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0 | lc_public_repos/langgraph/docs | lc_public_repos/langgraph/docs/cassettes/LLMCompiler_38d3ea91-59ba-4267-8060-ed75bbc840c6.msgpack.zlib | 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 |
0 | lc_public_repos/langgraph/docs | lc_public_repos/langgraph/docs/cassettes/review-tool-calls_3f05f8b6-6128-4de5-8884-862fc93f1227.msgpack.zlib | 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 |
0 | lc_public_repos/langgraph/docs | lc_public_repos/langgraph/docs/docs/index.md | ---
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{!README.md!}
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0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/subgraph-transform-state.ipynb | from typing_extensions import TypedDict
from langgraph.graph.state import StateGraph, START, END
class GrandChildState(TypedDict):
my_grandchild_key: str
def grandchild_1(state: GrandChildState) -> GrandChildState:
# NOTE: child or parent keys will not be accessible here
return {"my_grandchild_key": state["my_grandchild_key"] + ", how are you"}
grandchild = StateGraph(GrandChildState)
grandchild.add_node("grandchild_1", grandchild_1)
grandchild.add_edge(START, "grandchild_1")
grandchild.add_edge("grandchild_1", END)
grandchild_graph = grandchild.compile()grandchild_graph.invoke({"my_grandchild_key": "hi Bob"})class ChildState(TypedDict):
my_child_key: str
def call_grandchild_graph(state: ChildState) -> ChildState:
# NOTE: parent or grandchild keys won't be accessible here
# we're transforming the state from the child state channels (`my_child_key`)
# to the child state channels (`my_grandchild_key`)
grandchild_graph_input = {"my_grandchild_key": state["my_child_key"]}
# we're transforming the state from the grandchild state channels (`my_grandchild_key`)
# back to the child state channels (`my_child_key`)
grandchild_graph_output = grandchild_graph.invoke(grandchild_graph_input)
return {"my_child_key": grandchild_graph_output["my_grandchild_key"] + " today?"}
child = StateGraph(ChildState)
# NOTE: we're passing a function here instead of just compiled graph (`child_graph`)
child.add_node("child_1", call_grandchild_graph)
child.add_edge(START, "child_1")
child.add_edge("child_1", END)
child_graph = child.compile()child_graph.invoke({"my_child_key": "hi Bob"})class ParentState(TypedDict):
my_key: str
def parent_1(state: ParentState) -> ParentState:
# NOTE: child or grandchild keys won't be accessible here
return {"my_key": "hi " + state["my_key"]}
def parent_2(state: ParentState) -> ParentState:
return {"my_key": state["my_key"] + " bye!"}
def call_child_graph(state: ParentState) -> ParentState:
# we're transforming the state from the parent state channels (`my_key`)
# to the child state channels (`my_child_key`)
child_graph_input = {"my_child_key": state["my_key"]}
# we're transforming the state from the child state channels (`my_child_key`)
# back to the parent state channels (`my_key`)
child_graph_output = child_graph.invoke(child_graph_input)
return {"my_key": child_graph_output["my_child_key"]}
parent = StateGraph(ParentState)
parent.add_node("parent_1", parent_1)
# NOTE: we're passing a function here instead of just a compiled graph (`<code>child_graph</code>`)
parent.add_node("child", call_child_graph)
parent.add_node("parent_2", parent_2)
parent.add_edge(START, "parent_1")
parent.add_edge("parent_1", "child")
parent.add_edge("child", "parent_2")
parent.add_edge("parent_2", END)
parent_graph = parent.compile()parent_graph.invoke({"my_key": "Bob"}) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/streaming-content.ipynb | from langchain_core.messages import AIMessage
from langgraph.graph import START, StateGraph, MessagesState, END
from langgraph.types import StreamWriter
async def my_node(
state: MessagesState,
writer: StreamWriter, # <-- provide StreamWriter to write chunks to be streamed
):
chunks = [
"Four",
"score",
"and",
"seven",
"years",
"ago",
"our",
"fathers",
"...",
]
for chunk in chunks:
# write the chunk to be streamed using stream_mode=custom
writer(chunk)
return {"messages": [AIMessage(content=" ".join(chunks))]}
# Define a new graph
workflow = StateGraph(MessagesState)
workflow.add_node("model", my_node)
workflow.add_edge(START, "model")
workflow.add_edge("model", END)
app = workflow.compile()from langchain_core.messages import HumanMessage
inputs = [HumanMessage(content="What are you thinking about?")]
async for chunk in app.astream({"messages": inputs}, stream_mode="custom"):
print(chunk, flush=True)from langchain_core.messages import HumanMessage
inputs = [HumanMessage(content="What are you thinking about?")]
async for chunk in app.astream({"messages": inputs}, stream_mode=["custom", "updates"]):
print(chunk, flush=True)from langchain_core.runnables import RunnableConfig, RunnableLambda
from langchain_core.callbacks.manager import adispatch_custom_event
async def my_node(state: MessagesState, config: RunnableConfig):
chunks = [
"Four",
"score",
"and",
"seven",
"years",
"ago",
"our",
"fathers",
"...",
]
for chunk in chunks:
await adispatch_custom_event(
"my_custom_event",
{"chunk": chunk},
config=config, # <-- propagate config
)
return {"messages": [AIMessage(content=" ".join(chunks))]}
# Define a new graph
workflow = StateGraph(MessagesState)
workflow.add_node("model", my_node)
workflow.add_edge(START, "model")
workflow.add_edge("model", END)
app = workflow.compile()from langchain_core.messages import HumanMessage
inputs = [HumanMessage(content="What are you thinking about?")]
async for event in app.astream_events({"messages": inputs}, version="v2"):
tags = event.get("tags", [])
if event["event"] == "on_custom_event" and event["name"] == "my_custom_event":
data = event["data"]
if data:
print(data["chunk"], end="|", flush=True) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/create-react-agent.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")# First we initialize the model we want to use.
from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-4o", temperature=0)
# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)
from typing import Literal
from langchain_core.tools import tool
@tool
def get_weather(city: Literal["nyc", "sf"]):
"""Use this to get weather information."""
if city == "nyc":
return "It might be cloudy in nyc"
elif city == "sf":
return "It's always sunny in sf"
else:
raise AssertionError("Unknown city")
tools = [get_weather]
# Define the graph
from langgraph.prebuilt import create_react_agent
graph = create_react_agent(model, tools=tools)from IPython.display import Image, display
display(Image(graph.get_graph().draw_mermaid_png()))def print_stream(stream):
for s in stream:
message = s["messages"][-1]
if isinstance(message, tuple):
print(message)
else:
message.pretty_print()inputs = {"messages": [("user", "what is the weather in sf")]}
print_stream(graph.stream(inputs, stream_mode="values"))inputs = {"messages": [("user", "who built you?")]}
print_stream(graph.stream(inputs, stream_mode="values")) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/many-tools.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")import re
import uuid
from langchain_core.tools import StructuredTool
def create_tool(company: str) -> dict:
"""Create schema for a placeholder tool."""
# Remove non-alphanumeric characters and replace spaces with underscores for the tool name
formatted_company = re.sub(r"[^\w\s]", "", company).replace(" ", "_")
def company_tool(year: int) -> str:
# Placeholder function returning static revenue information for the company and year
return f"{company} had revenues of $100 in {year}."
return StructuredTool.from_function(
company_tool,
name=formatted_company,
description=f"Information about {company}",
)
# Abbreviated list of S&P 500 companies for demonstration
s_and_p_500_companies = [
"3M",
"A.O. Smith",
"Abbott",
"Accenture",
"Advanced Micro Devices",
"Yum! Brands",
"Zebra Technologies",
"Zimmer Biomet",
"Zoetis",
]
# Create a tool for each company and store it in a registry with a unique UUID as the key
tool_registry = {
str(uuid.uuid4()): create_tool(company) for company in s_and_p_500_companies
}from langchain_core.documents import Document
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import OpenAIEmbeddings
tool_documents = [
Document(
page_content=tool.description,
id=id,
metadata={"tool_name": tool.name},
)
for id, tool in tool_registry.items()
]
vector_store = InMemoryVectorStore(embedding=OpenAIEmbeddings())
document_ids = vector_store.add_documents(tool_documents)from typing import Annotated
from langchain_openai import ChatOpenAI
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition
# Define the state structure using TypedDict.
# It includes a list of messages (processed by add_messages)
# and a list of selected tool IDs.
class State(TypedDict):
messages: Annotated[list, add_messages]
selected_tools: list[str]
builder = StateGraph(State)
# Retrieve all available tools from the tool registry.
tools = list(tool_registry.values())
llm = ChatOpenAI()
# The agent function processes the current state
# by binding selected tools to the LLM.
def agent(state: State):
# Map tool IDs to actual tools
# based on the state's selected_tools list.
selected_tools = [tool_registry[id] for id in state["selected_tools"]]
# Bind the selected tools to the LLM for the current interaction.
llm_with_tools = llm.bind_tools(selected_tools)
# Invoke the LLM with the current messages and return the updated message list.
return {"messages": [llm_with_tools.invoke(state["messages"])]}
# The select_tools function selects tools based on the user's last message content.
def select_tools(state: State):
last_user_message = state["messages"][-1]
query = last_user_message.content
tool_documents = vector_store.similarity_search(query)
return {"selected_tools": [document.id for document in tool_documents]}
builder.add_node("agent", agent)
builder.add_node("select_tools", select_tools)
tool_node = ToolNode(tools=tools)
builder.add_node("tools", tool_node)
builder.add_conditional_edges("agent", tools_condition, path_map=["tools", "__end__"])
builder.add_edge("tools", "agent")
builder.add_edge("select_tools", "agent")
builder.add_edge(START, "select_tools")
graph = builder.compile()from IPython.display import Image, display
try:
display(Image(graph.get_graph().draw_mermaid_png()))
except Exception:
# This requires some extra dependencies and is optional
passuser_input = "Can you give me some information about AMD in 2022?"
result = graph.invoke({"messages": [("user", user_input)]})print(result["selected_tools"])for message in result["messages"]:
message.pretty_print()from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage
from langgraph.pregel.retry import RetryPolicy
from pydantic import BaseModel, Field
class QueryForTools(BaseModel):
"""Generate a query for additional tools."""
query: str = Field(..., description="Query for additional tools.")
def select_tools(state: State):
"""Selects tools based on the last message in the conversation state.
If the last message is from a human, directly uses the content of the message
as the query. Otherwise, constructs a query using a system message and invokes
the LLM to generate tool suggestions.
"""
last_message = state["messages"][-1]
hack_remove_tool_condition = False # Simulate an error in the first tool selection
if isinstance(last_message, HumanMessage):
query = last_message.content
hack_remove_tool_condition = True # Simulate wrong tool selection
else:
assert isinstance(last_message, ToolMessage)
system = SystemMessage(
"Given this conversation, generate a query for additional tools. "
"The query should be a short string containing what type of information "
"is needed. If no further information is needed, "
"set more_information_needed False and populate a blank string for the query."
)
input_messages = [system] + state["messages"]
response = llm.bind_tools([QueryForTools], tool_choice=True).invoke(
input_messages
)
query = response.tool_calls[0]["args"]["query"]
# Search the tool vector store using the generated query
tool_documents = vector_store.similarity_search(query)
if hack_remove_tool_condition:
# Simulate error by removing the correct tool from the selection
selected_tools = [
document.id
for document in tool_documents
if document.metadata["tool_name"] != "Advanced_Micro_Devices"
]
else:
selected_tools = [document.id for document in tool_documents]
return {"selected_tools": selected_tools}
graph_builder = StateGraph(State)
graph_builder.add_node("agent", agent)
graph_builder.add_node("select_tools", select_tools, retry=RetryPolicy(max_attempts=3))
tool_node = ToolNode(tools=tools)
graph_builder.add_node("tools", tool_node)
graph_builder.add_conditional_edges(
"agent",
tools_condition,
)
graph_builder.add_edge("tools", "select_tools")
graph_builder.add_edge("select_tools", "agent")
graph_builder.add_edge(START, "select_tools")
graph = graph_builder.compile()from IPython.display import Image, display
try:
display(Image(graph.get_graph().draw_mermaid_png()))
except Exception:
# This requires some extra dependencies and is optional
passuser_input = "Can you give me some information about AMD in 2022?"
result = graph.invoke({"messages": [("user", user_input)]})for message in result["messages"]:
message.pretty_print() |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/streaming-subgraphs.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")from typing import Optional, Annotated
from typing_extensions import TypedDict
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import StateGraph, START, END
# The structure of the logs
class Logs(TypedDict):
id: str
question: str
answer: str
grade: Optional[int]
feedback: Optional[str]
# Define custom reducer (see more on this in the "Custom reducer" section below)
def add_logs(left: list[Logs], right: list[Logs]) -> list[Logs]:
if not left:
left = []
if not right:
right = []
logs = left.copy()
left_id_to_idx = {log["id"]: idx for idx, log in enumerate(logs)}
# update if the new logs are already in the state, otherwise append
for log in right:
idx = left_id_to_idx.get(log["id"])
if idx is not None:
logs[idx] = log
else:
logs.append(log)
return logs
# Failure Analysis Subgraph
class FailureAnalysisState(TypedDict):
# keys shared with the parent graph (EntryGraphState)
logs: Annotated[list[Logs], add_logs]
failure_report: str
# subgraph key
failures: list[Logs]
def get_failures(state: FailureAnalysisState):
failures = [log for log in state["logs"] if log["grade"] == 0]
return {"failures": failures}
def generate_summary(state: FailureAnalysisState):
failures = state["failures"]
# NOTE: you can implement custom summarization logic here
failure_ids = [log["id"] for log in failures]
fa_summary = f"Poor quality of retrieval for document IDs: {', '.join(failure_ids)}"
return {"failure_report": fa_summary}
fa_builder = StateGraph(FailureAnalysisState)
fa_builder.add_node("get_failures", get_failures)
fa_builder.add_node("generate_summary", generate_summary)
fa_builder.add_edge(START, "get_failures")
fa_builder.add_edge("get_failures", "generate_summary")
fa_builder.add_edge("generate_summary", END)
# Summarization subgraph
class QuestionSummarizationState(TypedDict):
# keys that are shared with the parent graph (EntryGraphState)
summary_report: str
logs: Annotated[list[Logs], add_logs]
# subgraph keys
summary: str
def generate_summary(state: QuestionSummarizationState):
docs = state["logs"]
# NOTE: you can implement custom summarization logic here
summary = "Questions focused on usage of ChatOllama and Chroma vector store."
return {"summary": summary}
def send_to_slack(state: QuestionSummarizationState):
summary = state["summary"]
# NOTE: you can implement custom logic here, for example sending the summary generated in the previous step to Slack
return {"summary_report": summary}
qs_builder = StateGraph(QuestionSummarizationState)
qs_builder.add_node("generate_summary", generate_summary)
qs_builder.add_node("send_to_slack", send_to_slack)
qs_builder.add_edge(START, "generate_summary")
qs_builder.add_edge("generate_summary", "send_to_slack")
qs_builder.add_edge("send_to_slack", END)# Entry Graph
class EntryGraphState(TypedDict):
raw_logs: Annotated[list[Logs], add_logs]
logs: Annotated[list[Logs], add_logs] # This will be used in subgraphs
failure_report: str # This will be generated in the FA subgraph
summary_report: str # This will be generated in the QS subgraph
def select_logs(state):
return {"logs": [log for log in state["raw_logs"] if "grade" in log]}
entry_builder = StateGraph(EntryGraphState)
entry_builder.add_node("select_logs", select_logs)
entry_builder.add_node("question_summarization", qs_builder.compile())
entry_builder.add_node("failure_analysis", fa_builder.compile())
entry_builder.add_edge(START, "select_logs")
entry_builder.add_edge("select_logs", "failure_analysis")
entry_builder.add_edge("select_logs", "question_summarization")
entry_builder.add_edge("failure_analysis", END)
entry_builder.add_edge("question_summarization", END)
graph = entry_builder.compile()
from IPython.display import Image, display
# Setting xray to 1 will show the internal structure of the nested graph
display(Image(graph.get_graph(xray=1).draw_mermaid_png()))# Dummy logs
dummy_logs = [
Logs(
id="1",
question="How can I import ChatOllama?",
grade=1,
answer="To import ChatOllama, use: 'from langchain_community.chat_models import ChatOllama.'",
),
Logs(
id="2",
question="How can I use Chroma vector store?",
answer="To use Chroma, define: rag_chain = create_retrieval_chain(retriever, question_answer_chain).",
grade=0,
feedback="The retrieved documents discuss vector stores in general, but not Chroma specifically",
),
Logs(
id="3",
question="How do I create react agent in langgraph?",
answer="from langgraph.prebuilt import create_react_agent",
),
]
input = {"raw_logs": dummy_logs}for chunk in graph.stream(input, stream_mode="updates"):
node_name = list(chunk.keys())[0]
print(f"---------- Update from node {node_name} ---------")
print(chunk[node_name])# Format the namespace slightly nicer
def format_namespace(namespace):
return (
namespace[-1].split(":")[0] + " subgraph"
if len(namespace) > 0
else "parent graph"
)
for namespace, chunk in graph.stream(input, stream_mode="updates", subgraphs=True):
node_name = list(chunk.keys())[0]
print(
f"---------- Update from node {node_name} in {format_namespace(namespace)} ---------"
)
print(chunk[node_name]) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/subgraph-persistence.ipynb | from langgraph.graph import START, StateGraph
from langgraph.checkpoint.memory import MemorySaver
from typing import TypedDict
# subgraph
class SubgraphState(TypedDict):
foo: str # note that this key is shared with the parent graph state
bar: str
def subgraph_node_1(state: SubgraphState):
return {"bar": "bar"}
def subgraph_node_2(state: SubgraphState):
# note that this node is using a state key ('bar') that is only available in the subgraph
# and is sending update on the shared state key ('foo')
return {"foo": state["foo"] + state["bar"]}
subgraph_builder = StateGraph(SubgraphState)
subgraph_builder.add_node(subgraph_node_1)
subgraph_builder.add_node(subgraph_node_2)
subgraph_builder.add_edge(START, "subgraph_node_1")
subgraph_builder.add_edge("subgraph_node_1", "subgraph_node_2")
subgraph = subgraph_builder.compile()
# parent graph
class State(TypedDict):
foo: str
def node_1(state: State):
return {"foo": "hi! " + state["foo"]}
builder = StateGraph(State)
builder.add_node("node_1", node_1)
# note that we're adding the compiled subgraph as a node to the parent graph
builder.add_node("node_2", subgraph)
builder.add_edge(START, "node_1")
builder.add_edge("node_1", "node_2")checkpointer = MemorySaver()
# You must only pass checkpointer when compiling the parent graph.
# LangGraph will automatically propagate the checkpointer to the child subgraphs.
graph = builder.compile(checkpointer=checkpointer)config = {"configurable": {"thread_id": "1"}}for _, chunk in graph.stream({"foo": "foo"}, config, subgraphs=True):
print(chunk)graph.get_state(config).valuesstate_with_subgraph = [
s for s in graph.get_state_history(config) if s.next == ("node_2",)
][0]subgraph_config = state_with_subgraph.tasks[0].state
subgraph_configgraph.get_state(subgraph_config).values |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/.meta.yml | tags:
- how-tos
- how-to
- howto
- how to |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/streaming-events-from-within-tools.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")from langchain_core.callbacks import Callbacks
from langchain_core.messages import HumanMessage
from langchain_core.tools import tool
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
@tool
async def get_items(
place: str,
callbacks: Callbacks, # <--- Manually accept callbacks (needed for Python <= 3.10)
) -> str:
"""Use this tool to look up which items are in the given place."""
# Attention when using async, you should be invoking the LLM using ainvoke!
# If you fail to do so, streaming will not WORK.
return await llm.ainvoke(
[
{
"role": "user",
"content": f"Can you tell me what kind of items i might find in the following place: '{place}'. "
"List at least 3 such items separating them by a comma. And include a brief description of each item..",
}
],
{"callbacks": callbacks},
)
llm = ChatOpenAI(model_name="gpt-4o")
tools = [get_items]
agent = create_react_agent(llm, tools=tools)final_message = ""
async for msg, metadata in agent.astream(
{"messages": [("human", "what items are on the shelf?")]}, stream_mode="messages"
):
# Stream all messages from the tool node
if (
msg.content
and not isinstance(msg, HumanMessage)
and metadata["langgraph_node"] == "tools"
and not msg.name
):
print(msg.content, end="|", flush=True)
# Final message should come from our agent
if msg.content and metadata["langgraph_node"] == "agent":
final_message += msg.contentfrom langchain_core.messages import HumanMessage
async for event in agent.astream_events(
{"messages": [{"role": "user", "content": "what's in the bedroom."}]}, version="v2"
):
if (
event["event"] == "on_chat_model_stream"
and event["metadata"].get("langgraph_node") == "tools"
):
print(event["data"]["chunk"].content, end="|", flush=True) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/pass-config-to-tools.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("ANTHROPIC_API_KEY")from typing import List
from langchain_core.tools import tool
from langchain_core.runnables.config import RunnableConfig
from langgraph.prebuilt import ToolNode
user_to_pets = {}
@tool(parse_docstring=True)
def update_favorite_pets(
# NOTE: config arg does not need to be added to docstring, as we don't want it to be included in the function signature attached to the LLM
pets: List[str],
config: RunnableConfig,
) -> None:
"""Add the list of favorite pets.
Args:
pets: List of favorite pets to set.
"""
user_id = config.get("configurable", {}).get("user_id")
user_to_pets[user_id] = pets
@tool
def delete_favorite_pets(config: RunnableConfig) -> None:
"""Delete the list of favorite pets."""
user_id = config.get("configurable", {}).get("user_id")
if user_id in user_to_pets:
del user_to_pets[user_id]
@tool
def list_favorite_pets(config: RunnableConfig) -> None:
"""List favorite pets if any."""
user_id = config.get("configurable", {}).get("user_id")
return ", ".join(user_to_pets.get(user_id, []))tools = [update_favorite_pets, delete_favorite_pets, list_favorite_pets]
tool_node = ToolNode(tools)from langchain_anthropic import ChatAnthropic
from langgraph.graph import StateGraph, MessagesState
from langgraph.prebuilt import ToolNode
model_with_tools = ChatAnthropic(
model="claude-3-haiku-20240307", temperature=0
).bind_tools(tools)from typing import Literal
from langgraph.graph import StateGraph, MessagesState, START, END
def should_continue(state: MessagesState):
messages = state["messages"]
last_message = messages[-1]
if last_message.tool_calls:
return "tools"
return END
def call_model(state: MessagesState):
messages = state["messages"]
response = model_with_tools.invoke(messages)
return {"messages": [response]}
builder = StateGraph(MessagesState)
# Define the two nodes we will cycle between
builder.add_node("agent", call_model)
builder.add_node("tools", tool_node)
builder.add_edge(START, "agent")
builder.add_conditional_edges("agent", should_continue, ["tools", END])
builder.add_edge("tools", "agent")
graph = builder.compile()from IPython.display import Image, display
try:
display(Image(graph.get_graph().draw_mermaid_png()))
except Exception:
# This requires some extra dependencies and is optional
passfrom langchain_core.messages import HumanMessage
user_to_pets.clear() # Clear the state
print(f"User information prior to run: {user_to_pets}")
inputs = {"messages": [HumanMessage(content="my favorite pets are cats and dogs")]}
for chunk in graph.stream(
inputs, {"configurable": {"user_id": "123"}}, stream_mode="values"
):
chunk["messages"][-1].pretty_print()
print(f"User information after the run: {user_to_pets}")from langchain_core.messages import HumanMessage
print(f"User information prior to run: {user_to_pets}")
inputs = {"messages": [HumanMessage(content="what are my favorite pets")]}
for chunk in graph.stream(
inputs, {"configurable": {"user_id": "123"}}, stream_mode="values"
):
chunk["messages"][-1].pretty_print()
print(f"User information prior to run: {user_to_pets}")print(f"User information prior to run: {user_to_pets}")
inputs = {
"messages": [
HumanMessage(content="please forget what i told you about my favorite animals")
]
}
for chunk in graph.stream(
inputs, {"configurable": {"user_id": "123"}}, stream_mode="values"
):
chunk["messages"][-1].pretty_print()
print(f"User information prior to run: {user_to_pets}") |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/react-agent-structured-output.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("ANTHROPIC_API_KEY")from pydantic import BaseModel, Field
from typing import Literal
from langchain_core.tools import tool
from langchain_anthropic import ChatAnthropic
from langgraph.graph import MessagesState
class WeatherResponse(BaseModel):
"""Respond to the user with this"""
temperature: float = Field(description="The temperature in fahrenheit")
wind_directon: str = Field(
description="The direction of the wind in abbreviated form"
)
wind_speed: float = Field(description="The speed of the wind in km/h")
# Inherit 'messages' key from MessagesState, which is a list of chat messages
class AgentState(MessagesState):
# Final structured response from the agent
final_response: WeatherResponse
@tool
def get_weather(city: Literal["nyc", "sf"]):
"""Use this to get weather information."""
if city == "nyc":
return "It is cloudy in NYC, with 5 mph winds in the North-East direction and a temperature of 70 degrees"
elif city == "sf":
return "It is 75 degrees and sunny in SF, with 3 mph winds in the South-East direction"
else:
raise AssertionError("Unknown city")
tools = [get_weather]
model = ChatAnthropic(model="claude-3-opus-20240229")
model_with_tools = model.bind_tools(tools)
model_with_structured_output = model.with_structured_output(WeatherResponse)from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode
tools = [get_weather, WeatherResponse]
# Force the model to use tools by passing tool_choice="any"
model_with_response_tool = model.bind_tools(tools, tool_choice="any")
# Define the function that calls the model
def call_model(state: AgentState):
response = model_with_response_tool.invoke(state["messages"])
# We return a list, because this will get added to the existing list
return {"messages": [response]}
# Define the function that responds to the user
def respond(state: AgentState):
# Construct the final answer from the arguments of the last tool call
response = WeatherResponse(**state["messages"][-1].tool_calls[0]["args"])
# We return the final answer
return {"final_response": response}
# Define the function that determines whether to continue or not
def should_continue(state: AgentState):
messages = state["messages"]
last_message = messages[-1]
# If there is only one tool call and it is the response tool call we respond to the user
if (
len(last_message.tool_calls) == 1
and last_message.tool_calls[0]["name"] == "WeatherResponse"
):
return "respond"
# Otherwise we will use the tool node again
else:
return "continue"
# Define a new graph
workflow = StateGraph(AgentState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("respond", respond)
workflow.add_node("tools", ToolNode(tools))
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.set_entry_point("agent")
# We now add a conditional edge
workflow.add_conditional_edges(
"agent",
should_continue,
{
"continue": "tools",
"respond": "respond",
},
)
workflow.add_edge("tools", "agent")
workflow.add_edge("respond", END)
graph = workflow.compile()answer = graph.invoke(input={"messages": [("human", "what's the weather in SF?")]})[
"final_response"
]answerfrom langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode
from langchain_core.messages import HumanMessage
# Define the function that calls the model
def call_model(state: AgentState):
response = model_with_tools.invoke(state["messages"])
# We return a list, because this will get added to the existing list
return {"messages": [response]}
# Define the function that responds to the user
def respond(state: AgentState):
# We call the model with structured output in order to return the same format to the user every time
# state['messages'][-2] is the last ToolMessage in the convo, which we convert to a HumanMessage for the model to use
# We could also pass the entire chat history, but this saves tokens since all we care to structure is the output of the tool
response = model_with_structured_output.invoke(
[HumanMessage(content=state["messages"][-2].content)]
)
# We return the final answer
return {"final_response": response}
# Define the function that determines whether to continue or not
def should_continue(state: AgentState):
messages = state["messages"]
last_message = messages[-1]
# If there is no function call, then we respond to the user
if not last_message.tool_calls:
return "respond"
# Otherwise if there is, we continue
else:
return "continue"
# Define a new graph
workflow = StateGraph(AgentState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("respond", respond)
workflow.add_node("tools", ToolNode(tools))
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.set_entry_point("agent")
# We now add a conditional edge
workflow.add_conditional_edges(
"agent",
should_continue,
{
"continue": "tools",
"respond": "respond",
},
)
workflow.add_edge("tools", "agent")
workflow.add_edge("respond", END)
graph = workflow.compile()answer = graph.invoke(input={"messages": [("human", "what's the weather in SF?")]})[
"final_response"
]answer |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/persistence_postgres.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")from typing import Literal
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
from langgraph.checkpoint.postgres import PostgresSaver
from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
@tool
def get_weather(city: Literal["nyc", "sf"]):
"""Use this to get weather information."""
if city == "nyc":
return "It might be cloudy in nyc"
elif city == "sf":
return "It's always sunny in sf"
else:
raise AssertionError("Unknown city")
tools = [get_weather]
model = ChatOpenAI(model_name="gpt-4o-mini", temperature=0)DB_URI = "postgresql://postgres:postgres@localhost:5442/postgres?sslmode=disable"connection_kwargs = {
"autocommit": True,
"prepare_threshold": 0,
}from psycopg_pool import ConnectionPool
with ConnectionPool(
# Example configuration
conninfo=DB_URI,
max_size=20,
kwargs=connection_kwargs,
) as pool:
checkpointer = PostgresSaver(pool)
# NOTE: you need to call .setup() the first time you're using your checkpointer
checkpointer.setup()
graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)
config = {"configurable": {"thread_id": "1"}}
res = graph.invoke({"messages": [("human", "what's the weather in sf")]}, config)
checkpoint = checkpointer.get(config)rescheckpointfrom psycopg import Connection
with Connection.connect(DB_URI, **connection_kwargs) as conn:
checkpointer = PostgresSaver(conn)
# NOTE: you need to call .setup() the first time you're using your checkpointer
# checkpointer.setup()
graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)
config = {"configurable": {"thread_id": "2"}}
res = graph.invoke({"messages": [("human", "what's the weather in sf")]}, config)
checkpoint_tuple = checkpointer.get_tuple(config)checkpoint_tuplewith PostgresSaver.from_conn_string(DB_URI) as checkpointer:
graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)
config = {"configurable": {"thread_id": "3"}}
res = graph.invoke({"messages": [("human", "what's the weather in sf")]}, config)
checkpoint_tuples = list(checkpointer.list(config))checkpoint_tuplesfrom psycopg_pool import AsyncConnectionPool
async with AsyncConnectionPool(
# Example configuration
conninfo=DB_URI,
max_size=20,
kwargs=connection_kwargs,
) as pool:
checkpointer = AsyncPostgresSaver(pool)
# NOTE: you need to call .setup() the first time you're using your checkpointer
await checkpointer.setup()
graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)
config = {"configurable": {"thread_id": "4"}}
res = await graph.ainvoke(
{"messages": [("human", "what's the weather in nyc")]}, config
)
checkpoint = await checkpointer.aget(config)checkpointfrom psycopg import AsyncConnection
async with await AsyncConnection.connect(DB_URI, **connection_kwargs) as conn:
checkpointer = AsyncPostgresSaver(conn)
graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)
config = {"configurable": {"thread_id": "5"}}
res = await graph.ainvoke(
{"messages": [("human", "what's the weather in nyc")]}, config
)
checkpoint_tuple = await checkpointer.aget_tuple(config)checkpoint_tupleasync with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer:
graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)
config = {"configurable": {"thread_id": "6"}}
res = await graph.ainvoke(
{"messages": [("human", "what's the weather in nyc")]}, config
)
checkpoint_tuples = [c async for c in checkpointer.alist(config)]checkpoint_tuples |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/recursion-limit.ipynb | import operator
from typing import Annotated, Any
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
class State(TypedDict):
# The operator.add reducer fn makes this append-only
aggregate: Annotated[list, operator.add]
def node_a(state):
return {"aggregate": ["I'm A"]}
def node_b(state):
return {"aggregate": ["I'm B"]}
def node_c(state):
return {"aggregate": ["I'm C"]}
def node_d(state):
return {"aggregate": ["I'm A"]}
builder = StateGraph(State)
builder.add_node("a", node_a)
builder.add_edge(START, "a")
builder.add_node("b", node_b)
builder.add_node("c", node_c)
builder.add_node("d", node_d)
builder.add_edge("a", "b")
builder.add_edge("a", "c")
builder.add_edge("b", "d")
builder.add_edge("c", "d")
builder.add_edge("d", END)
graph = builder.compile()from IPython.display import Image, display
display(Image(graph.get_graph().draw_mermaid_png()))from langgraph.errors import GraphRecursionError
try:
graph.invoke({"aggregate": []}, {"recursion_limit": 3})
except GraphRecursionError:
print("Recursion Error")try:
graph.invoke({"aggregate": []}, {"recursion_limit": 4})
except GraphRecursionError:
print("Recursion Error") |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/stream-updates.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")from typing import Literal
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.runnables import ConfigurableField
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
@tool
def get_weather(city: Literal["nyc", "sf"]):
"""Use this to get weather information."""
if city == "nyc":
return "It might be cloudy in nyc"
elif city == "sf":
return "It's always sunny in sf"
else:
raise AssertionError("Unknown city")
tools = [get_weather]
model = ChatOpenAI(model_name="gpt-4o", temperature=0)
graph = create_react_agent(model, tools)inputs = {"messages": [("human", "what's the weather in sf")]}
async for chunk in graph.astream(inputs, stream_mode="updates"):
for node, values in chunk.items():
print(f"Receiving update from node: '{node}'")
print(values)
print("\n\n") |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/input_output_schema.ipynb | from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
# Define the schema for the input
class InputState(TypedDict):
question: str
# Define the schema for the output
class OutputState(TypedDict):
answer: str
# Define the overall schema, combining both input and output
class OverallState(InputState, OutputState):
pass
# Define the node that processes the input and generates an answer
def answer_node(state: InputState):
# Example answer and an extra key
return {"answer": "bye", "question": state["question"]}
# Build the graph with input and output schemas specified
builder = StateGraph(OverallState, input=InputState, output=OutputState)
builder.add_node(answer_node) # Add the answer node
builder.add_edge(START, "answer_node") # Define the starting edge
builder.add_edge("answer_node", END) # Define the ending edge
graph = builder.compile() # Compile the graph
# Invoke the graph with an input and print the result
print(graph.invoke({"question": "hi"})) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/create-react-agent-memory.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")# First we initialize the model we want to use.
from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-4o", temperature=0)
# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)
from typing import Literal
from langchain_core.tools import tool
@tool
def get_weather(city: Literal["nyc", "sf"]):
"""Use this to get weather information."""
if city == "nyc":
return "It might be cloudy in nyc"
elif city == "sf":
return "It's always sunny in sf"
else:
raise AssertionError("Unknown city")
tools = [get_weather]
# We can add "chat memory" to the graph with LangGraph's checkpointer
# to retain the chat context between interactions
from langgraph.checkpoint.memory import MemorySaver
memory = MemorySaver()
# Define the graph
from langgraph.prebuilt import create_react_agent
graph = create_react_agent(model, tools=tools, checkpointer=memory)def print_stream(stream):
for s in stream:
message = s["messages"][-1]
if isinstance(message, tuple):
print(message)
else:
message.pretty_print()config = {"configurable": {"thread_id": "1"}}
inputs = {"messages": [("user", "What's the weather in NYC?")]}
print_stream(graph.stream(inputs, config=config, stream_mode="values"))inputs = {"messages": [("user", "What's it known for?")]}
print_stream(graph.stream(inputs, config=config, stream_mode="values")) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/configuration.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("ANTHROPIC_API_KEY")import operator
from typing import Annotated, Sequence
from typing_extensions import TypedDict
from langchain_anthropic import ChatAnthropic
from langchain_core.messages import BaseMessage, HumanMessage
from langgraph.graph import END, StateGraph, START
model = ChatAnthropic(model_name="claude-2.1")
class AgentState(TypedDict):
messages: Annotated[Sequence[BaseMessage], operator.add]
def _call_model(state):
state["messages"]
response = model.invoke(state["messages"])
return {"messages": [response]}
# Define a new graph
builder = StateGraph(AgentState)
builder.add_node("model", _call_model)
builder.add_edge(START, "model")
builder.add_edge("model", END)
graph = builder.compile()from langchain_openai import ChatOpenAI
from typing import Optional
from langchain_core.runnables.config import RunnableConfig
openai_model = ChatOpenAI()
models = {
"anthropic": model,
"openai": openai_model,
}
def _call_model(state: AgentState, config: RunnableConfig):
# Access the config through the configurable key
model_name = config["configurable"].get("model", "anthropic")
model = models[model_name]
response = model.invoke(state["messages"])
return {"messages": [response]}
# Define a new graph
builder = StateGraph(AgentState)
builder.add_node("model", _call_model)
builder.add_edge(START, "model")
builder.add_edge("model", END)
graph = builder.compile()graph.invoke({"messages": [HumanMessage(content="hi")]})config = {"configurable": {"model": "openai"}}
graph.invoke({"messages": [HumanMessage(content="hi")]}, config=config)from langchain_core.messages import SystemMessage
# We can define a config schema to specify the configuration options for the graph
# A config schema is useful for indicating which fields are available in the configurable dict inside the config
class ConfigSchema(TypedDict):
model: Optional[str]
system_message: Optional[str]
def _call_model(state: AgentState, config: RunnableConfig):
# Access the config through the configurable key
model_name = config["configurable"].get("model", "anthropic")
model = models[model_name]
messages = state["messages"]
if "system_message" in config["configurable"]:
messages = [
SystemMessage(content=config["configurable"]["system_message"])
] + messages
response = model.invoke(messages)
return {"messages": [response]}
# Define a new graph - note that we pass in the configuration schema here, but it is not necessary
workflow = StateGraph(AgentState, ConfigSchema)
workflow.add_node("model", _call_model)
workflow.add_edge(START, "model")
workflow.add_edge("model", END)
graph = workflow.compile()graph.invoke({"messages": [HumanMessage(content="hi")]})config = {"configurable": {"system_message": "respond in italian"}}
graph.invoke({"messages": [HumanMessage(content="hi")]}, config=config) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/stream-values.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")from typing import Literal
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.runnables import ConfigurableField
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
@tool
def get_weather(city: Literal["nyc", "sf"]):
"""Use this to get weather information."""
if city == "nyc":
return "It might be cloudy in nyc"
elif city == "sf":
return "It's always sunny in sf"
else:
raise AssertionError("Unknown city")
tools = [get_weather]
model = ChatOpenAI(model_name="gpt-4o", temperature=0)
graph = create_react_agent(model, tools)inputs = {"messages": [("human", "what's the weather in sf")]}
async for chunk in graph.astream(inputs, stream_mode="values"):
chunk["messages"][-1].pretty_print()inputs = {"messages": [("human", "what's the weather in sf")]}
async for chunk in graph.astream(inputs, stream_mode="values"):
final_result = chunkfinal_resultfinal_result["messages"][-1].pretty_print() |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/pass_private_state.ipynb | from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
# The overall state of the graph (this is the public state shared across nodes)
class OverallState(TypedDict):
a: str
# Output from node_1 contains private data that is not part of the overall state
class Node1Output(TypedDict):
private_data: str
# The private data is only shared between node_1 and node_2
def node_1(state: OverallState) -> Node1Output:
output = {"private_data": "set by node_1"}
print(f"Entered node `node_1`:\n\tInput: {state}.\n\tReturned: {output}")
return output
# Node 2 input only requests the private data available after node_1
class Node2Input(TypedDict):
private_data: str
def node_2(state: Node2Input) -> OverallState:
output = {"a": "set by node_2"}
print(f"Entered node `node_2`:\n\tInput: {state}.\n\tReturned: {output}")
return output
# Node 3 only has access to the overall state (no access to private data from node_1)
def node_3(state: OverallState) -> OverallState:
output = {"a": "set by node_3"}
print(f"Entered node `node_3`:\n\tInput: {state}.\n\tReturned: {output}")
return output
# Build the state graph
builder = StateGraph(OverallState)
builder.add_node(node_1) # node_1 is the first node
builder.add_node(
node_2
) # node_2 is the second node and accepts private data from node_1
builder.add_node(node_3) # node_3 is the third node and does not see the private data
builder.add_edge(START, "node_1") # Start the graph with node_1
builder.add_edge("node_1", "node_2") # Pass from node_1 to node_2
builder.add_edge(
"node_2", "node_3"
) # Pass from node_2 to node_3 (only overall state is shared)
builder.add_edge("node_3", END) # End the graph after node_3
graph = builder.compile()
# Invoke the graph with the initial state
response = graph.invoke(
{
"a": "set at start",
}
)
print()
print(f"Output of graph invocation: {response}") |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/local-studio.md | # How to connect a local agent to LangGraph Studio
This guide shows you how to connect your local agent to [LangGraph Studio](../concepts/langgraph_studio.md) for visualization, interaction, and debugging.
## Connection Options
There are two ways to connect your local agent to LangGraph Studio:
- [LangGraph Desktop](../concepts/langgraph_studio.md#desktop-app): Application, Mac only, requires Docker
- [Development Server](../concepts/langgraph_studio.md#dev-server): Python package, all platforms, no Docker
In this guide we will cover how to use the development server as that is generally an easier and better experience.
## Setup your application
First, you will need to setup your application in the proper format.
This means defining a `langgraph.json` file which contains paths to your agent(s).
See [this guide](../concepts/application_structure.md) for information on how to do so.
## Install langgraph-cli
You will need to install [`langgraph-cli`](../cloud/reference/cli.md#langgraph-cli) (version `0.1.55` or higher).
You will need to make sure to install the `inmem` extras.
```shell
pip install "langgraph-cli[inmem]==0.1.55"
```
## Run the development server
1. Navigate to your project directory (where `langgraph.json` is located)
2. Start the server:
```bash
langgraph dev
```
This will look for the `langgraph.json` file in your current directory.
In there, it will find the paths to the graph(s), and start those up.
It will then automatically connect to the cloud-hosted studio.
## Use the studio
After connecting to the studio, a browser window should automatically pop up.
This will use the cloud hosted studio UI to connect to your local development server.
Your graph is still running locally, the UI is connecting to visualizing the agent and threads that are defined locally.
The graph will always use the most up-to-date code, so you will be able to change the underlying code and have it automatically reflected in the studio.
This is useful for debugging workflows.
You can run your graph in the UI until it messes up, go in and change your code, and then rerun from the node that failed.
# (Optional) Attach a debugger
For step-by-step debugging with breakpoints and variable inspection:
```bash
# Install debugpy package
pip install debugpy
# Start server with debugging enabled
langgraph dev --debug-port 5678
```
Then attach your preferred debugger:
=== "VS Code"
Add this configuration to `launch.json`:
```json
{
"name": "Attach to LangGraph",
"type": "debugpy",
"request": "attach",
"connect": {
"host": "0.0.0.0",
"port": 5678
}
}
```
Specify the port number you chose in the previous step.
=== "PyCharm"
1. Go to Run → Edit Configurations
2. Click + and select "Python Debug Server"
3. Set IDE host name: `localhost`
4. Set port: `5678` (or the port number you chose in the previous step)
5. Click "OK" and start debugging |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/node-retries.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("ANTHROPIC_API_KEY")from langgraph.pregel import RetryPolicy
RetryPolicy()import operator
import sqlite3
from typing import Annotated, Sequence
from typing_extensions import TypedDict
from langchain_anthropic import ChatAnthropic
from langchain_core.messages import BaseMessage
from langgraph.graph import END, StateGraph, START
from langchain_community.utilities import SQLDatabase
from langchain_core.messages import AIMessage
db = SQLDatabase.from_uri("sqlite:///:memory:")
model = ChatAnthropic(model_name="claude-2.1")
class AgentState(TypedDict):
messages: Annotated[Sequence[BaseMessage], operator.add]
def query_database(state):
query_result = db.run("SELECT * FROM Artist LIMIT 10;")
return {"messages": [AIMessage(content=query_result)]}
def call_model(state):
response = model.invoke(state["messages"])
return {"messages": [response]}
# Define a new graph
builder = StateGraph(AgentState)
builder.add_node(
"query_database",
query_database,
retry=RetryPolicy(retry_on=sqlite3.OperationalError),
)
builder.add_node("model", call_model, retry=RetryPolicy(max_attempts=5))
builder.add_edge(START, "model")
builder.add_edge("model", "query_database")
builder.add_edge("query_database", END)
graph = builder.compile() |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/react-agent-from-scratch.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")from typing import (
Annotated,
Sequence,
TypedDict,
)
from langchain_core.messages import BaseMessage
from langgraph.graph.message import add_messages
class AgentState(TypedDict):
"""The state of the agent."""
# add_messages is a reducer
# See https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers
messages: Annotated[Sequence[BaseMessage], add_messages]from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
model = ChatOpenAI(model="gpt-4o-mini")
@tool
def get_weather(location: str):
"""Call to get the weather from a specific location."""
# This is a placeholder for the actual implementation
# Don't let the LLM know this though 😊
if any([city in location.lower() for city in ["sf", "san francisco"]]):
return "It's sunny in San Francisco, but you better look out if you're a Gemini 😈."
else:
return f"I am not sure what the weather is in {location}"
tools = [get_weather]
model = model.bind_tools(tools)import json
from langchain_core.messages import ToolMessage, SystemMessage
from langchain_core.runnables import RunnableConfig
tools_by_name = {tool.name: tool for tool in tools}
# Define our tool node
def tool_node(state: AgentState):
outputs = []
for tool_call in state["messages"][-1].tool_calls:
tool_result = tools_by_name[tool_call["name"]].invoke(tool_call["args"])
outputs.append(
ToolMessage(
content=json.dumps(tool_result),
name=tool_call["name"],
tool_call_id=tool_call["id"],
)
)
return {"messages": outputs}
# Define the node that calls the model
def call_model(
state: AgentState,
config: RunnableConfig,
):
# this is similar to customizing the create_react_agent with state_modifier, but is a lot more flexible
system_prompt = SystemMessage(
"You are a helpful AI assistant, please respond to the users query to the best of your ability!"
)
response = model.invoke([system_prompt] + state["messages"], config)
# We return a list, because this will get added to the existing list
return {"messages": [response]}
# Define the conditional edge that determines whether to continue or not
def should_continue(state: AgentState):
messages = state["messages"]
last_message = messages[-1]
# If there is no function call, then we finish
if not last_message.tool_calls:
return "end"
# Otherwise if there is, we continue
else:
return "continue"from langgraph.graph import StateGraph, END
# Define a new graph
workflow = StateGraph(AgentState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("tools", tool_node)
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.set_entry_point("agent")
# We now add a conditional edge
workflow.add_conditional_edges(
# First, we define the start node. We use `agent`.
# This means these are the edges taken after the `agent` node is called.
"agent",
# Next, we pass in the function that will determine which node is called next.
should_continue,
# Finally we pass in a mapping.
# The keys are strings, and the values are other nodes.
# END is a special node marking that the graph should finish.
# What will happen is we will call `should_continue`, and then the output of that
# will be matched against the keys in this mapping.
# Based on which one it matches, that node will then be called.
{
# If `tools`, then we call the tool node.
"continue": "tools",
# Otherwise we finish.
"end": END,
},
)
# We now add a normal edge from `tools` to `agent`.
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge("tools", "agent")
# Now we can compile and visualize our graph
graph = workflow.compile()
from IPython.display import Image, display
try:
display(Image(graph.get_graph().draw_mermaid_png()))
except Exception:
# This requires some extra dependencies and is optional
pass# Helper function for formatting the stream nicely
def print_stream(stream):
for s in stream:
message = s["messages"][-1]
if isinstance(message, tuple):
print(message)
else:
message.pretty_print()
inputs = {"messages": [("user", "what is the weather in sf")]}
print_stream(graph.stream(inputs, stream_mode="values")) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/streaming-from-final-node.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")from typing import Literal
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.runnables import ConfigurableField
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
from langgraph.prebuilt import ToolNode
@tool
def get_weather(city: Literal["nyc", "sf"]):
"""Use this to get weather information."""
if city == "nyc":
return "It might be cloudy in nyc"
elif city == "sf":
return "It's always sunny in sf"
else:
raise AssertionError("Unknown city")
tools = [get_weather]
model = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
final_model = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
model = model.bind_tools(tools)
# NOTE: this is where we're adding a tag that we'll can use later to filter the model stream events to only the model called in the final node.
# This is not necessary if you call a single LLM but might be important in case you call multiple models within the node and want to filter events
# from only one of them.
final_model = final_model.with_config(tags=["final_node"])
tool_node = ToolNode(tools=tools)from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import END, StateGraph, START
from langgraph.graph.message import MessagesState
from langchain_core.messages import BaseMessage, SystemMessage, HumanMessage
def should_continue(state: MessagesState) -> Literal["tools", "final"]:
messages = state["messages"]
last_message = messages[-1]
# If the LLM makes a tool call, then we route to the "tools" node
if last_message.tool_calls:
return "tools"
# Otherwise, we stop (reply to the user)
return "final"
def call_model(state: MessagesState):
messages = state["messages"]
response = model.invoke(messages)
# We return a list, because this will get added to the existing list
return {"messages": [response]}
def call_final_model(state: MessagesState):
messages = state["messages"]
last_ai_message = messages[-1]
response = final_model.invoke(
[
SystemMessage("Rewrite this in the voice of Al Roker"),
HumanMessage(last_ai_message.content),
]
)
# overwrite the last AI message from the agent
response.id = last_ai_message.id
return {"messages": [response]}
builder = StateGraph(MessagesState)
builder.add_node("agent", call_model)
builder.add_node("tools", tool_node)
# add a separate final node
builder.add_node("final", call_final_model)
builder.add_edge(START, "agent")
builder.add_conditional_edges(
"agent",
should_continue,
)
builder.add_edge("tools", "agent")
builder.add_edge("final", END)
graph = builder.compile()from IPython.display import display, Image
display(Image(graph.get_graph().draw_mermaid_png()))from langchain_core.messages import HumanMessage
inputs = {"messages": [HumanMessage(content="what is the weather in sf")]}
for msg, metadata in graph.stream(inputs, stream_mode="messages"):
if (
msg.content
and not isinstance(msg, HumanMessage)
and metadata["langgraph_node"] == "final"
):
print(msg.content, end="|", flush=True)inputs = {"messages": [HumanMessage(content="what's the weather in nyc?")]}
async for event in graph.astream_events(inputs, version="v2"):
kind = event["event"]
tags = event.get("tags", [])
# filter on the custom tag
if kind == "on_chat_model_stream" and "final_node" in event.get("tags", []):
data = event["data"]
if data["chunk"].content:
# Empty content in the context of OpenAI or Anthropic usually means
# that the model is asking for a tool to be invoked.
# So we only print non-empty content
print(data["chunk"].content, end="|", flush=True) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/persistence.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("ANTHROPIC_API_KEY")from langchain_anthropic import ChatAnthropic
model = ChatAnthropic(model="claude-3-5-sonnet-20240620")from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, MessagesState, START
def call_model(state: MessagesState):
response = model.invoke(state["messages"])
return {"messages": response}
builder = StateGraph(MessagesState)
builder.add_node("call_model", call_model)
builder.add_edge(START, "call_model")
graph = builder.compile()input_message = {"type": "user", "content": "hi! I'm bob"}
for chunk in graph.stream({"messages": [input_message]}, stream_mode="values"):
chunk["messages"][-1].pretty_print()
input_message = {"type": "user", "content": "what's my name?"}
for chunk in graph.stream({"messages": [input_message]}, stream_mode="values"):
chunk["messages"][-1].pretty_print()from langgraph.checkpoint.memory import MemorySaver
memory = MemorySaver()
graph = builder.compile(checkpointer=memory)
# If you're using LangGraph Cloud or LangGraph Studio, you don't need to pass the checkpointer when compiling the graph, since it's done automatically.config = {"configurable": {"thread_id": "1"}}
input_message = {"type": "user", "content": "hi! I'm bob"}
for chunk in graph.stream({"messages": [input_message]}, config, stream_mode="values"):
chunk["messages"][-1].pretty_print()input_message = {"type": "user", "content": "what's my name?"}
for chunk in graph.stream({"messages": [input_message]}, config, stream_mode="values"):
chunk["messages"][-1].pretty_print()input_message = {"type": "user", "content": "what's my name?"}
for chunk in graph.stream(
{"messages": [input_message]},
{"configurable": {"thread_id": "2"}},
stream_mode="values",
):
chunk["messages"][-1].pretty_print() |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/persistence_mongodb.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")from contextlib import asynccontextmanager, contextmanager
from typing import Any, AsyncIterator, Dict, Iterator, Optional, Sequence, Tuple
from langchain_core.runnables import RunnableConfig
from motor.motor_asyncio import AsyncIOMotorClient, AsyncIOMotorDatabase
from pymongo import MongoClient, UpdateOne
from pymongo.database import Database as MongoDatabase
from langgraph.checkpoint.base import (
BaseCheckpointSaver,
ChannelVersions,
Checkpoint,
CheckpointMetadata,
CheckpointTuple,
get_checkpoint_id,
)
class MongoDBSaver(BaseCheckpointSaver):
"""A checkpoint saver that stores checkpoints in a MongoDB database."""
client: MongoClient
db: MongoDatabase
def __init__(
self,
client: MongoClient,
db_name: str,
) -> None:
super().__init__()
self.client = client
self.db = self.client[db_name]
@classmethod
@contextmanager
def from_conn_info(
cls, *, host: str, port: int, db_name: str
) -> Iterator["MongoDBSaver"]:
client = None
try:
client = MongoClient(host=host, port=port)
yield MongoDBSaver(client, db_name)
finally:
if client:
client.close()
def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
"""Get a checkpoint tuple from the database.
This method retrieves a checkpoint tuple from the MongoDB database based on the
provided config. If the config contains a "checkpoint_id" key, the checkpoint with
the matching thread ID and checkpoint ID is retrieved. Otherwise, the latest checkpoint
for the given thread ID is retrieved.
Args:
config (RunnableConfig): The config to use for retrieving the checkpoint.
Returns:
Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
if checkpoint_id := get_checkpoint_id(config):
query = {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
}
else:
query = {"thread_id": thread_id, "checkpoint_ns": checkpoint_ns}
result = self.db["checkpoints"].find(query).sort("checkpoint_id", -1).limit(1)
for doc in result:
config_values = {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": doc["checkpoint_id"],
}
checkpoint = self.serde.loads_typed((doc["type"], doc["checkpoint"]))
serialized_writes = self.db["checkpoint_writes"].find(config_values)
pending_writes = [
(
doc["task_id"],
doc["channel"],
self.serde.loads_typed((doc["type"], doc["value"])),
)
for doc in serialized_writes
]
return CheckpointTuple(
{"configurable": config_values},
checkpoint,
self.serde.loads(doc["metadata"]),
(
{
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": doc["parent_checkpoint_id"],
}
}
if doc.get("parent_checkpoint_id")
else None
),
pending_writes,
)
def list(
self,
config: Optional[RunnableConfig],
*,
filter: Optional[Dict[str, Any]] = None,
before: Optional[RunnableConfig] = None,
limit: Optional[int] = None,
) -> Iterator[CheckpointTuple]:
"""List checkpoints from the database.
This method retrieves a list of checkpoint tuples from the MongoDB database based
on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first).
Args:
config (RunnableConfig): The config to use for listing the checkpoints.
filter (Optional[Dict[str, Any]]): Additional filtering criteria for metadata. Defaults to None.
before (Optional[RunnableConfig]): If provided, only checkpoints before the specified checkpoint ID are returned. Defaults to None.
limit (Optional[int]): The maximum number of checkpoints to return. Defaults to None.
Yields:
Iterator[CheckpointTuple]: An iterator of checkpoint tuples.
"""
query = {}
if config is not None:
query = {
"thread_id": config["configurable"]["thread_id"],
"checkpoint_ns": config["configurable"].get("checkpoint_ns", ""),
}
if filter:
for key, value in filter.items():
query[f"metadata.{key}"] = value
if before is not None:
query["checkpoint_id"] = {"$lt": before["configurable"]["checkpoint_id"]}
result = self.db["checkpoints"].find(query).sort("checkpoint_id", -1)
if limit is not None:
result = result.limit(limit)
for doc in result:
checkpoint = self.serde.loads_typed((doc["type"], doc["checkpoint"]))
yield CheckpointTuple(
{
"configurable": {
"thread_id": doc["thread_id"],
"checkpoint_ns": doc["checkpoint_ns"],
"checkpoint_id": doc["checkpoint_id"],
}
},
checkpoint,
self.serde.loads(doc["metadata"]),
(
{
"configurable": {
"thread_id": doc["thread_id"],
"checkpoint_ns": doc["checkpoint_ns"],
"checkpoint_id": doc["parent_checkpoint_id"],
}
}
if doc.get("parent_checkpoint_id")
else None
),
)
def put(
self,
config: RunnableConfig,
checkpoint: Checkpoint,
metadata: CheckpointMetadata,
new_versions: ChannelVersions,
) -> RunnableConfig:
"""Save a checkpoint to the database.
This method saves a checkpoint to the MongoDB database. The checkpoint is associated
with the provided config and its parent config (if any).
Args:
config (RunnableConfig): The config to associate with the checkpoint.
checkpoint (Checkpoint): The checkpoint to save.
metadata (CheckpointMetadata): Additional metadata to save with the checkpoint.
new_versions (ChannelVersions): New channel versions as of this write.
Returns:
RunnableConfig: Updated configuration after storing the checkpoint.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"]["checkpoint_ns"]
checkpoint_id = checkpoint["id"]
type_, serialized_checkpoint = self.serde.dumps_typed(checkpoint)
doc = {
"parent_checkpoint_id": config["configurable"].get("checkpoint_id"),
"type": type_,
"checkpoint": serialized_checkpoint,
"metadata": self.serde.dumps(metadata),
}
upsert_query = {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
}
# Perform your operations here
self.db["checkpoints"].update_one(upsert_query, {"$set": doc}, upsert=True)
return {
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
}
}
def put_writes(
self,
config: RunnableConfig,
writes: Sequence[Tuple[str, Any]],
task_id: str,
) -> None:
"""Store intermediate writes linked to a checkpoint.
This method saves intermediate writes associated with a checkpoint to the MongoDB database.
Args:
config (RunnableConfig): Configuration of the related checkpoint.
writes (Sequence[Tuple[str, Any]]): List of writes to store, each as (channel, value) pair.
task_id (str): Identifier for the task creating the writes.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"]["checkpoint_ns"]
checkpoint_id = config["configurable"]["checkpoint_id"]
operations = []
for idx, (channel, value) in enumerate(writes):
upsert_query = {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
"task_id": task_id,
"idx": idx,
}
type_, serialized_value = self.serde.dumps_typed(value)
operations.append(
UpdateOne(
upsert_query,
{
"$set": {
"channel": channel,
"type": type_,
"value": serialized_value,
}
},
upsert=True,
)
)
self.db["checkpoint_writes"].bulk_write(operations)class AsyncMongoDBSaver(BaseCheckpointSaver):
"""A checkpoint saver that stores checkpoints in a MongoDB database asynchronously."""
client: AsyncIOMotorClient
db: AsyncIOMotorDatabase
def __init__(
self,
client: AsyncIOMotorClient,
db_name: str,
) -> None:
super().__init__()
self.client = client
self.db = self.client[db_name]
@classmethod
@asynccontextmanager
async def from_conn_info(
cls, *, host: str, port: int, db_name: str
) -> AsyncIterator["AsyncMongoDBSaver"]:
client = None
try:
client = AsyncIOMotorClient(host=host, port=port)
yield AsyncMongoDBSaver(client, db_name)
finally:
if client:
client.close()
async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
"""Get a checkpoint tuple from the database asynchronously.
This method retrieves a checkpoint tuple from the MongoDB database based on the
provided config. If the config contains a "checkpoint_id" key, the checkpoint with
the matching thread ID and checkpoint ID is retrieved. Otherwise, the latest checkpoint
for the given thread ID is retrieved.
Args:
config (RunnableConfig): The config to use for retrieving the checkpoint.
Returns:
Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
if checkpoint_id := get_checkpoint_id(config):
query = {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
}
else:
query = {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
}
result = self.db["checkpoints"].find(query).sort("checkpoint_id", -1).limit(1)
async for doc in result:
config_values = {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": doc["checkpoint_id"],
}
checkpoint = self.serde.loads_typed((doc["type"], doc["checkpoint"]))
serialized_writes = self.db["checkpoint_writes"].find(config_values)
pending_writes = [
(
doc["task_id"],
doc["channel"],
self.serde.loads_typed((doc["type"], doc["value"])),
)
async for doc in serialized_writes
]
return CheckpointTuple(
{"configurable": config_values},
checkpoint,
self.serde.loads(doc["metadata"]),
(
{
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": doc["parent_checkpoint_id"],
}
}
if doc.get("parent_checkpoint_id")
else None
),
pending_writes,
)
async def alist(
self,
config: Optional[RunnableConfig],
*,
filter: Optional[Dict[str, Any]] = None,
before: Optional[RunnableConfig] = None,
limit: Optional[int] = None,
) -> AsyncIterator[CheckpointTuple]:
"""List checkpoints from the database asynchronously.
This method retrieves a list of checkpoint tuples from the MongoDB database based
on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first).
Args:
config (Optional[RunnableConfig]): Base configuration for filtering checkpoints.
filter (Optional[Dict[str, Any]]): Additional filtering criteria for metadata.
before (Optional[RunnableConfig]): If provided, only checkpoints before the specified checkpoint ID are returned. Defaults to None.
limit (Optional[int]): Maximum number of checkpoints to return.
Yields:
AsyncIterator[CheckpointTuple]: An asynchronous iterator of matching checkpoint tuples.
"""
query = {}
if config is not None:
query = {
"thread_id": config["configurable"]["thread_id"],
"checkpoint_ns": config["configurable"].get("checkpoint_ns", ""),
}
if filter:
for key, value in filter.items():
query[f"metadata.{key}"] = value
if before is not None:
query["checkpoint_id"] = {"$lt": before["configurable"]["checkpoint_id"]}
result = self.db["checkpoints"].find(query).sort("checkpoint_id", -1)
if limit is not None:
result = result.limit(limit)
async for doc in result:
checkpoint = self.serde.loads_typed((doc["type"], doc["checkpoint"]))
yield CheckpointTuple(
{
"configurable": {
"thread_id": doc["thread_id"],
"checkpoint_ns": doc["checkpoint_ns"],
"checkpoint_id": doc["checkpoint_id"],
}
},
checkpoint,
self.serde.loads(doc["metadata"]),
(
{
"configurable": {
"thread_id": doc["thread_id"],
"checkpoint_ns": doc["checkpoint_ns"],
"checkpoint_id": doc["parent_checkpoint_id"],
}
}
if doc.get("parent_checkpoint_id")
else None
),
)
async def aput(
self,
config: RunnableConfig,
checkpoint: Checkpoint,
metadata: CheckpointMetadata,
new_versions: ChannelVersions,
) -> RunnableConfig:
"""Save a checkpoint to the database asynchronously.
This method saves a checkpoint to the MongoDB database. The checkpoint is associated
with the provided config and its parent config (if any).
Args:
config (RunnableConfig): The config to associate with the checkpoint.
checkpoint (Checkpoint): The checkpoint to save.
metadata (CheckpointMetadata): Additional metadata to save with the checkpoint.
new_versions (ChannelVersions): New channel versions as of this write.
Returns:
RunnableConfig: Updated configuration after storing the checkpoint.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"]["checkpoint_ns"]
checkpoint_id = checkpoint["id"]
type_, serialized_checkpoint = self.serde.dumps_typed(checkpoint)
doc = {
"parent_checkpoint_id": config["configurable"].get("checkpoint_id"),
"type": type_,
"checkpoint": serialized_checkpoint,
"metadata": self.serde.dumps(metadata),
}
upsert_query = {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
}
# Perform your operations here
await self.db["checkpoints"].update_one(
upsert_query, {"$set": doc}, upsert=True
)
return {
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
}
}
async def aput_writes(
self,
config: RunnableConfig,
writes: Sequence[Tuple[str, Any]],
task_id: str,
) -> None:
"""Store intermediate writes linked to a checkpoint asynchronously.
This method saves intermediate writes associated with a checkpoint to the database.
Args:
config (RunnableConfig): Configuration of the related checkpoint.
writes (Sequence[Tuple[str, Any]]): List of writes to store, each as (channel, value) pair.
task_id (str): Identifier for the task creating the writes.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"]["checkpoint_ns"]
checkpoint_id = config["configurable"]["checkpoint_id"]
operations = []
for idx, (channel, value) in enumerate(writes):
upsert_query = {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
"task_id": task_id,
"idx": idx,
}
type_, serialized_value = self.serde.dumps_typed(value)
operations.append(
UpdateOne(
upsert_query,
{
"$set": {
"channel": channel,
"type": type_,
"value": serialized_value,
}
},
upsert=True,
)
)
await self.db["checkpoint_writes"].bulk_write(operations)from typing import Literal
from langchain_core.runnables import ConfigurableField
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
@tool
def get_weather(city: Literal["nyc", "sf"]):
"""Use this to get weather information."""
if city == "nyc":
return "It might be cloudy in nyc"
elif city == "sf":
return "It's always sunny in sf"
else:
raise AssertionError("Unknown city")
tools = [get_weather]
model = ChatOpenAI(model_name="gpt-4o-mini", temperature=0)with MongoDBSaver.from_conn_info(
host="localhost", port=27017, db_name="checkpoints"
) as checkpointer:
graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)
config = {"configurable": {"thread_id": "1"}}
res = graph.invoke({"messages": [("human", "what's the weather in sf")]}, config)
latest_checkpoint = checkpointer.get(config)
latest_checkpoint_tuple = checkpointer.get_tuple(config)
checkpoint_tuples = list(checkpointer.list(config))latest_checkpointlatest_checkpoint_tuplecheckpoint_tuplesasync with AsyncMongoDBSaver.from_conn_info(
host="localhost", port=27017, db_name="checkpoints"
) as checkpointer:
graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)
config = {"configurable": {"thread_id": "2"}}
res = await graph.ainvoke(
{"messages": [("human", "what's the weather in nyc")]}, config
)
latest_checkpoint = await checkpointer.aget(config)
latest_checkpoint_tuple = await checkpointer.aget_tuple(config)
checkpoint_tuples = [c async for c in checkpointer.alist(config)]latest_checkpointlatest_checkpoint_tuplecheckpoint_tuples |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/visualization.ipynb | import random
from typing import Annotated, Literal
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
class State(TypedDict):
messages: Annotated[list, add_messages]
class MyNode:
def __init__(self, name: str):
self.name = name
def __call__(self, state: State):
return {"messages": [("assistant", f"Called node {self.name}")]}
def route(state) -> Literal["entry_node", "__end__"]:
if len(state["messages"]) > 10:
return "__end__"
return "entry_node"
def add_fractal_nodes(builder, current_node, level, max_level):
if level > max_level:
return
# Number of nodes to create at this level
num_nodes = random.randint(1, 3) # Adjust randomness as needed
for i in range(num_nodes):
nm = ["A", "B", "C"][i]
node_name = f"node_{current_node}_{nm}"
builder.add_node(node_name, MyNode(node_name))
builder.add_edge(current_node, node_name)
# Recursively add more nodes
r = random.random()
if r > 0.2 and level + 1 < max_level:
add_fractal_nodes(builder, node_name, level + 1, max_level)
elif r > 0.05:
builder.add_conditional_edges(node_name, route, node_name)
else:
# End
builder.add_edge(node_name, "__end__")
def build_fractal_graph(max_level: int):
builder = StateGraph(State)
entry_point = "entry_node"
builder.add_node(entry_point, MyNode(entry_point))
builder.add_edge(START, entry_point)
add_fractal_nodes(builder, entry_point, 1, max_level)
# Optional: set a finish point if required
builder.add_edge(entry_point, END) # or any specific node
return builder.compile()
app = build_fractal_graph(3)print(app.get_graph().draw_mermaid())from IPython.display import Image, display
from langchain_core.runnables.graph import CurveStyle, MermaidDrawMethod, NodeStyles
display(
Image(
app.get_graph().draw_mermaid_png(
draw_method=MermaidDrawMethod.API,
)
)
)import nest_asyncio
nest_asyncio.apply() # Required for Jupyter Notebook to run async functions
display(
Image(
app.get_graph().draw_mermaid_png(
curve_style=CurveStyle.LINEAR,
node_colors=NodeStyles(first="#ffdfba", last="#baffc9", default="#fad7de"),
wrap_label_n_words=9,
output_file_path=None,
draw_method=MermaidDrawMethod.PYPPETEER,
background_color="white",
padding=10,
)
)
)try:
display(Image(app.get_graph().draw_png()))
except ImportError:
print(
"You likely need to install dependencies for pygraphviz, see more here https://github.com/pygraphviz/pygraphviz/blob/main/INSTALL.txt"
) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/autogen-langgraph-platform.ipynb | %pip install autogen langgraphimport getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")import autogen
import os
config_list = [{"model": "gpt-4o", "api_key": os.environ["OPENAI_API_KEY"]}]
llm_config = {
"timeout": 600,
"cache_seed": 42,
"config_list": config_list,
"temperature": 0,
}
autogen_agent = autogen.AssistantAgent(
name="assistant",
llm_config=llm_config,
)
user_proxy = autogen.UserProxyAgent(
name="user_proxy",
human_input_mode="NEVER",
max_consecutive_auto_reply=10,
is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
code_execution_config={
"work_dir": "web",
"use_docker": False,
}, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.
llm_config=llm_config,
system_message="Reply TERMINATE if the task has been solved at full satisfaction. Otherwise, reply CONTINUE, or the reason why the task is not solved yet.",
)from langgraph.graph import StateGraph, MessagesState
def call_autogen_agent(state: MessagesState):
last_message = state["messages"][-1]
response = user_proxy.initiate_chat(autogen_agent, message=last_message.content)
# get the final response from the agent
content = response.chat_history[-1]["content"]
return {"messages": {"role": "assistant", "content": content}}
graph = StateGraph(MessagesState)
graph.add_node(call_autogen_agent)
graph.set_entry_point("call_autogen_agent")
graph = graph.compile() |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/streaming-tokens-without-langchain.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")from openai import AsyncOpenAI
from langchain_core.language_models.chat_models import ChatGenerationChunk
from langchain_core.messages import AIMessageChunk
from langchain_core.runnables.config import (
ensure_config,
get_callback_manager_for_config,
)
openai_client = AsyncOpenAI()
# define tool schema for openai tool calling
tool = {
"type": "function",
"function": {
"name": "get_items",
"description": "Use this tool to look up which items are in the given place.",
"parameters": {
"type": "object",
"properties": {"place": {"type": "string"}},
"required": ["place"],
},
},
}
async def call_model(state, config=None):
config = ensure_config(config | {"tags": ["agent_llm"]})
callback_manager = get_callback_manager_for_config(config)
messages = state["messages"]
llm_run_manager = callback_manager.on_chat_model_start({}, [messages])[0]
response = await openai_client.chat.completions.create(
messages=messages, model="gpt-3.5-turbo", tools=[tool], stream=True
)
response_content = ""
role = None
tool_call_id = None
tool_call_function_name = None
tool_call_function_arguments = ""
async for chunk in response:
delta = chunk.choices[0].delta
if delta.role is not None:
role = delta.role
if delta.content:
response_content += delta.content
# note: we're wrapping the response in ChatGenerationChunk so that we can stream this back using stream_mode="messages"
chunk = ChatGenerationChunk(
message=AIMessageChunk(
content=delta.content,
)
)
llm_run_manager.on_llm_new_token(delta.content, chunk=chunk)
if delta.tool_calls:
# note: for simplicity we're only handling a single tool call here
if delta.tool_calls[0].function.name is not None:
tool_call_function_name = delta.tool_calls[0].function.name
tool_call_id = delta.tool_calls[0].id
# note: we're wrapping the tools calls in ChatGenerationChunk so that we can stream this back using stream_mode="messages"
tool_call_chunk = ChatGenerationChunk(
message=AIMessageChunk(
content="",
additional_kwargs={"tool_calls": [delta.tool_calls[0].dict()]},
)
)
llm_run_manager.on_llm_new_token("", chunk=tool_call_chunk)
tool_call_function_arguments += delta.tool_calls[0].function.arguments
if tool_call_function_name is not None:
tool_calls = [
{
"id": tool_call_id,
"function": {
"name": tool_call_function_name,
"arguments": tool_call_function_arguments,
},
"type": "function",
}
]
else:
tool_calls = None
response_message = {
"role": role,
"content": response_content,
"tool_calls": tool_calls,
}
return {"messages": [response_message]}import json
async def get_items(place: str) -> str:
"""Use this tool to look up which items are in the given place."""
if "bed" in place: # For under the bed
return "socks, shoes and dust bunnies"
if "shelf" in place: # For 'shelf'
return "books, penciles and pictures"
else: # if the agent decides to ask about a different place
return "cat snacks"
# define mapping to look up functions when running tools
function_name_to_function = {"get_items": get_items}
async def call_tools(state):
messages = state["messages"]
tool_call = messages[-1]["tool_calls"][0]
function_name = tool_call["function"]["name"]
function_arguments = tool_call["function"]["arguments"]
arguments = json.loads(function_arguments)
function_response = await function_name_to_function[function_name](**arguments)
tool_message = {
"tool_call_id": tool_call["id"],
"role": "tool",
"name": function_name,
"content": function_response,
}
return {"messages": [tool_message]}import operator
from typing import Annotated, Literal
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, END, START
class State(TypedDict):
messages: Annotated[list, operator.add]
def should_continue(state) -> Literal["tools", END]:
messages = state["messages"]
last_message = messages[-1]
if last_message["tool_calls"]:
return "tools"
return END
workflow = StateGraph(State)
workflow.add_edge(START, "model")
workflow.add_node("model", call_model) # i.e. our "agent"
workflow.add_node("tools", call_tools)
workflow.add_conditional_edges("model", should_continue)
workflow.add_edge("tools", "model")
graph = workflow.compile()from langchain_core.messages import AIMessageChunk
first = True
async for msg, metadata in graph.astream(
{"messages": [{"role": "user", "content": "what's in the bedroom"}]},
stream_mode="messages",
):
if msg.content:
print(msg.content, end="|", flush=True)
if isinstance(msg, AIMessageChunk):
if first:
gathered = msg
first = False
else:
gathered = gathered + msg
if msg.tool_call_chunks:
print(gathered.tool_calls) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/pass-run-time-values-to-tools.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")from typing import List
# this is the state schema used by the prebuilt create_react_agent we'll be using below
from langgraph.prebuilt.chat_agent_executor import AgentState
from langchain_core.documents import Document
class State(AgentState):
docs: List[str]from typing import List, Tuple
from typing_extensions import Annotated
from langchain_core.messages import ToolMessage
from langchain_core.tools import tool
from langgraph.prebuilt import InjectedState
@tool
def get_context(question: str, state: Annotated[dict, InjectedState]):
"""Get relevant context for answering the question."""
return "\n\n".join(doc for doc in state["docs"])get_context.get_input_schema().schema()get_context.tool_call_schema.schema()from langchain_openai import ChatOpenAI
from langgraph.prebuilt import ToolNode, create_react_agent
from langgraph.checkpoint.memory import MemorySaver
model = ChatOpenAI(model="gpt-4o", temperature=0)
tools = [get_context]
# ToolNode will automatically take care of injecting state into tools
tool_node = ToolNode(tools)
checkpointer = MemorySaver()
graph = create_react_agent(model, tools, state_schema=State, checkpointer=checkpointer)docs = [
"FooBar company just raised 1 Billion dollars!",
"FooBar company was founded in 2019",
]
inputs = {
"messages": [{"type": "user", "content": "what's the latest news about FooBar"}],
"docs": docs,
}
config = {"configurable": {"thread_id": "1"}}
for chunk in graph.stream(inputs, config, stream_mode="values"):
chunk["messages"][-1].pretty_print()from langgraph.store.memory import InMemoryStore
doc_store = InMemoryStore()
namespace = ("documents", "1") # user ID
doc_store.put(
namespace, "doc_0", {"doc": "FooBar company just raised 1 Billion dollars!"}
)
namespace = ("documents", "2") # user ID
doc_store.put(namespace, "doc_1", {"doc": "FooBar company was founded in 2019"})from langgraph.store.base import BaseStore
from langchain_core.runnables import RunnableConfig
from langgraph.prebuilt import InjectedStore
@tool
def get_context(
question: str,
config: RunnableConfig,
store: Annotated[BaseStore, InjectedStore()],
) -> Tuple[str, List[Document]]:
"""Get relevant context for answering the question."""
user_id = config.get("configurable", {}).get("user_id")
docs = [item.value["doc"] for item in store.search(("documents", user_id))]
return "\n\n".join(doc for doc in docs)get_context.tool_call_schema.schema()tools = [get_context]
# ToolNode will automatically take care of injecting Store into tools
tool_node = ToolNode(tools)
checkpointer = MemorySaver()
# NOTE: we need to pass our store to `create_react_agent` to make sure our graph is aware of it
graph = create_react_agent(model, tools, checkpointer=checkpointer, store=doc_store)messages = [{"type": "user", "content": "what's the latest news about FooBar"}]
config = {"configurable": {"thread_id": "1", "user_id": "1"}}
for chunk in graph.stream({"messages": messages}, config, stream_mode="values"):
chunk["messages"][-1].pretty_print()messages = [{"type": "user", "content": "what's the latest news about FooBar"}]
config = {"configurable": {"thread_id": "2", "user_id": "2"}}
for chunk in graph.stream({"messages": messages}, config, stream_mode="values"):
chunk["messages"][-1].pretty_print() |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/create-react-agent-system-prompt.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")# First we initialize the model we want to use.
from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-4o", temperature=0)
# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)
from typing import Literal
from langchain_core.tools import tool
@tool
def get_weather(city: Literal["nyc", "sf"]):
"""Use this to get weather information."""
if city == "nyc":
return "It might be cloudy in nyc"
elif city == "sf":
return "It's always sunny in sf"
else:
raise AssertionError("Unknown city")
tools = [get_weather]
# We can add our system prompt here
prompt = "Respond in Italian"
# Define the graph
from langgraph.prebuilt import create_react_agent
graph = create_react_agent(model, tools=tools, state_modifier=prompt)def print_stream(stream):
for s in stream:
message = s["messages"][-1]
if isinstance(message, tuple):
print(message)
else:
message.pretty_print()inputs = {"messages": [("user", "What's the weather in NYC?")]}
print_stream(graph.stream(inputs, stream_mode="values")) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/stream-multiple.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")from typing import Literal
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.runnables import ConfigurableField
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
@tool
def get_weather(city: Literal["nyc", "sf"]):
"""Use this to get weather information."""
if city == "nyc":
return "It might be cloudy in nyc"
elif city == "sf":
return "It's always sunny in sf"
else:
raise AssertionError("Unknown city")
tools = [get_weather]
model = ChatOpenAI(model_name="gpt-4o", temperature=0)
graph = create_react_agent(model, tools)inputs = {"messages": [("human", "what's the weather in sf")]}
async for event, chunk in graph.astream(inputs, stream_mode=["updates", "debug"]):
print(f"Receiving new event of type: {event}...")
print(chunk)
print("\n\n") |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/use-remote-graph.md | # How to interact with the deployment using RemoteGraph
!!! info "Prerequisites"
- [LangGraph Platform](../concepts/langgraph_platform.md)
- [LangGraph Server](../concepts/langgraph_server.md)
`RemoteGraph` is an interface that allows you to interact with your LangGraph Platform deployment as if it were a regular, locally-defined LangGraph graph (e.g. a `CompiledGraph`). This guide shows you how you can initialize a `RemoteGraph` and interact with it.
## Initializing the graph
When initializing a `RemoteGraph`, you must always specify:
- `name`: the name of the graph you want to interact with. This is the same graph name you use in `langgraph.json` configuration file for your deployment.
- `api_key`: a valid LangSmith API key. Can be set as an environment variable (`LANGSMITH_API_KEY`) or passed directly via the `api_key` argument. The API key could also be provided via the `client` / `sync_client` arguments, if `LangGraphClient` / `SyncLangGraphClient` were initialized with `api_key` argument.
Additionally, you have to provide one of the following:
- `url`: URL of the deployment you want to interact with. If you pass `url` argument, both sync and async clients will be created using the provided URL, headers (if provided) and default configuration values (e.g. timeout, etc).
- `client`: a `LangGraphClient` instance for interacting with the deployment asynchronously (e.g. using `.astream()`, `.ainvoke()`, `.aget_state()`, `.aupdate_state()`, etc.)
- `sync_client`: a `SyncLangGraphClient` instance for interacting with the deployment synchronously (e.g. using `.stream()`, `.invoke()`, `.get_state()`, `.update_state()`, etc.)
!!! Note
If you pass both `client` or `sync_client` as well as `url` argument, they will take precedence over the `url` argument. If none of the `client` / `sync_client` / `url` arguments are provided, `RemoteGraph` will raise a `ValueError` at runtime.
### Using URL
=== "Python"
```python
from langgraph.pregel.remote import RemoteGraph
url = <DEPLOYMENT_URL>
graph_name = "agent"
remote_graph = RemoteGraph(graph_name, url=url)
```
=== "JavaScript"
```ts
import { RemoteGraph } from "@langchain/langgraph/remote";
const url = `<DEPLOYMENT_URL>`;
const graphName = "agent";
const remoteGraph = new RemoteGraph({ graphId: graphName, url });
```
### Using clients
=== "Python"
```python
from langgraph_sdk import get_client, get_sync_client
from langgraph.pregel.remote import RemoteGraph
url = <DEPLOYMENT_URL>
graph_name = "agent"
client = get_client(url=url)
sync_client = get_sync_client(url=url)
remote_graph = RemoteGraph(graph_name, client=client, sync_client=sync_client)
```
=== "JavaScript"
```ts
import { Client } from "@langchain/langgraph-sdk";
import { RemoteGraph } from "@langchain/langgraph/remote";
const client = new Client({ apiUrl: `<DEPLOYMENT_URL>` });
const graphName = "agent";
const remoteGraph = new RemoteGraph({ graphId: graphName, client });
```
## Invoking the graph
Since `RemoteGraph` is a `Runnable` that implements the same methods as `CompiledGraph`, you can interact with it the same way you normally would with a compiled graph, i.e. by calling `.invoke()`, `.stream()`, `.get_state()`, `.update_state()`, etc (as well as their async counterparts).
### Asynchronously
!!! Note
To use the graph asynchronously, you must provide either the `url` or `client` when initializing the `RemoteGraph`.
=== "Python"
```python
# invoke the graph
result = await remote_graph.ainvoke({
"messages": [{"role": "user", "content": "what's the weather in sf"}]
})
# stream outputs from the graph
async for chunk in remote_graph.astream({
"messages": [{"role": "user", "content": "what's the weather in la"}]
}):
print(chunk)
```
=== "JavaScript"
```ts
// invoke the graph
const result = await remoteGraph.invoke({
messages: [{role: "user", content: "what's the weather in sf"}]
})
// stream outputs from the graph
for await (const chunk of await remoteGraph.stream({
messages: [{role: "user", content: "what's the weather in la"}]
})):
console.log(chunk)
```
### Synchronously
!!! Note
To use the graph synchronously, you must provide either the `url` or `sync_client` when initializing the `RemoteGraph`.
=== "Python"
```python
# invoke the graph
result = remote_graph.invoke({
"messages": [{"role": "user", "content": "what's the weather in sf"}]
})
# stream outputs from the graph
for chunk in remote_graph.stream({
"messages": [{"role": "user", "content": "what's the weather in la"}]
}):
print(chunk)
```
## Thread-level persistence
By default, the graph runs (i.e. `.invoke()` or `.stream()` invocations) are stateless - the checkpoints and the final state of the graph are not persisted. If you would like to persist the outputs of the graph run (for example, to enable human-in-the-loop features), you can create a thread and provide the thread ID via the `config` argument, same as you would with a regular compiled graph:
=== "Python"
```python
from langgraph_sdk import get_sync_client
url = <DEPLOYMENT_URL>
graph_name = "agent"
sync_client = get_sync_client(url=url)
remote_graph = RemoteGraph(graph_name, url=url)
# create a thread (or use an existing thread instead)
thread = sync_client.threads.create()
# invoke the graph with the thread config
config = {"configurable": {"thread_id": thread["thread_id"]}}
result = remote_graph.invoke({
"messages": [{"role": "user", "content": "what's the weather in sf"}]
}, config=config)
# verify that the state was persisted to the thread
thread_state = remote_graph.get_state(config)
print(thread_state)
```
=== "JavaScript"
```ts
import { Client } from "@langchain/langgraph-sdk";
import { RemoteGraph } from "@langchain/langgraph/remote";
const url = `<DEPLOYMENT_URL>`;
const graphName = "agent";
const client = new Client({ apiUrl: url });
const remoteGraph = new RemoteGraph({ graphId: graphName, url });
// create a thread (or use an existing thread instead)
const thread = await client.threads.create();
// invoke the graph with the thread config
const config = { configurable: { thread_id: thread.thread_id }};
const result = await remoteGraph.invoke({
messages: [{ role: "user", content: "what's the weather in sf" }],
}, config);
// verify that the state was persisted to the thread
const threadState = await remoteGraph.getState(config);
console.log(threadState);
```
## Using as a subgraph
!!! Note
If you need to use a `checkpointer` with a graph that has a `RemoteGraph` subgraph node, make sure to use UUIDs as thread IDs.
Since the `RemoteGraph` behaves the same way as a regular `CompiledGraph`, it can be also used as a subgraph in another graph. For example:
=== "Python"
```python
from langgraph_sdk import get_sync_client
from langgraph.graph import StateGraph, MessagesState, START
from typing import TypedDict
url = <DEPLOYMENT_URL>
graph_name = "agent"
remote_graph = RemoteGraph(graph_name, url=url)
# define parent graph
builder = StateGraph(MessagesState)
# add remote graph directly as a node
builder.add_node("child", remote_graph)
builder.add_edge(START, "child")
graph = builder.compile()
# invoke the parent graph
result = graph.invoke({
"messages": [{"role": "user", "content": "what's the weather in sf"}]
})
print(result)
# stream outputs from both the parent graph and subgraph
for chunk in graph.stream({
"messages": [{"role": "user", "content": "what's the weather in sf"}]
}, subgraphs=True):
print(chunk)
```
=== "JavaScript"
```ts
import { MessagesAnnotation, StateGraph, START } from "@langchain/langgraph";
import { RemoteGraph } from "@langchain/langgraph/remote";
const url = `<DEPLOYMENT_URL>`;
const graphName = "agent";
const remoteGraph = new RemoteGraph({ graphId: graphName, url });
// define parent graph and add remote graph directly as a node
const graph = new StateGraph(MessagesAnnotation)
.addNode("child", remoteGraph)
.addEdge(START, "child")
.compile()
// invoke the parent graph
const result = await graph.invoke({
messages: [{ role: "user", content: "what's the weather in sf" }]
});
console.log(result);
// stream outputs from both the parent graph and subgraph
for await (const chunk of await graph.stream({
messages: [{ role: "user", content: "what's the weather in la" }]
}, { subgraphs: true })) {
console.log(chunk);
}
``` |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/subgraph.ipynb | from langgraph.graph import START, StateGraph
from typing import TypedDict
# Define subgraph
class SubgraphState(TypedDict):
foo: str # note that this key is shared with the parent graph state
bar: str
def subgraph_node_1(state: SubgraphState):
return {"bar": "bar"}
def subgraph_node_2(state: SubgraphState):
# note that this node is using a state key ('bar') that is only available in the subgraph
# and is sending update on the shared state key ('foo')
return {"foo": state["foo"] + state["bar"]}
subgraph_builder = StateGraph(SubgraphState)
subgraph_builder.add_node(subgraph_node_1)
subgraph_builder.add_node(subgraph_node_2)
subgraph_builder.add_edge(START, "subgraph_node_1")
subgraph_builder.add_edge("subgraph_node_1", "subgraph_node_2")
subgraph = subgraph_builder.compile()
# Define parent graph
class ParentState(TypedDict):
foo: str
def node_1(state: ParentState):
return {"foo": "hi! " + state["foo"]}
builder = StateGraph(ParentState)
builder.add_node("node_1", node_1)
# note that we're adding the compiled subgraph as a node to the parent graph
builder.add_node("node_2", subgraph)
builder.add_edge(START, "node_1")
builder.add_edge("node_1", "node_2")
graph = builder.compile()for chunk in graph.stream({"foo": "foo"}):
print(chunk)for chunk in graph.stream({"foo": "foo"}, subgraphs=True):
print(chunk)# Define subgraph
class SubgraphState(TypedDict):
# note that none of these keys are shared with the parent graph state
bar: str
baz: str
def subgraph_node_1(state: SubgraphState):
return {"baz": "baz"}
def subgraph_node_2(state: SubgraphState):
return {"bar": state["bar"] + state["baz"]}
subgraph_builder = StateGraph(SubgraphState)
subgraph_builder.add_node(subgraph_node_1)
subgraph_builder.add_node(subgraph_node_2)
subgraph_builder.add_edge(START, "subgraph_node_1")
subgraph_builder.add_edge("subgraph_node_1", "subgraph_node_2")
subgraph = subgraph_builder.compile()
# Define parent graph
class ParentState(TypedDict):
foo: str
def node_1(state: ParentState):
return {"foo": "hi! " + state["foo"]}
def node_2(state: ParentState):
# transform the state to the subgraph state
response = subgraph.invoke({"bar": state["foo"]})
# transform response back to the parent state
return {"foo": response["bar"]}
builder = StateGraph(ParentState)
builder.add_node("node_1", node_1)
# note that instead of using the compiled subgraph we are using `node_2` function that is calling the subgraph
builder.add_node("node_2", node_2)
builder.add_edge(START, "node_1")
builder.add_edge("node_1", "node_2")
graph = builder.compile()for chunk in graph.stream({"foo": "foo"}, subgraphs=True):
print(chunk) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/cross-thread-persistence.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("ANTHROPIC_API_KEY")from langgraph.store.memory import InMemoryStore
from langchain_openai import OpenAIEmbeddings
in_memory_store = InMemoryStore(
index={
"embed": OpenAIEmbeddings(model="text-embedding-3-small"),
"dims": 1536,
}
)import uuid
from typing import Annotated
from typing_extensions import TypedDict
from langchain_anthropic import ChatAnthropic
from langchain_core.runnables import RunnableConfig
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.memory import MemorySaver
from langgraph.store.base import BaseStore
model = ChatAnthropic(model="claude-3-5-sonnet-20240620")
# NOTE: we're passing the Store param to the node --
# this is the Store we compile the graph with
def call_model(state: MessagesState, config: RunnableConfig, *, store: BaseStore):
user_id = config["configurable"]["user_id"]
namespace = ("memories", user_id)
memories = store.search(namespace, query=str(state["messages"][-1].content))
info = "\n".join([d.value["data"] for d in memories])
system_msg = f"You are a helpful assistant talking to the user. User info: {info}"
# Store new memories if the user asks the model to remember
last_message = state["messages"][-1]
if "remember" in last_message.content.lower():
memory = "User name is Bob"
store.put(namespace, str(uuid.uuid4()), {"data": memory})
response = model.invoke(
[{"type": "system", "content": system_msg}] + state["messages"]
)
return {"messages": response}
builder = StateGraph(MessagesState)
builder.add_node("call_model", call_model)
builder.add_edge(START, "call_model")
# NOTE: we're passing the store object here when compiling the graph
graph = builder.compile(checkpointer=MemorySaver(), store=in_memory_store)
# If you're using LangGraph Cloud or LangGraph Studio, you don't need to pass the store or checkpointer when compiling the graph, since it's done automatically.config = {"configurable": {"thread_id": "1", "user_id": "1"}}
input_message = {"type": "user", "content": "Hi! Remember: my name is Bob"}
for chunk in graph.stream({"messages": [input_message]}, config, stream_mode="values"):
chunk["messages"][-1].pretty_print()config = {"configurable": {"thread_id": "2", "user_id": "1"}}
input_message = {"type": "user", "content": "what is my name?"}
for chunk in graph.stream({"messages": [input_message]}, config, stream_mode="values"):
chunk["messages"][-1].pretty_print()for memory in in_memory_store.search(("memories", "1")):
print(memory.value)config = {"configurable": {"thread_id": "3", "user_id": "2"}}
input_message = {"type": "user", "content": "what is my name?"}
for chunk in graph.stream({"messages": [input_message]}, config, stream_mode="values"):
chunk["messages"][-1].pretty_print() |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/command.ipynb | import random
from typing_extensions import TypedDict, Literal
from langgraph.graph import StateGraph, START
from langgraph.types import Command
# Define graph state
class State(TypedDict):
foo: str
# Define the nodes
def node_a(state: State) -> Command[Literal["node_b", "node_c"]]:
print("Called A")
value = random.choice(["a", "b"])
# this is a replacement for a conditional edge function
if value == "a":
goto = "node_b"
else:
goto = "node_c"
# note how Command allows you to BOTH update the graph state AND route to the next node
return Command(
# this is the state update
update={"foo": value},
# this is a replacement for an edge
goto=goto,
)
# Nodes B and C are unchanged
def node_b(state: State):
print("Called B")
return {"foo": state["foo"] + "b"}
def node_c(state: State):
print("Called C")
return {"foo": state["foo"] + "c"}builder = StateGraph(State)
builder.add_edge(START, "node_a")
builder.add_node(node_a)
builder.add_node(node_b)
builder.add_node(node_c)
# NOTE: there are no edges between nodes A, B and C!
graph = builder.compile()from IPython.display import display, Image
display(Image(graph.get_graph().draw_mermaid_png()))graph.invoke({"foo": ""}) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/map-reduce.ipynb | import os
import getpass
def _set_env(name: str):
if not os.getenv(name):
os.environ[name] = getpass.getpass(f"{name}: ")
_set_env("ANTHROPIC_API_KEY")import operator
from typing import Annotated
from typing_extensions import TypedDict
from langchain_anthropic import ChatAnthropic
from langgraph.types import Send
from langgraph.graph import END, StateGraph, START
from pydantic import BaseModel, Field
# Model and prompts
# Define model and prompts we will use
subjects_prompt = """Generate a comma separated list of between 2 and 5 examples related to: {topic}."""
joke_prompt = """Generate a joke about {subject}"""
best_joke_prompt = """Below are a bunch of jokes about {topic}. Select the best one! Return the ID of the best one.
{jokes}"""
class Subjects(BaseModel):
subjects: list[str]
class Joke(BaseModel):
joke: str
class BestJoke(BaseModel):
id: int = Field(description="Index of the best joke, starting with 0", ge=0)
model = ChatAnthropic(model="claude-3-5-sonnet-20240620")
# Graph components: define the components that will make up the graph
# This will be the overall state of the main graph.
# It will contain a topic (which we expect the user to provide)
# and then will generate a list of subjects, and then a joke for
# each subject
class OverallState(TypedDict):
topic: str
subjects: list
# Notice here we use the operator.add
# This is because we want combine all the jokes we generate
# from individual nodes back into one list - this is essentially
# the "reduce" part
jokes: Annotated[list, operator.add]
best_selected_joke: str
# This will be the state of the node that we will "map" all
# subjects to in order to generate a joke
class JokeState(TypedDict):
subject: str
# This is the function we will use to generate the subjects of the jokes
def generate_topics(state: OverallState):
prompt = subjects_prompt.format(topic=state["topic"])
response = model.with_structured_output(Subjects).invoke(prompt)
return {"subjects": response.subjects}
# Here we generate a joke, given a subject
def generate_joke(state: JokeState):
prompt = joke_prompt.format(subject=state["subject"])
response = model.with_structured_output(Joke).invoke(prompt)
return {"jokes": [response.joke]}
# Here we define the logic to map out over the generated subjects
# We will use this an edge in the graph
def continue_to_jokes(state: OverallState):
# We will return a list of `Send` objects
# Each `Send` object consists of the name of a node in the graph
# as well as the state to send to that node
return [Send("generate_joke", {"subject": s}) for s in state["subjects"]]
# Here we will judge the best joke
def best_joke(state: OverallState):
jokes = "\n\n".join(state["jokes"])
prompt = best_joke_prompt.format(topic=state["topic"], jokes=jokes)
response = model.with_structured_output(BestJoke).invoke(prompt)
return {"best_selected_joke": state["jokes"][response.id]}
# Construct the graph: here we put everything together to construct our graph
graph = StateGraph(OverallState)
graph.add_node("generate_topics", generate_topics)
graph.add_node("generate_joke", generate_joke)
graph.add_node("best_joke", best_joke)
graph.add_edge(START, "generate_topics")
graph.add_conditional_edges("generate_topics", continue_to_jokes, ["generate_joke"])
graph.add_edge("generate_joke", "best_joke")
graph.add_edge("best_joke", END)
app = graph.compile()from IPython.display import Image
Image(app.get_graph().draw_mermaid_png())# Call the graph: here we call it to generate a list of jokes
for s in app.stream({"topic": "animals"}):
print(s) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/branching.ipynb | import operator
from typing import Annotated, Any
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
class State(TypedDict):
# The operator.add reducer fn makes this append-only
aggregate: Annotated[list, operator.add]
class ReturnNodeValue:
def __init__(self, node_secret: str):
self._value = node_secret
def __call__(self, state: State) -> Any:
print(f"Adding {self._value} to {state['aggregate']}")
return {"aggregate": [self._value]}
builder = StateGraph(State)
builder.add_node("a", ReturnNodeValue("I'm A"))
builder.add_edge(START, "a")
builder.add_node("b", ReturnNodeValue("I'm B"))
builder.add_node("c", ReturnNodeValue("I'm C"))
builder.add_node("d", ReturnNodeValue("I'm D"))
builder.add_edge("a", "b")
builder.add_edge("a", "c")
builder.add_edge("b", "d")
builder.add_edge("c", "d")
builder.add_edge("d", END)
graph = builder.compile()from IPython.display import Image, display
display(Image(graph.get_graph().draw_mermaid_png()))graph.invoke({"aggregate": []}, {"configurable": {"thread_id": "foo"}})import operator
from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import StateGraph
class State(TypedDict):
# The operator.add reducer fn makes this append-only
aggregate: Annotated[list, operator.add]
class ReturnNodeValue:
def __init__(self, node_secret: str):
self._value = node_secret
def __call__(self, state: State) -> Any:
print(f"Adding {self._value} to {state['aggregate']}")
return {"aggregate": [self._value]}
builder = StateGraph(State)
builder.add_node("a", ReturnNodeValue("I'm A"))
builder.add_edge(START, "a")
builder.add_node("b", ReturnNodeValue("I'm B"))
builder.add_node("b2", ReturnNodeValue("I'm B2"))
builder.add_node("c", ReturnNodeValue("I'm C"))
builder.add_node("d", ReturnNodeValue("I'm D"))
builder.add_edge("a", "b")
builder.add_edge("a", "c")
builder.add_edge("b", "b2")
builder.add_edge(["b2", "c"], "d")
builder.add_edge("d", END)
graph = builder.compile()from IPython.display import Image, display
display(Image(graph.get_graph().draw_mermaid_png()))graph.invoke({"aggregate": []})import operator
from typing import Annotated, Sequence
from typing_extensions import TypedDict
from langgraph.graph import END, START, StateGraph
class State(TypedDict):
# The operator.add reducer fn makes this append-only
aggregate: Annotated[list, operator.add]
which: str
class ReturnNodeValue:
def __init__(self, node_secret: str):
self._value = node_secret
def __call__(self, state: State) -> Any:
print(f"Adding {self._value} to {state['aggregate']}")
return {"aggregate": [self._value]}
builder = StateGraph(State)
builder.add_node("a", ReturnNodeValue("I'm A"))
builder.add_edge(START, "a")
builder.add_node("b", ReturnNodeValue("I'm B"))
builder.add_node("c", ReturnNodeValue("I'm C"))
builder.add_node("d", ReturnNodeValue("I'm D"))
builder.add_node("e", ReturnNodeValue("I'm E"))
def route_bc_or_cd(state: State) -> Sequence[str]:
if state["which"] == "cd":
return ["c", "d"]
return ["b", "c"]
intermediates = ["b", "c", "d"]
builder.add_conditional_edges(
"a",
route_bc_or_cd,
intermediates,
)
for node in intermediates:
builder.add_edge(node, "e")
builder.add_edge("e", END)
graph = builder.compile()from IPython.display import Image, display
display(Image(graph.get_graph().draw_mermaid_png()))graph.invoke({"aggregate": [], "which": "bc"})graph.invoke({"aggregate": [], "which": "cd"})import operator
from typing import Annotated, Sequence
from typing_extensions import TypedDict
from langgraph.graph import StateGraph
def reduce_fanouts(left, right):
if left is None:
left = []
if not right:
# Overwrite
return []
return left + right
class State(TypedDict):
# The operator.add reducer fn makes this append-only
aggregate: Annotated[list, operator.add]
fanout_values: Annotated[list, reduce_fanouts]
which: str
builder = StateGraph(State)
builder.add_node("a", ReturnNodeValue("I'm A"))
builder.add_edge(START, "a")
class ParallelReturnNodeValue:
def __init__(
self,
node_secret: str,
reliability: float,
):
self._value = node_secret
self._reliability = reliability
def __call__(self, state: State) -> Any:
print(f"Adding {self._value} to {state['aggregate']} in parallel.")
return {
"fanout_values": [
{
"value": [self._value],
"reliability": self._reliability,
}
]
}
builder.add_node("b", ParallelReturnNodeValue("I'm B", reliability=0.9))
builder.add_node("c", ParallelReturnNodeValue("I'm C", reliability=0.1))
builder.add_node("d", ParallelReturnNodeValue("I'm D", reliability=0.3))
def aggregate_fanout_values(state: State) -> Any:
# Sort by reliability
ranked_values = sorted(
state["fanout_values"], key=lambda x: x["reliability"], reverse=True
)
return {
"aggregate": [x["value"] for x in ranked_values] + ["I'm E"],
"fanout_values": [],
}
builder.add_node("e", aggregate_fanout_values)
def route_bc_or_cd(state: State) -> Sequence[str]:
if state["which"] == "cd":
return ["c", "d"]
return ["b", "c"]
intermediates = ["b", "c", "d"]
builder.add_conditional_edges("a", route_bc_or_cd, intermediates)
for node in intermediates:
builder.add_edge(node, "e")
builder.add_edge("e", END)
graph = builder.compile()from IPython.display import Image, display
display(Image(graph.get_graph().draw_mermaid_png()))graph.invoke({"aggregate": [], "which": "bc", "fanout_values": []})graph.invoke({"aggregate": [], "which": "cd"}) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/tool-calling-errors.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("ANTHROPIC_API_KEY")from langchain_core.tools import tool
@tool
def get_weather(location: str):
"""Call to get the current weather."""
if location == "san francisco":
raise ValueError("Input queries must be proper nouns")
elif location == "San Francisco":
return "It's 60 degrees and foggy."
else:
raise ValueError("Invalid input.")from typing import Literal
from langchain_anthropic import ChatAnthropic
from langgraph.graph import StateGraph, MessagesState, START, END
from langgraph.prebuilt import ToolNode
tool_node = ToolNode([get_weather])
model_with_tools = ChatAnthropic(
model="claude-3-haiku-20240307", temperature=0
).bind_tools([get_weather])
def should_continue(state: MessagesState):
messages = state["messages"]
last_message = messages[-1]
if last_message.tool_calls:
return "tools"
return END
def call_model(state: MessagesState):
messages = state["messages"]
response = model_with_tools.invoke(messages)
return {"messages": [response]}
workflow = StateGraph(MessagesState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("tools", tool_node)
workflow.add_edge(START, "agent")
workflow.add_conditional_edges("agent", should_continue, ["tools", END])
workflow.add_edge("tools", "agent")
app = workflow.compile()from IPython.display import Image, display
try:
display(Image(app.get_graph().draw_mermaid_png()))
except Exception:
# This requires some extra dependencies and is optional
passresponse = app.invoke(
{"messages": [("human", "what is the weather in san francisco?")]},
)
for message in response["messages"]:
string_representation = f"{message.type.upper()}: {message.content}\n"
print(string_representation)from langchain_core.output_parsers import StrOutputParser
from pydantic import BaseModel, Field
class HaikuRequest(BaseModel):
topic: list[str] = Field(
max_length=3,
min_length=3,
)
@tool
def master_haiku_generator(request: HaikuRequest):
"""Generates a haiku based on the provided topics."""
model = ChatAnthropic(model="claude-3-haiku-20240307", temperature=0)
chain = model | StrOutputParser()
topics = ", ".join(request.topic)
haiku = chain.invoke(f"Write a haiku about {topics}")
return haiku
tool_node = ToolNode([master_haiku_generator])
model = ChatAnthropic(model="claude-3-haiku-20240307", temperature=0)
model_with_tools = model.bind_tools([master_haiku_generator])
def should_continue(state: MessagesState):
messages = state["messages"]
last_message = messages[-1]
if last_message.tool_calls:
return "tools"
return END
def call_model(state: MessagesState):
messages = state["messages"]
response = model_with_tools.invoke(messages)
return {"messages": [response]}
workflow = StateGraph(MessagesState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("tools", tool_node)
workflow.add_edge(START, "agent")
workflow.add_conditional_edges("agent", should_continue, ["tools", END])
workflow.add_edge("tools", "agent")
app = workflow.compile()
response = app.invoke(
{"messages": [("human", "Write me an incredible haiku about water.")]},
{"recursion_limit": 10},
)
for message in response["messages"]:
string_representation = f"{message.type.upper()}: {message.content}\n"
print(string_representation)import json
from langchain_core.messages import AIMessage, ToolMessage
from langchain_core.messages.modifier import RemoveMessage
@tool
def master_haiku_generator(request: HaikuRequest):
"""Generates a haiku based on the provided topics."""
model = ChatAnthropic(model="claude-3-haiku-20240307", temperature=0)
chain = model | StrOutputParser()
topics = ", ".join(request.topic)
haiku = chain.invoke(f"Write a haiku about {topics}")
return haiku
def call_tool(state: MessagesState):
tools_by_name = {master_haiku_generator.name: master_haiku_generator}
messages = state["messages"]
last_message = messages[-1]
output_messages = []
for tool_call in last_message.tool_calls:
try:
tool_result = tools_by_name[tool_call["name"]].invoke(tool_call["args"])
output_messages.append(
ToolMessage(
content=json.dumps(tool_result),
name=tool_call["name"],
tool_call_id=tool_call["id"],
)
)
except Exception as e:
# Return the error if the tool call fails
output_messages.append(
ToolMessage(
content="",
name=tool_call["name"],
tool_call_id=tool_call["id"],
additional_kwargs={"error": e},
)
)
return {"messages": output_messages}
model = ChatAnthropic(model="claude-3-haiku-20240307", temperature=0)
model_with_tools = model.bind_tools([master_haiku_generator])
better_model = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0)
better_model_with_tools = better_model.bind_tools([master_haiku_generator])
def should_continue(state: MessagesState):
messages = state["messages"]
last_message = messages[-1]
if last_message.tool_calls:
return "tools"
return END
def should_fallback(
state: MessagesState,
) -> Literal["agent", "remove_failed_tool_call_attempt"]:
messages = state["messages"]
failed_tool_messages = [
msg
for msg in messages
if isinstance(msg, ToolMessage)
and msg.additional_kwargs.get("error") is not None
]
if failed_tool_messages:
return "remove_failed_tool_call_attempt"
return "agent"
def call_model(state: MessagesState):
messages = state["messages"]
response = model_with_tools.invoke(messages)
return {"messages": [response]}
def remove_failed_tool_call_attempt(state: MessagesState):
messages = state["messages"]
# Remove all messages from the most recent
# instance of AIMessage onwards.
last_ai_message_index = next(
i
for i, msg in reversed(list(enumerate(messages)))
if isinstance(msg, AIMessage)
)
messages_to_remove = messages[last_ai_message_index:]
return {"messages": [RemoveMessage(id=m.id) for m in messages_to_remove]}
# Fallback to a better model if a tool call fails
def call_fallback_model(state: MessagesState):
messages = state["messages"]
response = better_model_with_tools.invoke(messages)
return {"messages": [response]}
workflow = StateGraph(MessagesState)
workflow.add_node("agent", call_model)
workflow.add_node("tools", call_tool)
workflow.add_node("remove_failed_tool_call_attempt", remove_failed_tool_call_attempt)
workflow.add_node("fallback_agent", call_fallback_model)
workflow.add_edge(START, "agent")
workflow.add_conditional_edges("agent", should_continue, ["tools", END])
workflow.add_conditional_edges("tools", should_fallback)
workflow.add_edge("remove_failed_tool_call_attempt", "fallback_agent")
workflow.add_edge("fallback_agent", "tools")
app = workflow.compile()try:
display(Image(app.get_graph().draw_mermaid_png()))
except Exception:
# This requires some extra dependencies and is optional
passstream = app.stream(
{"messages": [("human", "Write me an incredible haiku about water.")]},
{"recursion_limit": 10},
)
for chunk in stream:
print(chunk) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/deploy-self-hosted.md | # How to do a Self-hosted deployment of LangGraph
!!! info "Prerequisites"
- [Application Structure](../concepts/application_structure.md)
- [Deployment Options](../concepts/deployment_options.md)
This how-to guide will walk you through how to create a docker image from an existing LangGraph application, so you can deploy it on your own infrastructure.
## How it works
With the self-hosted deployment option, you are responsible for managing the infrastructure, including setting up and maintaining necessary databases, Redis instances, and other services.
You will need to do the following:
1. Deploy Redis and Postgres instances on your own infrastructure.
2. Build a docker image with the [LangGraph Server](../concepts/langgraph_server.md) using the [LangGraph CLI](../concepts/langgraph_cli.md).
3. Deploy a web server that will run the docker image and pass in the necessary environment variables.
## Helm Chart
If you would like to deploy LangGraph Cloud on Kubernetes, you can use this [Helm chart](https://github.com/langchain-ai/helm/blob/main/charts/langgraph-cloud/README.md).
## Environment Variables
You will eventually need to pass in the following environment variables to the LangGraph Deploy server:
- `REDIS_URI`: Connection details to a Redis instance. Redis will be used as a pub-sub broker to enable streaming real time output from background runs.
- `DATABASE_URI`: Postgres connection details. Postgres will be used to store assistants, threads, runs, persist thread state and long term memory, and to manage the state of the background task queue with 'exactly once' semantics.
- `LANGSMITH_API_KEY`: (If using [Self-Hosted Lite](../concepts/deployment_options.md#self-hosted-lite)) LangSmith API key. This will be used to authenticate ONCE at server start up.
- `LANGGRAPH_CLOUD_LICENSE_KEY`: (If using [Self-Hosted Enterprise](../concepts/deployment_options.md#self-hosted-enterprise)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
## Build the Docker Image
Please read the [Application Structure](../concepts/application_structure.md) guide to understand how to structure your LangGraph application.
If the application is structured correctly, you can build a docker image with the LangGraph Deploy server.
To build the docker image, you first need to install the CLI:
```shell
pip install -U langgraph-cli
```
You can then use:
```
langgraph build -t my-image
```
This will build a docker image with the LangGraph Deploy server. The `-t my-image` is used to tag the image with a name.
When running this server, you need to pass three environment variables:
## Running the application locally
### Using Docker
```shell
docker run \
--env-file .env \
-p 8123:8000 \
-e REDIS_URI="foo" \
-e DATABASE_URI="bar" \
-e LANGSMITH_API_KEY="baz" \
my-image
```
If you want to run this quickly without setting up a separate Redis and Postgres instance, you can use this docker compose file.
!!! note
* You need to replace `my-image` with the name of the image you built in the previous step (from `langgraph build`).
and you should provide appropriate values for `REDIS_URI`, `DATABASE_URI`, and `LANGSMITH_API_KEY`.
* If your application requires additional environment variables, you can pass them in a similar way.
* If using [Self-Hosted Enterprise](../concepts/deployment_options.md#self-hosted-enterprise), you must provide `LANGGRAPH_CLOUD_LICENSE_KEY` as an additional environment variable.
### Using Docker Compose
```yml
volumes:
langgraph-data:
driver: local
services:
langgraph-redis:
image: redis:6
healthcheck:
test: redis-cli ping
interval: 5s
timeout: 1s
retries: 5
langgraph-postgres:
image: postgres:16
ports:
- "5433:5432"
environment:
POSTGRES_DB: postgres
POSTGRES_USER: postgres
POSTGRES_PASSWORD: postgres
volumes:
- langgraph-data:/var/lib/postgresql/data
healthcheck:
test: pg_isready -U postgres
start_period: 10s
timeout: 1s
retries: 5
interval: 5s
langgraph-api:
image: ${IMAGE_NAME}
ports:
- "8123:8000"
depends_on:
langgraph-redis:
condition: service_healthy
langgraph-postgres:
condition: service_healthy
env_file:
- .env
environment:
REDIS_URI: redis://langgraph-redis:6379
LANGSMITH_API_KEY: ${LANGSMITH_API_KEY}
POSTGRES_URI: postgres://postgres:postgres@langgraph-postgres:5432/postgres?sslmode=disable
```
You can then run `docker compose up` with this Docker compose file in the same folder.
This will spin up LangGraph Deploy on port `8123` (if you want to change this, you can change this by changing the ports in the `langgraph-api` volume).
You can test that the application is up by checking:
```shell
curl --request GET --url 0.0.0.0:8123/ok
```
Assuming everything is running correctly, you should see a response like:
```shell
{"ok":true}
```
|
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/create-react-agent-hitl.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")# First we initialize the model we want to use.
from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-4o", temperature=0)
# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)
from typing import Literal
from langchain_core.tools import tool
@tool
def get_weather(location: str):
"""Use this to get weather information from a given location."""
if location.lower() in ["nyc", "new york"]:
return "It might be cloudy in nyc"
elif location.lower() in ["sf", "san francisco"]:
return "It's always sunny in sf"
else:
raise AssertionError("Unknown Location")
tools = [get_weather]
# We need a checkpointer to enable human-in-the-loop patterns
from langgraph.checkpoint.memory import MemorySaver
memory = MemorySaver()
# Define the graph
from langgraph.prebuilt import create_react_agent
graph = create_react_agent(
model, tools=tools, interrupt_before=["tools"], checkpointer=memory
)def print_stream(stream):
"""A utility to pretty print the stream."""
for s in stream:
message = s["messages"][-1]
if isinstance(message, tuple):
print(message)
else:
message.pretty_print()from langchain_core.messages import HumanMessage
config = {"configurable": {"thread_id": "42"}}
inputs = {"messages": [("user", "what is the weather in SF, CA?")]}
print_stream(graph.stream(inputs, config, stream_mode="values"))snapshot = graph.get_state(config)
print("Next step: ", snapshot.next)print_stream(graph.stream(None, config, stream_mode="values"))state = graph.get_state(config)
last_message = state.values["messages"][-1]
last_message.tool_calls[0]["args"] = {"location": "San Francisco"}
graph.update_state(config, {"messages": [last_message]})print_stream(graph.stream(None, config, stream_mode="values")) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/return-when-recursion-limit-hits.ipynb | from typing_extensions import TypedDict
from langgraph.graph import StateGraph
from langgraph.graph import START, END
class State(TypedDict):
value: str
action_result: str
def router(state: State):
if state["value"] == "end":
return END
else:
return "action"
def decision_node(state):
return {"value": "keep going!"}
def action_node(state: State):
# Do your action here ...
return {"action_result": "what a great result!"}
workflow = StateGraph(State)
workflow.add_node("decision", decision_node)
workflow.add_node("action", action_node)
workflow.add_edge(START, "decision")
workflow.add_conditional_edges("decision", router, ["action", END])
workflow.add_edge("action", "decision")
app = workflow.compile()from IPython.display import Image, display
display(Image(app.get_graph().draw_mermaid_png()))from langgraph.errors import GraphRecursionError
try:
app.invoke({"value": "hi!"})
except GraphRecursionError:
print("Recursion Error")from typing_extensions import TypedDict
from langgraph.graph import StateGraph
from typing import Annotated
from langgraph.managed.is_last_step import RemainingSteps
class State(TypedDict):
value: str
action_result: str
remaining_steps: RemainingSteps
def router(state: State):
# Force the agent to end
if state["remaining_steps"] <= 2:
return END
if state["value"] == "end":
return END
else:
return "action"
def decision_node(state):
return {"value": "keep going!"}
def action_node(state: State):
# Do your action here ...
return {"action_result": "what a great result!"}
workflow = StateGraph(State)
workflow.add_node("decision", decision_node)
workflow.add_node("action", action_node)
workflow.add_edge(START, "decision")
workflow.add_conditional_edges("decision", router, ["action", END])
workflow.add_edge("action", "decision")
app = workflow.compile()app.invoke({"value": "hi!"}) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/state-model.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
from pydantic import BaseModel
# The overall state of the graph (this is the public state shared across nodes)
class OverallState(BaseModel):
a: str
def node(state: OverallState):
return {"a": "goodbye"}
# Build the state graph
builder = StateGraph(OverallState)
builder.add_node(node) # node_1 is the first node
builder.add_edge(START, "node") # Start the graph with node_1
builder.add_edge("node", END) # End the graph after node_1
graph = builder.compile()
# Test the graph with a valid input
graph.invoke({"a": "hello"})try:
graph.invoke({"a": 123}) # Should be a string
except Exception as e:
print("An exception was raised because `a` is an integer rather than a string.")
print(e)from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
from pydantic import BaseModel
# The overall state of the graph (this is the public state shared across nodes)
class OverallState(BaseModel):
a: str
def bad_node(state: OverallState):
return {
"a": 123 # Invalid
}
def ok_node(state: OverallState):
return {"a": "goodbye"}
# Build the state graph
builder = StateGraph(OverallState)
builder.add_node(bad_node)
builder.add_node(ok_node)
builder.add_edge(START, "bad_node")
builder.add_edge("bad_node", "ok_node")
builder.add_edge("ok_node", END)
graph = builder.compile()
# Test the graph with a valid input
try:
graph.invoke({"a": "hello"})
except Exception as e:
print("An exception was raised because bad_node sets `a` to an integer.")
print(e) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/async.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("ANTHROPIC_API_KEY")from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph.message import add_messages
# Add messages essentially does this with more
# robust handling
# def add_messages(left: list, right: list):
# return left + right
class State(TypedDict):
messages: Annotated[list, add_messages]from langchain_core.tools import tool
@tool
def search(query: str):
"""Call to surf the web."""
# This is a placeholder, but don't tell the LLM that...
return ["The answer to your question lies within."]
tools = [search]from langgraph.prebuilt import ToolNode
tool_node = ToolNode(tools)from langchain_anthropic import ChatAnthropic
model = ChatAnthropic(model="claude-3-haiku-20240307")model = model.bind_tools(tools)from typing import Literal
# Define the function that determines whether to continue or not
def should_continue(state: State) -> Literal["end", "continue"]:
messages = state["messages"]
last_message = messages[-1]
# If there is no tool call, then we finish
if not last_message.tool_calls:
return "end"
# Otherwise if there is, we continue
else:
return "continue"
# Define the function that calls the model
async def call_model(state: State):
messages = state["messages"]
response = await model.ainvoke(messages)
# We return a list, because this will get added to the existing list
return {"messages": [response]}from langgraph.graph import END, StateGraph, START
# Define a new graph
workflow = StateGraph(State)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("action", tool_node)
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.add_edge(START, "agent")
# We now add a conditional edge
workflow.add_conditional_edges(
# First, we define the start node. We use `agent`.
# This means these are the edges taken after the `agent` node is called.
"agent",
# Next, we pass in the function that will determine which node is called next.
should_continue,
# Finally we pass in a mapping.
# The keys are strings, and the values are other nodes.
# END is a special node marking that the graph should finish.
# What will happen is we will call `should_continue`, and then the output of that
# will be matched against the keys in this mapping.
# Based on which one it matches, that node will then be called.
{
# If `tools`, then we call the tool node.
"continue": "action",
# Otherwise we finish.
"end": END,
},
)
# We now add a normal edge from `tools` to `agent`.
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge("action", "agent")
# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable
app = workflow.compile()from IPython.display import Image, display
display(Image(app.get_graph().draw_mermaid_png()))from langchain_core.messages import HumanMessage
inputs = {"messages": [HumanMessage(content="what is the weather in sf")]}
await app.ainvoke(inputs)inputs = {"messages": [HumanMessage(content="what is the weather in sf")]}
async for output in app.astream(inputs, stream_mode="updates"):
# stream_mode="updates" yields dictionaries with output keyed by node name
for key, value in output.items():
print(f"Output from node '{key}':")
print("---")
print(value["messages"][-1].pretty_print())
print("\n---\n")inputs = {"messages": [HumanMessage(content="what is the weather in sf")]}
async for output in app.astream_log(inputs, include_types=["llm"]):
# astream_log() yields the requested logs (here LLMs) in JSONPatch format
for op in output.ops:
if op["path"] == "/streamed_output/-":
# this is the output from .stream()
...
elif op["path"].startswith("/logs/") and op["path"].endswith(
"/streamed_output/-"
):
# because we chose to only include LLMs, these are LLM tokens
try:
content = op["value"].content[0]
if "partial_json" in content:
print(content["partial_json"], end="|")
elif "text" in content:
print(content["text"], end="|")
else:
print(content, end="|")
except:
pass |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/run-id-langsmith.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")
_set_env("LANGSMITH_API_KEY")from langchain_openai import ChatOpenAI
from typing import Literal
from langgraph.prebuilt import create_react_agent
from langchain_core.tools import tool
# First we initialize the model we want to use.
model = ChatOpenAI(model="gpt-4o", temperature=0)
# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)
@tool
def get_weather(city: Literal["nyc", "sf"]):
"""Use this to get weather information."""
if city == "nyc":
return "It might be cloudy in nyc"
elif city == "sf":
return "It's always sunny in sf"
else:
raise AssertionError("Unknown city")
tools = [get_weather]
# Define the graph
graph = create_react_agent(model, tools=tools)import uuid
def print_stream(stream):
for s in stream:
message = s["messages"][-1]
if isinstance(message, tuple):
print(message)
else:
message.pretty_print()
inputs = {"messages": [("user", "what is the weather in sf")]}
config = {"run_name": "agent_007", "tags": ["cats are awesome"]}
print_stream(graph.stream(inputs, config, stream_mode="values")) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/subgraphs-manage-state.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")from langgraph.graph import StateGraph, END, START, MessagesState
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
@tool
def get_weather(city: str):
"""Get the weather for a specific city"""
return f"It's sunny in {city}!"
raw_model = ChatOpenAI()
model = raw_model.with_structured_output(get_weather)
class SubGraphState(MessagesState):
city: str
def model_node(state: SubGraphState):
result = model.invoke(state["messages"])
return {"city": result["city"]}
def weather_node(state: SubGraphState):
result = get_weather.invoke({"city": state["city"]})
return {"messages": [{"role": "assistant", "content": result}]}
subgraph = StateGraph(SubGraphState)
subgraph.add_node(model_node)
subgraph.add_node(weather_node)
subgraph.add_edge(START, "model_node")
subgraph.add_edge("model_node", "weather_node")
subgraph.add_edge("weather_node", END)
subgraph = subgraph.compile(interrupt_before=["weather_node"])from typing import Literal
from typing_extensions import TypedDict
from langgraph.checkpoint.memory import MemorySaver
memory = MemorySaver()
class RouterState(MessagesState):
route: Literal["weather", "other"]
class Router(TypedDict):
route: Literal["weather", "other"]
router_model = raw_model.with_structured_output(Router)
def router_node(state: RouterState):
system_message = "Classify the incoming query as either about weather or not."
messages = [{"role": "system", "content": system_message}] + state["messages"]
route = router_model.invoke(messages)
return {"route": route["route"]}
def normal_llm_node(state: RouterState):
response = raw_model.invoke(state["messages"])
return {"messages": [response]}
def route_after_prediction(
state: RouterState,
) -> Literal["weather_graph", "normal_llm_node"]:
if state["route"] == "weather":
return "weather_graph"
else:
return "normal_llm_node"
graph = StateGraph(RouterState)
graph.add_node(router_node)
graph.add_node(normal_llm_node)
graph.add_node("weather_graph", subgraph)
graph.add_edge(START, "router_node")
graph.add_conditional_edges("router_node", route_after_prediction)
graph.add_edge("normal_llm_node", END)
graph.add_edge("weather_graph", END)
graph = graph.compile(checkpointer=memory)from IPython.display import Image, display
# Setting xray to 1 will show the internal structure of the nested graph
display(Image(graph.get_graph(xray=1).draw_mermaid_png()))config = {"configurable": {"thread_id": "1"}}
inputs = {"messages": [{"role": "user", "content": "hi!"}]}
for update in graph.stream(inputs, config=config, stream_mode="updates"):
print(update)config = {"configurable": {"thread_id": "2"}}
inputs = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
for update in graph.stream(inputs, config=config, stream_mode="updates"):
print(update)config = {"configurable": {"thread_id": "3"}}
inputs = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
for update in graph.stream(inputs, config=config, stream_mode="values", subgraphs=True):
print(update)state = graph.get_state(config)
state.nextstate.tasksstate = graph.get_state(config, subgraphs=True)
state.tasks[0]for update in graph.stream(None, config=config, stream_mode="values", subgraphs=True):
print(update)parent_graph_state_before_subgraph = next(
h for h in graph.get_state_history(config) if h.next == ("weather_graph",)
)subgraph_state_before_model_node = next(
h
for h in graph.get_state_history(parent_graph_state_before_subgraph.tasks[0].state)
if h.next == ("model_node",)
)
# This pattern can be extended no matter how many levels deep
# subsubgraph_stat_history = next(h for h in graph.get_state_history(subgraph_state_before_model_node.tasks[0].state) if h.next == ('my_subsubgraph_node',))subgraph_state_before_model_node.nextfor value in graph.stream(
None,
config=subgraph_state_before_model_node.config,
stream_mode="values",
subgraphs=True,
):
print(value)config = {"configurable": {"thread_id": "4"}}
inputs = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
for update in graph.stream(inputs, config=config, stream_mode="updates"):
print(update)state = graph.get_state(config, subgraphs=True)
state.values["messages"]graph.update_state(state.tasks[0].state.config, {"city": "la"})for update in graph.stream(None, config=config, stream_mode="updates", subgraphs=True):
print(update)config = {"configurable": {"thread_id": "14"}}
inputs = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
for update in graph.stream(
inputs, config=config, stream_mode="updates", subgraphs=True
):
print(update)
# Graph execution should stop before the weather node
print("interrupted!")
state = graph.get_state(config, subgraphs=True)
# We update the state by passing in the message we want returned from the weather node, and make sure to use as_node
graph.update_state(
state.tasks[0].state.config,
{"messages": [{"role": "assistant", "content": "rainy"}]},
as_node="weather_node",
)
for update in graph.stream(None, config=config, stream_mode="updates", subgraphs=True):
print(update)
print(graph.get_state(config).values["messages"])config = {"configurable": {"thread_id": "8"}}
inputs = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
for update in graph.stream(
inputs, config=config, stream_mode="updates", subgraphs=True
):
print(update)
# Graph execution should stop before the weather node
print("interrupted!")
# We update the state by passing in the message we want returned from the weather graph, making sure to use as_node
# Note that we don't need to pass in the subgraph config, since we aren't updating the state inside the subgraph
graph.update_state(
config,
{"messages": [{"role": "assistant", "content": "rainy"}]},
as_node="weather_graph",
)
for update in graph.stream(None, config=config, stream_mode="updates"):
print(update)
print(graph.get_state(config).values["messages"])from typing import Literal
from typing_extensions import TypedDict
from langgraph.checkpoint.memory import MemorySaver
memory = MemorySaver()
class RouterState(MessagesState):
route: Literal["weather", "other"]
class Router(TypedDict):
route: Literal["weather", "other"]
router_model = raw_model.with_structured_output(Router)
def router_node(state: RouterState):
system_message = "Classify the incoming query as either about weather or not."
messages = [{"role": "system", "content": system_message}] + state["messages"]
route = router_model.invoke(messages)
return {"route": route["route"]}
def normal_llm_node(state: RouterState):
response = raw_model.invoke(state["messages"])
return {"messages": [response]}
def route_after_prediction(
state: RouterState,
) -> Literal["weather_graph", "normal_llm_node"]:
if state["route"] == "weather":
return "weather_graph"
else:
return "normal_llm_node"
graph = StateGraph(RouterState)
graph.add_node(router_node)
graph.add_node(normal_llm_node)
graph.add_node("weather_graph", subgraph)
graph.add_edge(START, "router_node")
graph.add_conditional_edges("router_node", route_after_prediction)
graph.add_edge("normal_llm_node", END)
graph.add_edge("weather_graph", END)
graph = graph.compile()from langgraph.checkpoint.memory import MemorySaver
memory = MemorySaver()
class GrandfatherState(MessagesState):
to_continue: bool
def router_node(state: GrandfatherState):
# Dummy logic that will always continue
return {"to_continue": True}
def route_after_prediction(state: GrandfatherState):
if state["to_continue"]:
return "graph"
else:
return END
grandparent_graph = StateGraph(GrandfatherState)
grandparent_graph.add_node(router_node)
grandparent_graph.add_node("graph", graph)
grandparent_graph.add_edge(START, "router_node")
grandparent_graph.add_conditional_edges(
"router_node", route_after_prediction, ["graph", END]
)
grandparent_graph.add_edge("graph", END)
grandparent_graph = grandparent_graph.compile(checkpointer=MemorySaver())from IPython.display import Image, display
# Setting xray to 1 will show the internal structure of the nested graph
display(Image(grandparent_graph.get_graph(xray=2).draw_mermaid_png()))config = {"configurable": {"thread_id": "2"}}
inputs = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
for update in grandparent_graph.stream(
inputs, config=config, stream_mode="updates", subgraphs=True
):
print(update)state = grandparent_graph.get_state(config, subgraphs=True)
print("Grandparent State:")
print(state.values)
print("---------------")
print("Parent Graph State:")
print(state.tasks[0].state.values)
print("---------------")
print("Subgraph State:")
print(state.tasks[0].state.tasks[0].state.values)grandparent_graph_state = state
parent_graph_state = grandparent_graph_state.tasks[0].state
subgraph_state = parent_graph_state.tasks[0].state
grandparent_graph.update_state(
subgraph_state.config,
{"messages": [{"role": "assistant", "content": "rainy"}]},
as_node="weather_node",
)
for update in grandparent_graph.stream(
None, config=config, stream_mode="updates", subgraphs=True
):
print(update)
print(grandparent_graph.get_state(config).values["messages"])for state in grandparent_graph.get_state_history(config):
print(state)
print("-----") |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/streaming-events-from-within-tools-without-langchain.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")from openai import AsyncOpenAI
from langchain_core.language_models.chat_models import ChatGenerationChunk
from langchain_core.messages import AIMessageChunk
from langchain_core.runnables.config import (
ensure_config,
get_callback_manager_for_config,
)
openai_client = AsyncOpenAI()
# define tool schema for openai tool calling
tool = {
"type": "function",
"function": {
"name": "get_items",
"description": "Use this tool to look up which items are in the given place.",
"parameters": {
"type": "object",
"properties": {"place": {"type": "string"}},
"required": ["place"],
},
},
}
async def call_model(state, config=None):
config = ensure_config(config | {"tags": ["agent_llm"]})
callback_manager = get_callback_manager_for_config(config)
messages = state["messages"]
llm_run_manager = callback_manager.on_chat_model_start({}, [messages])[0]
response = await openai_client.chat.completions.create(
messages=messages, model="gpt-3.5-turbo", tools=[tool], stream=True
)
response_content = ""
role = None
tool_call_id = None
tool_call_function_name = None
tool_call_function_arguments = ""
async for chunk in response:
delta = chunk.choices[0].delta
if delta.role is not None:
role = delta.role
if delta.content:
response_content += delta.content
llm_run_manager.on_llm_new_token(delta.content)
if delta.tool_calls:
# note: for simplicity we're only handling a single tool call here
if delta.tool_calls[0].function.name is not None:
tool_call_function_name = delta.tool_calls[0].function.name
tool_call_id = delta.tool_calls[0].id
# note: we're wrapping the tools calls in ChatGenerationChunk so that the events from .astream_events in the graph can render tool calls correctly
tool_call_chunk = ChatGenerationChunk(
message=AIMessageChunk(
content="",
additional_kwargs={"tool_calls": [delta.tool_calls[0].dict()]},
)
)
llm_run_manager.on_llm_new_token("", chunk=tool_call_chunk)
tool_call_function_arguments += delta.tool_calls[0].function.arguments
if tool_call_function_name is not None:
tool_calls = [
{
"id": tool_call_id,
"function": {
"name": tool_call_function_name,
"arguments": tool_call_function_arguments,
},
"type": "function",
}
]
else:
tool_calls = None
response_message = {
"role": role,
"content": response_content,
"tool_calls": tool_calls,
}
return {"messages": [response_message]}import json
from langchain_core.callbacks import adispatch_custom_event
async def get_items(place: str) -> str:
"""Use this tool to look up which items are in the given place."""
# this can be replaced with any actual streaming logic that you might have
def stream(place: str):
if "bed" in place: # For under the bed
yield from ["socks", "shoes", "dust bunnies"]
elif "shelf" in place: # For 'shelf'
yield from ["books", "penciles", "pictures"]
else: # if the agent decides to ask about a different place
yield "cat snacks"
tokens = []
for token in stream(place):
await adispatch_custom_event(
# this will allow you to filter events by name
"tool_call_token_stream",
{
"function_name": "get_items",
"arguments": {"place": place},
"tool_output_token": token,
},
# this will allow you to filter events by tags
config={"tags": ["tool_call"]},
)
tokens.append(token)
return ", ".join(tokens)
# define mapping to look up functions when running tools
function_name_to_function = {"get_items": get_items}
async def call_tools(state):
messages = state["messages"]
tool_call = messages[-1]["tool_calls"][0]
function_name = tool_call["function"]["name"]
function_arguments = tool_call["function"]["arguments"]
arguments = json.loads(function_arguments)
function_response = await function_name_to_function[function_name](**arguments)
tool_message = {
"tool_call_id": tool_call["id"],
"role": "tool",
"name": function_name,
"content": function_response,
}
return {"messages": [tool_message]}import operator
from typing import Annotated, Literal
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, END, START
class State(TypedDict):
messages: Annotated[list, operator.add]
def should_continue(state) -> Literal["tools", END]:
messages = state["messages"]
last_message = messages[-1]
if last_message["tool_calls"]:
return "tools"
return END
workflow = StateGraph(State)
workflow.add_edge(START, "model")
workflow.add_node("model", call_model) # i.e. our "agent"
workflow.add_node("tools", call_tools)
workflow.add_conditional_edges("model", should_continue)
workflow.add_edge("tools", "model")
graph = workflow.compile()async for event in graph.astream_events(
{"messages": [{"role": "user", "content": "what's in the bedroom"}]}, version="v2"
):
tags = event.get("tags", [])
if event["event"] == "on_custom_event" and "tool_call" in tags:
print("Tool token", event["data"]["tool_output_token"]) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/index.md | ---
hide:
- navigation
title: How-to Guides
description: How to accomplish common tasks in LangGraph
---
# How-to Guides
Here you’ll find answers to “How do I...?” types of questions. These guides are **goal-oriented** and concrete; they're meant to help you complete a specific task. For conceptual explanations see the [Conceptual guide](../concepts/index.md). For end-to-end walk-throughs see [Tutorials](../tutorials/index.md). For comprehensive descriptions of every class and function see the [API Reference](../reference/index.md).
## LangGraph
### Controllability
LangGraph offers a high level of control over the execution of your graph.
These how-to guides show how to achieve that controllability.
- [How to create branches for parallel execution](branching.ipynb)
- [How to create map-reduce branches for parallel execution](map-reduce.ipynb)
- [How to control graph recursion limit](recursion-limit.ipynb)
- [How to combine control flow and state updates with Command](command.ipynb)
### Persistence
[LangGraph Persistence](../concepts/persistence.md) makes it easy to persist state across graph runs (thread-level persistence) and across threads (cross-thread persistence). These how-to guides show how to add persistence to your graph.
- [How to add thread-level persistence to your graph](persistence.ipynb)
- [How to add thread-level persistence to subgraphs](subgraph-persistence.ipynb)
- [How to add cross-thread persistence to your graph](cross-thread-persistence.ipynb)
- [How to use Postgres checkpointer for persistence](persistence_postgres.ipynb)
- [How to create a custom checkpointer using MongoDB](persistence_mongodb.ipynb)
- [How to create a custom checkpointer using Redis](persistence_redis.ipynb)
### Memory
LangGraph makes it easy to manage conversation [memory](../concepts/memory.md) in your graph. These how-to guides show how to implement different strategies for that.
- [How to manage conversation history](memory/manage-conversation-history.ipynb)
- [How to delete messages](memory/delete-messages.ipynb)
- [How to add summary conversation memory](memory/add-summary-conversation-history.ipynb)
- [How to add long-term memory (cross-thread)](cross-thread-persistence.ipynb)
- [How to use semantic search for long-term memory](memory/semantic-search.ipynb)
### Human-in-the-loop
[Human-in-the-loop](../concepts/human_in_the_loop.md) functionality allows
you to involve humans in the decision-making process of your graph. These how-to guides show how to implement human-in-the-loop workflows in your graph.
- [How to add breakpoints](human_in_the_loop/breakpoints.ipynb)
- [How to add dynamic breakpoints](human_in_the_loop/dynamic_breakpoints.ipynb)
- [How to edit graph state](human_in_the_loop/edit-graph-state.ipynb)
- [How to wait for user input](human_in_the_loop/wait-user-input.ipynb)
- [How to view and update past graph state](human_in_the_loop/time-travel.ipynb)
- [How to review tool calls](human_in_the_loop/review-tool-calls.ipynb)
### Streaming
[Streaming](../concepts/streaming.md) is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.
- [How to stream full state of your graph](stream-values.ipynb)
- [How to stream state updates of your graph](stream-updates.ipynb)
- [How to stream LLM tokens](streaming-tokens.ipynb)
- [How to stream LLM tokens without LangChain models](streaming-tokens-without-langchain.ipynb)
- [How to stream custom data](streaming-content.ipynb)
- [How to configure multiple streaming modes at the same time](stream-multiple.ipynb)
- [How to stream events from within a tool](streaming-events-from-within-tools.ipynb)
- [How to stream events from within a tool without LangChain models](streaming-events-from-within-tools-without-langchain.ipynb)
- [How to stream events from the final node](streaming-from-final-node.ipynb)
- [How to stream from subgraphs](streaming-subgraphs.ipynb)
- [How to disable streaming for models that don't support it](disable-streaming.ipynb)
### Tool calling
[Tool calling](https://python.langchain.com/docs/concepts/tool_calling/) is a type of chat model API that accepts tool schemas, along with messages, as input and returns invocations of those tools as part of the output message.
These how-to guides show common patterns for tool calling with LangGraph:
- [How to call tools using ToolNode](tool-calling.ipynb)
- [How to handle tool calling errors](tool-calling-errors.ipynb)
- [How to pass runtime values to tools](pass-run-time-values-to-tools.ipynb)
- [How to pass config to tools](pass-config-to-tools.ipynb)
- [How to handle large numbers of tools](many-tools.ipynb)
### Subgraphs
[Subgraphs](../concepts/low_level.md#subgraphs) allow you to reuse an existing graph from another graph. These how-to guides show how to use subgraphs:
- [How to add and use subgraphs](subgraph.ipynb)
- [How to view and update state in subgraphs](subgraphs-manage-state.ipynb)
- [How to transform inputs and outputs of a subgraph](subgraph-transform-state.ipynb)
### State Management
- [How to use Pydantic model as state](state-model.ipynb)
- [How to define input/output schema for your graph](input_output_schema.ipynb)
- [How to pass private state between nodes inside the graph](pass_private_state.ipynb)
### Other
- [How to run graph asynchronously](async.ipynb)
- [How to visualize your graph](visualization.ipynb)
- [How to add runtime configuration to your graph](configuration.ipynb)
- [How to add node retries](node-retries.ipynb)
- [How to force function calling agent to structure output](react-agent-structured-output.ipynb)
- [How to pass custom LangSmith run ID for graph runs](run-id-langsmith.ipynb)
- [How to return state before hitting recursion limit](return-when-recursion-limit-hits.ipynb)
- [How to integrate LangGraph with AutoGen, CrewAI, and other frameworks](autogen-integration.ipynb)
### Prebuilt ReAct Agent
The LangGraph [prebuilt ReAct agent](../reference/prebuilt.md#langgraph.prebuilt.chat_agent_executor.create_react_agent) is pre-built implementation of a [tool calling agent](../concepts/agentic_concepts.md#tool-calling-agent).
One of the big benefits of LangGraph is that you can easily create your own agent architectures. So while it's fine to start here to build an agent quickly, we would strongly recommend learning how to build your own agent so that you can take full advantage of LangGraph.
These guides show how to use the prebuilt ReAct agent:
- [How to create a ReAct agent](create-react-agent.ipynb)
- [How to add memory to a ReAct agent](create-react-agent-memory.ipynb)
- [How to add a custom system prompt to a ReAct agent](create-react-agent-system-prompt.ipynb)
- [How to add human-in-the-loop processes to a ReAct agent](create-react-agent-hitl.ipynb)
- [How to create prebuilt ReAct agent from scratch](react-agent-from-scratch.ipynb)
- [How to add semantic search for long-term memory to a ReAct agent](memory/semantic-search.ipynb#using-in-create-react-agent)
## LangGraph Platform
This section includes how-to guides for LangGraph Platform.
LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
The LangGraph Platform offers a few different deployment options described in the [deployment options guide](../concepts/deployment_options.md).
!!! tip
* LangGraph is an MIT-licensed open-source library, which we are committed to maintaining and growing for the community.
* You can always deploy LangGraph applications on your own infrastructure using the open-source LangGraph project without using LangGraph Platform.
### Application Structure
Learn how to set up your app for deployment to LangGraph Platform:
- [How to set up app for deployment (requirements.txt)](../cloud/deployment/setup.md)
- [How to set up app for deployment (pyproject.toml)](../cloud/deployment/setup_pyproject.md)
- [How to set up app for deployment (JavaScript)](../cloud/deployment/setup_javascript.md)
- [How to add semantic search](../cloud/deployment/semantic_search.md)
- [How to customize Dockerfile](../cloud/deployment/custom_docker.md)
- [How to test locally](../cloud/deployment/test_locally.md)
- [How to rebuild graph at runtime](../cloud/deployment/graph_rebuild.md)
- [How to use LangGraph Platform to deploy CrewAI, AutoGen, and other frameworks](autogen-langgraph-platform.ipynb)
### Deployment
LangGraph applications can be deployed using LangGraph Cloud, which provides a range of services to help you deploy, manage, and scale your applications.
- [How to deploy to LangGraph cloud](../cloud/deployment/cloud.md)
- [How to deploy to a self-hosted environment](./deploy-self-hosted.md)
- [How to interact with the deployment using RemoteGraph](./use-remote-graph.md)
### Assistants
[Assistants](../concepts/assistants.md) is a configured instance of a template.
- [How to configure agents](../cloud/how-tos/configuration_cloud.md)
- [How to version assistants](../cloud/how-tos/assistant_versioning.md)
### Threads
- [How to copy threads](../cloud/how-tos/copy_threads.md)
- [How to check status of your threads](../cloud/how-tos/check_thread_status.md)
### Runs
LangGraph Platform supports multiple types of runs besides streaming runs.
- [How to run an agent in the background](../cloud/how-tos/background_run.md)
- [How to run multiple agents in the same thread](../cloud/how-tos/same-thread.md)
- [How to create cron jobs](../cloud/how-tos/cron_jobs.md)
- [How to create stateless runs](../cloud/how-tos/stateless_runs.md)
### Streaming
Streaming the results of your LLM application is vital for ensuring a good user experience, especially when your graph may call multiple models and take a long time to fully complete a run. Read about how to stream values from your graph in these how to guides:
- [How to stream values](../cloud/how-tos/stream_values.md)
- [How to stream updates](../cloud/how-tos/stream_updates.md)
- [How to stream messages](../cloud/how-tos/stream_messages.md)
- [How to stream events](../cloud/how-tos/stream_events.md)
- [How to stream in debug mode](../cloud/how-tos/stream_debug.md)
- [How to stream multiple modes](../cloud/how-tos/stream_multiple.md)
### Human-in-the-loop
When designing complex graphs, relying entirely on the LLM for decision-making can be risky, particularly when it involves tools that interact with files, APIs, or databases. These interactions may lead to unintended data access or modifications, depending on the use case. To mitigate these risks, LangGraph allows you to integrate human-in-the-loop behavior, ensuring your LLM applications operate as intended without undesirable outcomes.
- [How to add a breakpoint](../cloud/how-tos/human_in_the_loop_breakpoint.md)
- [How to wait for user input](../cloud/how-tos/human_in_the_loop_user_input.md)
- [How to edit graph state](../cloud/how-tos/human_in_the_loop_edit_state.md)
- [How to replay and branch from prior states](../cloud/how-tos/human_in_the_loop_time_travel.md)
- [How to review tool calls](../cloud/how-tos/human_in_the_loop_review_tool_calls.md)
### Double-texting
Graph execution can take a while, and sometimes users may change their mind about the input they wanted to send before their original input has finished running. For example, a user might notice a typo in their original request and will edit the prompt and resend it. Deciding what to do in these cases is important for ensuring a smooth user experience and preventing your graphs from behaving in unexpected ways.
- [How to use the interrupt option](../cloud/how-tos/interrupt_concurrent.md)
- [How to use the rollback option](../cloud/how-tos/rollback_concurrent.md)
- [How to use the reject option](../cloud/how-tos/reject_concurrent.md)
- [How to use the enqueue option](../cloud/how-tos/enqueue_concurrent.md)
### Webhooks
- [How to integrate webhooks](../cloud/how-tos/webhooks.md)
### Cron Jobs
- [How to create cron jobs](../cloud/how-tos/cron_jobs.md)
### LangGraph Studio
LangGraph Studio is a built-in UI for visualizing, testing, and debugging your agents.
- [How to connect to a LangGraph Cloud deployment](../cloud/how-tos/test_deployment.md)
- [How to connect to a local dev server](../how-tos/local-studio.md)
- [How to connect to a local deployment (Docker)](../cloud/how-tos/test_local_deployment.md)
- [How to test your graph in LangGraph Studio (MacOS only)](../cloud/how-tos/invoke_studio.md)
- [How to interact with threads in LangGraph Studio](../cloud/how-tos/threads_studio.md)
## Troubleshooting
These are the guides for resolving common errors you may find while building with LangGraph. Errors referenced below will have an `lc_error_code` property corresponding to one of the below codes when they are thrown in code.
- [GRAPH_RECURSION_LIMIT](../troubleshooting/errors/GRAPH_RECURSION_LIMIT.md)
- [INVALID_CONCURRENT_GRAPH_UPDATE](../troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE.md)
- [INVALID_GRAPH_NODE_RETURN_VALUE](../troubleshooting/errors/INVALID_GRAPH_NODE_RETURN_VALUE.md)
- [MULTIPLE_SUBGRAPHS](../troubleshooting/errors/MULTIPLE_SUBGRAPHS.md)
- [INVALID_CHAT_HISTORY](../troubleshooting/errors/INVALID_CHAT_HISTORY.md)
|
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/autogen-integration.ipynb | %pip install autogen bs4 langgraph langchain-openai langchain-communityimport getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")
_set_env("TAVILY_API_KEY")import autogen
import os
config_list = [{"model": "gpt-4o", "api_key": os.environ["OPENAI_API_KEY"]}]
llm_config = {
"timeout": 600,
"cache_seed": 42,
"config_list": config_list,
"temperature": 0,
}
autogen_agent = autogen.AssistantAgent(
name="assistant",
llm_config=llm_config,
)
user_proxy = autogen.UserProxyAgent(
name="user_proxy",
human_input_mode="NEVER",
max_consecutive_auto_reply=10,
is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
code_execution_config={
"work_dir": "web",
"use_docker": False,
}, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.
llm_config=llm_config,
system_message="Reply TERMINATE if the task has been solved at full satisfaction. Otherwise, reply CONTINUE, or the reason why the task is not solved yet.",
)from langgraph.graph import StateGraph, START, MessagesState
from langgraph.prebuilt import ToolNode, create_react_agent
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, AIMessage
model = ChatOpenAI(model="gpt-4o")
tools = [TavilySearchResults(max_results=1)]
web_search_agent = create_react_agent(
model, tools, state_modifier="You are an agent specializing in web search"
)from typing import Literal, TypedDict
class Route(TypedDict):
"""Decide where to go next"""
goto: Literal["web_search_assistant", "coding_assistant"]
def route(state: MessagesState) -> Literal["web_search_assistant", "coding_assistant"]:
messages = [
{
"role": "system",
"content": "Based on the conversation so far, decide who to call next: web search assistant or coding assistant.",
}
] + state["messages"]
response = model.with_structured_output(Route).invoke(messages)
return response["goto"]
def call_autogen_agent(state: MessagesState):
last_message = state["messages"][-1]
response = user_proxy.initiate_chat(autogen_agent, message=last_message.content)
# get the final response from the agent
content = response.chat_history[-1]["content"]
return {"messages": AIMessage(content=content)}
builder = StateGraph(MessagesState)
builder.add_conditional_edges(START, route)
builder.add_node("coding_assistant", call_autogen_agent)
builder.add_node("web_search_assistant", web_search_agent)
graph = builder.compile()from IPython.display import display, Image
display(Image(graph.get_graph().draw_mermaid_png()))for chunk in graph.stream(
{
"messages": [
{
"role": "user",
"content": "Find numbers between 10 and 30 in fibonacci sequence",
}
]
}
):
print(chunk)for chunk in graph.stream(
{"messages": [{"role": "user", "content": "what's the weather in nyc?"}]},
subgraphs=True,
):
print(chunk) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/persistence_redis.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")"""Implementation of a langgraph checkpoint saver using Redis."""
from contextlib import asynccontextmanager, contextmanager
from typing import (
Any,
AsyncGenerator,
AsyncIterator,
Iterator,
List,
Optional,
Tuple,
)
from langchain_core.runnables import RunnableConfig
from langgraph.checkpoint.base import (
BaseCheckpointSaver,
ChannelVersions,
Checkpoint,
CheckpointMetadata,
CheckpointTuple,
PendingWrite,
get_checkpoint_id,
)
from langgraph.checkpoint.serde.base import SerializerProtocol
from redis import Redis
from redis.asyncio import Redis as AsyncRedis
REDIS_KEY_SEPARATOR = ":"
# Utilities shared by both RedisSaver and AsyncRedisSaver
def _make_redis_checkpoint_key(
thread_id: str, checkpoint_ns: str, checkpoint_id: str
) -> str:
return REDIS_KEY_SEPARATOR.join(
["checkpoint", thread_id, checkpoint_ns, checkpoint_id]
)
def _make_redis_checkpoint_writes_key(
thread_id: str,
checkpoint_ns: str,
checkpoint_id: str,
task_id: str,
idx: Optional[int],
) -> str:
if idx is None:
return REDIS_KEY_SEPARATOR.join(
["writes", thread_id, checkpoint_ns, checkpoint_id, task_id]
)
return REDIS_KEY_SEPARATOR.join(
["writes", thread_id, checkpoint_ns, checkpoint_id, task_id, str(idx)]
)
def _parse_redis_checkpoint_key(redis_key: str) -> dict:
namespace, thread_id, checkpoint_ns, checkpoint_id = redis_key.split(
REDIS_KEY_SEPARATOR
)
if namespace != "checkpoint":
raise ValueError("Expected checkpoint key to start with 'checkpoint'")
return {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
}
def _parse_redis_checkpoint_writes_key(redis_key: str) -> dict:
namespace, thread_id, checkpoint_ns, checkpoint_id, task_id, idx = redis_key.split(
REDIS_KEY_SEPARATOR
)
if namespace != "writes":
raise ValueError("Expected checkpoint key to start with 'checkpoint'")
return {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
"task_id": task_id,
"idx": idx,
}
def _filter_keys(
keys: List[str], before: Optional[RunnableConfig], limit: Optional[int]
) -> list:
"""Filter and sort Redis keys based on optional criteria."""
if before:
keys = [
k
for k in keys
if _parse_redis_checkpoint_key(k.decode())["checkpoint_id"]
< before["configurable"]["checkpoint_id"]
]
keys = sorted(
keys,
key=lambda k: _parse_redis_checkpoint_key(k.decode())["checkpoint_id"],
reverse=True,
)
if limit:
keys = keys[:limit]
return keys
def _dump_writes(serde: SerializerProtocol, writes: tuple[str, Any]) -> list[dict]:
"""Serialize pending writes."""
serialized_writes = []
for channel, value in writes:
type_, serialized_value = serde.dumps_typed(value)
serialized_writes.append(
{"channel": channel, "type": type_, "value": serialized_value}
)
return serialized_writes
def _load_writes(
serde: SerializerProtocol, task_id_to_data: dict[tuple[str, str], dict]
) -> list[PendingWrite]:
"""Deserialize pending writes."""
writes = [
(
task_id,
data[b"channel"].decode(),
serde.loads_typed((data[b"type"].decode(), data[b"value"])),
)
for (task_id, _), data in task_id_to_data.items()
]
return writes
def _parse_redis_checkpoint_data(
serde: SerializerProtocol,
key: str,
data: dict,
pending_writes: Optional[List[PendingWrite]] = None,
) -> Optional[CheckpointTuple]:
"""Parse checkpoint data retrieved from Redis."""
if not data:
return None
parsed_key = _parse_redis_checkpoint_key(key)
thread_id = parsed_key["thread_id"]
checkpoint_ns = parsed_key["checkpoint_ns"]
checkpoint_id = parsed_key["checkpoint_id"]
config = {
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
}
}
checkpoint = serde.loads_typed((data[b"type"].decode(), data[b"checkpoint"]))
metadata = serde.loads(data[b"metadata"].decode())
parent_checkpoint_id = data.get(b"parent_checkpoint_id", b"").decode()
parent_config = (
{
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": parent_checkpoint_id,
}
}
if parent_checkpoint_id
else None
)
return CheckpointTuple(
config=config,
checkpoint=checkpoint,
metadata=metadata,
parent_config=parent_config,
pending_writes=pending_writes,
)class RedisSaver(BaseCheckpointSaver):
"""Redis-based checkpoint saver implementation."""
conn: Redis
def __init__(self, conn: Redis):
super().__init__()
self.conn = conn
@classmethod
@contextmanager
def from_conn_info(cls, *, host: str, port: int, db: int) -> Iterator["RedisSaver"]:
conn = None
try:
conn = Redis(host=host, port=port, db=db)
yield RedisSaver(conn)
finally:
if conn:
conn.close()
def put(
self,
config: RunnableConfig,
checkpoint: Checkpoint,
metadata: CheckpointMetadata,
new_versions: ChannelVersions,
) -> RunnableConfig:
"""Save a checkpoint to Redis.
Args:
config (RunnableConfig): The config to associate with the checkpoint.
checkpoint (Checkpoint): The checkpoint to save.
metadata (CheckpointMetadata): Additional metadata to save with the checkpoint.
new_versions (ChannelVersions): New channel versions as of this write.
Returns:
RunnableConfig: Updated configuration after storing the checkpoint.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"]["checkpoint_ns"]
checkpoint_id = checkpoint["id"]
parent_checkpoint_id = config["configurable"].get("checkpoint_id")
key = _make_redis_checkpoint_key(thread_id, checkpoint_ns, checkpoint_id)
type_, serialized_checkpoint = self.serde.dumps_typed(checkpoint)
serialized_metadata = self.serde.dumps(metadata)
data = {
"checkpoint": serialized_checkpoint,
"type": type_,
"metadata": serialized_metadata,
"parent_checkpoint_id": parent_checkpoint_id
if parent_checkpoint_id
else "",
}
self.conn.hset(key, mapping=data)
return {
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
}
}
def put_writes(
self,
config: RunnableConfig,
writes: List[Tuple[str, Any]],
task_id: str,
) -> RunnableConfig:
"""Store intermediate writes linked to a checkpoint.
Args:
config (RunnableConfig): Configuration of the related checkpoint.
writes (Sequence[Tuple[str, Any]]): List of writes to store, each as (channel, value) pair.
task_id (str): Identifier for the task creating the writes.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"]["checkpoint_ns"]
checkpoint_id = config["configurable"]["checkpoint_id"]
for idx, data in enumerate(_dump_writes(self.serde, writes)):
key = _make_redis_checkpoint_writes_key(
thread_id, checkpoint_ns, checkpoint_id, task_id, idx
)
self.conn.hset(key, mapping=data)
return config
def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
"""Get a checkpoint tuple from Redis.
This method retrieves a checkpoint tuple from Redis based on the
provided config. If the config contains a "checkpoint_id" key, the checkpoint with
the matching thread ID and checkpoint ID is retrieved. Otherwise, the latest checkpoint
for the given thread ID is retrieved.
Args:
config (RunnableConfig): The config to use for retrieving the checkpoint.
Returns:
Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_id = get_checkpoint_id(config)
checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
checkpoint_key = self._get_checkpoint_key(
self.conn, thread_id, checkpoint_ns, checkpoint_id
)
if not checkpoint_key:
return None
checkpoint_data = self.conn.hgetall(checkpoint_key)
# load pending writes
checkpoint_id = (
checkpoint_id
or _parse_redis_checkpoint_key(checkpoint_key)["checkpoint_id"]
)
writes_key = _make_redis_checkpoint_writes_key(
thread_id, checkpoint_ns, checkpoint_id, "*", None
)
matching_keys = self.conn.keys(pattern=writes_key)
parsed_keys = [
_parse_redis_checkpoint_writes_key(key.decode()) for key in matching_keys
]
pending_writes = _load_writes(
self.serde,
{
(parsed_key["task_id"], parsed_key["idx"]): self.conn.hgetall(key)
for key, parsed_key in sorted(
zip(matching_keys, parsed_keys), key=lambda x: x[1]["idx"]
)
},
)
return _parse_redis_checkpoint_data(
self.serde, checkpoint_key, checkpoint_data, pending_writes=pending_writes
)
def list(
self,
config: Optional[RunnableConfig],
*,
# TODO: implement filtering
filter: Optional[dict[str, Any]] = None,
before: Optional[RunnableConfig] = None,
limit: Optional[int] = None,
) -> Iterator[CheckpointTuple]:
"""List checkpoints from the database.
This method retrieves a list of checkpoint tuples from Redis based
on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first).
Args:
config (RunnableConfig): The config to use for listing the checkpoints.
filter (Optional[Dict[str, Any]]): Additional filtering criteria for metadata. Defaults to None.
before (Optional[RunnableConfig]): If provided, only checkpoints before the specified checkpoint ID are returned. Defaults to None.
limit (Optional[int]): The maximum number of checkpoints to return. Defaults to None.
Yields:
Iterator[CheckpointTuple]: An iterator of checkpoint tuples.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
pattern = _make_redis_checkpoint_key(thread_id, checkpoint_ns, "*")
keys = _filter_keys(self.conn.keys(pattern), before, limit)
for key in keys:
data = self.conn.hgetall(key)
if data and b"checkpoint" in data and b"metadata" in data:
yield _parse_redis_checkpoint_data(self.serde, key.decode(), data)
def _get_checkpoint_key(
self, conn, thread_id: str, checkpoint_ns: str, checkpoint_id: Optional[str]
) -> Optional[str]:
"""Determine the Redis key for a checkpoint."""
if checkpoint_id:
return _make_redis_checkpoint_key(thread_id, checkpoint_ns, checkpoint_id)
all_keys = conn.keys(_make_redis_checkpoint_key(thread_id, checkpoint_ns, "*"))
if not all_keys:
return None
latest_key = max(
all_keys,
key=lambda k: _parse_redis_checkpoint_key(k.decode())["checkpoint_id"],
)
return latest_key.decode()class AsyncRedisSaver(BaseCheckpointSaver):
"""Async redis-based checkpoint saver implementation."""
conn: AsyncRedis
def __init__(self, conn: AsyncRedis):
super().__init__()
self.conn = conn
@classmethod
@asynccontextmanager
async def from_conn_info(
cls, *, host: str, port: int, db: int
) -> AsyncIterator["AsyncRedisSaver"]:
conn = None
try:
conn = AsyncRedis(host=host, port=port, db=db)
yield AsyncRedisSaver(conn)
finally:
if conn:
await conn.aclose()
async def aput(
self,
config: RunnableConfig,
checkpoint: Checkpoint,
metadata: CheckpointMetadata,
new_versions: ChannelVersions,
) -> RunnableConfig:
"""Save a checkpoint to the database asynchronously.
This method saves a checkpoint to Redis. The checkpoint is associated
with the provided config and its parent config (if any).
Args:
config (RunnableConfig): The config to associate with the checkpoint.
checkpoint (Checkpoint): The checkpoint to save.
metadata (CheckpointMetadata): Additional metadata to save with the checkpoint.
new_versions (ChannelVersions): New channel versions as of this write.
Returns:
RunnableConfig: Updated configuration after storing the checkpoint.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"]["checkpoint_ns"]
checkpoint_id = checkpoint["id"]
parent_checkpoint_id = config["configurable"].get("checkpoint_id")
key = _make_redis_checkpoint_key(thread_id, checkpoint_ns, checkpoint_id)
type_, serialized_checkpoint = self.serde.dumps_typed(checkpoint)
serialized_metadata = self.serde.dumps(metadata)
data = {
"checkpoint": serialized_checkpoint,
"type": type_,
"checkpoint_id": checkpoint_id,
"metadata": serialized_metadata,
"parent_checkpoint_id": parent_checkpoint_id
if parent_checkpoint_id
else "",
}
await self.conn.hset(key, mapping=data)
return {
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
}
}
async def aput_writes(
self,
config: RunnableConfig,
writes: List[Tuple[str, Any]],
task_id: str,
) -> RunnableConfig:
"""Store intermediate writes linked to a checkpoint asynchronously.
This method saves intermediate writes associated with a checkpoint to the database.
Args:
config (RunnableConfig): Configuration of the related checkpoint.
writes (Sequence[Tuple[str, Any]]): List of writes to store, each as (channel, value) pair.
task_id (str): Identifier for the task creating the writes.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"]["checkpoint_ns"]
checkpoint_id = config["configurable"]["checkpoint_id"]
for idx, data in enumerate(_dump_writes(self.serde, writes)):
key = _make_redis_checkpoint_writes_key(
thread_id, checkpoint_ns, checkpoint_id, task_id, idx
)
await self.conn.hset(key, mapping=data)
return config
async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
"""Get a checkpoint tuple from Redis asynchronously.
This method retrieves a checkpoint tuple from Redis based on the
provided config. If the config contains a "checkpoint_id" key, the checkpoint with
the matching thread ID and checkpoint ID is retrieved. Otherwise, the latest checkpoint
for the given thread ID is retrieved.
Args:
config (RunnableConfig): The config to use for retrieving the checkpoint.
Returns:
Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_id = get_checkpoint_id(config)
checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
checkpoint_key = await self._aget_checkpoint_key(
self.conn, thread_id, checkpoint_ns, checkpoint_id
)
if not checkpoint_key:
return None
checkpoint_data = await self.conn.hgetall(checkpoint_key)
# load pending writes
checkpoint_id = (
checkpoint_id
or _parse_redis_checkpoint_key(checkpoint_key)["checkpoint_id"]
)
writes_key = _make_redis_checkpoint_writes_key(
thread_id, checkpoint_ns, checkpoint_id, "*", None
)
matching_keys = await self.conn.keys(pattern=writes_key)
parsed_keys = [
_parse_redis_checkpoint_writes_key(key.decode()) for key in matching_keys
]
pending_writes = _load_writes(
self.serde,
{
(parsed_key["task_id"], parsed_key["idx"]): await self.conn.hgetall(key)
for key, parsed_key in sorted(
zip(matching_keys, parsed_keys), key=lambda x: x[1]["idx"]
)
},
)
return _parse_redis_checkpoint_data(
self.serde, checkpoint_key, checkpoint_data, pending_writes=pending_writes
)
async def alist(
self,
config: Optional[RunnableConfig],
*,
# TODO: implement filtering
filter: Optional[dict[str, Any]] = None,
before: Optional[RunnableConfig] = None,
limit: Optional[int] = None,
) -> AsyncGenerator[CheckpointTuple, None]:
"""List checkpoints from Redis asynchronously.
This method retrieves a list of checkpoint tuples from Redis based
on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first).
Args:
config (Optional[RunnableConfig]): Base configuration for filtering checkpoints.
filter (Optional[Dict[str, Any]]): Additional filtering criteria for metadata.
before (Optional[RunnableConfig]): If provided, only checkpoints before the specified checkpoint ID are returned. Defaults to None.
limit (Optional[int]): Maximum number of checkpoints to return.
Yields:
AsyncIterator[CheckpointTuple]: An asynchronous iterator of matching checkpoint tuples.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
pattern = _make_redis_checkpoint_key(thread_id, checkpoint_ns, "*")
keys = _filter_keys(await self.conn.keys(pattern), before, limit)
for key in keys:
data = await self.conn.hgetall(key)
if data and b"checkpoint" in data and b"metadata" in data:
yield _parse_redis_checkpoint_data(self.serde, key.decode(), data)
async def _aget_checkpoint_key(
self, conn, thread_id: str, checkpoint_ns: str, checkpoint_id: Optional[str]
) -> Optional[str]:
"""Asynchronously determine the Redis key for a checkpoint."""
if checkpoint_id:
return _make_redis_checkpoint_key(thread_id, checkpoint_ns, checkpoint_id)
all_keys = await conn.keys(
_make_redis_checkpoint_key(thread_id, checkpoint_ns, "*")
)
if not all_keys:
return None
latest_key = max(
all_keys,
key=lambda k: _parse_redis_checkpoint_key(k.decode())["checkpoint_id"],
)
return latest_key.decode()from typing import Literal
from langchain_core.runnables import ConfigurableField
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
@tool
def get_weather(city: Literal["nyc", "sf"]):
"""Use this to get weather information."""
if city == "nyc":
return "It might be cloudy in nyc"
elif city == "sf":
return "It's always sunny in sf"
else:
raise AssertionError("Unknown city")
tools = [get_weather]
model = ChatOpenAI(model_name="gpt-4o-mini", temperature=0)with RedisSaver.from_conn_info(host="localhost", port=6379, db=0) as checkpointer:
graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)
config = {"configurable": {"thread_id": "1"}}
res = graph.invoke({"messages": [("human", "what's the weather in sf")]}, config)
latest_checkpoint = checkpointer.get(config)
latest_checkpoint_tuple = checkpointer.get_tuple(config)
checkpoint_tuples = list(checkpointer.list(config))latest_checkpointlatest_checkpoint_tuplecheckpoint_tuplesasync with AsyncRedisSaver.from_conn_info(
host="localhost", port=6379, db=0
) as checkpointer:
graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)
config = {"configurable": {"thread_id": "2"}}
res = await graph.ainvoke(
{"messages": [("human", "what's the weather in nyc")]}, config
)
latest_checkpoint = await checkpointer.aget(config)
latest_checkpoint_tuple = await checkpointer.aget_tuple(config)
checkpoint_tuples = [c async for c in checkpointer.alist(config)]latest_checkpointlatest_checkpoint_tuplecheckpoint_tuples |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/streaming-tokens.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph.message import add_messages
# Add messages essentially does this with more
# robust handling
# def add_messages(left: list, right: list):
# return left + right
class State(TypedDict):
messages: Annotated[list, add_messages]from langchain_core.tools import tool
@tool
def search(query: str):
"""Call to surf the web."""
# This is a placeholder, but don't tell the LLM that...
return ["Cloudy with a chance of hail."]
tools = [search]from langgraph.prebuilt import ToolNode
tool_node = ToolNode(tools)from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-3.5-turbo")model = model.bind_tools(tools)from typing import Literal
from langchain_core.runnables import RunnableConfig
from langgraph.graph import END, START, StateGraph
# Define the function that determines whether to continue or not
def should_continue(state: State):
messages = state["messages"]
last_message = messages[-1]
# If there is no function call, then we finish
if not last_message.tool_calls:
return END
# Otherwise if there is, we continue
else:
return "tools"
# Define the function that calls the model
async def call_model(state: State, config: RunnableConfig):
messages = state["messages"]
# Note: Passing the config through explicitly is required for python < 3.11
# Since context var support wasn't added before then: https://docs.python.org/3/library/asyncio-task.html#creating-tasks
response = await model.ainvoke(messages, config)
# We return a list, because this will get added to the existing list
return {"messages": response}# Define a new graph
workflow = StateGraph(State)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("tools", tool_node)
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.add_edge(START, "agent")
# We now add a conditional edge
workflow.add_conditional_edges(
# First, we define the start node. We use `agent`.
# This means these are the edges taken after the `agent` node is called.
"agent",
# Next, we pass in the function that will determine which node is called next.
should_continue,
# Next we pass in the path map - all the nodes this edge could go to
["tools", END],
)
workflow.add_edge("tools", "agent")
# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable
app = workflow.compile()from IPython.display import Image, display
display(Image(app.get_graph().draw_mermaid_png()))from langchain_core.messages import AIMessageChunk, HumanMessage
inputs = [HumanMessage(content="what is the weather in sf")]
first = True
async for msg, metadata in app.astream({"messages": inputs}, stream_mode="messages"):
if msg.content and not isinstance(msg, HumanMessage):
print(msg.content, end="|", flush=True)
if isinstance(msg, AIMessageChunk):
if first:
gathered = msg
first = False
else:
gathered = gathered + msg
if msg.tool_call_chunks:
print(gathered.tool_calls) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/disable-streaming.ipynb | from langchain_openai import ChatOpenAI
from langgraph.graph import MessagesState
from langgraph.graph import StateGraph, START, END
llm = ChatOpenAI(model="o1-preview", temperature=1)
graph_builder = StateGraph(MessagesState)
def chatbot(state: MessagesState):
return {"messages": [llm.invoke(state["messages"])]}
graph_builder.add_node("chatbot", chatbot)
graph_builder.add_edge(START, "chatbot")
graph_builder.add_edge("chatbot", END)
graph = graph_builder.compile()from IPython.display import Image, display
display(Image(graph.get_graph().draw_mermaid_png()))input = {"messages": {"role": "user", "content": "how many r's are in strawberry?"}}
try:
async for event in graph.astream_events(input, version="v2"):
if event["event"] == "on_chat_model_end":
print(event["data"]["output"].content, end="", flush=True)
except:
print("Streaming not supported!")llm = ChatOpenAI(model="o1-preview", temperature=1, disable_streaming=True)
graph_builder = StateGraph(MessagesState)
def chatbot(state: MessagesState):
return {"messages": [llm.invoke(state["messages"])]}
graph_builder.add_node("chatbot", chatbot)
graph_builder.add_edge(START, "chatbot")
graph_builder.add_edge("chatbot", END)
graph = graph_builder.compile()input = {"messages": {"role": "user", "content": "how many r's are in strawberry?"}}
async for event in graph.astream_events(input, version="v2"):
if event["event"] == "on_chat_model_end":
print(event["data"]["output"].content, end="", flush=True) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/how-tos/tool-calling.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("ANTHROPIC_API_KEY")from langchain_core.messages import AIMessage
from langchain_core.tools import tool
from langgraph.prebuilt import ToolNode@tool
def get_weather(location: str):
"""Call to get the current weather."""
if location.lower() in ["sf", "san francisco"]:
return "It's 60 degrees and foggy."
else:
return "It's 90 degrees and sunny."
@tool
def get_coolest_cities():
"""Get a list of coolest cities"""
return "nyc, sf"tools = [get_weather, get_coolest_cities]
tool_node = ToolNode(tools)message_with_single_tool_call = AIMessage(
content="",
tool_calls=[
{
"name": "get_weather",
"args": {"location": "sf"},
"id": "tool_call_id",
"type": "tool_call",
}
],
)
tool_node.invoke({"messages": [message_with_single_tool_call]})message_with_multiple_tool_calls = AIMessage(
content="",
tool_calls=[
{
"name": "get_coolest_cities",
"args": {},
"id": "tool_call_id_1",
"type": "tool_call",
},
{
"name": "get_weather",
"args": {"location": "sf"},
"id": "tool_call_id_2",
"type": "tool_call",
},
],
)
tool_node.invoke({"messages": [message_with_multiple_tool_calls]})from typing import Literal
from langchain_anthropic import ChatAnthropic
from langgraph.graph import StateGraph, MessagesState
from langgraph.prebuilt import ToolNode
model_with_tools = ChatAnthropic(
model="claude-3-haiku-20240307", temperature=0
).bind_tools(tools)model_with_tools.invoke("what's the weather in sf?").tool_callstool_node.invoke({"messages": [model_with_tools.invoke("what's the weather in sf?")]})from typing import Literal
from langgraph.graph import StateGraph, MessagesState, START, END
def should_continue(state: MessagesState):
messages = state["messages"]
last_message = messages[-1]
if last_message.tool_calls:
return "tools"
return END
def call_model(state: MessagesState):
messages = state["messages"]
response = model_with_tools.invoke(messages)
return {"messages": [response]}
workflow = StateGraph(MessagesState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("tools", tool_node)
workflow.add_edge(START, "agent")
workflow.add_conditional_edges("agent", should_continue, ["tools", END])
workflow.add_edge("tools", "agent")
app = workflow.compile()from IPython.display import Image, display
try:
display(Image(app.get_graph().draw_mermaid_png()))
except Exception:
# This requires some extra dependencies and is optional
pass# example with a single tool call
for chunk in app.stream(
{"messages": [("human", "what's the weather in sf?")]}, stream_mode="values"
):
chunk["messages"][-1].pretty_print()# example with a multiple tool calls in succession
for chunk in app.stream(
{"messages": [("human", "what's the weather in the coolest cities?")]},
stream_mode="values",
):
chunk["messages"][-1].pretty_print() |
0 | lc_public_repos/langgraph/docs/docs/how-tos | lc_public_repos/langgraph/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")
_set_env("ANTHROPIC_API_KEY")from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import MemorySaver
from IPython.display import Image, display
class State(TypedDict):
input: str
user_feedback: str
def step_1(state):
print("---Step 1---")
pass
def human_feedback(state):
print("---human_feedback---")
pass
def step_3(state):
print("---Step 3---")
pass
builder = StateGraph(State)
builder.add_node("step_1", step_1)
builder.add_node("human_feedback", human_feedback)
builder.add_node("step_3", step_3)
builder.add_edge(START, "step_1")
builder.add_edge("step_1", "human_feedback")
builder.add_edge("human_feedback", "step_3")
builder.add_edge("step_3", END)
# Set up memory
memory = MemorySaver()
# Add
graph = builder.compile(checkpointer=memory, interrupt_before=["human_feedback"])
# View
display(Image(graph.get_graph().draw_mermaid_png()))# Input
initial_input = {"input": "hello world"}
# Thread
thread = {"configurable": {"thread_id": "1"}}
# Run the graph until the first interruption
for event in graph.stream(initial_input, thread, stream_mode="values"):
print(event)# Get user input
try:
user_input = input("Tell me how you want to update the state: ")
except:
user_input = "go to step 3!"
# We now update the state as if we are the human_feedback node
graph.update_state(thread, {"user_feedback": user_input}, as_node="human_feedback")
# We can check the state
print("--State after update--")
print(graph.get_state(thread))
# We can check the next node, showing that it is node 3 (which follows human_feedback)
graph.get_state(thread).next# Continue the graph execution
for event in graph.stream(None, thread, stream_mode="values"):
print(event)graph.get_state(thread).values# Set up the state
from langgraph.graph import MessagesState, START
# Set up the tool
# We will have one real tool - a search tool
# We'll also have one "fake" tool - a "ask_human" tool
# Here we define any ACTUAL tools
from langchain_core.tools import tool
from langgraph.prebuilt import ToolNode
@tool
def search(query: str):
"""Call to surf the web."""
# This is a placeholder for the actual implementation
# Don't let the LLM know this though 😊
return f"I looked up: {query}. Result: It's sunny in San Francisco, but you better look out if you're a Gemini 😈."
tools = [search]
tool_node = ToolNode(tools)
# Set up the model
from langchain_anthropic import ChatAnthropic
from langchain_openai import ChatOpenAI
model = ChatAnthropic(model="claude-3-5-sonnet-20240620")
model = ChatOpenAI(model="gpt-4o")
from pydantic import BaseModel
# We are going "bind" all tools to the model
# We have the ACTUAL tools from above, but we also need a mock tool to ask a human
# Since `bind_tools` takes in tools but also just tool definitions,
# We can define a tool definition for `ask_human`
class AskHuman(BaseModel):
"""Ask the human a question"""
question: str
model = model.bind_tools(tools + [AskHuman])
# Define nodes and conditional edges
# Define the function that determines whether to continue or not
def should_continue(state):
messages = state["messages"]
last_message = messages[-1]
# If there is no function call, then we finish
if not last_message.tool_calls:
return "end"
# If tool call is asking Human, we return that node
# You could also add logic here to let some system know that there's something that requires Human input
# For example, send a slack message, etc
elif last_message.tool_calls[0]["name"] == "AskHuman":
return "ask_human"
# Otherwise if there is, we continue
else:
return "continue"
# Define the function that calls the model
def call_model(state):
messages = state["messages"]
response = model.invoke(messages)
# We return a list, because this will get added to the existing list
return {"messages": [response]}
# We define a fake node to ask the human
def ask_human(state):
pass
# Build the graph
from langgraph.graph import END, StateGraph
# Define a new graph
workflow = StateGraph(MessagesState)
# Define the three nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("action", tool_node)
workflow.add_node("ask_human", ask_human)
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.add_edge(START, "agent")
# We now add a conditional edge
workflow.add_conditional_edges(
# First, we define the start node. We use `agent`.
# This means these are the edges taken after the `agent` node is called.
"agent",
# Next, we pass in the function that will determine which node is called next.
should_continue,
# Finally we pass in a mapping.
# The keys are strings, and the values are other nodes.
# END is a special node marking that the graph should finish.
# What will happen is we will call `should_continue`, and then the output of that
# will be matched against the keys in this mapping.
# Based on which one it matches, that node will then be called.
{
# If `tools`, then we call the tool node.
"continue": "action",
# We may ask the human
"ask_human": "ask_human",
# Otherwise we finish.
"end": END,
},
)
# We now add a normal edge from `tools` to `agent`.
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge("action", "agent")
# After we get back the human response, we go back to the agent
workflow.add_edge("ask_human", "agent")
# Set up memory
from langgraph.checkpoint.memory import MemorySaver
memory = MemorySaver()
# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable
# We add a breakpoint BEFORE the `ask_human` node so it never executes
app = workflow.compile(checkpointer=memory, interrupt_before=["ask_human"])
display(Image(app.get_graph().draw_mermaid_png()))from langchain_core.messages import HumanMessage
config = {"configurable": {"thread_id": "2"}}
input_message = HumanMessage(
content="Use the search tool to ask the user where they are, then look up the weather there"
)
for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
event["messages"][-1].pretty_print()tool_call_id = app.get_state(config).values["messages"][-1].tool_calls[0]["id"]
# We now create the tool call with the id and the response we want
tool_message = [
{"tool_call_id": tool_call_id, "type": "tool", "content": "san francisco"}
]
# # This is equivalent to the below, either one works
# from langchain_core.messages import ToolMessage
# tool_message = [ToolMessage(tool_call_id=tool_call_id, content="san francisco")]
# We now update the state
# Notice that we are also specifying `as_node="ask_human"`
# This will apply this update as this node,
# which will make it so that afterwards it continues as normal
app.update_state(config, {"messages": tool_message}, as_node="ask_human")
# We can check the state
# We can see that the state currently has the `agent` node next
# This is based on how we define our graph,
# where after the `ask_human` node goes (which we just triggered)
# there is an edge to the `agent` node
app.get_state(config).nextfor event in app.stream(None, config, stream_mode="values"):
event["messages"][-1].pretty_print() |
0 | lc_public_repos/langgraph/docs/docs/how-tos | lc_public_repos/langgraph/docs/docs/how-tos/human_in_the_loop/breakpoints.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("ANTHROPIC_API_KEY")from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import MemorySaver
from IPython.display import Image, display
class State(TypedDict):
input: str
def step_1(state):
print("---Step 1---")
pass
def step_2(state):
print("---Step 2---")
pass
def step_3(state):
print("---Step 3---")
pass
builder = StateGraph(State)
builder.add_node("step_1", step_1)
builder.add_node("step_2", step_2)
builder.add_node("step_3", step_3)
builder.add_edge(START, "step_1")
builder.add_edge("step_1", "step_2")
builder.add_edge("step_2", "step_3")
builder.add_edge("step_3", END)
# Set up memory
memory = MemorySaver()
# Add
graph = builder.compile(checkpointer=memory, interrupt_before=["step_3"])
# View
display(Image(graph.get_graph().draw_mermaid_png()))# Input
initial_input = {"input": "hello world"}
# Thread
thread = {"configurable": {"thread_id": "1"}}
# Run the graph until the first interruption
for event in graph.stream(initial_input, thread, stream_mode="values"):
print(event)
try:
user_approval = input("Do you want to go to Step 3? (yes/no): ")
except:
user_approval = "yes"
if user_approval.lower() == "yes":
# If approved, continue the graph execution
for event in graph.stream(None, thread, stream_mode="values"):
print(event)
else:
print("Operation cancelled by user.")# Set up the tool
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
from langgraph.graph import MessagesState, START
from langgraph.prebuilt import ToolNode
from langgraph.graph import END, StateGraph
from langgraph.checkpoint.memory import MemorySaver
@tool
def search(query: str):
"""Call to surf the web."""
# This is a placeholder for the actual implementation
# Don't let the LLM know this though 😊
return [
"It's sunny in San Francisco, but you better look out if you're a Gemini 😈."
]
tools = [search]
tool_node = ToolNode(tools)
# Set up the model
model = ChatAnthropic(model="claude-3-5-sonnet-20240620")
model = model.bind_tools(tools)
# Define nodes and conditional edges
# Define the function that determines whether to continue or not
def should_continue(state):
messages = state["messages"]
last_message = messages[-1]
# If there is no function call, then we finish
if not last_message.tool_calls:
return "end"
# Otherwise if there is, we continue
else:
return "continue"
# Define the function that calls the model
def call_model(state):
messages = state["messages"]
response = model.invoke(messages)
# We return a list, because this will get added to the existing list
return {"messages": [response]}
# Define a new graph
workflow = StateGraph(MessagesState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("action", tool_node)
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.add_edge(START, "agent")
# We now add a conditional edge
workflow.add_conditional_edges(
# First, we define the start node. We use `agent`.
# This means these are the edges taken after the `agent` node is called.
"agent",
# Next, we pass in the function that will determine which node is called next.
should_continue,
# Finally we pass in a mapping.
# The keys are strings, and the values are other nodes.
# END is a special node marking that the graph should finish.
# What will happen is we will call `should_continue`, and then the output of that
# will be matched against the keys in this mapping.
# Based on which one it matches, that node will then be called.
{
# If `tools`, then we call the tool node.
"continue": "action",
# Otherwise we finish.
"end": END,
},
)
# We now add a normal edge from `tools` to `agent`.
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge("action", "agent")
# Set up memory
memory = MemorySaver()
# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable
# We add in `interrupt_before=["action"]`
# This will add a breakpoint before the `action` node is called
app = workflow.compile(checkpointer=memory, interrupt_before=["action"])
display(Image(app.get_graph().draw_mermaid_png()))from langchain_core.messages import HumanMessage
thread = {"configurable": {"thread_id": "3"}}
inputs = [HumanMessage(content="search for the weather in sf now")]
for event in app.stream({"messages": inputs}, thread, stream_mode="values"):
event["messages"][-1].pretty_print()for event in app.stream(None, thread, stream_mode="values"):
event["messages"][-1].pretty_print() |
0 | lc_public_repos/langgraph/docs/docs/how-tos | lc_public_repos/langgraph/docs/docs/how-tos/human_in_the_loop/edit-graph-state.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("ANTHROPIC_API_KEY")from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import MemorySaver
from IPython.display import Image, display
class State(TypedDict):
input: str
def step_1(state):
print("---Step 1---")
pass
def step_2(state):
print("---Step 2---")
pass
def step_3(state):
print("---Step 3---")
pass
builder = StateGraph(State)
builder.add_node("step_1", step_1)
builder.add_node("step_2", step_2)
builder.add_node("step_3", step_3)
builder.add_edge(START, "step_1")
builder.add_edge("step_1", "step_2")
builder.add_edge("step_2", "step_3")
builder.add_edge("step_3", END)
# Set up memory
memory = MemorySaver()
# Add
graph = builder.compile(checkpointer=memory, interrupt_before=["step_2"])
# View
display(Image(graph.get_graph().draw_mermaid_png()))# Input
initial_input = {"input": "hello world"}
# Thread
thread = {"configurable": {"thread_id": "1"}}
# Run the graph until the first interruption
for event in graph.stream(initial_input, thread, stream_mode="values"):
print(event)print("Current state!")
print(graph.get_state(thread).values)
graph.update_state(thread, {"input": "hello universe!"})
print("---\n---\nUpdated state!")
print(graph.get_state(thread).values)# Continue the graph execution
for event in graph.stream(None, thread, stream_mode="values"):
print(event)# Set up the tool
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
from langgraph.graph import MessagesState, START, END, StateGraph
from langgraph.prebuilt import ToolNode
from langgraph.checkpoint.memory import MemorySaver
@tool
def search(query: str):
"""Call to surf the web."""
# This is a placeholder for the actual implementation
# Don't let the LLM know this though 😊
return [
"It's sunny in San Francisco, but you better look out if you're a Gemini 😈."
]
tools = [search]
tool_node = ToolNode(tools)
# Set up the model
model = ChatAnthropic(model="claude-3-5-sonnet-20240620")
model = model.bind_tools(tools)
# Define nodes and conditional edges
# Define the function that determines whether to continue or not
def should_continue(state):
messages = state["messages"]
last_message = messages[-1]
# If there is no function call, then we finish
if not last_message.tool_calls:
return "end"
# Otherwise if there is, we continue
else:
return "continue"
# Define the function that calls the model
def call_model(state):
messages = state["messages"]
response = model.invoke(messages)
# We return a list, because this will get added to the existing list
return {"messages": [response]}
# Define a new graph
workflow = StateGraph(MessagesState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("action", tool_node)
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.add_edge(START, "agent")
# We now add a conditional edge
workflow.add_conditional_edges(
# First, we define the start node. We use `agent`.
# This means these are the edges taken after the `agent` node is called.
"agent",
# Next, we pass in the function that will determine which node is called next.
should_continue,
# Finally we pass in a mapping.
# The keys are strings, and the values are other nodes.
# END is a special node marking that the graph should finish.
# What will happen is we will call `should_continue`, and then the output of that
# will be matched against the keys in this mapping.
# Based on which one it matches, that node will then be called.
{
# If `tools`, then we call the tool node.
"continue": "action",
# Otherwise we finish.
"end": END,
},
)
# We now add a normal edge from `tools` to `agent`.
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge("action", "agent")
# Set up memory
memory = MemorySaver()
# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable
# We add in `interrupt_before=["action"]`
# This will add a breakpoint before the `action` node is called
app = workflow.compile(checkpointer=memory, interrupt_before=["action"])from langchain_core.messages import HumanMessage
thread = {"configurable": {"thread_id": "3"}}
inputs = [HumanMessage(content="search for the weather in sf now")]
for event in app.stream({"messages": inputs}, thread, stream_mode="values"):
event["messages"][-1].pretty_print()# First, lets get the current state
current_state = app.get_state(thread)
# Let's now get the last message in the state
# This is the one with the tool calls that we want to update
last_message = current_state.values["messages"][-1]
# Let's now update the args for that tool call
last_message.tool_calls[0]["args"] = {"query": "current weather in SF"}
# Let's now call `update_state` to pass in this message in the `messages` key
# This will get treated as any other update to the state
# It will get passed to the reducer function for the `messages` key
# That reducer function will use the ID of the message to update it
# It's important that it has the right ID! Otherwise it would get appended
# as a new message
app.update_state(thread, {"messages": last_message})current_state = app.get_state(thread).values["messages"][-1].tool_calls
current_statefor event in app.stream(None, thread, stream_mode="values"):
event["messages"][-1].pretty_print() |
0 | lc_public_repos/langgraph/docs/docs/how-tos | lc_public_repos/langgraph/docs/docs/how-tos/human_in_the_loop/time-travel.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")# Set up the tool
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from langgraph.graph import MessagesState, START
from langgraph.prebuilt import ToolNode
from langgraph.graph import END, StateGraph
from langgraph.checkpoint.memory import MemorySaver
@tool
def play_song_on_spotify(song: str):
"""Play a song on Spotify"""
# Call the spotify API ...
return f"Successfully played {song} on Spotify!"
@tool
def play_song_on_apple(song: str):
"""Play a song on Apple Music"""
# Call the apple music API ...
return f"Successfully played {song} on Apple Music!"
tools = [play_song_on_apple, play_song_on_spotify]
tool_node = ToolNode(tools)
# Set up the model
model = ChatOpenAI(model="gpt-4o-mini")
model = model.bind_tools(tools, parallel_tool_calls=False)
# Define nodes and conditional edges
# Define the function that determines whether to continue or not
def should_continue(state):
messages = state["messages"]
last_message = messages[-1]
# If there is no function call, then we finish
if not last_message.tool_calls:
return "end"
# Otherwise if there is, we continue
else:
return "continue"
# Define the function that calls the model
def call_model(state):
messages = state["messages"]
response = model.invoke(messages)
# We return a list, because this will get added to the existing list
return {"messages": [response]}
# Define a new graph
workflow = StateGraph(MessagesState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("action", tool_node)
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.add_edge(START, "agent")
# We now add a conditional edge
workflow.add_conditional_edges(
# First, we define the start node. We use `agent`.
# This means these are the edges taken after the `agent` node is called.
"agent",
# Next, we pass in the function that will determine which node is called next.
should_continue,
# Finally we pass in a mapping.
# The keys are strings, and the values are other nodes.
# END is a special node marking that the graph should finish.
# What will happen is we will call `should_continue`, and then the output of that
# will be matched against the keys in this mapping.
# Based on which one it matches, that node will then be called.
{
# If `tools`, then we call the tool node.
"continue": "action",
# Otherwise we finish.
"end": END,
},
)
# We now add a normal edge from `tools` to `agent`.
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge("action", "agent")
# Set up memory
memory = MemorySaver()
# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable
# We add in `interrupt_before=["action"]`
# This will add a breakpoint before the `action` node is called
app = workflow.compile(checkpointer=memory)from langchain_core.messages import HumanMessage
config = {"configurable": {"thread_id": "1"}}
input_message = HumanMessage(content="Can you play Taylor Swift's most popular song?")
for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
event["messages"][-1].pretty_print()app.get_state(config).values["messages"]all_states = []
for state in app.get_state_history(config):
print(state)
all_states.append(state)
print("--")to_replay = all_states[2]to_replay.valuesto_replay.nextfor event in app.stream(None, to_replay.config):
for v in event.values():
print(v)# Let's now get the last message in the state
# This is the one with the tool calls that we want to update
last_message = to_replay.values["messages"][-1]
# Let's now update the tool we are calling
last_message.tool_calls[0]["name"] = "play_song_on_spotify"
branch_config = app.update_state(
to_replay.config,
{"messages": [last_message]},
)for event in app.stream(None, branch_config):
for v in event.values():
print(v)from langchain_core.messages import AIMessage
# Let's now get the last message in the state
# This is the one with the tool calls that we want to update
last_message = to_replay.values["messages"][-1]
# Let's now get the ID for the last message, and create a new message with that ID.
new_message = AIMessage(
content="It's quiet hours so I can't play any music right now!", id=last_message.id
)
branch_config = app.update_state(
to_replay.config,
{"messages": [new_message]},
)branch_state = app.get_state(branch_config)branch_state.valuesbranch_state.next |
0 | lc_public_repos/langgraph/docs/docs/how-tos | lc_public_repos/langgraph/docs/docs/how-tos/human_in_the_loop/review-tool-calls.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("ANTHROPIC_API_KEY")from typing_extensions import TypedDict, Literal
from langgraph.graph import StateGraph, START, END, MessagesState
from langgraph.checkpoint.memory import MemorySaver
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
from langchain_core.messages import AIMessage
from IPython.display import Image, display
@tool
def weather_search(city: str):
"""Search for the weather"""
print("----")
print(f"Searching for: {city}")
print("----")
return "Sunny!"
model = ChatAnthropic(model_name="claude-3-5-sonnet-20240620").bind_tools(
[weather_search]
)
class State(MessagesState):
"""Simple state."""
def call_llm(state):
return {"messages": [model.invoke(state["messages"])]}
def human_review_node(state):
pass
def run_tool(state):
new_messages = []
tools = {"weather_search": weather_search}
tool_calls = state["messages"][-1].tool_calls
for tool_call in tool_calls:
tool = tools[tool_call["name"]]
result = tool.invoke(tool_call["args"])
new_messages.append(
{
"role": "tool",
"name": tool_call["name"],
"content": result,
"tool_call_id": tool_call["id"],
}
)
return {"messages": new_messages}
def route_after_llm(state) -> Literal[END, "human_review_node"]:
if len(state["messages"][-1].tool_calls) == 0:
return END
else:
return "human_review_node"
def route_after_human(state) -> Literal["run_tool", "call_llm"]:
if isinstance(state["messages"][-1], AIMessage):
return "run_tool"
else:
return "call_llm"
builder = StateGraph(State)
builder.add_node(call_llm)
builder.add_node(run_tool)
builder.add_node(human_review_node)
builder.add_edge(START, "call_llm")
builder.add_conditional_edges("call_llm", route_after_llm)
builder.add_conditional_edges("human_review_node", route_after_human)
builder.add_edge("run_tool", "call_llm")
# Set up memory
memory = MemorySaver()
# Add
graph = builder.compile(checkpointer=memory, interrupt_before=["human_review_node"])
# View
display(Image(graph.get_graph().draw_mermaid_png()))# Input
initial_input = {"messages": [{"role": "user", "content": "hi!"}]}
# Thread
thread = {"configurable": {"thread_id": "1"}}
# Run the graph until the first interruption
for event in graph.stream(initial_input, thread, stream_mode="values"):
print(event)print("Pending Executions!")
print(graph.get_state(thread).next)# Input
initial_input = {"messages": [{"role": "user", "content": "what's the weather in sf?"}]}
# Thread
thread = {"configurable": {"thread_id": "2"}}
# Run the graph until the first interruption
for event in graph.stream(initial_input, thread, stream_mode="values"):
print(event)print("Pending Executions!")
print(graph.get_state(thread).next)for event in graph.stream(None, thread, stream_mode="values"):
print(event)# Input
initial_input = {"messages": [{"role": "user", "content": "what's the weather in sf?"}]}
# Thread
thread = {"configurable": {"thread_id": "5"}}
# Run the graph until the first interruption
for event in graph.stream(initial_input, thread, stream_mode="values"):
print(event)print("Pending Executions!")
print(graph.get_state(thread).next)# To get the ID of the message we want to replace, we need to fetch the current state and find it there.
state = graph.get_state(thread)
print("Current State:")
print(state.values)
print("\nCurrent Tool Call ID:")
current_content = state.values["messages"][-1].content
current_id = state.values["messages"][-1].id
tool_call_id = state.values["messages"][-1].tool_calls[0]["id"]
print(tool_call_id)
# We now need to construct a replacement tool call.
# We will change the argument to be `San Francisco, USA`
# Note that we could change any number of arguments or tool names - it just has to be a valid one
new_message = {
"role": "assistant",
"content": current_content,
"tool_calls": [
{
"id": tool_call_id,
"name": "weather_search",
"args": {"city": "San Francisco, USA"},
}
],
# This is important - this needs to be the same as the message you replacing!
# Otherwise, it will show up as a separate message
"id": current_id,
}
graph.update_state(
# This is the config which represents this thread
thread,
# This is the updated value we want to push
{"messages": [new_message]},
# We push this update acting as our human_review_node
as_node="human_review_node",
)
# Let's now continue executing from here
for event in graph.stream(None, thread, stream_mode="values"):
print(event)# Input
initial_input = {"messages": [{"role": "user", "content": "what's the weather in sf?"}]}
# Thread
thread = {"configurable": {"thread_id": "6"}}
# Run the graph until the first interruption
for event in graph.stream(initial_input, thread, stream_mode="values"):
print(event)print("Pending Executions!")
print(graph.get_state(thread).next)# To get the ID of the message we want to replace, we need to fetch the current state and find it there.
state = graph.get_state(thread)
print("Current State:")
print(state.values)
print("\nCurrent Tool Call ID:")
tool_call_id = state.values["messages"][-1].tool_calls[0]["id"]
print(tool_call_id)
# We now need to construct a replacement tool call.
# We will change the argument to be `San Francisco, USA`
# Note that we could change any number of arguments or tool names - it just has to be a valid one
new_message = {
"role": "tool",
# This is our natural language feedback
"content": "User requested changes: pass in the country as well",
"name": "weather_search",
"tool_call_id": tool_call_id,
}
graph.update_state(
# This is the config which represents this thread
thread,
# This is the updated value we want to push
{"messages": [new_message]},
# We push this update acting as our human_review_node
as_node="human_review_node",
)
# Let's now continue executing from here
for event in graph.stream(None, thread, stream_mode="values"):
print(event)print("Pending Executions!")
print(graph.get_state(thread).next)
for event in graph.stream(None, thread, stream_mode="values"):
print(event) |
0 | lc_public_repos/langgraph/docs/docs/how-tos | lc_public_repos/langgraph/docs/docs/how-tos/human_in_the_loop/dynamic_breakpoints.ipynb | from typing_extensions import TypedDict
from IPython.display import Image, display
from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import MemorySaver
from langgraph.errors import NodeInterrupt
class State(TypedDict):
input: str
def step_1(state: State) -> State:
print("---Step 1---")
return state
def step_2(state: State) -> State:
# Let's optionally raise a NodeInterrupt
# if the length of the input is longer than 5 characters
if len(state["input"]) > 5:
raise NodeInterrupt(
f"Received input that is longer than 5 characters: {state['input']}"
)
print("---Step 2---")
return state
def step_3(state: State) -> State:
print("---Step 3---")
return state
builder = StateGraph(State)
builder.add_node("step_1", step_1)
builder.add_node("step_2", step_2)
builder.add_node("step_3", step_3)
builder.add_edge(START, "step_1")
builder.add_edge("step_1", "step_2")
builder.add_edge("step_2", "step_3")
builder.add_edge("step_3", END)
# Set up memory
memory = MemorySaver()
# Compile the graph with memory
graph = builder.compile(checkpointer=memory)
# View
display(Image(graph.get_graph().draw_mermaid_png()))initial_input = {"input": "hello"}
thread_config = {"configurable": {"thread_id": "1"}}
for event in graph.stream(initial_input, thread_config, stream_mode="values"):
print(event)state = graph.get_state(thread_config)
print(state.next)
print(state.tasks)initial_input = {"input": "hello world"}
thread_config = {"configurable": {"thread_id": "2"}}
# Run the graph until the first interruption
for event in graph.stream(initial_input, thread_config, stream_mode="values"):
print(event)state = graph.get_state(thread_config)
print(state.next)
print(state.tasks)# NOTE: to resume the graph from a dynamic interrupt we use the same syntax as with regular interrupts -- we pass None as the input
for event in graph.stream(None, thread_config, stream_mode="values"):
print(event)state = graph.get_state(thread_config)
print(state.next)
print(state.tasks)# NOTE: this update will be applied as of the last successful node before the interrupt, i.e. `step_1`, right before the node with an interrupt
graph.update_state(config=thread_config, values={"input": "foo"})
for event in graph.stream(None, thread_config, stream_mode="values"):
print(event)
state = graph.get_state(thread_config)
print(state.next)
print(state.values)initial_input = {"input": "hello world"}
thread_config = {"configurable": {"thread_id": "3"}}
# Run the graph until the first interruption
for event in graph.stream(initial_input, thread_config, stream_mode="values"):
print(event)# NOTE: this update will skip the node `step_2` altogether
graph.update_state(config=thread_config, values=None, as_node="step_2")
for event in graph.stream(None, thread_config, stream_mode="values"):
print(event)
state = graph.get_state(thread_config)
print(state.next)
print(state.values) |
0 | lc_public_repos/langgraph/docs/docs/how-tos | lc_public_repos/langgraph/docs/docs/how-tos/memory/add-summary-conversation-history.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("ANTHROPIC_API_KEY")from typing import Literal
from langchain_anthropic import ChatAnthropic
from langchain_core.messages import SystemMessage, RemoveMessage
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import MessagesState, StateGraph, START, END
memory = MemorySaver()
# We will add a `summary` attribute (in addition to `messages` key,
# which MessagesState already has)
class State(MessagesState):
summary: str
# We will use this model for both the conversation and the summarization
model = ChatAnthropic(model_name="claude-3-haiku-20240307")
# Define the logic to call the model
def call_model(state: State):
# If a summary exists, we add this in as a system message
summary = state.get("summary", "")
if summary:
system_message = f"Summary of conversation earlier: {summary}"
messages = [SystemMessage(content=system_message)] + state["messages"]
else:
messages = state["messages"]
response = model.invoke(messages)
# We return a list, because this will get added to the existing list
return {"messages": [response]}
# We now define the logic for determining whether to end or summarize the conversation
def should_continue(state: State) -> Literal["summarize_conversation", END]:
"""Return the next node to execute."""
messages = state["messages"]
# If there are more than six messages, then we summarize the conversation
if len(messages) > 6:
return "summarize_conversation"
# Otherwise we can just end
return END
def summarize_conversation(state: State):
# First, we summarize the conversation
summary = state.get("summary", "")
if summary:
# If a summary already exists, we use a different system prompt
# to summarize it than if one didn't
summary_message = (
f"This is summary of the conversation to date: {summary}\n\n"
"Extend the summary by taking into account the new messages above:"
)
else:
summary_message = "Create a summary of the conversation above:"
messages = state["messages"] + [HumanMessage(content=summary_message)]
response = model.invoke(messages)
# We now need to delete messages that we no longer want to show up
# I will delete all but the last two messages, but you can change this
delete_messages = [RemoveMessage(id=m.id) for m in state["messages"][:-2]]
return {"summary": response.content, "messages": delete_messages}
# Define a new graph
workflow = StateGraph(State)
# Define the conversation node and the summarize node
workflow.add_node("conversation", call_model)
workflow.add_node(summarize_conversation)
# Set the entrypoint as conversation
workflow.add_edge(START, "conversation")
# We now add a conditional edge
workflow.add_conditional_edges(
# First, we define the start node. We use `conversation`.
# This means these are the edges taken after the `conversation` node is called.
"conversation",
# Next, we pass in the function that will determine which node is called next.
should_continue,
)
# We now add a normal edge from `summarize_conversation` to END.
# This means that after `summarize_conversation` is called, we end.
workflow.add_edge("summarize_conversation", END)
# Finally, we compile it!
app = workflow.compile(checkpointer=memory)def print_update(update):
for k, v in update.items():
for m in v["messages"]:
m.pretty_print()
if "summary" in v:
print(v["summary"])from langchain_core.messages import HumanMessage
config = {"configurable": {"thread_id": "4"}}
input_message = HumanMessage(content="hi! I'm bob")
input_message.pretty_print()
for event in app.stream({"messages": [input_message]}, config, stream_mode="updates"):
print_update(event)
input_message = HumanMessage(content="what's my name?")
input_message.pretty_print()
for event in app.stream({"messages": [input_message]}, config, stream_mode="updates"):
print_update(event)
input_message = HumanMessage(content="i like the celtics!")
input_message.pretty_print()
for event in app.stream({"messages": [input_message]}, config, stream_mode="updates"):
print_update(event)values = app.get_state(config).values
valuesinput_message = HumanMessage(content="i like how much they win")
input_message.pretty_print()
for event in app.stream({"messages": [input_message]}, config, stream_mode="updates"):
print_update(event)values = app.get_state(config).values
valuesinput_message = HumanMessage(content="what's my name?")
input_message.pretty_print()
for event in app.stream({"messages": [input_message]}, config, stream_mode="updates"):
print_update(event)input_message = HumanMessage(content="what NFL team do you think I like?")
input_message.pretty_print()
for event in app.stream({"messages": [input_message]}, config, stream_mode="updates"):
print_update(event)input_message = HumanMessage(content="i like the patriots!")
input_message.pretty_print()
for event in app.stream({"messages": [input_message]}, config, stream_mode="updates"):
print_update(event) |
0 | lc_public_repos/langgraph/docs/docs/how-tos | lc_public_repos/langgraph/docs/docs/how-tos/memory/delete-messages.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("ANTHROPIC_API_KEY")from typing import Literal
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import MessagesState, StateGraph, START, END
from langgraph.prebuilt import ToolNode
memory = MemorySaver()
@tool
def search(query: str):
"""Call to surf the web."""
# This is a placeholder for the actual implementation
# Don't let the LLM know this though 😊
return "It's sunny in San Francisco, but you better look out if you're a Gemini 😈."
tools = [search]
tool_node = ToolNode(tools)
model = ChatAnthropic(model_name="claude-3-haiku-20240307")
bound_model = model.bind_tools(tools)
def should_continue(state: MessagesState):
"""Return the next node to execute."""
last_message = state["messages"][-1]
# If there is no function call, then we finish
if not last_message.tool_calls:
return END
# Otherwise if there is, we continue
return "action"
# Define the function that calls the model
def call_model(state: MessagesState):
response = model.invoke(state["messages"])
# We return a list, because this will get added to the existing list
return {"messages": response}
# Define a new graph
workflow = StateGraph(MessagesState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("action", tool_node)
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.add_edge(START, "agent")
# We now add a conditional edge
workflow.add_conditional_edges(
# First, we define the start node. We use `agent`.
# This means these are the edges taken after the `agent` node is called.
"agent",
# Next, we pass in the function that will determine which node is called next.
should_continue,
# Next, we pass in the path map - all the possible nodes this edge could go to
["action", END],
)
# We now add a normal edge from `tools` to `agent`.
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge("action", "agent")
# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable
app = workflow.compile(checkpointer=memory)from langchain_core.messages import HumanMessage
config = {"configurable": {"thread_id": "2"}}
input_message = HumanMessage(content="hi! I'm bob")
for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
event["messages"][-1].pretty_print()
input_message = HumanMessage(content="what's my name?")
for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
event["messages"][-1].pretty_print()messages = app.get_state(config).values["messages"]
messagesfrom langchain_core.messages import RemoveMessage
app.update_state(config, {"messages": RemoveMessage(id=messages[0].id)})messages = app.get_state(config).values["messages"]
messagesfrom langchain_core.messages import RemoveMessage
from langgraph.graph import END
def delete_messages(state):
messages = state["messages"]
if len(messages) > 3:
return {"messages": [RemoveMessage(id=m.id) for m in messages[:-3]]}
# We need to modify the logic to call delete_messages rather than end right away
def should_continue(state: MessagesState) -> Literal["action", "delete_messages"]:
"""Return the next node to execute."""
last_message = state["messages"][-1]
# If there is no function call, then we call our delete_messages function
if not last_message.tool_calls:
return "delete_messages"
# Otherwise if there is, we continue
return "action"
# Define a new graph
workflow = StateGraph(MessagesState)
workflow.add_node("agent", call_model)
workflow.add_node("action", tool_node)
# This is our new node we're defining
workflow.add_node(delete_messages)
workflow.add_edge(START, "agent")
workflow.add_conditional_edges(
"agent",
should_continue,
)
workflow.add_edge("action", "agent")
# This is the new edge we're adding: after we delete messages, we finish
workflow.add_edge("delete_messages", END)
app = workflow.compile(checkpointer=memory)from langchain_core.messages import HumanMessage
config = {"configurable": {"thread_id": "3"}}
input_message = HumanMessage(content="hi! I'm bob")
for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
print([(message.type, message.content) for message in event["messages"]])
input_message = HumanMessage(content="what's my name?")
for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
print([(message.type, message.content) for message in event["messages"]])messages = app.get_state(config).values["messages"]
messages |
0 | lc_public_repos/langgraph/docs/docs/how-tos | lc_public_repos/langgraph/docs/docs/how-tos/memory/manage-conversation-history.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("ANTHROPIC_API_KEY")from typing import Literal
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import MessagesState, StateGraph, START, END
from langgraph.prebuilt import ToolNode
memory = MemorySaver()
@tool
def search(query: str):
"""Call to surf the web."""
# This is a placeholder for the actual implementation
# Don't let the LLM know this though 😊
return "It's sunny in San Francisco, but you better look out if you're a Gemini 😈."
tools = [search]
tool_node = ToolNode(tools)
model = ChatAnthropic(model_name="claude-3-haiku-20240307")
bound_model = model.bind_tools(tools)
def should_continue(state: MessagesState):
"""Return the next node to execute."""
last_message = state["messages"][-1]
# If there is no function call, then we finish
if not last_message.tool_calls:
return END
# Otherwise if there is, we continue
return "action"
# Define the function that calls the model
def call_model(state: MessagesState):
response = bound_model.invoke(state["messages"])
# We return a list, because this will get added to the existing list
return {"messages": response}
# Define a new graph
workflow = StateGraph(MessagesState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("action", tool_node)
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.add_edge(START, "agent")
# We now add a conditional edge
workflow.add_conditional_edges(
# First, we define the start node. We use `agent`.
# This means these are the edges taken after the `agent` node is called.
"agent",
# Next, we pass in the function that will determine which node is called next.
should_continue,
# Next, we pass in the path map - all the possible nodes this edge could go to
["action", END],
)
# We now add a normal edge from `tools` to `agent`.
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge("action", "agent")
# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable
app = workflow.compile(checkpointer=memory)from langchain_core.messages import HumanMessage
config = {"configurable": {"thread_id": "2"}}
input_message = HumanMessage(content="hi! I'm bob")
for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
event["messages"][-1].pretty_print()
input_message = HumanMessage(content="what's my name?")
for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
event["messages"][-1].pretty_print()from typing import Literal
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import MessagesState, StateGraph, START
from langgraph.prebuilt import ToolNode
memory = MemorySaver()
@tool
def search(query: str):
"""Call to surf the web."""
# This is a placeholder for the actual implementation
# Don't let the LLM know this though 😊
return "It's sunny in San Francisco, but you better look out if you're a Gemini 😈."
tools = [search]
tool_node = ToolNode(tools)
model = ChatAnthropic(model_name="claude-3-haiku-20240307")
bound_model = model.bind_tools(tools)
def should_continue(state: MessagesState):
"""Return the next node to execute."""
last_message = state["messages"][-1]
# If there is no function call, then we finish
if not last_message.tool_calls:
return END
# Otherwise if there is, we continue
return "action"
def filter_messages(messages: list):
# This is very simple helper function which only ever uses the last message
return messages[-1:]
# Define the function that calls the model
def call_model(state: MessagesState):
messages = filter_messages(state["messages"])
response = bound_model.invoke(messages)
# We return a list, because this will get added to the existing list
return {"messages": response}
# Define a new graph
workflow = StateGraph(MessagesState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("action", tool_node)
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.add_edge(START, "agent")
# We now add a conditional edge
workflow.add_conditional_edges(
# First, we define the start node. We use `agent`.
# This means these are the edges taken after the `agent` node is called.
"agent",
# Next, we pass in the function that will determine which node is called next.
should_continue,
# Next, we pass in the pathmap - all the possible nodes this edge could go to
["action", END],
)
# We now add a normal edge from `tools` to `agent`.
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge("action", "agent")
# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable
app = workflow.compile(checkpointer=memory)from langchain_core.messages import HumanMessage
config = {"configurable": {"thread_id": "2"}}
input_message = HumanMessage(content="hi! I'm bob")
for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
event["messages"][-1].pretty_print()
# This will now not remember the previous messages
# (because we set `messages[-1:]` in the filter messages argument)
input_message = HumanMessage(content="what's my name?")
for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
event["messages"][-1].pretty_print() |
0 | lc_public_repos/langgraph/docs/docs/how-tos | lc_public_repos/langgraph/docs/docs/how-tos/memory/semantic-search.ipynb | import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")from langchain.embeddings import init_embeddings
from langgraph.store.memory import InMemoryStore
# Create store with semantic search enabled
embeddings = init_embeddings("openai:text-embedding-3-small")
store = InMemoryStore(
index={
"embed": embeddings,
"dims": 1536,
}
)# Store some memories
store.put(("user_123", "memories"), "1", {"text": "I love pizza"})
store.put(("user_123", "memories"), "2", {"text": "I prefer Italian food"})
store.put(("user_123", "memories"), "3", {"text": "I don't like spicy food"})
store.put(("user_123", "memories"), "3", {"text": "I am studying econometrics"})
store.put(("user_123", "memories"), "3", {"text": "I am a plumber"})# Find memories about food preferences
memories = store.search(("user_123", "memories"), query="I like food?", limit=5)
for memory in memories:
print(f'Memory: {memory.value["text"]} (similarity: {memory.score})')from typing import Optional
from langchain.chat_models import init_chat_model
from langgraph.store.base import BaseStore
from langgraph.graph import START, MessagesState, StateGraph
llm = init_chat_model("openai:gpt-4o-mini")
def chat(state, *, store: BaseStore):
# Search based on user's last message
items = store.search(
("user_123", "memories"), query=state["messages"][-1].content, limit=2
)
memories = "\n".join(item.value["text"] for item in items)
memories = f"## Memories of user\n{memories}" if memories else ""
response = llm.invoke(
[
{"role": "system", "content": f"You are a helpful assistant.\n{memories}"},
*state["messages"],
]
)
return {"messages": [response]}
builder = StateGraph(MessagesState)
builder.add_node(chat)
builder.add_edge(START, "chat")
graph = builder.compile(store=store)
for message, metadata in graph.stream(
input={"messages": [{"role": "user", "content": "I'm hungry"}]},
stream_mode="messages",
):
print(message.content, end="")import uuid
from typing import Optional
from langchain.chat_models import init_chat_model
from langchain_core.tools import InjectedToolArg
from langgraph.store.base import BaseStore
from typing_extensions import Annotated
from langgraph.prebuilt import create_react_agent
def prepare_messages(state, *, store: BaseStore):
# Search based on user's last message
items = store.search(
("user_123", "memories"), query=state["messages"][-1].content, limit=2
)
memories = "\n".join(item.value["text"] for item in items)
memories = f"## Memories of user\n{memories}" if memories else ""
return [
{"role": "system", "content": f"You are a helpful assistant.\n{memories}"}
] + state["messages"]
# You can also use the store directly within a tool!
def upsert_memory(
content: str,
*,
memory_id: Optional[uuid.UUID] = None,
store: Annotated[BaseStore, InjectedToolArg],
):
"""Upsert a memory in the database."""
# The LLM can use this tool to store a new memory
mem_id = memory_id or uuid.uuid4()
store.put(
("user_123", "memories"),
key=str(mem_id),
value={"text": content},
)
return f"Stored memory {mem_id}"
agent = create_react_agent(
init_chat_model("openai:gpt-4o-mini"),
tools=[upsert_memory],
# The state_modifier is run to prepare the messages for the LLM. It is called
# right before each LLM call
state_modifier=prepare_messages,
store=store,
)for message, metadata in agent.stream(
input={"messages": [{"role": "user", "content": "I'm hungry"}]},
stream_mode="messages",
):
print(message.content, end="")# Configure store to embed both memory content and emotional context
store = InMemoryStore(
index={"embed": embeddings, "dims": 1536, "fields": ["memory", "emotional_context"]}
)
# Store memories with different content/emotion pairs
store.put(
("user_123", "memories"),
"mem1",
{
"memory": "Had pizza with friends at Mario's",
"emotional_context": "felt happy and connected",
"this_isnt_indexed": "I prefer ravioli though",
},
)
store.put(
("user_123", "memories"),
"mem2",
{
"memory": "Ate alone at home",
"emotional_context": "felt a bit lonely",
"this_isnt_indexed": "I like pie",
},
)
# Search focusing on emotional state - matches mem2
results = store.search(
("user_123", "memories"), query="times they felt isolated", limit=1
)
print("Expect mem 2")
for r in results:
print(f"Item: {r.key}; Score ({r.score})")
print(f"Memory: {r.value['memory']}")
print(f"Emotion: {r.value['emotional_context']}\n")
# Search focusing on social eating - matches mem1
print("Expect mem1")
results = store.search(("user_123", "memories"), query="fun pizza", limit=1)
for r in results:
print(f"Item: {r.key}; Score ({r.score})")
print(f"Memory: {r.value['memory']}")
print(f"Emotion: {r.value['emotional_context']}\n")
print("Expect random lower score (ravioli not indexed)")
results = store.search(("user_123", "memories"), query="ravioli", limit=1)
for r in results:
print(f"Item: {r.key}; Score ({r.score})")
print(f"Memory: {r.value['memory']}")
print(f"Emotion: {r.value['emotional_context']}\n")store = InMemoryStore(
index={
"embed": embeddings,
"dims": 1536,
"fields": ["memory"],
} # Default to embed memory field
)
# Store one memory with default indexing
store.put(
("user_123", "memories"),
"mem1",
{"memory": "I love spicy food", "context": "At a Thai restaurant"},
)
# Store another overriding which fields to embed
store.put(
("user_123", "memories"),
"mem2",
{"memory": "The restaurant was too loud", "context": "Dinner at an Italian place"},
index=["context"], # Override: only embed the context
)
# Search about food - matches mem1 (using default field)
print("Expect mem1")
results = store.search(
("user_123", "memories"), query="what food do they like", limit=1
)
for r in results:
print(f"Item: {r.key}; Score ({r.score})")
print(f"Memory: {r.value['memory']}")
print(f"Context: {r.value['context']}\n")
# Search about restaurant atmosphere - matches mem2 (using overridden field)
print("Expect mem2")
results = store.search(
("user_123", "memories"), query="restaurant environment", limit=1
)
for r in results:
print(f"Item: {r.key}; Score ({r.score})")
print(f"Memory: {r.value['memory']}")
print(f"Context: {r.value['context']}\n")store = InMemoryStore(index={"embed": embeddings, "dims": 1536, "fields": ["memory"]})
# Store a normal indexed memory
store.put(
("user_123", "memories"),
"mem1",
{"memory": "I love chocolate ice cream", "type": "preference"},
)
# Store a system memory without indexing
store.put(
("user_123", "memories"),
"mem2",
{"memory": "User completed onboarding", "type": "system"},
index=False, # Disable indexing entirely
)
# Search about food preferences - finds mem1
print("Expect mem1")
results = store.search(("user_123", "memories"), query="what food preferences", limit=1)
for r in results:
print(f"Item: {r.key}; Score ({r.score})")
print(f"Memory: {r.value['memory']}")
print(f"Type: {r.value['type']}\n")
# Search about onboarding - won't find mem2 (not indexed)
print("Expect low score (mem2 not indexed)")
results = store.search(("user_123", "memories"), query="onboarding status", limit=1)
for r in results:
print(f"Item: {r.key}; Score ({r.score})")
print(f"Memory: {r.value['memory']}")
print(f"Type: {r.value['type']}\n") |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/persistence.md | # Persistence
LangGraph has a built-in persistence layer, implemented through checkpointers. When you compile graph with a checkpointer, the checkpointer saves a `checkpoint` of the graph state at every super-step. Those checkpoints are saved to a `thread`, which can be accessed after graph execution. Because `threads` allow access to graph's state after execution, several powerful capabilities including human-in-the-loop, memory, time travel, and fault-tolerance are all possible. See [this how-to guide](../how-tos/persistence.ipynb) for an end-to-end example on how to add and use checkpointers with your graph. Below, we'll discuss each of these concepts in more detail.
## Threads
A thread is a unique ID or [thread identifier](#threads) assigned to each checkpoint saved by a checkpointer. When invoking graph with a checkpointer, you **must** specify a `thread_id` as part of the `configurable` portion of the config:
```python
{"configurable": {"thread_id": "1"}}
```
## Checkpoints
Checkpoint is a snapshot of the graph state saved at each super-step and is represented by `StateSnapshot` object with the following key properties:
- `config`: Config associated with this checkpoint.
- `metadata`: Metadata associated with this checkpoint.
- `values`: Values of the state channels at this point in time.
- `next` A tuple of the node names to execute next in the graph.
- `tasks`: A tuple of `PregelTask` objects that contain information about next tasks to be executed. If the step was previously attempted, it will include error information. If a graph was interrupted [dynamically](../how-tos/human_in_the_loop/dynamic_breakpoints.ipynb) from within a node, tasks will contain additional data associated with interrupts.
Let's see what checkpoints are saved when a simple graph is invoked as follows:
```python
from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import MemorySaver
from typing import Annotated
from typing_extensions import TypedDict
from operator import add
class State(TypedDict):
foo: int
bar: Annotated[list[str], add]
def node_a(state: State):
return {"foo": "a", "bar": ["a"]}
def node_b(state: State):
return {"foo": "b", "bar": ["b"]}
workflow = StateGraph(State)
workflow.add_node(node_a)
workflow.add_node(node_b)
workflow.add_edge(START, "node_a")
workflow.add_edge("node_a", "node_b")
workflow.add_edge("node_b", END)
checkpointer = MemorySaver()
graph = workflow.compile(checkpointer=checkpointer)
config = {"configurable": {"thread_id": "1"}}
graph.invoke({"foo": ""}, config)
```
After we run the graph, we expect to see exactly 4 checkpoints:
* empty checkpoint with `START` as the next node to be executed
* checkpoint with the user input `{'foo': '', 'bar': []}` and `node_a` as the next node to be executed
* checkpoint with the outputs of `node_a` `{'foo': 'a', 'bar': ['a']}` and `node_b` as the next node to be executed
* checkpoint with the outputs of `node_b` `{'foo': 'b', 'bar': ['a', 'b']}` and no next nodes to be executed
Note that we `bar` channel values contain outputs from both nodes as we have a reducer for `bar` channel.
### Get state
When interacting with the saved graph state, you **must** specify a [thread identifier](#threads). You can view the *latest* state of the graph by calling `graph.get_state(config)`. This will return a `StateSnapshot` object that corresponds to the latest checkpoint associated with the thread ID provided in the config or a checkpoint associated with a checkpoint ID for the thread, if provided.
```python
# get the latest state snapshot
config = {"configurable": {"thread_id": "1"}}
graph.get_state(config)
# get a state snapshot for a specific checkpoint_id
config = {"configurable": {"thread_id": "1", "checkpoint_id": "1ef663ba-28fe-6528-8002-5a559208592c"}}
graph.get_state(config)
```
In our example, the output of `get_state` will look like this:
```
StateSnapshot(
values={'foo': 'b', 'bar': ['a', 'b']},
next=(),
config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef663ba-28fe-6528-8002-5a559208592c'}},
metadata={'source': 'loop', 'writes': {'node_b': {'foo': 'b', 'bar': ['b']}}, 'step': 2},
created_at='2024-08-29T19:19:38.821749+00:00',
parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef663ba-28f9-6ec4-8001-31981c2c39f8'}}, tasks=()
)
```
### Get state history
You can get the full history of the graph execution for a given thread by calling `graph.get_state_history(config)`. This will return a list of `StateSnapshot` objects associated with the thread ID provided in the config. Importantly, the checkpoints will be ordered chronologically with the most recent checkpoint / `StateSnapshot` being the first in the list.
```python
config = {"configurable": {"thread_id": "1"}}
list(graph.get_state_history(config))
```
In our example, the output of `get_state_history` will look like this:
```
[
StateSnapshot(
values={'foo': 'b', 'bar': ['a', 'b']},
next=(),
config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef663ba-28fe-6528-8002-5a559208592c'}},
metadata={'source': 'loop', 'writes': {'node_b': {'foo': 'b', 'bar': ['b']}}, 'step': 2},
created_at='2024-08-29T19:19:38.821749+00:00',
parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef663ba-28f9-6ec4-8001-31981c2c39f8'}},
tasks=(),
),
StateSnapshot(
values={'foo': 'a', 'bar': ['a']}, next=('node_b',),
config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef663ba-28f9-6ec4-8001-31981c2c39f8'}},
metadata={'source': 'loop', 'writes': {'node_a': {'foo': 'a', 'bar': ['a']}}, 'step': 1},
created_at='2024-08-29T19:19:38.819946+00:00',
parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef663ba-28f4-6b4a-8000-ca575a13d36a'}},
tasks=(PregelTask(id='6fb7314f-f114-5413-a1f3-d37dfe98ff44', name='node_b', error=None, interrupts=()),),
),
StateSnapshot(
values={'foo': '', 'bar': []},
next=('node_a',),
config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef663ba-28f4-6b4a-8000-ca575a13d36a'}},
metadata={'source': 'loop', 'writes': None, 'step': 0},
created_at='2024-08-29T19:19:38.817813+00:00',
parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef663ba-28f0-6c66-bfff-6723431e8481'}},
tasks=(PregelTask(id='f1b14528-5ee5-579c-949b-23ef9bfbed58', name='node_a', error=None, interrupts=()),),
),
StateSnapshot(
values={'bar': []},
next=('__start__',),
config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef663ba-28f0-6c66-bfff-6723431e8481'}},
metadata={'source': 'input', 'writes': {'foo': ''}, 'step': -1},
created_at='2024-08-29T19:19:38.816205+00:00',
parent_config=None,
tasks=(PregelTask(id='6d27aa2e-d72b-5504-a36f-8620e54a76dd', name='__start__', error=None, interrupts=()),),
)
]
```
### Replay
It's also possible to play-back a prior graph execution. If we `invoking` a graph with a `thread_id` and a `checkpoint_id`, then we will *re-play* the graph from a checkpoint that corresponds to the `checkpoint_id`.
* `thread_id` is simply the ID of a thread. This is always required.
* `checkpoint_id` This identifier refers to a specific checkpoint within a thread.
You must pass these when invoking the graph as part of the `configurable` portion of the config:
```python
# {"configurable": {"thread_id": "1"}} # valid config
# {"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}} # also valid config
config = {"configurable": {"thread_id": "1"}}
graph.invoke(None, config=config)
```
Importantly, LangGraph knows whether a particular checkpoint has been executed previously. If it has, LangGraph simply *re-plays* that particular step in the graph and does not re-execute the step. See this [how to guide on time-travel to learn more about replaying](../how-tos/human_in_the_loop/time-travel.ipynb).
### Update state
In addition to re-playing the graph from specific `checkpoints`, we can also *edit* the graph state. We do this using `graph.update_state()`. This method three different arguments:
#### `config`
The config should contain `thread_id` specifying which thread to update. When only the `thread_id` is passed, we update (or fork) the current state. Optionally, if we include `checkpoint_id` field, then we fork that selected checkpoint.
#### `values`
These are the values that will be used to update the state. Note that this update is treated exactly as any update from a node is treated. This means that these values will be passed to the [reducer](./low_level.md#reducers) functions, if they are defined for some of the channels in the graph state. This means that `update_state` does NOT automatically overwrite the channel values for every channel, but only for the channels without reducers. Let's walk through an example.
Let's assume you have defined the state of your graph with the following schema (see full example above):
```python
from typing import Annotated
from typing_extensions import TypedDict
from operator import add
class State(TypedDict):
foo: int
bar: Annotated[list[str], add]
```
Let's now assume the current state of the graph is
```
{"foo": 1, "bar": ["a"]}
```
If you update the state as below:
```
graph.update_state(config, {"foo": 2, "bar": ["b"]})
```
Then the new state of the graph will be:
```
{"foo": 2, "bar": ["a", "b"]}
```
The `foo` key (channel) is completely changed (because there is no reducer specified for that channel, so `update_state` overwrites it). However, there is a reducer specified for the `bar` key, and so it appends `"b"` to the state of `bar`.
#### `as_node`
The final thing you can optionally specify when calling `update_state` is `as_node`. If you provided it, the update will be applied as if it came from node `as_node`. If `as_node` is not provided, it will be set to the last node that updated the state, if not ambiguous. The reason this matters is that the next steps to execute depend on the last node to have given an update, so this can be used to control which node executes next. See this [how to guide on time-travel to learn more about forking state](../how-tos/human_in_the_loop/time-travel.ipynb).
## Memory Store
A [state schema](low_level.md#schema) specifies a set of keys that are populated as a graph is executed. As discussed above, state can be written by a checkpointer to a thread at each graph step, enabling state persistence.
But, what if we want to retrain some information *across threads*? Consider the case of a chatbot where we want to retain specific information about the user across *all* chat conversations (e.g., threads) with that user!
With checkpointers alone, we cannot share information across threads. This motivates the need for the [`Store`](../reference/store.md#langgraph.store.base.BaseStore) interface. As an illustration, we can define an `InMemoryStore` to store information about a user across threads. We simply compile our graph with a checkpointer, as before, and with our new `in_memory_store` variable.
### Basic Usage
First, let's showcase this in isolation without using LangGraph.
```python
from langgraph.store.memory import InMemoryStore
in_memory_store = InMemoryStore()
```
Memories are namespaced by a `tuple`, which in this specific example will be `(<user_id>, "memories")`. The namespace can be any length and represent anything, does not have be user specific.
```python
user_id = "1"
namespace_for_memory = (user_id, "memories")
```
We use the `store.put` method to save memories to our namespace in the store. When we do this, we specify the namespace, as defined above, and a key-value pair for the memory: the key is simply a unique identifier for the memory (`memory_id`) and the value (a dictionary) is the memory itself.
```python
memory_id = str(uuid.uuid4())
memory = {"food_preference" : "I like pizza"}
in_memory_store.put(namespace_for_memory, memory_id, memory)
```
We can read out memories in our namespace using the `store.search` method, which will return all memories for a given user as a list. The most recent memory is the last in the list.
```python
memories = in_memory_store.search(namespace_for_memory)
memories[-1].dict()
{'value': {'food_preference': 'I like pizza'},
'key': '07e0caf4-1631-47b7-b15f-65515d4c1843',
'namespace': ['1', 'memories'],
'created_at': '2024-10-02T17:22:31.590602+00:00',
'updated_at': '2024-10-02T17:22:31.590605+00:00'}
```
Each memory type is a Python class ([`Item`](https://langchain-ai.github.io/langgraph/reference/store/#langgraph.store.base.Item)) with certain attributes. We can access it as a dictionary by converting via `.dict` as above.
The attributes it has are:
- `value`: The value (itself a dictionary) of this memory
- `key`: A unique key for this memory in this namespace
- `namespace`: A list of strings, the namespace of this memory type
- `created_at`: Timestamp for when this memory was created
- `updated_at`: Timestamp for when this memory was updated
### Semantic Search
Beyond simple retrieval, the store also supports semantic search, allowing you to find memories based on meaning rather than exact matches. To enable this, configure the store with an embedding model:
```python
from langchain.embeddings import init_embeddings
store = InMemoryStore(
index={
"embed": init_embeddings("openai:text-embedding-3-small"), # Embedding provider
"dims": 1536, # Embedding dimensions
"fields": ["food_preference", "$"] # Fields to embed
}
)
```
Now when searching, you can use natural language queries to find relevant memories:
```python
# Find memories about food preferences
# (This can be done after putting memories into the store)
memories = store.search(
namespace_for_memory,
query="What does the user like to eat?",
limit=3 # Return top 3 matches
)
```
You can control which parts of your memories get embedded by configuring the `fields` parameter or by specifying the `index` parameter when storing memories:
```python
# Store with specific fields to embed
store.put(
namespace_for_memory,
str(uuid.uuid4()),
{
"food_preference": "I love Italian cuisine",
"context": "Discussing dinner plans"
},
index=["food_preference"] # Only embed "food_preferences" field
)
# Store without embedding (still retrievable, but not searchable)
store.put(
namespace_for_memory,
str(uuid.uuid4()),
{"system_info": "Last updated: 2024-01-01"},
index=False
)
```
### Using in LangGraph
With this all in place, we use the `in_memory_store` in LangGraph. The `in_memory_store` works hand-in-hand with the checkpointer: the checkpointer saves state to threads, as discussed above, and the `in_memory_store` allows us to store arbitrary information for access *across* threads. We compile the graph with both the checkpointer and the `in_memory_store` as follows.
```python
from langgraph.checkpoint.memory import MemorySaver
# We need this because we want to enable threads (conversations)
checkpointer = MemorySaver()
# ... Define the graph ...
# Compile the graph with the checkpointer and store
graph = graph.compile(checkpointer=checkpointer, store=in_memory_store)
```
We invoke the graph with a `thread_id`, as before, and also with a `user_id`, which we'll use to namespace our memories to this particular user as we showed above.
```python
# Invoke the graph
user_id = "1"
config = {"configurable": {"thread_id": "1", "user_id": user_id}}
# First let's just say hi to the AI
for update in graph.stream(
{"messages": [{"role": "user", "content": "hi"}]}, config, stream_mode="updates"
):
print(update)
```
We can access the `in_memory_store` and the `user_id` in *any node* by passing `store: BaseStore` and `config: RunnableConfig` as node arguments. Here's how we might use semantic search in a node to find relevant memories:
```python
def update_memory(state: MessagesState, config: RunnableConfig, *, store: BaseStore):
# Get the user id from the config
user_id = config["configurable"]["user_id"]
# Namespace the memory
namespace = (user_id, "memories")
# ... Analyze conversation and create a new memory
# Create a new memory ID
memory_id = str(uuid.uuid4())
# We create a new memory
store.put(namespace, memory_id, {"memory": memory})
```
As we showed above, we can also access the store in any node and use the `store.search` method to get memories. Recall the the memories are returned as a list of objects that can be converted to a dictionary.
```python
memories[-1].dict()
{'value': {'food_preference': 'I like pizza'},
'key': '07e0caf4-1631-47b7-b15f-65515d4c1843',
'namespace': ['1', 'memories'],
'created_at': '2024-10-02T17:22:31.590602+00:00',
'updated_at': '2024-10-02T17:22:31.590605+00:00'}
```
We can access the memories and use them in our model call.
```python
def call_model(state: MessagesState, config: RunnableConfig, *, store: BaseStore):
# Get the user id from the config
user_id = config["configurable"]["user_id"]
# Search based on the most recent message
memories = store.search(
namespace,
query=state["messages"][-1].content,
limit=3
)
info = "\n".join([d.value["memory"] for d in memories])
# ... Use memories in the model call
```
If we create a new thread, we can still access the same memories so long as the `user_id` is the same.
```python
# Invoke the graph
config = {"configurable": {"thread_id": "2", "user_id": "1"}}
# Let's say hi again
for update in graph.stream(
{"messages": [{"role": "user", "content": "hi, tell me about my memories"}]}, config, stream_mode="updates"
):
print(update)
```
When we use the LangGraph Platform, either locally (e.g., in LangGraph Studio) or with LangGraph Cloud, the base store is available to use by default and does not need to be specified during graph compilation. To enable semantic search, however, you **do** need to configure the indexing settings in your `langgraph.json` file. For example:
```json
{
...
"store": {
"index": {
"embed": "openai:text-embeddings-3-small",
"dims": 1536,
"fields": ["$"]
}
}
}
```
See the [deployment guide](../cloud/deployment/semantic_search.md) for more details and configuration options.
## Checkpointer libraries
Under the hood, checkpointing is powered by checkpointer objects that conform to [BaseCheckpointSaver][langgraph.checkpoint.base.BaseCheckpointSaver] interface. LangGraph provides several checkpointer implementations, all implemented via standalone, installable libraries:
* `langgraph-checkpoint`: The base interface for checkpointer savers ([BaseCheckpointSaver][langgraph.checkpoint.base.BaseCheckpointSaver]) and serialization/deserialization interface ([SerializerProtocol][langgraph.checkpoint.serde.base.SerializerProtocol]). Includes in-memory checkpointer implementation ([MemorySaver][langgraph.checkpoint.memory.MemorySaver]) for experimentation. LangGraph comes with `langgraph-checkpoint` included.
* `langgraph-checkpoint-sqlite`: An implementation of LangGraph checkpointer that uses SQLite database ([SqliteSaver][langgraph.checkpoint.sqlite.SqliteSaver] / [AsyncSqliteSaver][langgraph.checkpoint.sqlite.aio.AsyncSqliteSaver]). Ideal for experimentation and local workflows. Needs to be installed separately.
* `langgraph-checkpoint-postgres`: An advanced checkpointer that uses Postgres database ([PostgresSaver][langgraph.checkpoint.postgres.PostgresSaver] / [AsyncPostgresSaver][langgraph.checkpoint.postgres.aio.AsyncPostgresSaver]), used in LangGraph Cloud. Ideal for using in production. Needs to be installed separately.
### Checkpointer interface
Each checkpointer conforms to [BaseCheckpointSaver][langgraph.checkpoint.base.BaseCheckpointSaver] interface and implements the following methods:
* `.put` - Store a checkpoint with its configuration and metadata.
* `.put_writes` - Store intermediate writes linked to a checkpoint (i.e. [pending writes](#pending-writes)).
* `.get_tuple` - Fetch a checkpoint tuple using for a given configuration (`thread_id` and `checkpoint_id`). This is used to populate `StateSnapshot` in `graph.get_state()`.
* `.list` - List checkpoints that match a given configuration and filter criteria. This is used to populate state history in `graph.get_state_history()`
If the checkpointer is used with asynchronous graph execution (i.e. executing the graph via `.ainvoke`, `.astream`, `.abatch`), asynchronous versions of the above methods will be used (`.aput`, `.aput_writes`, `.aget_tuple`, `.alist`).
!!! note Note
For running your graph asynchronously, you can use `MemorySaver`, or async versions of Sqlite/Postgres checkpointers -- `AsyncSqliteSaver` / `AsyncPostgresSaver` checkpointers.
### Serializer
When checkpointers save the graph state, they need to serialize the channel values in the state. This is done using serializer objects.
`langgraph_checkpoint` defines [protocol][langgraph.checkpoint.serde.base.SerializerProtocol] for implementing serializers provides a default implementation ([JsonPlusSerializer][langgraph.checkpoint.serde.jsonplus.JsonPlusSerializer]) that handles a wide variety of types, including LangChain and LangGraph primitives, datetimes, enums and more.
## Capabilities
### Human-in-the-loop
First, checkpointers facilitate [human-in-the-loop workflows](agentic_concepts.md#human-in-the-loop) workflows by allowing humans to inspect, interrupt, and approve graph steps. Checkpointers are needed for these workflows as the human has to be able to view the state of a graph at any point in time, and the graph has to be to resume execution after the human has made any updates to the state. See [these how-to guides](../how-tos/human_in_the_loop/breakpoints.ipynb) for concrete examples.
### Memory
Second, checkpointers allow for ["memory"](agentic_concepts.md#memory) between interactions. In the case of repeated human interactions (like conversations) any follow up messages can be sent to that thread, which will retain its memory of previous ones. See [this how-to guide](../how-tos/memory/manage-conversation-history.ipynb) for an end-to-end example on how to add and manage conversation memory using checkpointers.
### Time Travel
Third, checkpointers allow for ["time travel"](../how-tos/human_in_the_loop/time-travel.ipynb), allowing users to replay prior graph executions to review and / or debug specific graph steps. In addition, checkpointers make it possible to fork the graph state at arbitrary checkpoints to explore alternative trajectories.
### Fault-tolerance
Lastly, checkpointing also provides fault-tolerance and error recovery: if one or more nodes fail at a given superstep, you can restart your graph from the last successful step. Additionally, when a graph node fails mid-execution at a given superstep, LangGraph stores pending checkpoint writes from any other nodes that completed successfully at that superstep, so that whenever we resume graph execution from that superstep we don't re-run the successful nodes.
#### Pending writes
Additionally, when a graph node fails mid-execution at a given superstep, LangGraph stores pending checkpoint writes from any other nodes that completed successfully at that superstep, so that whenever we resume graph execution from that superstep we don't re-run the successful nodes.
|
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/sdk.md | # LangGraph SDK
!!! info "Prerequisites"
- [LangGraph Platform](./langgraph_platform.md)
- [LangGraph Server](./langgraph_server.md)
The LangGraph Platform provides both a Python and JS SDK for interacting with the [LangGraph Server API](./langgraph_server.md).
## Installation
You can install the packages using the appropriate package manager for your language.
=== "Python"
```bash
pip install langgraph-sdk
```
=== "JS"
```bash
yarn add @langchain/langgraph-sdk
```
## API Reference
You can find the API reference for the SDKs here:
- [Python SDK Reference](../cloud/reference/sdk/python_sdk_ref.md)
- [JS/TS SDK Reference](../cloud/reference/sdk/js_ts_sdk_ref.md)
## Python Sync vs. Async
The Python SDK provides both synchronous (`get_sync_client`) and asynchronous (`get_client`) clients for interacting with the LangGraph Server API.
=== "Async"
```python
from langgraph_sdk import get_client
client = get_client(url=..., api_key=...)
await client.assistants.search()
```
=== "Sync"
```python
from langgraph_sdk import get_sync_client
client = get_sync_client(url=..., api_key=...)
client.assistants.search()
```
## Related
- [LangGraph CLI API Reference](../cloud/reference/cli.md)
- [Python SDK Reference](../cloud/reference/sdk/python_sdk_ref.md)
- [JS/TS SDK Reference](../cloud/reference/sdk/js_ts_sdk_ref.md) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/langgraph_server.md | # LangGraph Server
!!! info "Prerequisites"
- [LangGraph Platform](./langgraph_platform.md)
- [LangGraph Glossary](low_level.md)
## Overview
LangGraph Server offers an API for creating and managing agent-based applications. It is built on the concept of [assistants](assistants.md), which are agents configured for specific tasks, and includes built-in [persistence](persistence.md#memory-store) and a **task queue**. This versatile API supports a wide range of agentic application use cases, from background processing to real-time interactions.
## Key Features
The LangGraph Platform incorporates best practices for agent deployment, so you can focus on building your agent logic.
* **Streaming endpoints**: Endpoints that expose [multiple different streaming modes](streaming.md). We've made these work even for long-running agents that may go minutes between consecutive stream events.
* **Background runs**: The LangGraph Server supports launching assistants in the background with endpoints for polling the status of the assistant's run and webhooks to monitor run status effectively.
- **Support for long runs**: Our blocking endpoints for running assistants send regular heartbeat signals, preventing unexpected connection closures when handling requests that take a long time to complete.
* **Task queue**: We've added a task queue to make sure we don't drop any requests if they arrive in a bursty nature.
* **Horizontally scalable infrastructure**: LangGraph Server is designed to be horizontally scalable, allowing you to scale up and down your usage as needed.
* **Double texting support**: Many times users might interact with your graph in unintended ways. For instance, a user may send one message and before the graph has finished running send a second message. We call this ["double texting"](double_texting.md) and have added four different ways to handle this.
* **Optimized checkpointer**: LangGraph Platform comes with a built-in [checkpointer](./persistence.md#checkpoints) optimized for LangGraph applications.
* **Human-in-the-loop endpoints**: We've exposed all endpoints needed to support [human-in-the-loop](human_in_the_loop.md) features.
* **Memory**: In addition to thread-level persistence (covered above by [checkpointers]l(./persistence.md#checkpoints)), LangGraph Platform also comes with a built-in [memory store](persistence.md#memory-store).
* **Cron jobs**: Built-in support for scheduling tasks, enabling you to automate regular actions like data clean-up or batch processing within your applications.
* **Webhooks**: Allows your application to send real-time notifications and data updates to external systems, making it easy to integrate with third-party services and trigger actions based on specific events.
* **Monitoring**: LangGraph Server integrates seamlessly with the [LangSmith](https://docs.smith.langchain.com/) monitoring platform, providing real-time insights into your application's performance and health.
## What are you deploying?
When you deploy a LangGraph Server, you are deploying one or more [graphs](#graphs), a database for [persistence](persistence.md), and a task queue.
### Graphs
When you deploy a graph with LangGraph Server, you are deploying a "blueprint" for an [Assistant](assistants.md).
An [Assistant](assistants.md) is a graph paired with specific configuration settings. You can create multiple assistants per graph, each with unique settings to accommodate different use cases
that can be served by the same graph.
Upon deployment, LangGraph Server will automatically create a default assistant for each graph using the graph's default configuration settings.
You can interact with assistants through the [LangGraph Server API](#langgraph-server-api).
!!! note
We often think of a graph as implementing an [agent](agentic_concepts.md), but a graph does not necessarily need to implement an agent. For example, a graph could implement a simple
chatbot that only supports back-and-forth conversation, without the ability to influence any application control flow. In reality, as applications get more complex, a graph will often implement a more complex flow that may use [multiple agents](./multi_agent.md) working in tandem.
### Persistence and Task Queue
The LangGraph Server leverages a database for [persistence](persistence.md) and a task queue.
Currently, only [Postgres](https://www.postgresql.org/) is supported as a database for LangGraph Server and [Redis](https://redis.io/) as the task queue.
If you're deploying using [LangGraph Cloud](./langgraph_cloud.md), these components are managed for you. If you're deploying LangGraph Server on your own infrastructure, you'll need to set up and manage these components yourself.
Please review the [deployment options](./deployment_options.md) guide for more information on how these components are set up and managed.
## Application Structure
To deploy a LangGraph Server application, you need to specify the graph(s) you want to deploy, as well as any relevant configuration settings, such as dependencies and environment variables.
Read the [application structure](./application_structure.md) guide to learn how to structure your LangGraph application for deployment.
## LangGraph Server API
The LangGraph Server API allows you to create and manage [assistants](assistants.md), [threads](#threads), [runs](#runs), [cron jobs](#cron-jobs), and more.
The [LangGraph Cloud API Reference](../cloud/reference/api/api_ref.html) provides detailed information on the API endpoints and data models.
### Assistants
An [Assistant](assistants.md) refers to a [graph](#graphs) plus specific [configuration](low_level.md#configuration) settings for that graph.
You can think of an assistant as a saved configuration of an [agent](agentic_concepts.md).
When building agents, it is fairly common to make rapid changes that *do not* alter the graph logic. For example, simply changing prompts or the LLM selection can have significant impacts on the behavior of the agents. Assistants offer an easy way to make and save these types of changes to agent configuration.
### Threads
A thread contains the accumulated state of a sequence of [runs](#runs). If a run is executed on a thread, then the [state](low_level.md#state) of the underlying graph of the assistant will be persisted to the thread.
A thread's current and historical state can be retrieved. To persist state, a thread must be created prior to executing a run.
The state of a thread at a particular point in time is called a [checkpoint](persistence.md#checkpoints). Checkpoints can be used to restore the state of a thread at a later time.
For more on threads and checkpoints, see this section of the [LangGraph conceptual guide](low_level.md#persistence).
The LangGraph Cloud API provides several endpoints for creating and managing threads and thread state. See the [API reference](../cloud/reference/api/api_ref.html#tag/threads) for more details.
### Runs
A run is an invocation of an [assistant](#assistants). Each run may have its own input, configuration, and metadata, which may affect execution and output of the underlying graph. A run can optionally be executed on a [thread](#threads).
The LangGraph Cloud API provides several endpoints for creating and managing runs. See the [API reference](../cloud/reference/api/api_ref.html#tag/thread-runs/) for more details.
### Store
Store is an API for managing persistent [key-value store](./persistence.md#memory-store) that is available from any [thread](#threads).
Stores are useful for implementing [memory](./memory.md) in your LangGraph application.
### Cron Jobs
There are many situations in which it is useful to run an assistant on a schedule.
For example, say that you're building an assistant that runs daily and sends an email summary
of the day's news. You could use a cron job to run the assistant every day at 8:00 PM.
LangGraph Cloud supports cron jobs, which run on a user-defined schedule. The user specifies a schedule, an assistant, and some input. After that, on the specified schedule, the server will:
- Create a new thread with the specified assistant
- Send the specified input to that thread
Note that this sends the same input to the thread every time. See the [how-to guide](../cloud/how-tos/cron_jobs.md) for creating cron jobs.
The LangGraph Cloud API provides several endpoints for creating and managing cron jobs. See the [API reference](../cloud/reference/api/api_ref.html#tag/runscreate/POST/threads/{thread_id}/runs/crons) for more details.
### Webhooks
Webhooks enable event-driven communication from your LangGraph Cloud application to external services. For example, you may want to issue an update to a separate service once an API call to LangGraph Cloud has finished running.
Many LangGraph Cloud endpoints accept a `webhook` parameter. If this parameter is specified by a an endpoint that can accept POST requests, LangGraph Cloud will send a request at the completion of a run.
See the corresponding [how-to guide](../cloud/how-tos/webhooks.md) for more detail.
## Related
* LangGraph [Application Structure](./application_structure.md) guide explains how to structure your LangGraph application for deployment.
* [How-to guides for the LangGraph Platform](../how-tos/index.md).
* The [LangGraph Cloud API Reference](../cloud/reference/api/api_ref.html) provides detailed information on the API endpoints and data models.
|
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/.meta.yml | tags:
- concepts
- conceptual guide
- explanation |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/deployment_options.md | # Deployment Options
!!! info "Prerequisites"
- [LangGraph Platform](./langgraph_platform.md)
- [LangGraph Server](./langgraph_server.md)
- [LangGraph Platform Plans](./plans.md)
## Overview
There are 4 main options for deploying with the LangGraph Platform:
1. **[Self-Hosted Lite](#self-hosted-lite)**: Available for all plans.
2. **[Self-Hosted Enterprise](#self-hosted-enterprise)**: Available for the **Enterprise** plan.
3. **[Cloud SaaS](#cloud-saas)**: Available for **Plus** and **Enterprise** plans.
4. **[Bring Your Own Cloud](#bring-your-own-cloud)**: Available only for **Enterprise** plans and **only on AWS**.
Please see the [LangGraph Platform Plans](./plans.md) for more information on the different plans.
The guide below will explain the differences between the deployment options.
## Self-Hosted Enterprise
!!! important
The Self-Hosted Enterprise version is only available for the **Enterprise** plan.
With a Self-Hosted Enterprise deployment, you are responsible for managing the infrastructure, including setting up and maintaining required databases and Redis instances.
You’ll build a Docker image using the [LangGraph CLI](./langgraph_cli.md), which can then be deployed on your own infrastructure.
For more information, please see:
* [Self-Hosted conceptual guide](./self_hosted.md)
* [Self-Hosted Deployment how-to guide](../how-tos/deploy-self-hosted.md)
## Self-Hosted Lite
!!! important
The Self-Hosted Lite version is available for all plans.
The Self-Hosted Lite deployment option is a free (up to 1 million nodes executed), limited version of LangGraph Platform that you can run locally or in a self-hosted manner.
With a Self-Hosted Lite deployment, you are responsible for managing the infrastructure, including setting up and maintaining required databases and Redis instances.
You’ll build a Docker image using the [LangGraph CLI](./langgraph_cli.md), which can then be deployed on your own infrastructure.
For more information, please see:
* [Self-Hosted conceptual guide](./self_hosted.md)
* [Self-Hosted deployment how-to guide](../how-tos/deploy-self-hosted.md)
## Cloud SaaS
!!! important
The Cloud SaaS version of LangGraph Platform is only available for **Plus** and **Enterprise** plans.
The [Cloud SaaS](./langgraph_cloud.md) version of LangGraph Platform is hosted as part of [LangSmith](https://smith.langchain.com/).
The Cloud SaaS version of LangGraph Platform provides a simple way to deploy and manage your LangGraph applications.
This deployment option provides an integration with GitHub, allowing you to deploy code from any of your repositories on GitHub.
For more information, please see:
* [Cloud SaaS Conceptual Guide](./langgraph_cloud.md)
* [How to deploy to Cloud SaaS](../cloud/deployment/cloud.md)
## Bring Your Own Cloud
!!! important
The Bring Your Own Cloud version of LangGraph Platform is only available for **Enterprise** plans.
This combines the best of both worlds for Cloud and Self-Hosted. We manage the infrastructure, so you don't have to, but the infrastructure all runs within your cloud. This is currently only available on AWS.
For more information please see:
* [Bring Your Own Cloud Conceptual Guide](./bring_your_own_cloud.md)
## Related
For more information, please see:
* [LangGraph Platform plans](./plans.md)
* [LangGraph Platform pricing](https://www.langchain.com/langgraph-platform-pricing)
* [Deployment how-to guides](../how-tos/index.md#deployment)
|
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/low_level.md | # LangGraph Glossary
## Graphs
At its core, LangGraph models agent workflows as graphs. You define the behavior of your agents using three key components:
1. [`State`](#state): A shared data structure that represents the current snapshot of your application. It can be any Python type, but is typically a `TypedDict` or Pydantic `BaseModel`.
2. [`Nodes`](#nodes): Python functions that encode the logic of your agents. They receive the current `State` as input, perform some computation or side-effect, and return an updated `State`.
3. [`Edges`](#edges): Python functions that determine which `Node` to execute next based on the current `State`. They can be conditional branches or fixed transitions.
By composing `Nodes` and `Edges`, you can create complex, looping workflows that evolve the `State` over time. The real power, though, comes from how LangGraph manages that `State`. To emphasize: `Nodes` and `Edges` are nothing more than Python functions - they can contain an LLM or just good ol' Python code.
In short: _nodes do the work. edges tell what to do next_.
LangGraph's underlying graph algorithm uses [message passing](https://en.wikipedia.org/wiki/Message_passing) to define a general program. When a Node completes its operation, it sends messages along one or more edges to other node(s). These recipient nodes then execute their functions, pass the resulting messages to the next set of nodes, and the process continues. Inspired by Google's [Pregel](https://research.google/pubs/pregel-a-system-for-large-scale-graph-processing/) system, the program proceeds in discrete "super-steps."
A super-step can be considered a single iteration over the graph nodes. Nodes that run in parallel are part of the same super-step, while nodes that run sequentially belong to separate super-steps. At the start of graph execution, all nodes begin in an `inactive` state. A node becomes `active` when it receives a new message (state) on any of its incoming edges (or "channels"). The active node then runs its function and responds with updates. At the end of each super-step, nodes with no incoming messages vote to `halt` by marking themselves as `inactive`. The graph execution terminates when all nodes are `inactive` and no messages are in transit.
### StateGraph
The `StateGraph` class is the main graph class to use. This is parameterized by a user defined `State` object.
### MessageGraph
The `MessageGraph` class is a special type of graph. The `State` of a `MessageGraph` is ONLY a list of messages. This class is rarely used except for chatbots, as most applications require the `State` to be more complex than a list of messages.
### Compiling your graph
To build your graph, you first define the [state](#state), you then add [nodes](#nodes) and [edges](#edges), and then you compile it. What exactly is compiling your graph and why is it needed?
Compiling is a pretty simple step. It provides a few basic checks on the structure of your graph (no orphaned nodes, etc). It is also where you can specify runtime args like [checkpointers](./persistence.md) and [breakpoints](#breakpoints). You compile your graph by just calling the `.compile` method:
```python
graph = graph_builder.compile(...)
```
You **MUST** compile your graph before you can use it.
## State
The first thing you do when you define a graph is define the `State` of the graph. The `State` consists of the [schema of the graph](#schema) as well as [`reducer` functions](#reducers) which specify how to apply updates to the state. The schema of the `State` will be the input schema to all `Nodes` and `Edges` in the graph, and can be either a `TypedDict` or a `Pydantic` model. All `Nodes` will emit updates to the `State` which are then applied using the specified `reducer` function.
### Schema
The main documented way to specify the schema of a graph is by using `TypedDict`. However, we also support [using a Pydantic BaseModel](../how-tos/state-model.ipynb) as your graph state to add **default values** and additional data validation.
By default, the graph will have the same input and output schemas. If you want to change this, you can also specify explicit input and output schemas directly. This is useful when you have a lot of keys, and some are explicitly for input and others for output. See the [notebook here](../how-tos/input_output_schema.ipynb) for how to use.
#### Multiple schemas
Typically, all graph nodes communicate with a single schema. This means that they will read and write to the same state channels. But, there are cases where we want more control over this:
- Internal nodes can pass information that is not required in the graph's input / output.
- We may also want to use different input / output schemas for the graph. The output might, for example, only contain a single relevant output key.
It is possible to have nodes write to private state channels inside the graph for internal node communication. We can simply define a private schema, `PrivateState`. See [this notebook](../how-tos/pass_private_state.ipynb) for more detail.
It is also possible to define explicit input and output schemas for a graph. In these cases, we define an "internal" schema that contains _all_ keys relevant to graph operations. But, we also define `input` and `output` schemas that are sub-sets of the "internal" schema to constrain the input and output of the graph. See [this notebook](../how-tos/input_output_schema.ipynb) for more detail.
Let's look at an example:
```python
class InputState(TypedDict):
user_input: str
class OutputState(TypedDict):
graph_output: str
class OverallState(TypedDict):
foo: str
user_input: str
graph_output: str
class PrivateState(TypedDict):
bar: str
def node_1(state: InputState) -> OverallState:
# Write to OverallState
return {"foo": state["user_input"] + " name"}
def node_2(state: OverallState) -> PrivateState:
# Read from OverallState, write to PrivateState
return {"bar": state["foo"] + " is"}
def node_3(state: PrivateState) -> OutputState:
# Read from PrivateState, write to OutputState
return {"graph_output": state["bar"] + " Lance"}
builder = StateGraph(OverallState,input=InputState,output=OutputState)
builder.add_node("node_1", node_1)
builder.add_node("node_2", node_2)
builder.add_node("node_3", node_3)
builder.add_edge(START, "node_1")
builder.add_edge("node_1", "node_2")
builder.add_edge("node_2", "node_3")
builder.add_edge("node_3", END)
graph = builder.compile()
graph.invoke({"user_input":"My"})
{'graph_output': 'My name is Lance'}
```
There are two subtle and important points to note here:
1. We pass `state: InputState` as the input schema to `node_1`. But, we write out to `foo`, a channel in `OverallState`. How can we write out to a state channel that is not included in the input schema? This is because a node _can write to any state channel in the graph state._ The graph state is the union of of the state channels defined at initialization, which includes `OverallState` and the filters `InputState` and `OutputState`.
2. We initialize the graph with `StateGraph(OverallState,input=InputState,output=OutputState)`. So, how can we write to `PrivateState` in `node_2`? How does the graph gain access to this schema if it was not passed in the `StateGraph` initialization? We can do this because _nodes can also declare additional state channels_ as long as the state schema definition exists. In this case, the `PrivateState` schema is defined, so we can add `bar` as a new state channel in the graph and write to it.
### Reducers
Reducers are key to understanding how updates from nodes are applied to the `State`. Each key in the `State` has its own independent reducer function. If no reducer function is explicitly specified then it is assumed that all updates to that key should override it. There are a few different types of reducers, starting with the default type of reducer:
#### Default Reducer
These two examples show how to use the default reducer:
**Example A:**
```python
from typing_extensions import TypedDict
class State(TypedDict):
foo: int
bar: list[str]
```
In this example, no reducer functions are specified for any key. Let's assume the input to the graph is `{"foo": 1, "bar": ["hi"]}`. Let's then assume the first `Node` returns `{"foo": 2}`. This is treated as an update to the state. Notice that the `Node` does not need to return the whole `State` schema - just an update. After applying this update, the `State` would then be `{"foo": 2, "bar": ["hi"]}`. If the second node returns `{"bar": ["bye"]}` then the `State` would then be `{"foo": 2, "bar": ["bye"]}`
**Example B:**
```python
from typing import Annotated
from typing_extensions import TypedDict
from operator import add
class State(TypedDict):
foo: int
bar: Annotated[list[str], add]
```
In this example, we've used the `Annotated` type to specify a reducer function (`operator.add`) for the second key (`bar`). Note that the first key remains unchanged. Let's assume the input to the graph is `{"foo": 1, "bar": ["hi"]}`. Let's then assume the first `Node` returns `{"foo": 2}`. This is treated as an update to the state. Notice that the `Node` does not need to return the whole `State` schema - just an update. After applying this update, the `State` would then be `{"foo": 2, "bar": ["hi"]}`. If the second node returns `{"bar": ["bye"]}` then the `State` would then be `{"foo": 2, "bar": ["hi", "bye"]}`. Notice here that the `bar` key is updated by adding the two lists together.
### Working with Messages in Graph State
#### Why use messages?
Most modern LLM providers have a chat model interface that accepts a list of messages as input. LangChain's [`ChatModel`](https://python.langchain.com/docs/concepts/#chat-models) in particular accepts a list of `Message` objects as inputs. These messages come in a variety of forms such as `HumanMessage` (user input) or `AIMessage` (LLM response). To read more about what message objects are, please refer to [this](https://python.langchain.com/docs/concepts/#messages) conceptual guide.
#### Using Messages in your Graph
In many cases, it is helpful to store prior conversation history as a list of messages in your graph state. To do so, we can add a key (channel) to the graph state that stores a list of `Message` objects and annotate it with a reducer function (see `messages` key in the example below). The reducer function is vital to telling the graph how to update the list of `Message` objects in the state with each state update (for example, when a node sends an update). If you don't specify a reducer, every state update will overwrite the list of messages with the most recently provided value. If you wanted to simply append messages to the existing list, you could use `operator.add` as a reducer.
However, you might also want to manually update messages in your graph state (e.g. human-in-the-loop). If you were to use `operator.add`, the manual state updates you send to the graph would be appended to the existing list of messages, instead of updating existing messages. To avoid that, you need a reducer that can keep track of message IDs and overwrite existing messages, if updated. To achieve this, you can use the prebuilt `add_messages` function. For brand new messages, it will simply append to existing list, but it will also handle the updates for existing messages correctly.
#### Serialization
In addition to keeping track of message IDs, the `add_messages` function will also try to deserialize messages into LangChain `Message` objects whenever a state update is received on the `messages` channel. See more information on LangChain serialization/deserialization [here](https://python.langchain.com/docs/how_to/serialization/). This allows sending graph inputs / state updates in the following format:
```python
# this is supported
{"messages": [HumanMessage(content="message")]}
# and this is also supported
{"messages": [{"type": "human", "content": "message"}]}
```
Since the state updates are always deserialized into LangChain `Messages` when using `add_messages`, you should use dot notation to access message attributes, like `state["messages"][-1].content`. Below is an example of a graph that uses `add_messages` as it's reducer function.
```python
from langchain_core.messages import AnyMessage
from langgraph.graph.message import add_messages
from typing import Annotated
from typing_extensions import TypedDict
class GraphState(TypedDict):
messages: Annotated[list[AnyMessage], add_messages]
```
#### MessagesState
Since having a list of messages in your state is so common, there exists a prebuilt state called `MessagesState` which makes it easy to use messages. `MessagesState` is defined with a single `messages` key which is a list of `AnyMessage` objects and uses the `add_messages` reducer. Typically, there is more state to track than just messages, so we see people subclass this state and add more fields, like:
```python
from langgraph.graph import MessagesState
class State(MessagesState):
documents: list[str]
```
## Nodes
In LangGraph, nodes are typically python functions (sync or `async`) where the **first** positional argument is the [state](#state), and (optionally), the **second** positional argument is a "config", containing optional [configurable parameters](#configuration) (such as a `thread_id`).
Similar to `NetworkX`, you add these nodes to a graph using the [add_node][langgraph.graph.StateGraph.add_node] method:
```python
from langchain_core.runnables import RunnableConfig
from langgraph.graph import StateGraph
builder = StateGraph(dict)
def my_node(state: dict, config: RunnableConfig):
print("In node: ", config["configurable"]["user_id"])
return {"results": f"Hello, {state['input']}!"}
# The second argument is optional
def my_other_node(state: dict):
return state
builder.add_node("my_node", my_node)
builder.add_node("other_node", my_other_node)
...
```
Behind the scenes, functions are converted to [RunnableLambda's](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableLambda.html#langchain_core.runnables.base.RunnableLambda), which add batch and async support to your function, along with native tracing and debugging.
If you add a node to graph without specifying a name, it will be given a default name equivalent to the function name.
```python
builder.add_node(my_node)
# You can then create edges to/from this node by referencing it as `"my_node"`
```
### `START` Node
The `START` Node is a special node that represents the node sends user input to the graph. The main purpose for referencing this node is to determine which nodes should be called first.
```python
from langgraph.graph import START
graph.add_edge(START, "node_a")
```
### `END` Node
The `END` Node is a special node that represents a terminal node. This node is referenced when you want to denote which edges have no actions after they are done.
```
from langgraph.graph import END
graph.add_edge("node_a", END)
```
## Edges
Edges define how the logic is routed and how the graph decides to stop. This is a big part of how your agents work and how different nodes communicate with each other. There are a few key types of edges:
- Normal Edges: Go directly from one node to the next.
- Conditional Edges: Call a function to determine which node(s) to go to next.
- Entry Point: Which node to call first when user input arrives.
- Conditional Entry Point: Call a function to determine which node(s) to call first when user input arrives.
A node can have MULTIPLE outgoing edges. If a node has multiple out-going edges, **all** of those destination nodes will be executed in parallel as a part of the next superstep.
### Normal Edges
If you **always** want to go from node A to node B, you can use the [add_edge][langgraph.graph.StateGraph.add_edge] method directly.
```python
graph.add_edge("node_a", "node_b")
```
### Conditional Edges
If you want to **optionally** route to 1 or more edges (or optionally terminate), you can use the [add_conditional_edges][langgraph.graph.StateGraph.add_conditional_edges] method. This method accepts the name of a node and a "routing function" to call after that node is executed:
```python
graph.add_conditional_edges("node_a", routing_function)
```
Similar to nodes, the `routing_function` accept the current `state` of the graph and return a value.
By default, the return value `routing_function` is used as the name of the node (or a list of nodes) to send the state to next. All those nodes will be run in parallel as a part of the next superstep.
You can optionally provide a dictionary that maps the `routing_function`'s output to the name of the next node.
```python
graph.add_conditional_edges("node_a", routing_function, {True: "node_b", False: "node_c"})
```
!!! tip
Use [`Command`](#command) instead of conditional edges if you want to combine state updates and routing in a single function.
### Entry Point
The entry point is the first node(s) that are run when the graph starts. You can use the [`add_edge`][langgraph.graph.StateGraph.add_edge] method from the virtual [`START`][langgraph.constants.START] node to the first node to execute to specify where to enter the graph.
```python
from langgraph.graph import START
graph.add_edge(START, "node_a")
```
### Conditional Entry Point
A conditional entry point lets you start at different nodes depending on custom logic. You can use [`add_conditional_edges`][langgraph.graph.StateGraph.add_conditional_edges] from the virtual [`START`][langgraph.constants.START] node to accomplish this.
```python
from langgraph.graph import START
graph.add_conditional_edges(START, routing_function)
```
You can optionally provide a dictionary that maps the `routing_function`'s output to the name of the next node.
```python
graph.add_conditional_edges(START, routing_function, {True: "node_b", False: "node_c"})
```
## `Send`
By default, `Nodes` and `Edges` are defined ahead of time and operate on the same shared state. However, there can be cases where the exact edges are not known ahead of time and/or you may want different versions of `State` to exist at the same time. A common of example of this is with `map-reduce` design patterns. In this design pattern, a first node may generate a list of objects, and you may want to apply some other node to all those objects. The number of objects may be unknown ahead of time (meaning the number of edges may not be known) and the input `State` to the downstream `Node` should be different (one for each generated object).
To support this design pattern, LangGraph supports returning [`Send`][langgraph.types.Send] objects from conditional edges. `Send` takes two arguments: first is the name of the node, and second is the state to pass to that node.
```python
def continue_to_jokes(state: OverallState):
return [Send("generate_joke", {"subject": s}) for s in state['subjects']]
graph.add_conditional_edges("node_a", continue_to_jokes)
```
## `Command`
It can be useful to combine control flow (edges) and state updates (nodes). For example, you might want to BOTH perform state updates AND decide which node to go to next in the SAME node. LangGraph provides a way to do so by returning a [`Command`][langgraph.types.Command] object from node functions:
```python
def my_node(state: State) -> Command[Literal["my_other_node"]]:
return Command(
# state update
update={"foo": "bar"},
# control flow
goto="my_other_node"
)
```
`Command` has the following properties:
| Property | Description |
| --- | --- |
| `graph` | Graph to send the command to. Supported values:<br>- `None`: the current graph (default)<br>- `Command.PARENT`: closest parent graph |
| `update` | Update to apply to the graph's state. |
| `resume` | Value to resume execution with. To be used together with [`interrupt()`][langgraph.types.interrupt]. |
| `goto` | Can be one of the following:<br>- name of the node to navigate to next (any node that belongs to the specified `graph`)<br>- sequence of node names to navigate to next<br>- `Send` object (to execute a node with the input provided)<br>- sequence of `Send` objects<br>If `goto` is not specified and there are no other tasks left in the graph, the graph will halt after executing the current superstep. |
```python
from langgraph.graph import StateGraph, START
from langgraph.types import Command
from typing_extensions import Literal, TypedDict
class State(TypedDict):
foo: str
def my_node(state: State) -> Command[Literal["my_other_node"]]:
return Command(update={"foo": "bar"}, goto="my_other_node")
def my_other_node(state: State):
return {"foo": state["foo"] + "baz"}
builder = StateGraph(State)
builder.add_edge(START, "my_node")
builder.add_node("my_node", my_node)
builder.add_node("my_other_node", my_other_node)
graph = builder.compile()
```
With `Command` you can also achieve dynamic control flow behavior (identical to [conditional edges](#conditional-edges)):
```python
def my_node(state: State) -> Command[Literal["my_other_node"]]:
if state["foo"] == "bar":
return Command(update={"foo": "baz"}, goto="my_other_node")
```
!!! important
When returning `Command` in your node functions, you must add return type annotations with the list of node names the node is routing to, e.g. `Command[Literal["node_b", "node_c"]]`. This is necessary for the graph compilation and rendering, and tells LangGraph that `node_a` can navigate to `node_b` and `node_c`.
Check out this [how-to guide](../how-tos/command.ipynb) for an end-to-end example of how to use `Command`.
## Persistence
LangGraph provides built-in persistence for your agent's state using [checkpointers][langgraph.checkpoint.base.BaseCheckpointSaver]. Checkpointers save snapshots of the graph state at every superstep, allowing resumption at any time. This enables features like human-in-the-loop interactions, memory management, and fault-tolerance. You can even directly manipulate a graph's state after its execution using the
appropriate `get` and `update` methods. For more details, see the [persistence conceptual guide](./persistence.md).
## Threads
Threads in LangGraph represent individual sessions or conversations between your graph and a user. When using checkpointing, turns in a single conversation (and even steps within a single graph execution) are organized by a unique thread ID.
## Storage
LangGraph provides built-in document storage through the [BaseStore][langgraph.store.base.BaseStore] interface. Unlike checkpointers, which save state by thread ID, stores use custom namespaces for organizing data. This enables cross-thread persistence, allowing agents to maintain long-term memories, learn from past interactions, and accumulate knowledge over time. Common use cases include storing user profiles, building knowledge bases, and managing global preferences across all threads.
## Graph Migrations
LangGraph can easily handle migrations of graph definitions (nodes, edges, and state) even when using a checkpointer to track state.
- For threads at the end of the graph (i.e. not interrupted) you can change the entire topology of the graph (i.e. all nodes and edges, remove, add, rename, etc)
- For threads currently interrupted, we support all topology changes other than renaming / removing nodes (as that thread could now be about to enter a node that no longer exists) -- if this is a blocker please reach out and we can prioritize a solution.
- For modifying state, we have full backwards and forwards compatibility for adding and removing keys
- State keys that are renamed lose their saved state in existing threads
- State keys whose types change in incompatible ways could currently cause issues in threads with state from before the change -- if this is a blocker please reach out and we can prioritize a solution.
## Configuration
When creating a graph, you can also mark that certain parts of the graph are configurable. This is commonly done to enable easily switching between models or system prompts. This allows you to create a single "cognitive architecture" (the graph) but have multiple different instance of it.
You can optionally specify a `config_schema` when creating a graph.
```python
class ConfigSchema(TypedDict):
llm: str
graph = StateGraph(State, config_schema=ConfigSchema)
```
You can then pass this configuration into the graph using the `configurable` config field.
```python
config = {"configurable": {"llm": "anthropic"}}
graph.invoke(inputs, config=config)
```
You can then access and use this configuration inside a node:
```python
def node_a(state, config):
llm_type = config.get("configurable", {}).get("llm", "openai")
llm = get_llm(llm_type)
...
```
See [this guide](../how-tos/configuration.ipynb) for a full breakdown on configuration.
### Recursion Limit
The recursion limit sets the maximum number of [super-steps](#graphs) the graph can execute during a single execution. Once the limit is reached, LangGraph will raise `GraphRecursionError`. By default this value is set to 25 steps. The recursion limit can be set on any graph at runtime, and is passed to `.invoke`/`.stream` via the config dictionary. Importantly, `recursion_limit` is a standalone `config` key and should not be passed inside the `configurable` key as all other user-defined configuration. See the example below:
```python
graph.invoke(inputs, config={"recursion_limit": 5, "configurable":{"llm": "anthropic"}})
```
Read [this how-to](https://langchain-ai.github.io/langgraph/how-tos/recursion-limit/) to learn more about how the recursion limit works.
## Breakpoints
It can often be useful to set breakpoints before or after certain nodes execute. This can be used to wait for human approval before continuing. These can be set when you ["compile" a graph](#compiling-your-graph). You can set breakpoints either _before_ a node executes (using `interrupt_before`) or after a node executes (using `interrupt_after`.)
You **MUST** use a [checkpointer](./persistence.md) when using breakpoints. This is because your graph needs to be able to resume execution.
In order to resume execution, you can just invoke your graph with `None` as the input.
```python
# Initial run of graph
graph.invoke(inputs, config=config)
# Let's assume it hit a breakpoint somewhere, you can then resume by passing in None
graph.invoke(None, config=config)
```
See [this guide](../how-tos/human_in_the_loop/breakpoints.ipynb) for a full walkthrough of how to add breakpoints.
### Dynamic Breakpoints
It may be helpful to **dynamically** interrupt the graph from inside a given node based on some condition. In `LangGraph` you can do so by using `NodeInterrupt` -- a special exception that can be raised from inside a node.
```python
def my_node(state: State) -> State:
if len(state['input']) > 5:
raise NodeInterrupt(f"Received input that is longer than 5 characters: {state['input']}")
return state
```
## Subgraphs
A subgraph is a [graph](#graphs) that is used as a [node](#nodes) in another graph. This is nothing more than the age-old concept of encapsulation, applied to LangGraph. Some reasons for using subgraphs are:
- building [multi-agent systems](./multi_agent.md)
- when you want to reuse a set of nodes in multiple graphs, which maybe share some state, you can define them once in a subgraph and then use them in multiple parent graphs
- when you want different teams to work on different parts of the graph independently, you can define each part as a subgraph, and as long as the subgraph interface (the input and output schemas) is respected, the parent graph can be built without knowing any details of the subgraph
There are two ways to add subgraphs to a parent graph:
- add a node with the compiled subgraph: this is useful when the parent graph and the subgraph share state keys and you don't need to transform state on the way in or out
```python
builder.add_node("subgraph", subgraph_builder.compile())
```
- add a node with a function that invokes the subgraph: this is useful when the parent graph and the subgraph have different state schemas and you need to transform state before or after calling the subgraph
```python
subgraph = subgraph_builder.compile()
def call_subgraph(state: State):
return subgraph.invoke({"subgraph_key": state["parent_key"]})
builder.add_node("subgraph", call_subgraph)
```
Let's take a look at examples for each.
### As a compiled graph
The simplest way to create subgraph nodes is by using a [compiled subgraph](#compiling-your-graph) directly. When doing so, it is **important** that the parent graph and the subgraph [state schemas](#state) share at least one key which they can use to communicate. If your graph and subgraph do not share any keys, you should use write a function [invoking the subgraph](#as-a-function) instead.
!!! Note
If you pass extra keys to the subgraph node (i.e., in addition to the shared keys), they will be ignored by the subgraph node. Similarly, if you return extra keys from the subgraph, they will be ignored by the parent graph.
```python
from langgraph.graph import START, StateGraph
from typing import TypedDict
class State(TypedDict):
foo: str
class SubgraphState(TypedDict):
foo: str # note that this key is shared with the parent graph state
bar: str
# Define subgraph
def subgraph_node(state: SubgraphState):
# note that this subgraph node can communicate with the parent graph via the shared "foo" key
return {"foo": state["foo"] + "bar"}
subgraph_builder = StateGraph(SubgraphState)
subgraph_builder.add_node(subgraph_node)
...
subgraph = subgraph_builder.compile()
# Define parent graph
builder = StateGraph(State)
builder.add_node("subgraph", subgraph)
...
graph = builder.compile()
```
### As a function
You might want to define a subgraph with a completely different schema. In this case, you can create a node function that invokes the subgraph. This function will need to [transform](../how-tos/subgraph-transform-state.ipynb) the input (parent) state to the subgraph state before invoking the subgraph, and transform the results back to the parent state before returning the state update from the node.
```python
class State(TypedDict):
foo: str
class SubgraphState(TypedDict):
# note that none of these keys are shared with the parent graph state
bar: str
baz: str
# Define subgraph
def subgraph_node(state: SubgraphState):
return {"bar": state["bar"] + "baz"}
subgraph_builder = StateGraph(SubgraphState)
subgraph_builder.add_node(subgraph_node)
...
subgraph = subgraph_builder.compile()
# Define parent graph
def node(state: State):
# transform the state to the subgraph state
response = subgraph.invoke({"bar": state["foo"]})
# transform response back to the parent state
return {"foo": response["bar"]}
builder = StateGraph(State)
# note that we are using `node` function instead of a compiled subgraph
builder.add_node(node)
...
graph = builder.compile()
```
## Visualization
It's often nice to be able to visualize graphs, especially as they get more complex. LangGraph comes with several built-in ways to visualize graphs. See [this how-to guide](../how-tos/visualization.ipynb) for more info.
## Streaming
LangGraph is built with first class support for streaming, including streaming updates from graph nodes during the execution, streaming tokens from LLM calls and more. See this [conceptual guide](./streaming.md) for more information.
|
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/streaming.md | # Streaming
LangGraph is built with first class support for streaming. There are several different ways to stream back outputs from a graph run
## Streaming graph outputs (`.stream` and `.astream`)
`.stream` and `.astream` are sync and async methods for streaming back outputs from a graph run.
There are several different modes you can specify when calling these methods (e.g. `graph.stream(..., mode="...")):
- [`"values"`](../how-tos/stream-values.ipynb): This streams the full value of the state after each step of the graph.
- [`"updates"`](../how-tos/stream-updates.ipynb): This streams the updates to the state after each step of the graph. If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are streamed separately.
- [`"custom"`](../how-tos/streaming-content.ipynb): This streams custom data from inside your graph nodes.
- [`"messages"`](../how-tos/streaming-tokens.ipynb): This streams LLM tokens and metadata for the graph node where LLM is invoked.
- `"debug"`: This streams as much information as possible throughout the execution of the graph.
You can also specify multiple streaming modes at the same time by passing them as a list. When you do this, the streamed outputs will be tuples `(stream_mode, data)`. For example:
```python
graph.stream(..., stream_mode=["updates", "messages"])
```
```
...
('messages', (AIMessageChunk(content='Hi'), {'langgraph_step': 3, 'langgraph_node': 'agent', ...}))
...
('updates', {'agent': {'messages': [AIMessage(content="Hi, how can I help you?")]}})
```
The below visualization shows the difference between the `values` and `updates` modes:
## Streaming LLM tokens and events (`.astream_events`)
In addition, you can use the [`astream_events`](../how-tos/streaming-events-from-within-tools.ipynb) method to stream back events that happen _inside_ nodes. This is useful for [streaming tokens of LLM calls](../how-tos/streaming-tokens.ipynb).
This is a standard method on all [LangChain objects](https://python.langchain.com/docs/concepts/#runnable-interface). This means that as the graph is executed, certain events are emitted along the way and can be seen if you run the graph using `.astream_events`.
All events have (among other things) `event`, `name`, and `data` fields. What do these mean?
- `event`: This is the type of event that is being emitted. You can find a detailed table of all callback events and triggers [here](https://python.langchain.com/docs/concepts/#callback-events).
- `name`: This is the name of event.
- `data`: This is the data associated with the event.
What types of things cause events to be emitted?
* each node (runnable) emits `on_chain_start` when it starts execution, `on_chain_stream` during the node execution and `on_chain_end` when the node finishes. Node events will have the node name in the event's `name` field
* the graph will emit `on_chain_start` in the beginning of the graph execution, `on_chain_stream` after each node execution and `on_chain_end` when the graph finishes. Graph events will have the `LangGraph` in the event's `name` field
* Any writes to state channels (i.e. anytime you update the value of one of your state keys) will emit `on_chain_start` and `on_chain_end` events
Additionally, any events that are created inside your nodes (LLM events, tool events, manually emitted events, etc.) will also be visible in the output of `.astream_events`.
To make this more concrete and to see what this looks like, let's see what events are returned when we run a simple graph:
```python
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, MessagesState, START, END
model = ChatOpenAI(model="gpt-4o-mini")
def call_model(state: MessagesState):
response = model.invoke(state['messages'])
return {"messages": response}
workflow = StateGraph(MessagesState)
workflow.add_node(call_model)
workflow.add_edge(START, "call_model")
workflow.add_edge("call_model", END)
app = workflow.compile()
inputs = [{"role": "user", "content": "hi!"}]
async for event in app.astream_events({"messages": inputs}, version="v1"):
kind = event["event"]
print(f"{kind}: {event['name']}")
```
```shell
on_chain_start: LangGraph
on_chain_start: __start__
on_chain_end: __start__
on_chain_start: call_model
on_chat_model_start: ChatOpenAI
on_chat_model_stream: ChatOpenAI
on_chat_model_stream: ChatOpenAI
on_chat_model_stream: ChatOpenAI
on_chat_model_stream: ChatOpenAI
on_chat_model_stream: ChatOpenAI
on_chat_model_stream: ChatOpenAI
on_chat_model_stream: ChatOpenAI
on_chat_model_stream: ChatOpenAI
on_chat_model_stream: ChatOpenAI
on_chat_model_stream: ChatOpenAI
on_chat_model_stream: ChatOpenAI
on_chat_model_end: ChatOpenAI
on_chain_start: ChannelWrite<call_model,messages>
on_chain_end: ChannelWrite<call_model,messages>
on_chain_stream: call_model
on_chain_end: call_model
on_chain_stream: LangGraph
on_chain_end: LangGraph
```
We start with the overall graph start (`on_chain_start: LangGraph`). We then write to the `__start__` node (this is special node to handle input).
We then start the `call_model` node (`on_chain_start: call_model`). We then start the chat model invocation (`on_chat_model_start: ChatOpenAI`),
stream back token by token (`on_chat_model_stream: ChatOpenAI`) and then finish the chat model (`on_chat_model_end: ChatOpenAI`). From there,
we write the results back to the channel (`ChannelWrite<call_model,messages>`) and then finish the `call_model` node and then the graph as a whole.
This should hopefully give you a good sense of what events are emitted in a simple graph. But what data do these events contain?
Each type of event contains data in a different format. Let's look at what `on_chat_model_stream` events look like. This is an important type of event
since it is needed for streaming tokens from an LLM response.
These events look like:
```shell
{'event': 'on_chat_model_stream',
'name': 'ChatOpenAI',
'run_id': '3fdbf494-acce-402e-9b50-4eab46403859',
'tags': ['seq:step:1'],
'metadata': {'langgraph_step': 1,
'langgraph_node': 'call_model',
'langgraph_triggers': ['start:call_model'],
'langgraph_task_idx': 0,
'checkpoint_id': '1ef657a0-0f9d-61b8-bffe-0c39e4f9ad6c',
'checkpoint_ns': 'call_model',
'ls_provider': 'openai',
'ls_model_name': 'gpt-4o-mini',
'ls_model_type': 'chat',
'ls_temperature': 0.7},
'data': {'chunk': AIMessageChunk(content='Hello', id='run-3fdbf494-acce-402e-9b50-4eab46403859')},
'parent_ids': []}
```
We can see that we have the event type and name (which we knew from before).
We also have a bunch of stuff in metadata. Noticeably, `'langgraph_node': 'call_model',` is some really helpful information
which tells us which node this model was invoked inside of.
Finally, `data` is a really important field. This contains the actual data for this event! Which in this case
is an AIMessageChunk. This contains the `content` for the message, as well as an `id`.
This is the ID of the overall AIMessage (not just this chunk) and is super helpful - it helps
us track which chunks are part of the same message (so we can show them together in the UI).
This information contains all that is needed for creating a UI for streaming LLM tokens. You can see a
guide for that [here](../how-tos/streaming-tokens.ipynb).
!!! warning "ASYNC IN PYTHON<=3.10"
You may fail to see events being emitted from inside a node when using `.astream_events` in Python <= 3.10. If you're using a Langchain RunnableLambda, a RunnableGenerator, or Tool asynchronously inside your node, you will have to propagate callbacks to these objects manually. This is because LangChain cannot automatically propagate callbacks to child objects in this case. Please see examples [here](../how-tos/streaming-content.ipynb) and [here](../how-tos/streaming-events-from-within-tools.ipynb).
## LangGraph Platform
Streaming is critical for making LLM applications feel responsive to end users. When creating a streaming run, the streaming mode determines what data is streamed back to the API client. LangGraph Platform supports five streaming modes:
- `values`: Stream the full state of the graph after each [super-step](https://langchain-ai.github.io/langgraph/concepts/low_level/#graphs) is executed. See the [how-to guide](../cloud/how-tos/stream_values.md) for streaming values.
- `messages-tuple`: Stream LLM tokens for any messages generated inside a node. This mode is primarily meant for powering chat applications. See the [how-to guide](../cloud/how-tos/stream_messages.md) for streaming messages.
- `updates`: Streams updates to the state of the graph after each node is executed. See the [how-to guide](../cloud/how-tos/stream_updates.md) for streaming updates.
- `events`: Stream all events (including the state of the graph) that occur during graph execution. See the [how-to guide](../cloud/how-tos/stream_events.md) for streaming events. This can be used to do token-by-token streaming for LLMs.
- `debug`: Stream debug events throughout graph execution. See the [how-to guide](../cloud/how-tos/stream_debug.md) for streaming debug events.
You can also specify multiple streaming modes at the same time. See the [how-to guide](../cloud/how-tos/stream_multiple.md) for configuring multiple streaming modes at the same time.
See the [API reference](../cloud/reference/api/api_ref.html#tag/threads-runs/POST/threads/{thread_id}/runs/stream) for how to create streaming runs.
Streaming modes `values`, `updates`, `messages-tuple` and `debug` are very similar to modes available in the LangGraph library - for a deeper conceptual explanation of those, you can see the [previous section](#streaming-graph-outputs-stream-and-astream).
Streaming mode `events` is the same as using `.astream_events` in the LangGraph library - for a deeper conceptual explanation of this, you can see the [previous section](#streaming-graph-outputs-stream-and-astream).
All events emitted have two attributes:
- `event`: This is the name of the event
- `data`: This is data associated with the event |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/multi_agent.md | # Multi-agent Systems
An [agent](./agentic_concepts.md#agent-architectures) is _a system that uses an LLM to decide the control flow of an application_. As you develop these systems, they might grow more complex over time, making them harder to manage and scale. For example, you might run into the following problems:
- agent has too many tools at its disposal and makes poor decisions about which tool to call next
- context grows too complex for a single agent to keep track of
- there is a need for multiple specialization areas in the system (e.g. planner, researcher, math expert, etc.)
To tackle these, you might consider breaking your application into multiple smaller, independent agents and composing them into a **multi-agent system**. These independent agents can be as simple as a prompt and an LLM call, or as complex as a [ReAct](./agentic_concepts.md#react-implementation) agent (and more!).
The primary benefits of using multi-agent systems are:
- **Modularity**: Separate agents make it easier to develop, test, and maintain agentic systems.
- **Specialization**: You can create expert agents focused on specific domains, which helps with the overall system performance.
- **Control**: You can explicitly control how agents communicate (as opposed to relying on function calling).
## Multi-agent architectures
There are several ways to connect agents in a multi-agent system:
- **Network**: each agent can communicate with [every other agent](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/multi-agent-collaboration/). Any agent can decide which other agent to call next.
- **Supervisor**: each agent communicates with a single [supervisor](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/agent_supervisor/) agent. Supervisor agent makes decisions on which agent should be called next.
- **Supervisor (tool-calling)**: this is a special case of supervisor architecture. Individual agents can be represented as tools. In this case, a supervisor agent uses a tool-calling LLM to decide which of the agent tools to call, as well as the arguments to pass to those agents.
- **Hierarchical**: you can define a multi-agent system with [a supervisor of supervisors](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/hierarchical_agent_teams/). This is a generalization of the supervisor architecture and allows for more complex control flows.
- **Custom multi-agent workflow**: each agent communicates with only a subset of agents. Parts of the flow are deterministic, and only some agents can decide which other agents to call next.
### Network
In this architecture, agents are defined as graph nodes. Each agent can communicate with every other agent (many-to-many connections) and can decide which agent to call next. This architecture is good for problems that do not have a clear hierarchy of agents or a specific sequence in which agents should be called.
### Supervisor
In this architecture, we define agents as nodes and add a supervisor node (LLM) that decides which agent nodes should be called next. We use [conditional edges](./low_level.md#conditional-edges) to route execution to the appropriate agent node based on supervisor's decision. This architecture also lends itself well to running multiple agents in parallel or using [map-reduce](../how-tos/map-reduce.ipynb) pattern.
```python
from typing import Literal
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, MessagesState, START
model = ChatOpenAI()
class AgentState(MessagesState):
next: Literal["agent_1", "agent_2", "__end__"]
def supervisor(state: AgentState):
# you can pass relevant parts of the state to the LLM (e.g., state["messages"])
# to determine which agent to call next. a common pattern is to call the model
# with a structured output (e.g. force it to return an output with a "next_agent" field)
response = model.invoke(...)
# the "next" key will be used by the conditional edges to route execution
# to the appropriate agent
return {"next": response["next_agent"]}
def agent_1(state: AgentState):
# you can pass relevant parts of the state to the LLM (e.g., state["messages"])
# and add any additional logic (different models, custom prompts, structured output, etc.)
response = model.invoke(...)
return {"messages": [response]}
def agent_2(state: AgentState):
response = model.invoke(...)
return {"messages": [response]}
builder = StateGraph(AgentState)
builder.add_node(supervisor)
builder.add_node(agent_1)
builder.add_node(agent_2)
builder.add_edge(START, "supervisor")
# route to one of the agents or exit based on the supervisor's decisiion
# if the supervisor returns "__end__", the graph will finish execution
builder.add_conditional_edges("supervisor", lambda state: state["next"])
builder.add_edge("agent_1", "supervisor")
builder.add_edge("agent_2", "supervisor")
supervisor = builder.compile()
```
Check out this [tutorial](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/agent_supervisor/) for an example of supervisor multi-agent architecture.
### Supervisor (tool-calling)
In this variant of the [supervisor](#supervisor) architecture, we define individual agents as **tools** and use a tool-calling LLM in the supervisor node. This can be implemented as a [ReAct](./agentic_concepts.md#react-implementation)-style agent with two nodes — an LLM node (supervisor) and a tool-calling node that executes tools (agents in this case).
```python
from typing import Annotated
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import InjectedState, create_react_agent
model = ChatOpenAI()
# this is the agent function that will be called as tool
# notice that you can pass the state to the tool via InjectedState annotation
def agent_1(state: Annotated[dict, InjectedState]):
# you can pass relevant parts of the state to the LLM (e.g., state["messages"])
# and add any additional logic (different models, custom prompts, structured output, etc.)
response = model.invoke(...)
# return the LLM response as a string (expected tool response format)
# this will be automatically turned to ToolMessage
# by the prebuilt create_react_agent (supervisor)
return response.content
def agent_2(state: Annotated[dict, InjectedState]):
response = model.invoke(...)
return response.content
tools = [agent_1, agent_2]
# the simplest way to build a supervisor w/ tool-calling is to use prebuilt ReAct agent graph
# that consists of a tool-calling LLM node (i.e. supervisor) and a tool-executing node
supervisor = create_react_agent(model, tools)
```
### Hierarchical
As you add more agents to your system, it might become too hard for the supervisor to manage all of them. The supervisor might start making poor decisions about which agent to call next, the context might become too complex for a single supervisor to keep track of. In other words, you end up with the same problems that motivated the multi-agent architecture in the first place.
To address this, you can design your system _hierarchically_. For example, you can create separate, specialized teams of agents managed by individual supervisors, and a top-level supervisor to manage the teams.
```python
from typing import Literal
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, MessagesState, START
model = ChatOpenAI()
# define team 1 (same as the single supervisor example above)
class Team1State(MessagesState):
next: Literal["team_1_agent_1", "team_1_agent_2", "__end__"]
def team_1_supervisor(state: Team1State):
response = model.invoke(...)
return {"next": response["next_agent"]}
def team_1_agent_1(state: Team1State):
response = model.invoke(...)
return {"messages": [response]}
def team_1_agent_2(state: Team1State):
response = model.invoke(...)
return {"messages": [response]}
team_1_builder = StateGraph(Team1State)
team_1_builder.add_node(team_1_supervisor)
team_1_builder.add_node(team_1_agent_1)
team_1_builder.add_node(team_1_agent_2)
team_1_builder.add_edge(START, "team_1_supervisor")
# route to one of the agents or exit based on the supervisor's decisiion
# if the supervisor returns "__end__", the graph will finish execution
team_1_builder.add_conditional_edges("team_1_supervisor", lambda state: state["next"])
team_1_builder.add_edge("team_1_agent_1", "team_1_supervisor")
team_1_builder.add_edge("team_1_agent_2", "team_1_supervisor")
team_1_graph = team_1_builder.compile()
# define team 2 (same as the single supervisor example above)
class Team2State(MessagesState):
next: Literal["team_2_agent_1", "team_2_agent_2", "__end__"]
def team_2_supervisor(state: Team2State):
...
def team_2_agent_1(state: Team2State):
...
def team_2_agent_2(state: Team2State):
...
team_2_builder = StateGraph(Team2State)
...
team_2_graph = team_2_builder.compile()
# define top-level supervisor
class TopLevelState(MessagesState):
next: Literal["team_1", "team_2", "__end__"]
builder = StateGraph(TopLevelState)
def top_level_supervisor(state: TopLevelState):
# you can pass relevant parts of the state to the LLM (e.g., state["messages"])
# to determine which team to call next. a common pattern is to call the model
# with a structured output (e.g. force it to return an output with a "next_team" field)
response = model.invoke(...)
# the "next" key will be used by the conditional edges to route execution
# to the appropriate team
return {"next": response["next_team"]}
builder = StateGraph(TopLevelState)
builder.add_node(top_level_supervisor)
builder.add_node(team_1_graph)
builder.add_node(team_2_graph)
builder.add_edge(START, "top_level_supervisor")
# route to one of the teams or exit based on the supervisor's decision
# if the top-level supervisor returns "__end__", the graph will finish execution
builder.add_conditional_edges("top_level_supervisor", lambda state: state["next"])
builder.add_edge("team_1_graph", "top_level_supervisor")
builder.add_edge("team_2_graph", "top_level_supervisor")
graph = builder.compile()
```
### Custom multi-agent workflow
In this architecture we add individual agents as graph nodes and define the order in which agents are called ahead of time, in a custom workflow. In LangGraph the workflow can be defined in two ways:
- **Explicit control flow (normal edges)**: LangGraph allows you to explicitly define the control flow of your application (i.e. the sequence of how agents communicate) explicitly, via [normal graph edges](./low_level.md#normal-edges). This is the most deterministic variant of this architecture above — we always know which agent will be called next ahead of time.
- **Dynamic control flow (conditional edges)**: in LangGraph you can allow LLMs to decide parts of your application control flow. This can be achieved by using [conditional edges](./low_level.md#conditional-edges). A special case of this is a [supervisor tool-calling](#supervisor-tool-calling) architecture. In that case, the tool-calling LLM powering the supervisor agent will make decisions about the order in which the tools (agents) are being called.
```python
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, MessagesState, START
model = ChatOpenAI()
def agent_1(state: MessagesState):
response = model.invoke(...)
return {"messages": [response]}
def agent_2(state: MessagesState):
response = model.invoke(...)
return {"messages": [response]}
builder = StateGraph(MessagesState)
builder.add_node(agent_1)
builder.add_node(agent_2)
# define the flow explicitly
builder.add_edge(START, "agent_1")
builder.add_edge("agent_1", "agent_2")
```
## Communication between agents
The most important thing when building multi-agent systems is figuring out how the agents communicate. There are few different considerations:
- Do agents communicate via [**via graph state or via tool calls**](#graph-state-vs-tool-calls)?
- What if two agents have [**different state schemas**](#different-state-schemas)?
- How to communicate over a [**shared message list**](#shared-message-list)?
### Graph state vs tool calls
What is the "payload" that is being passed around between agents? In most of the architectures discussed above the agents communicate via the [graph state](./low_level.md#state). In the case of the [supervisor with tool-calling](#supervisor-tool-calling), the payloads are tool call arguments.
#### Graph state
To communicate via graph state, individual agents need to be defined as [graph nodes](./low_level.md#nodes). These can be added as functions or as entire [subgraphs](./low_level.md#subgraphs). At each step of the graph execution, agent node receives the current state of the graph, executes the agent code and then passes the updated state to the next nodes.
Typically agent nodes share a single [state schema](./low_level.md#schema). However, you might want to design agent nodes with [different state schemas](#different-state-schemas).
### Different state schemas
An agent might need to have a different state schema from the rest of the agents. For example, a search agent might only need to keep track of queries and retrieved documents. There are two ways to achieve this in LangGraph:
- Define [subgraph](./low_level.md#subgraphs) agents with a separate state schema. If there are no shared state keys (channels) between the subgraph and the parent graph, it’s important to [add input / output transformations](https://langchain-ai.github.io/langgraph/how-tos/subgraph-transform-state/) so that the parent graph knows how to communicate with the subgraphs.
- Define agent node functions with a [private input state schema](https://langchain-ai.github.io/langgraph/how-tos/pass_private_state/) that is distinct from the overall graph state schema. This allows passing information that is only needed for executing that particular agent.
### Shared message list
The most common way for the agents to communicate is via a shared state channel, typically a list of messages. This assumes that there is always at least a single channel (key) in the state that is shared by the agents. When communicating via a shared message list there is an additional consideration: should the agents [share the full history](#share-full-history) of their thought process or only [the final result](#share-final-result)?
#### Share full history
Agents can **share the full history** of their thought process (i.e. "scratchpad") with all other agents. This "scratchpad" would typically look like a [list of messages](./low_level.md#why-use-messages). The benefit of sharing full thought process is that it might help other agents make better decisions and improve reasoning ability for the system as a whole. The downside is that as the number of agents and their complexity grows, the "scratchpad" will grow quickly and might require additional strategies for [memory management](./memory.md/#managing-long-conversation-history).
#### Share final result
Agents can have their own private "scratchpad" and only **share the final result** with the rest of the agents. This approach might work better for systems with many agents or agents that are more complex. In this case, you would need to define agents with [different state schemas](#different-state-schemas)
For agents called as tools, the supervisor determines the inputs based on the tool schema. Additionally, LangGraph allows [passing state](https://langchain-ai.github.io/langgraph/how-tos/pass-run-time-values-to-tools/#pass-graph-state-to-tools) to individual tools at runtime, so subordinate agents can access parent state, if needed.
|
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/langgraph_cli.md | # LangGraph CLI
!!! info "Prerequisites"
- [LangGraph Platform](./langgraph_platform.md)
- [LangGraph Server](./langgraph_server.md)
The LangGraph CLI is a multi-platform command-line tool for building and running the [LangGraph API server](./langgraph_server.md) locally. This offers an alternative to the [LangGraph Studio desktop app](./langgraph_studio.md) for developing and testing agents across all major operating systems (Linux, Windows, MacOS). The resulting server includes all API endpoints for your graph's runs, threads, assistants, etc. as well as the other services required to run your agent, including a managed database for checkpointing and storage.
## Installation
The LangGraph CLI can be installed via Homebrew (on macOS) or pip:
=== "Homebrew"
```bash
brew install langgraph-cli
```
=== "pip"
```bash
pip install langgraph-cli
```
## Commands
The CLI provides the following core functionality:
### `build`
The `langgraph build` command builds a Docker image for the [LangGraph API server](./langgraph_server.md) that can be directly deployed.
### `dev`
!!! note "New in version 0.1.55"
The `langgraph dev` command was introduced in langgraph-cli version 0.1.55.
!!! note "Python only"
Currently, the CLI only supports Python >= 3.11.
JS support is coming soon.
The `langgraph dev` command starts a lightweight development server that requires no Docker installation. This server is ideal for rapid development and testing, with features like:
- Hot reloading: Changes to your code are automatically detected and reloaded
- Debugger support: Attach your IDE's debugger for line-by-line debugging
- In-memory state with local persistence: Server state is stored in memory for speed but persisted locally between restarts
To use this command, you need to install the CLI with the "inmem" extra:
```bash
pip install -U "langgraph-cli[inmem]"
```
**Note**: This command is intended for local development and testing only. It is not recommended for production use. Since it does not use Docker, we recommend using virtual environments to manage your project's dependencies.
### `up`
The `langgraph up` command starts an instance of the [LangGraph API server](./langgraph_server.md) locally in a docker container. This requires thedocker server to be running locally. It also requires a LangSmith API key for local development or a license key for production use.
The server includes all API endpoints for your graph's runs, threads, assistants, etc. as well as the other services required to run your agent, including a managed database for checkpointing and storage.
### `dockerfile`
The `langgraph dockerfile` command generates a [Dockerfile](https://docs.docker.com/reference/dockerfile/) that can be used to build images for and deploy instances of the [LangGraph API server](./langgraph_server.md). This is useful if you want to further customize the dockerfile or deploy in a more custom way.
## Related
- [LangGraph CLI API Reference](../cloud/reference/cli.md)
|
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/langgraph_cloud.md | # Cloud SaaS
!!! info "Prerequisites"
- [LangGraph Platform](./langgraph_platform.md)
- [LangGraph Server](./langgraph_server.md)
## Overview
LangGraph's Cloud SaaS is a managed service for deploying LangGraph APIs, regardless of its definition or dependencies. The service offers managed implementations of checkpointers and stores, allowing you to focus on building the right cognitive architecture for your use case. By handling scalable & secure infrastructure, LangGraph Cloud offers the fastest path to getting your LangGraph API deployed to production.
## Deployment
A **deployment** is an instance of a LangGraph API. A single deployment can have many [revisions](#revision). When a deployment is created, all the necessary infrastructure (e.g. database, containers, secrets store) are automatically provisioned. See the [architecture diagram](#architecture) below for more details.
See the [how-to guide](../cloud/deployment/cloud.md#create-new-deployment) for creating a new deployment.
## Resource Allocation
| **Deployment Type** | **CPU** | **Memory** | **Scaling** |
|---------------------|---------|------------|---------------------|
| Development | 1 CPU | 1 GB | Up to 1 container |
| Production | 1 CPU | 2 GB | Up to 10 containers |
## Revision
A revision is an iteration of a [deployment](#deployment). When a new deployment is created, an initial revision is automatically created. To deploy new code changes or update environment variable configurations for a deployment, a new revision must be created. When a revision is created, a new container image is built automatically.
See the [how-to guide](../cloud/deployment/cloud.md#create-new-revision) for creating a new revision.
## Asynchronous Deployment
Infrastructure for [deployments](#deployment) and [revisions](#revision) are provisioned and deployed asynchronously. They are not deployed immediately after submission. Currently, deployment can take up to several minutes.
## Architecture
!!! warning "Subject to Change"
The Cloud SaaS deployment architecture may change in the future.
A high-level diagram of a Cloud SaaS deployment.
## Related
- [Deployment Options](./deployment_options.md)
|
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/memory.md | # Memory
## What is Memory?
[Memory](https://pmc.ncbi.nlm.nih.gov/articles/PMC10410470/) is a cognitive function that allows people to store, retrieve, and use information to understand their present and future. Consider the frustration of working with a colleague who forgets everything you tell them, requiring constant repetition! As AI agents undertake more complex tasks involving numerous user interactions, equipping them with memory becomes equally crucial for efficiency and user satisfaction. With memory, agents can learn from feedback and adapt to users' preferences. This guide covers two types of memory based on recall scope:
**Short-term memory**, or [thread](persistence.md#threads)-scoped memory, can be recalled at any time **from within** a single conversational thread with a user. LangGraph manages short-term memory as a part of your agent's [state](low_level.md#state). State is persisted to a database using a [checkpointer](persistence.md#checkpoints) so the thread can be resumed at any time. Short-term memory updates when the graph is invoked or a step is completed, and the State is read at the start of each step.
**Long-term memory** is shared **across** conversational threads. It can be recalled _at any time_ and **in any thread**. Memories are scoped to any custom namespace, not just within a single thread ID. LangGraph provides [stores](persistence.md#memory-store) ([reference doc](https://langchain-ai.github.io/langgraph/reference/store/#langgraph.store.base.BaseStore)) to let you save and recall long-term memories.
Both are important to understand and implement for your application.
## Short-term memory
Short-term memory lets your application remember previous interactions within a single [thread](persistence.md#threads) or conversation. A [thread](persistence.md#threads) organizes multiple interactions in a session, similar to the way email groups messages in a single conversation.
LangGraph manages short-term memory as part of the agent's state, persisted via thread-scoped checkpoints. This state can normally include the conversation history along with other stateful data, such as uploaded files, retrieved documents, or generated artifacts. By storing these in the graph's state, the bot can access the full context for a given conversation while maintaining separation between different threads.
Since conversation history is the most common form of representing short-term memory, in the next section, we will cover techniques for managing conversation history when the list of messages becomes **long**. If you want to stick to the high-level concepts, continue on to the [long-term memory](#long-term-memory) section.
### Managing long conversation history
Long conversations pose a challenge to today's LLMs. The full history may not even fit inside an LLM's context window, resulting in an irrecoverable error. Even _if_ your LLM technically supports the full context length, most LLMs still perform poorly over long contexts. They get "distracted" by stale or off-topic content, all while suffering from slower response times and higher costs.
Managing short-term memory is an exercise of balancing [precision & recall](https://en.wikipedia.org/wiki/Precision_and_recall#:~:text=Precision%20can%20be%20seen%20as,irrelevant%20ones%20are%20also%20returned) with your application's other performance requirements (latency & cost). As always, it's important to think critically about how you represent information for your LLM and to look at your data. We cover a few common techniques for managing message lists below and hope to provide sufficient context for you to pick the best tradeoffs for your application:
- [Editing message lists](#editing-message-lists): How to think about trimming and filtering a list of messages before passing to language model.
- [Summarizing past conversations](#summarizing-past-conversations): A common technique to use when you don't just want to filter the list of messages.
### Editing message lists
Chat models accept context using [messages](https://python.langchain.com/docs/concepts/#messages), which include developer provided instructions (a system message) and user inputs (human messages). In chat applications, messages alternate between human inputs and model responses, resulting in a list of messages that grows longer over time. Because context windows are limited and token-rich message lists can be costly, many applications can benefit from using techniques to manually remove or forget stale information.
The most direct approach is to remove old messages from a list (similar to a [least-recently used cache](https://en.wikipedia.org/wiki/Page_replacement_algorithm#Least_recently_used)).
The typical technique for deleting content from a list in LangGraph is to return an update from a node telling the system to delete some portion of the list. You get to define what this update looks like, but a common approach would be to let you return an object or dictionary specifying which values to retain.
```python
def manage_list(existing: list, updates: Union[list, dict]):
if isinstance(updates, list):
# Normal case, add to the history
return existing + updates
elif isinstance(updates, dict) and updates["type"] == "keep":
# You get to decide what this looks like.
# For example, you could simplify and just accept a string "DELETE"
# and clear the entire list.
return existing[updates["from"]:updates["to"]]
# etc. We define how to interpret updates
class State(TypedDict):
my_list: Annotated[list, manage_list]
def my_node(state: State):
return {
# We return an update for the field "my_list" saying to
# keep only values from index -5 to the end (deleting the rest)
"my_list": {"type": "keep", "from": -5, "to": None}
}
```
LangGraph will call the `manage_list` "[reducer](low_level.md#reducers)" function any time an update is returned under the key "my_list". Within that function, we define what types of updates to accept. Typically, messages will be added to the existing list (the conversation will grow); however, we've also added support to accept a dictionary that lets you "keep" certain parts of the state. This lets you programmatically drop old message context.
Another common approach is to let you return a list of "remove" objects that specify the IDs of all messages to delete. If you're using the LangChain messages and the [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages) reducer (or `MessagesState`, which uses the same underlying functionality) in LangGraph, you can do this using a `RemoveMessage`.
```python
from langchain_core.messages import RemoveMessage, AIMessage
from langgraph.graph import add_messages
# ... other imports
class State(TypedDict):
# add_messages will default to upserting messages by ID to the existing list
# if a RemoveMessage is returned, it will delete the message in the list by ID
messages: Annotated[list, add_messages]
def my_node_1(state: State):
# Add an AI message to the `messages` list in the state
return {"messages": [AIMessage(content="Hi")]}
def my_node_2(state: State):
# Delete all but the last 2 messages from the `messages` list in the state
delete_messages = [RemoveMessage(id=m.id) for m in state['messages'][:-2]]
return {"messages": delete_messages}
```
In the example above, the `add_messages` reducer allows us to [append](https://langchain-ai.github.io/langgraph/concepts/low_level/#serialization) new messages to the `messages` state key as shown in `my_node_1`. When it sees a `RemoveMessage`, it will delete the message with that ID from the list (and the RemoveMessage will then be discarded). For more information on LangChain-specific message handling, check out [this how-to on using `RemoveMessage` ](https://langchain-ai.github.io/langgraph/how-tos/memory/delete-messages/).
See this how-to [guide](https://langchain-ai.github.io/langgraph/how-tos/memory/manage-conversation-history/) and module 2 from our [LangChain Academy](https://github.com/langchain-ai/langchain-academy/tree/main/module-2) course for example usage.
### Summarizing past conversations
The problem with trimming or removing messages, as shown above, is that we may lose information from culling of the message queue. Because of this, some applications benefit from a more sophisticated approach of summarizing the message history using a chat model.
Simple prompting and orchestration logic can be used to achieve this. As an example, in LangGraph we can extend the [MessagesState](https://langchain-ai.github.io/langgraph/concepts/low_level/#working-with-messages-in-graph-state) to include a `summary` key.
```python
from langgraph.graph import MessagesState
class State(MessagesState):
summary: str
```
Then, we can generate a summary of the chat history, using any existing summary as context for the next summary. This `summarize_conversation` node can be called after some number of messages have accumulated in the `messages` state key.
```python
def summarize_conversation(state: State):
# First, we get any existing summary
summary = state.get("summary", "")
# Create our summarization prompt
if summary:
# A summary already exists
summary_message = (
f"This is a summary of the conversation to date: {summary}\n\n"
"Extend the summary by taking into account the new messages above:"
)
else:
summary_message = "Create a summary of the conversation above:"
# Add prompt to our history
messages = state["messages"] + [HumanMessage(content=summary_message)]
response = model.invoke(messages)
# Delete all but the 2 most recent messages
delete_messages = [RemoveMessage(id=m.id) for m in state["messages"][:-2]]
return {"summary": response.content, "messages": delete_messages}
```
See this how-to [here](https://langchain-ai.github.io/langgraph/how-tos/memory/add-summary-conversation-history/) and module 2 from our [LangChain Academy](https://github.com/langchain-ai/langchain-academy/tree/main/module-2) course for example usage.
### Knowing **when** to remove messages
Most LLMs have a maximum supported context window (denominated in tokens). A simple way to decide when to truncate messages is to count the tokens in the message history and truncate whenever it approaches that limit. Naive truncation is straightforward to implement on your own, though there are a few "gotchas". Some model APIs further restrict the sequence of message types (must start with human message, cannot have consecutive messages of the same type, etc.). If you're using LangChain, you can use the [`trim_messages`](https://python.langchain.com/docs/how_to/trim_messages/#trimming-based-on-token-count) utility and specify the number of tokens to keep from the list, as well as the `strategy` (e.g., keep the last `max_tokens`) to use for handling the boundary.
Below is an example.
```python
from langchain_core.messages import trim_messages
trim_messages(
messages,
# Keep the last <= n_count tokens of the messages.
strategy="last",
# Remember to adjust based on your model
# or else pass a custom token_encoder
token_counter=ChatOpenAI(model="gpt-4"),
# Remember to adjust based on the desired conversation
# length
max_tokens=45,
# Most chat models expect that chat history starts with either:
# (1) a HumanMessage or
# (2) a SystemMessage followed by a HumanMessage
start_on="human",
# Most chat models expect that chat history ends with either:
# (1) a HumanMessage or
# (2) a ToolMessage
end_on=("human", "tool"),
# Usually, we want to keep the SystemMessage
# if it's present in the original history.
# The SystemMessage has special instructions for the model.
include_system=True,
)
```
## Long-term memory
Long-term memory in LangGraph allows systems to retain information across different conversations or sessions. Unlike short-term memory, which is **thread-scoped**, long-term memory is saved within custom "namespaces."
### Storing memories
LangGraph stores long-term memories as JSON documents in a [store](persistence.md#memory-store) ([reference doc](https://langchain-ai.github.io/langgraph/reference/store/#langgraph.store.base.BaseStore)). Each memory is organized under a custom `namespace` (similar to a folder) and a distinct `key` (like a filename). Namespaces often include user or org IDs or other labels that makes it easier to organize information. This structure enables hierarchical organization of memories. Cross-namespace searching is then supported through content filters. See the example below for an example.
```python
from langgraph.store.memory import InMemoryStore
def embed(texts: list[str]) -> list[list[float]]:
# Replace with an actual embedding function or LangChain embeddings object
return [[1.0, 2.0] * len(texts)]
# InMemoryStore saves data to an in-memory dictionary. Use a DB-backed store in production use.
store = InMemoryStore(index={"embed": embed, "dims": 2})
user_id = "my-user"
application_context = "chitchat"
namespace = (user_id, application_context)
store.put(
namespace,
"a-memory",
{
"rules": [
"User likes short, direct language",
"User only speaks English & python",
],
"my-key": "my-value",
},
)
# get the "memory" by ID
item = store.get(namespace, "a-memory")
# search for "memories" within this namespace, filtering on content equivalence, sorted by vector similarity
items = store.search(
namespace, filter={"my-key": "my-value"}, query="language preferences"
)
```
### Framework for thinking about long-term memory
Long-term memory is a complex challenge without a one-size-fits-all solution. However, the following questions provide a structure framework to help you navigate the different techniques:
**What is the type of memory?**
Humans use memories to remember [facts](https://en.wikipedia.org/wiki/Semantic_memory), [experiences](https://en.wikipedia.org/wiki/Episodic_memory), and [rules](https://en.wikipedia.org/wiki/Procedural_memory). AI agents can use memory in the same ways. For example, AI agents can use memory to remember specific facts about a user to accomplish a task. We expand on several types of memories in the [section below](#memory-types).
**When do you want to update memories?**
Memory can be updated as part of an agent's application logic (e.g. "on the hot path"). In this case, the agent typically decides to remember facts before responding to a user. Alternatively, memory can be updated as a background task (logic that runs in the background / asynchronously and generates memories). We explain the tradeoffs between these approaches in the [section below](#writing-memories).
## Memory types
Different applications require various types of memory. Although the analogy isn't perfect, examining [human memory types](https://www.psychologytoday.com/us/basics/memory/types-of-memory?ref=blog.langchain.dev) can be insightful. Some research (e.g., the [CoALA paper](https://arxiv.org/pdf/2309.02427)) have even mapped these human memory types to those used in AI agents.
| Memory Type | What is Stored | Human Example | Agent Example |
|-------------|----------------|---------------|---------------|
| Semantic | Facts | Things I learned in school | Facts about a user |
| Episodic | Experiences | Things I did | Past agent actions |
| Procedural | Instructions | Instincts or motor skills | Agent system prompt |
### Semantic Memory
[Semantic memory](https://en.wikipedia.org/wiki/Semantic_memory), both in humans and AI agents, involves the retention of specific facts and concepts. In humans, it can include information learned in school and the understanding of concepts and their relationships. For AI agents, semantic memory is often used to personalize applications by remembering facts or concepts from past interactions.
> Note: Not to be confused with "semantic search" which is a technique for finding similar content using "meaning" (usually as embeddings). Semantic memory is a term from psychology, referring to storing facts and knowledge, while semantic search is a method for retrieving information based on meaning rather than exact matches.
#### Profile
Semantic memories can be managed in different ways. For example, memories can be a single, continuously updated "profile" of well-scoped and specific information about a user, organization, or other entity (including the agent itself). A profile is generally just a JSON document with various key-value pairs you've selected to represent your domain.
When remembering a profile, you will want to make sure that you are **updating** the profile each time. As a result, you will want to pass in the previous profile and [ask the model to generate a new profile](https://github.com/langchain-ai/memory-template) (or some [JSON patch](https://github.com/hinthornw/trustcall) to apply to the old profile). This can be become error-prone as the profile gets larger, and may benefit from splitting a profile into multiple documents or **strict** decoding when generating documents to ensure the memory schemas remains valid.
#### Collection
Alternatively, memories can be a collection of documents that are continuously updated and extended over time. Each individual memory can be more narrowly scoped and easier to generate, which means that you're less likely to **lose** information over time. It's easier for an LLM to generate _new_ objects for new information than reconcile new information with an existing profile. As a result, a document collection tends to lead to [higher recall downstream](https://en.wikipedia.org/wiki/Precision_and_recall).
However, this shifts some complexity memory updating. The model must now _delete_ or _update_ existing items in the list, which can be tricky. In addition, some models may default to over-inserting and others may default to over-updating. See the [Trustcall](https://github.com/hinthornw/trustcall) package for one way to manage this and consider evaluation (e.g., with a tool like [LangSmith](https://docs.smith.langchain.com/tutorials/Developers/evaluation)) to help you tune the behavior.
Working with document collections also shifts complexity to memory **search** over the list. The `Store` currently supports both [semantic search](https://langchain-ai.github.io/langgraph/reference/store/#langgraph.store.base.SearchOp.query) and [filtering by content](https://langchain-ai.github.io/langgraph/reference/store/#langgraph.store.base.SearchOp.filter).
Finally, using a collection of memories can make it challenging to provide comprehensive context to the model. While individual memories may follow a specific schema, this structure might not capture the full context or relationships between memories. As a result, when using these memories to generate responses, the model may lack important contextual information that would be more readily available in a unified profile approach.
Regardless of memory management approach, the central point is that the agent will use the semantic memories to [ground its responses](https://python.langchain.com/docs/concepts/rag/), which often leads to more personalized and relevant interactions.
### Episodic Memory
[Episodic memory](https://en.wikipedia.org/wiki/Episodic_memory), in both humans and AI agents, involves recalling past events or actions. The [CoALA paper](https://arxiv.org/pdf/2309.02427) frames this well: facts can be written to semantic memory, whereas *experiences* can be written to episodic memory. For AI agents, episodic memory is often used to help an agent remember how to accomplish a task.
In practice, episodic memories are often implemented through [few-shot example prompting](https://python.langchain.com/docs/concepts/few_shot_prompting/), where agents learn from past sequences to perform tasks correctly. Sometimes it's easier to "show" than "tell" and LLMs learn well from examples. Few-shot learning lets you ["program"](https://x.com/karpathy/status/1627366413840322562) your LLM by updating the prompt with input-output examples to illustrate the intended behavior. While various [best-practices](https://python.langchain.com/docs/concepts/#1-generating-examples) can be used to generate few-shot examples, often the challenge lies in selecting the most relevant examples based on user input.
Note that the memory [store](persistence.md#memory-store) is just one way to store data as few-shot examples. If you want to have more developer involvement, or tie few-shots more closely to your evaluation harness, you can also use a [LangSmith Dataset](https://docs.smith.langchain.com/evaluation/how_to_guides/datasets/index_datasets_for_dynamic_few_shot_example_selection) to store your data. Then dynamic few-shot example selectors can be used out-of-the box to achieve this same goal. LangSmith will index the dataset for you and enable retrieval of few shot examples that are most relevant to the user input based upon keyword similarity ([using a BM25-like algorithm](https://docs.smith.langchain.com/how_to_guides/datasets/index_datasets_for_dynamic_few_shot_example_selection) for keyword based similarity).
See this how-to [video](https://www.youtube.com/watch?v=37VaU7e7t5o) for example usage of dynamic few-shot example selection in LangSmith. Also, see this [blog post](https://blog.langchain.dev/few-shot-prompting-to-improve-tool-calling-performance/) showcasing few-shot prompting to improve tool calling performance and this [blog post](https://blog.langchain.dev/aligning-llm-as-a-judge-with-human-preferences/) using few-shot example to align an LLMs to human preferences.
### Procedural Memory
[Procedural memory](https://en.wikipedia.org/wiki/Procedural_memory), in both humans and AI agents, involves remembering the rules used to perform tasks. In humans, procedural memory is like the internalized knowledge of how to perform tasks, such as riding a bike via basic motor skills and balance. Episodic memory, on the other hand, involves recalling specific experiences, such as the first time you successfully rode a bike without training wheels or a memorable bike ride through a scenic route. For AI agents, procedural memory is a combination of model weights, agent code, and agent's prompt that collectively determine the agent's functionality.
In practice, it is fairly uncommon for agents to modify their model weights or rewrite their code. However, it is more common for agents to [modify their own prompts](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/prompt-generator).
One effective approach to refining an agent's instructions is through ["Reflection"](https://blog.langchain.dev/reflection-agents/) or meta-prompting. This involves prompting the agent with its current instructions (e.g., the system prompt) along with recent conversations or explicit user feedback. The agent then refines its own instructions based on this input. This method is particularly useful for tasks where instructions are challenging to specify upfront, as it allows the agent to learn and adapt from its interactions.
For example, we built a [Tweet generator](https://www.youtube.com/watch?v=Vn8A3BxfplE) using external feedback and prompt re-writing to produce high-quality paper summaries for Twitter. In this case, the specific summarization prompt was difficult to specify *a priori*, but it was fairly easy for a user to critique the generated Tweets and provide feedback on how to improve the summarization process.
The below pseudo-code shows how you might implement this with the LangGraph memory [store](persistence.md#memory-store), using the store to save a prompt, the `update_instructions` node to get the current prompt (as well as feedback from the conversation with the user captured in `state["messages"]`), update the prompt, and save the new prompt back to the store. Then, the `call_model` get the updated prompt from the store and uses it to generate a response.
```python
# Node that *uses* the instructions
def call_model(state: State, store: BaseStore):
namespace = ("agent_instructions", )
instructions = store.get(namespace, key="agent_a")[0]
# Application logic
prompt = prompt_template.format(instructions=instructions.value["instructions"])
...
# Node that updates instructions
def update_instructions(state: State, store: BaseStore):
namespace = ("instructions",)
current_instructions = store.search(namespace)[0]
# Memory logic
prompt = prompt_template.format(instructions=instructions.value["instructions"], conversation=state["messages"])
output = llm.invoke(prompt)
new_instructions = output['new_instructions']
store.put(("agent_instructions",), "agent_a", {"instructions": new_instructions})
...
```
## Writing memories
While [humans often form long-term memories during sleep](https://medicine.yale.edu/news-article/sleeps-crucial-role-in-preserving-memory/), AI agents need a different approach. When and how should agents create new memories? There are at least two primary methods for agents to write memories: "on the hot path" and "in the background".
### Writing memories in the hot path
Creating memories during runtime offers both advantages and challenges. On the positive side, this approach allows for real-time updates, making new memories immediately available for use in subsequent interactions. It also enables transparency, as users can be notified when memories are created and stored.
However, this method also presents challenges. It may increase complexity if the agent requires a new tool to decide what to commit to memory. In addition, the process of reasoning about what to save to memory can impact agent latency. Finally, the agent must multitask between memory creation and its other responsibilities, potentially affecting the quantity and quality of memories created.
As an example, ChatGPT uses a [save_memories](https://openai.com/index/memory-and-new-controls-for-chatgpt/) tool to upsert memories as content strings, deciding whether and how to use this tool with each user message. See our [memory-agent](https://github.com/langchain-ai/memory-agent) template as an reference implementation.
### Writing memories in the background
Creating memories as a separate background task offers several advantages. It eliminates latency in the primary application, separates application logic from memory management, and allows for more focused task completion by the agent. This approach also provides flexibility in timing memory creation to avoid redundant work.
However, this method has its own challenges. Determining the frequency of memory writing becomes crucial, as infrequent updates may leave other threads without new context. Deciding when to trigger memory formation is also important. Common strategies include scheduling after a set time period (with rescheduling if new events occur), using a cron schedule, or allowing manual triggers by users or the application logic.
See our [memory-service](https://github.com/langchain-ai/memory-template) template as an reference implementation.
|
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/langgraph_platform.md | # LangGraph Platform
## Overview
LangGraph Platform is a commercial solution for deploying agentic applications to production, built on the open-source [LangGraph framework](./high_level.md).
The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications:
- [LangGraph Server](./langgraph_server.md): The server defines an opinionated API and architecture that incorporates best practices for deploying agentic applications, allowing you to focus on building your agent logic rather than developing server infrastructure.
- [LangGraph Studio](./langgraph_studio.md): LangGraph Studio is a specialized IDE that can connect to a LangGraph Server to enable visualization, interaction, and debugging of the application locally.
- [LangGraph CLI](./langgraph_cli.md): LangGraph CLI is a command-line interface that helps to interact with a local LangGraph
- [Python/JS SDK](./sdk.md): The Python/JS SDK provides a programmatic way to interact with deployed LangGraph Applications.
- [Remote Graph](../how-tos/use-remote-graph.md): A RemoteGraph allows you to interact with any deployed LangGraph application as though it were running locally.
The LangGraph Platform offers a few different deployment options described in the [deployment options guide](./deployment_options.md).
## Why Use LangGraph Platform?
LangGraph Platform is designed to make deploying agentic applications seamless and production-ready.
For simpler applications, deploying a LangGraph agent can be as straightforward as using your own server logic—for example, setting up a FastAPI endpoint and invoking LangGraph directly.
### Option 1: Deploying with Custom Server Logic
For basic LangGraph applications, you may choose to handle deployment using your custom server infrastructure. Setting up endpoints with frameworks like [FastAPI](https://fastapi.tiangolo.com/) allows you to quickly deploy and run LangGraph as you would any other Python application:
```python
from fastapi import FastAPI
from your_agent_package import graph
app = FastAPI()
@app.get("/foo")
async def foo(...):
return await graph.ainvoke({...})
```
This approach works well for simple applications with straightforward needs and provides you with full control over the deployment setup. For example, you might use this for a single-assistant application that doesn’t require long-running sessions or persistent memory.
### Option 2: Leveraging LangGraph Platform for Complex Deployments
As your applications scale or add complex features, the deployment requirements often evolve. Running an application with more nodes, longer processing times, or a need for persistent memory can introduce challenges that quickly become time-consuming and difficult to manage manually. [LangGraph Platform](./langgraph_platform.md) is built to handle these challenges seamlessly, allowing you to focus on agent logic rather than server infrastructure.
Here are some common issues that arise in complex deployments, which LangGraph Platform addresses:
- **[Streaming Support](streaming.md)**: As agents grow more sophisticated, they often benefit from streaming both token outputs and intermediate states back to the user. Without this, users are left waiting for potentially long operations with no feedback. LangGraph Server provides [multiple streaming modes](streaming.md) optimized for various application needs.
- **Background Runs**: For agents that take longer to process (e.g., hours), maintaining an open connection can be impractical. The LangGraph Server supports launching agent runs in the background and provides both polling endpoints and webhooks to monitor run status effectively.
- **Support for long runs**: Vanilla server setups often encounter timeouts or disruptions when handling requests that take a long time to complete. LangGraph Server’s API provides robust support for these tasks by sending regular heartbeat signals, preventing unexpected connection closures during prolonged processes.
- **Handling Burstiness**: Certain applications, especially those with real-time user interaction, may experience "bursty" request loads where numerous requests hit the server simultaneously. LangGraph Server includes a task queue, ensuring requests are handled consistently without loss, even under heavy loads.
- **[Double Texting](double_texting.md)**: In user-driven applications, it’s common for users to send multiple messages rapidly. This “double texting” can disrupt agent flows if not handled properly. LangGraph Server offers built-in strategies to address and manage such interactions.
- **[Checkpointers and Memory Management](persistence.md#checkpoints)**: For agents needing persistence (e.g., conversation memory), deploying a robust storage solution can be complex. LangGraph Platform includes optimized [checkpointers](persistence.md#checkpoints) and a [memory store](persistence.md#memory-store), managing state across sessions without the need for custom solutions.
- **[Human-in-the-loop Support](human_in_the_loop.md)**: In many applications, users require a way to intervene in agent processes. LangGraph Server provides specialized endpoints for human-in-the-loop scenarios, simplifying the integration of manual oversight into agent workflows.
By using LangGraph Platform, you gain access to a robust, scalable deployment solution that mitigates these challenges, saving you the effort of implementing and maintaining them manually. This allows you to focus more on building effective agent behavior and less on solving deployment infrastructure issues.
|
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/agentic_concepts.md | # Agent architectures
Many LLM applications implement a particular control flow of steps before and / or after LLM calls. As an example, [RAG](https://github.com/langchain-ai/rag-from-scratch) performs retrieval of relevant documents to a question, and passes those documents to an LLM in order to ground the model's response.
Instead of hard-coding a fixed control flow, we sometimes want LLM systems that can pick its own control flow to solve more complex problems! This is one definition of an [agent](https://blog.langchain.dev/what-is-an-agent/): *an agent is a system that uses an LLM to decide the control flow of an application.* There are many ways that an LLM can control application:
- An LLM can route between two potential paths
- An LLM can decide which of many tools to call
- An LLM can decide whether the generated answer is sufficient or more work is needed
As a result, there are many different types of [agent architectures](https://blog.langchain.dev/what-is-a-cognitive-architecture/), which given an LLM varying levels of control.
## Router
A router allows an LLM to select a single step from a specified set of options. This is an agent architecture that exhibits a relatively limited level of control because the LLM usually governs a single decision and can return a narrow set of outputs. Routers typically employ a few different concepts to achieve this.
### Structured Output
Structured outputs with LLMs work by providing a specific format or schema that the LLM should follow in its response. This is similar to tool calling, but more general. While tool calling typically involves selecting and using predefined functions, structured outputs can be used for any type of formatted response. Common methods to achieve structured outputs include:
1. Prompt engineering: Instructing the LLM to respond in a specific format.
2. Output parsers: Using post-processing to extract structured data from LLM responses.
3. Tool calling: Leveraging built-in tool calling capabilities of some LLMs to generate structured outputs.
Structured outputs are crucial for routing as they ensure the LLM's decision can be reliably interpreted and acted upon by the system. Learn more about [structured outputs in this how-to guide](https://python.langchain.com/docs/how_to/structured_output/).
## Tool calling agent
While a router allows an LLM to make a single decision, more complex agent architectures expand the LLM's control in two key ways:
1. Multi-step decision making: The LLM can control a sequence of decisions rather than just one.
2. Tool access: The LLM can choose from and use a variety of tools to accomplish tasks.
[ReAct](https://arxiv.org/abs/2210.03629) is a popular general purpose agent architecture that combines these expansions, integrating three core concepts.
1. `Tool calling`: Allowing the LLM to select and use various tools as needed.
2. `Memory`: Enabling the agent to retain and use information from previous steps.
3. `Planning`: Empowering the LLM to create and follow multi-step plans to achieve goals.
This architecture allows for more complex and flexible agent behaviors, going beyond simple routing to enable dynamic problem-solving across multiple steps. You can use it with [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent].
### Tool calling
Tools are useful whenever you want an agent to interact with external systems. External systems (e.g., APIs) often require a particular input schema or payload, rather than natural language. When we bind an API, for example, as a tool we given the model awareness of the required input schema. The model will choose to call a tool based upon the natural language input from the user and it will return an output that adheres to the tool's schema.
[Many LLM providers support tool calling](https://python.langchain.com/v0.1/docs/integrations/chat/) and [tool calling interface](https://blog.langchain.dev/improving-core-tool-interfaces-and-docs-in-langchain/) in LangChain is simple: you can simply pass any Python `function` into `ChatModel.bind_tools(function)`.
### Memory
Memory is crucial for agents, enabling them to retain and utilize information across multiple steps of problem-solving. It operates on different scales:
1. Short-term memory: Allows the agent to access information acquired during earlier steps in a sequence.
2. Long-term memory: Enables the agent to recall information from previous interactions, such as past messages in a conversation.
LangGraph provides full control over memory implementation:
- [`State`](./low_level.md#state): User-defined schema specifying the exact structure of memory to retain.
- [`Checkpointers`](./persistence.md): Mechanism to store state at every step across different interactions.
This flexible approach allows you to tailor the memory system to your specific agent architecture needs. For a practical guide on adding memory to your graph, see [this tutorial](../how-tos/persistence.ipynb).
Effective memory management enhances an agent's ability to maintain context, learn from past experiences, and make more informed decisions over time.
### Planning
In the ReAct architecture, an LLM is called repeatedly in a while-loop. At each step the agent decides which tools to call, and what the inputs to those tools should be. Those tools are then executed, and the outputs are fed back into the LLM as observations. The while-loop terminates when the agent decides it is not worth calling any more tools.
### ReAct implementation
There are several differences between this paper and the pre-built [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent] implementation:
- First, we use [tool-calling](#tool-calling) to have LLMs call tools, whereas the paper used prompting + parsing of raw output. This is because tool calling did not exist when the paper was written, but is generally better and more reliable.
- Second, we use messages to prompt the LLM, whereas the paper used string formatting. This is because at the time of writing, LLMs didn't even expose a message-based interface, whereas now that's the only interface they expose.
- Third, the paper required all inputs to the tools to be a single string. This was largely due to LLMs not being super capable at the time, and only really being able to generate a single input. Our implementation allows for using tools that require multiple inputs.
- Fourth, the paper only looks at calling a single tool at the time, largely due to limitations in LLMs performance at the time. Our implementation allows for calling multiple tools at a time.
- Finally, the paper asked the LLM to explicitly generate a "Thought" step before deciding which tools to call. This is the "Reasoning" part of "ReAct". Our implementation does not do this by default, largely because LLMs have gotten much better and that is not as necessary. Of course, if you wish to prompt it do so, you certainly can.
## Custom agent architectures
While routers and tool-calling agents (like ReAct) are common, [customizing agent architectures](https://blog.langchain.dev/why-you-should-outsource-your-agentic-infrastructure-but-own-your-cognitive-architecture/) often leads to better performance for specific tasks. LangGraph offers several powerful features for building tailored agent systems:
### Human-in-the-loop
Human involvement can significantly enhance agent reliability, especially for sensitive tasks. This can involve:
- Approving specific actions
- Providing feedback to update the agent's state
- Offering guidance in complex decision-making processes
Human-in-the-loop patterns are crucial when full automation isn't feasible or desirable. Learn more in our [human-in-the-loop guide](./human_in_the_loop.md).
### Parallelization
Parallel processing is vital for efficient multi-agent systems and complex tasks. LangGraph supports parallelization through its [Send](./low_level.md#send) API, enabling:
- Concurrent processing of multiple states
- Implementation of map-reduce-like operations
- Efficient handling of independent subtasks
For practical implementation, see our [map-reduce tutorial](../how-tos/map-reduce.ipynb).
### Subgraphs
[Subgraphs](./low_level.md#subgraphs) are essential for managing complex agent architectures, particularly in [multi-agent systems](./multi_agent.md). They allow:
- Isolated state management for individual agents
- Hierarchical organization of agent teams
- Controlled communication between agents and the main system
Subgraphs communicate with the parent graph through overlapping keys in the state schema. This enables flexible, modular agent design. For implementation details, refer to our [subgraph how-to guide](../how-tos/subgraph.ipynb).
### Reflection
Reflection mechanisms can significantly improve agent reliability by:
1. Evaluating task completion and correctness
2. Providing feedback for iterative improvement
3. Enabling self-correction and learning
While often LLM-based, reflection can also use deterministic methods. For instance, in coding tasks, compilation errors can serve as feedback. This approach is demonstrated in [this video using LangGraph for self-corrective code generation](https://www.youtube.com/watch?v=MvNdgmM7uyc).
By leveraging these features, LangGraph enables the creation of sophisticated, task-specific agent architectures that can handle complex workflows, collaborate effectively, and continuously improve their performance.
|
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/faq.md | # FAQ
Common questions and their answers!
## Do I need to use LangChain to use LangGraph? What’s the difference?
No. LangGraph is an orchestration framework for complex agentic systems and is more low-level and controllable than LangChain agents. LangChain provides a standard interface to interact with models and other components, useful for straight-forward chains and retrieval flows.
## How is LangGraph different from other agent frameworks?
Other agentic frameworks can work for simple, generic tasks but fall short for complex tasks bespoke to a company’s needs. LangGraph provides a more expressive framework to handle companies’ unique tasks without restricting users to a single black-box cognitive architecture.
## Does LangGraph impact the performance of my app?
LangGraph will not add any overhead to your code and is specifically designed with streaming workflows in mind.
## Is LangGraph open source? Is it free?
Yes. LangGraph is an MIT-licensed open-source library and is free to use.
## How are LangGraph and LangGraph Platform different?
LangGraph is a stateful, orchestration framework that brings added control to agent workflows. LangGraph Platform is a service for deploying and scaling LangGraph applications, with an opinionated API for building agent UXs, plus an integrated developer studio.
| Features | LangGraph (open source) | LangGraph Platform |
|----------|------------------------|-------------------|
| Description | Stateful orchestration framework for agentic applications | Scalable infrastructure for deploying LangGraph applications |
| SDKs | Python and JavaScript | Python and JavaScript |
| HTTP APIs | None | Yes - useful for retrieving & updating state or long-term memory, or creating a configurable assistant |
| Streaming | Basic | Dedicated mode for token-by-token messages |
| Checkpointer | Community contributed | Supported out-of-the-box |
| Persistence Layer | Self-managed | Managed Postgres with efficient storage |
| Deployment | Self-managed | • Cloud SaaS <br> • Free self-hosted <br> • Enterprise (BYOC or paid self-hosted) |
| Scalability | Self-managed | Auto-scaling of task queues and servers |
| Fault-tolerance | Self-managed | Automated retries |
| Concurrency Control | Simple threading | Supports double-texting |
| Scheduling | None | Cron scheduling |
| Monitoring | None | Integrated with LangSmith for observability |
| IDE integration | LangGraph Studio for Desktop | LangGraph Studio for Desktop & Cloud |
## What are my deployment options for LangGraph Platform?
We currently have the following deployment options for LangGraph applications:
- [Self-Hosted Lite](./deployment_options.md#self-hosted-lite): A free (up to 1M nodes executed), limited version of LangGraph Platform that you can run locally or in a self-hosted manner. This version requires a LangSmith API key and logs all usage to LangSmith. Fewer features are available than in paid plans.
- [Cloud SaaS](./deployment_options.md#cloud-saas): Fully managed and hosted as part of LangSmith, with automatic updates and zero maintenance.
- [Bring Your Own Cloud (BYOC)](./deployment_options.md#bring-your-own-cloud): Deploy LangGraph Platform within your VPC, provisioned and run as a service. Keep data in your environment while outsourcing the management of the service.
- [Self-Hosted Enterprise](./deployment_options.md#self-hosted-enterprise): Deploy LangGraph entirely on your own infrastructure.
## Is LangGraph Platform open source?
No. LangGraph Platform is proprietary software.
There is a free, self-hosted version of LangGraph Platform with access to basic features. The Cloud SaaS deployment option is free while in beta, but will eventually be a paid service. We will always give ample notice before charging for a service and reward our early adopters with preferential pricing. The Bring Your Own Cloud (BYOC) and Self-Hosted Enterprise options are also paid services. [Contact our sales team](https://www.langchain.com/contact-sales) to learn more.
For more information, see our [LangGraph Platform pricing page](https://www.langchain.com/pricing-langgraph-platform).
## Does LangGraph work with LLMs that don't support tool calling?
Yes! You can use LangGraph with any LLMs. The main reason we use LLMs that support tool calling is that this is often the most convenient way to have the LLM make its decision about what to do. If your LLM does not support tool calling, you can still use it - you just need to write a bit of logic to convert the raw LLM string response to a decision about what to do.
## Does LangGraph work with OSS LLMs?
Yes! LangGraph is totally ambivalent to what LLMs are used under the hood. The main reason we use closed LLMs in most of the tutorials is that they seamlessly support tool calling, while OSS LLMs often don't. But tool calling is not necessary (see [this section](#does-langgraph-work-with-llms-that-dont-support-tool-calling)) so you can totally use LangGraph with OSS LLMs.
|
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/human_in_the_loop.md | # Human-in-the-loop
Human-in-the-loop (or "on-the-loop") enhances agent capabilities through several common user interaction patterns.
Common interaction patterns include:
(1) `Approval` - We can interrupt our agent, surface the current state to a user, and allow the user to accept an action.
(2) `Editing` - We can interrupt our agent, surface the current state to a user, and allow the user to edit the agent state.
(3) `Input` - We can explicitly create a graph node to collect human input and pass that input directly to the agent state.
Use-cases for these interaction patterns include:
(1) `Reviewing tool calls` - We can interrupt an agent to review and edit the results of tool calls.
(2) `Time Travel` - We can manually re-play and / or fork past actions of an agent.
## Persistence
All of these interaction patterns are enabled by LangGraph's built-in [persistence](./persistence.md) layer, which will write a checkpoint of the graph state at each step. Persistence allows the graph to stop so that a human can review and / or edit the current state of the graph and then resume with the human's input.
### Breakpoints
Adding a [breakpoint](./low_level.md#breakpoints) a specific location in the graph flow is one way to enable human-in-the-loop. In this case, the developer knows *where* in the workflow human input is needed and simply places a breakpoint prior to or following that particular graph node.
Here, we compile our graph with a checkpointer and a breakpoint at the node we want to interrupt before, `step_for_human_in_the_loop`. We then perform one of the above interaction patterns, which will create a new checkpoint if a human edits the graph state. The new checkpoint is saved to the `thread` and we can resume the graph execution from there by passing in `None` as the input.
```python
# Compile our graph with a checkpointer and a breakpoint before "step_for_human_in_the_loop"
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["step_for_human_in_the_loop"])
# Run the graph up to the breakpoint
thread_config = {"configurable": {"thread_id": "1"}}
for event in graph.stream(inputs, thread_config, stream_mode="values"):
print(event)
# Perform some action that requires human in the loop
# Continue the graph execution from the current checkpoint
for event in graph.stream(None, thread_config, stream_mode="values"):
print(event)
```
### Dynamic Breakpoints
Alternatively, the developer can define some *condition* that must be met for a breakpoint to be triggered. This concept of [dynamic breakpoints](./low_level.md#dynamic-breakpoints) is useful when the developer wants to halt the graph under *a particular condition*. This uses a `NodeInterrupt`, which is a special type of exception that can be raised from within a node based upon some condition. As an example, we can define a dynamic breakpoint that triggers when the `input` is longer than 5 characters.
```python
def my_node(state: State) -> State:
if len(state['input']) > 5:
raise NodeInterrupt(f"Received input that is longer than 5 characters: {state['input']}")
return state
```
Let's assume we run the graph with an input that triggers the dynamic breakpoint and then attempt to resume the graph execution simply by passing in `None` for the input.
```python
# Attempt to continue the graph execution with no change to state after we hit the dynamic breakpoint
for event in graph.stream(None, thread_config, stream_mode="values"):
print(event)
```
The graph will *interrupt* again because this node will be *re-run* with the same graph state. We need to change the graph state such that the condition that triggers the dynamic breakpoint is no longer met. So, we can simply edit the graph state to an input that meets the condition of our dynamic breakpoint (< 5 characters) and re-run the node.
```python
# Update the state to pass the dynamic breakpoint
graph.update_state(config=thread_config, values={"input": "foo"})
for event in graph.stream(None, thread_config, stream_mode="values"):
print(event)
```
Alternatively, what if we want to keep our current input and skip the node (`my_node`) that performs the check? To do this, we can simply perform the graph update with `as_node="my_node"` and pass in `None` for the values. This will make no update the graph state, but run the update as `my_node`, effectively skipping the node and bypassing the dynamic breakpoint.
```python
# This update will skip the node `my_node` altogether
graph.update_state(config=thread_config, values=None, as_node="my_node")
for event in graph.stream(None, thread_config, stream_mode="values"):
print(event)
```
See [our guide](../how-tos/human_in_the_loop/dynamic_breakpoints.ipynb) for a detailed how-to on doing this!
## Interaction Patterns
### Approval
Sometimes we want to approve certain steps in our agent's execution.
We can interrupt our agent at a [breakpoint](./low_level.md#breakpoints) prior to the step that we want to approve.
This is generally recommend for sensitive actions (e.g., using external APIs or writing to a database).
With persistence, we can surface the current agent state as well as the next step to a user for review and approval.
If approved, the graph resumes execution from the last saved checkpoint, which is saved to the `thread`:
```python
# Compile our graph with a checkpointer and a breakpoint before the step to approve
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["node_2"])
# Run the graph up to the breakpoint
for event in graph.stream(inputs, thread, stream_mode="values"):
print(event)
# ... Get human approval ...
# If approved, continue the graph execution from the last saved checkpoint
for event in graph.stream(None, thread, stream_mode="values"):
print(event)
```
See [our guide](../how-tos/human_in_the_loop/breakpoints.ipynb) for a detailed how-to on doing this!
### Editing
Sometimes we want to review and edit the agent's state.
As with approval, we can interrupt our agent at a [breakpoint](./low_level.md#breakpoints) prior to the step we want to check.
We can surface the current state to a user and allow the user to edit the agent state.
This can, for example, be used to correct the agent if it made a mistake (e.g., see the section on tool calling below).
We can edit the graph state by forking the current checkpoint, which is saved to the `thread`.
We can then proceed with the graph from our forked checkpoint as done before.
```python
# Compile our graph with a checkpointer and a breakpoint before the step to review
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["node_2"])
# Run the graph up to the breakpoint
for event in graph.stream(inputs, thread, stream_mode="values"):
print(event)
# Review the state, decide to edit it, and create a forked checkpoint with the new state
graph.update_state(thread, {"state": "new state"})
# Continue the graph execution from the forked checkpoint
for event in graph.stream(None, thread, stream_mode="values"):
print(event)
```
See [this guide](../how-tos/human_in_the_loop/edit-graph-state.ipynb) for a detailed how-to on doing this!
### Input
Sometimes we want to explicitly get human input at a particular step in the graph.
We can create a graph node designated for this (e.g., `human_input` in our example diagram).
As with approval and editing, we can interrupt our agent at a [breakpoint](./low_level.md#breakpoints) prior to this node.
We can then perform a state update that includes the human input, just as we did with editing state.
But, we add one thing:
We can use `as_node=human_input` with the state update to specify that the state update *should be treated as a node*.
The is subtle, but important:
With editing, the user makes a decision about whether or not to edit the graph state.
With input, we explicitly define a node in our graph for collecting human input!
The state update with the human input then runs *as this node*.
```python
# Compile our graph with a checkpointer and a breakpoint before the step to to collect human input
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["human_input"])
# Run the graph up to the breakpoint
for event in graph.stream(inputs, thread, stream_mode="values"):
print(event)
# Update the state with the user input as if it was the human_input node
graph.update_state(thread, {"user_input": user_input}, as_node="human_input")
# Continue the graph execution from the checkpoint created by the human_input node
for event in graph.stream(None, thread, stream_mode="values"):
print(event)
```
See [this guide](../how-tos/human_in_the_loop/wait-user-input.ipynb) for a detailed how-to on doing this!
## Use-cases
### Reviewing Tool Calls
Some user interaction patterns combine the above ideas.
For example, many agents use [tool calling](https://python.langchain.com/docs/how_to/tool_calling/) to make decisions.
Tool calling presents a challenge because the agent must get two things right:
(1) The name of the tool to call
(2) The arguments to pass to the tool
Even if the tool call is correct, we may also want to apply discretion:
(3) The tool call may be a sensitive operation that we want to approve
With these points in mind, we can combine the above ideas to create a human-in-the-loop review of a tool call.
```python
# Compile our graph with a checkpointer and a breakpoint before the step to to review the tool call from the LLM
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["human_review"])
# Run the graph up to the breakpoint
for event in graph.stream(inputs, thread, stream_mode="values"):
print(event)
# Review the tool call and update it, if needed, as the human_review node
graph.update_state(thread, {"tool_call": "updated tool call"}, as_node="human_review")
# Otherwise, approve the tool call and proceed with the graph execution with no edits
# Continue the graph execution from either:
# (1) the forked checkpoint created by human_review or
# (2) the checkpoint saved when the tool call was originally made (no edits in human_review)
for event in graph.stream(None, thread, stream_mode="values"):
print(event)
```
See [this guide](../how-tos/human_in_the_loop/review-tool-calls.ipynb) for a detailed how-to on doing this!
### Time Travel
When working with agents, we often want closely examine their decision making process:
(1) Even when they arrive a desired final result, the reasoning that led to that result is often important to examine.
(2) When agents make mistakes, it is often valuable to understand why.
(3) In either of the above cases, it is useful to manually explore alternative decision making paths.
Collectively, we call these debugging concepts `time-travel` and they are composed of `replaying` and `forking`.
#### Replaying
Sometimes we want to simply replay past actions of an agent.
Above, we showed the case of executing an agent from the current state (or checkpoint) of the graph.
We by simply passing in `None` for the input with a `thread`.
```
thread = {"configurable": {"thread_id": "1"}}
for event in graph.stream(None, thread, stream_mode="values"):
print(event)
```
Now, we can modify this to replay past actions from a *specific* checkpoint by passing in the checkpoint ID.
To get a specific checkpoint ID, we can easily get all of the checkpoints in the thread and filter to the one we want.
```python
all_checkpoints = []
for state in app.get_state_history(thread):
all_checkpoints.append(state)
```
Each checkpoint has a unique ID, which we can use to replay from a specific checkpoint.
Assume from reviewing the checkpoints that we want to replay from one, `xxx`.
We just pass in the checkpoint ID when we run the graph.
```python
config = {'configurable': {'thread_id': '1', 'checkpoint_id': 'xxx'}}
for event in graph.stream(None, config, stream_mode="values"):
print(event)
```
Importantly, the graph knows which checkpoints have been previously executed.
So, it will re-play any previously executed nodes rather than re-executing them.
See [this additional conceptual guide](https://langchain-ai.github.io/langgraph/concepts/persistence/#replay) for related context on replaying.
See see [this guide](../how-tos/human_in_the_loop/time-travel.ipynb) for a detailed how-to on doing time-travel!
#### Forking
Sometimes we want to fork past actions of an agent, and explore different paths through the graph.
`Editing`, as discussed above, is *exactly* how we do this for the *current* state of the graph!
But, what if we want to fork *past* states of the graph?
For example, let's say we want to edit a particular checkpoint, `xxx`.
We pass this `checkpoint_id` when we update the state of the graph.
```python
config = {"configurable": {"thread_id": "1", "checkpoint_id": "xxx"}}
graph.update_state(config, {"state": "updated state"}, )
```
This creates a new forked checkpoint, `xxx-fork`, which we can then run the graph from.
```python
config = {'configurable': {'thread_id': '1', 'checkpoint_id': 'xxx-fork'}}
for event in graph.stream(None, config, stream_mode="values"):
print(event)
```
See [this additional conceptual guide](https://langchain-ai.github.io/langgraph/concepts/persistence/#update-state) for related context on forking.
See see [this guide](../how-tos/human_in_the_loop/time-travel.ipynb) for a detailed how-to on doing time-travel!
|
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/plans.md | # LangGraph Platform Plans
## Overview
LangGraph Platform is a commercial solution for deploying agentic applications in production.
There are three different plans for using it.
- **Developer**: All [LangSmith](https://smith.langchain.com/) users have access to this plan. You can sign up for this plan simply by creating a LangSmith account. This gives you access to the [Self-Hosted Lite](./deployment_options.md#self-hosted-lite) deployment option.
- **Plus**: All [LangSmith](https://smith.langchain.com/) users with a [Plus account](https://docs.smith.langchain.com/administration/pricing) have access to this plan. You can sign up for this plan simply by upgrading your LangSmith account to the Plus plan type. This gives you access to the [Cloud](./deployment_options.md#cloud-saas) deployment option.
- **Enterprise**: This is separate from LangSmith plans. You can sign up for this plan by contacting sales@langchain.dev. This gives you access to all deployment options: [Cloud](./deployment_options.md#cloud-saas), [Bring-Your-Own-Cloud](./deployment_options.md#bring-your-own-cloud), and [Self Hosted Enterprise](./deployment_options.md#self-hosted-enterprise)
## Plan Details
| | Developer | Plus | Enterprise |
|------------------------------------------------------------------|---------------------------------------------|-------------------------------------------------------|-----------------------------------------------------|
| Deployment Options | Self-Hosted Lite | Cloud | Self-Hosted Enterprise, Cloud, Bring-Your-Own-Cloud |
| Usage | Free, limited to 1M nodes executed per year | Free while in Beta, will be charged per node executed | Custom |
| APIs for retrieving and updating state and conversational history | ✅ | ✅ | ✅ |
| APIs for retrieving and updating long-term memory | ✅ | ✅ | ✅ |
| Horizontally scalable task queues and servers | ✅ | ✅ | ✅ |
| Real-time streaming of outputs and intermediate steps | ✅ | ✅ | ✅ |
| Assistants API (configurable templates for LangGraph apps) | ✅ | ✅ | ✅ |
| Cron scheduling | -- | ✅ | ✅ |
| LangGraph Studio for prototyping | Desktop only | Coming Soon! | Coming Soon! |
| Authentication & authorization to call the LangGraph APIs | -- | Coming Soon! | Coming Soon! |
| Smart caching to reduce traffic to LLM API | -- | Coming Soon! | Coming Soon! |
| Publish/subscribe API for state | -- | Coming Soon! | Coming Soon! |
| Scheduling prioritization | -- | Coming Soon! | Coming Soon! |
Please see the [LangGraph Platform Pricing](https://www.langchain.com/langgraph-platform-pricing) for information on pricing.
## Related
For more information, please see:
* [Deployment Options conceptual guide](./deployment_options.md)
* [LangGraph Platform Pricing](https://www.langchain.com/langgraph-platform-pricing)
* [LangSmith Plans](https://docs.smith.langchain.com/administration/pricing)
|
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/langgraph_studio.md | # LangGraph Studio
!!! info "Prerequisites"
- [LangGraph Platform](./langgraph_platform.md)
- [LangGraph Server](./langgraph_server.md)
LangGraph Studio offers a new way to develop LLM applications by providing a specialized agent IDE that enables visualization, interaction, and debugging of complex agentic applications.
With visual graphs and the ability to edit state, you can better understand agent workflows and iterate faster. LangGraph Studio integrates with LangSmith allowing you to collaborate with teammates to debug failure modes.
## Features
The key features of LangGraph Studio are:
- Visualizes your graph
- Test your graph by running it from the UI
- Debug your agent by [modifying its state and rerunning](human_in_the_loop.md)
- Create and manage [assistants](assistants.md)
- View and manage [threads](persistence.md#threads)
- View and manage [long term memory](memory.md)
- Add node input/outputs to [LangSmith](https://smith.langchain.com/) datasets for testing
## Types
### Desktop app
LangGraph Studio is available as a [desktop app](https://studio.langchain.com/) for MacOS users.
While in Beta, LangGraph Studio is available for free to all [LangSmith](https://smith.langchain.com/) users on any plan tier.
### Cloud studio
If you have deployed your LangGraph application on LangGraph Platform (Cloud), you can access the studio as part of that
### Development server
LangGraph CLI also contains a command for running an in-memory development server that can be used to connect a local LangGraph app with the studio.
See [instructions here](../cloud/reference/cli.md#dev) for more information.
The way this works is that it runs inside your local environment.
It will spin up an in-memory, development server to deploy the graph.
You can then connect to the studio via the Cloud hosted version of LangGraph Platform.
To be clear, the web studio will connect to your locally running server - your agent is still running locally and never leaves your device.
## Studio FAQs
### Why is my project failing to start?
There are a few reasons that your project might fail to start, here are some of the most common ones.
#### Docker issues (desktop only)
LangGraph Studio (desktop) requires Docker Desktop version 4.24 or higher. Please make sure you have a version of Docker installed that satisfies that requirement and also make sure you have the Docker Desktop app up and running before trying to use LangGraph Studio. In addition, make sure you have docker-compose updated to version 2.22.0 or higher.
#### Configuration or environment issues
Another reason your project might fail to start is because your configuration file is defined incorrectly, or you are missing required environment variables.
### How does interrupt work?
When you select the `Interrupts` dropdown and select a node to interrupt the graph will pause execution before and after (unless the node goes straight to `END`) that node has run. This means that you will be able to both edit the state before the node is ran and the state after the node has ran. This is intended to allow developers more fine-grained control over the behavior of a node and make it easier to observe how the node is behaving. You will not be able to edit the state after the node has ran if the node is the final node in the graph.
### How do I reload the app? (desktop only)
If you would like to reload the app, don't use Command+R as you might normally do. Instead, close and reopen the app for a full refresh.
### How does automatic rebuilding work? (desktop only)
One of the key features of LangGraph Studio is that it automatically rebuilds your image when you change the source code. This allows for a super fast development and testing cycle which makes it easy to iterate on your graph. There are two different ways that LangGraph rebuilds your image: either by editing the image or completely rebuilding it.
#### Rebuilds from source code changes
If you modified the source code only (no configuration or dependency changes!) then the image does not require a full rebuild, and LangGraph Studio will only update the relevant parts. The UI status in the bottom left will switch from `Online` to `Stopping` temporarily while the image gets edited. The logs will be shown as this process is happening, and after the image has been edited the status will change back to `Online` and you will be able to run your graph with the modified code!
#### Rebuilds from configuration or dependency changes
If you edit your graph configuration file (`langgraph.json`) or the dependencies (either `pyproject.toml` or `requirements.txt`) then the entire image will be rebuilt. This will cause the UI to switch away from the graph view and start showing the logs of the new image building process. This can take a minute or two, and once it is done your updated image will be ready to use!
### Why is my graph taking so long to startup? (desktop only)
The LangGraph Studio interacts with a local LangGraph API server. To stay aligned with ongoing updates, the LangGraph API requires regular rebuilding. As a result, you may occasionally experience slight delays when starting up your project.
## Why are extra edges showing up in my graph?
If you don't define your conditional edges carefully, you might notice extra edges appearing in your graph. This is because without proper definition, LangGraph Studio assumes the conditional edge could access all other nodes. In order for this to not be the case, you need to be explicit about how you define the nodes the conditional edge routes to. There are two ways you can do this:
### Solution 1: Include a path map
The first way to solve this is to add path maps to your conditional edges. A path map is just a dictionary or array that maps the possible outputs of your router function with the names of the nodes that each output corresponds to. The path map is passed as the third argument to the `add_conditional_edges` function like so:
=== "Python"
```python
graph.add_conditional_edges("node_a", routing_function, {True: "node_b", False: "node_c"})
```
=== "Javascript"
```ts
graph.addConditionalEdges("node_a", routingFunction, { true: "node_b", false: "node_c" });
```
In this case, the routing function returns either True or False, which map to `node_b` and `node_c` respectively.
### Solution 2: Update the typing of the router (Python only)
Instead of passing a path map, you can also be explicit about the typing of your routing function by specifying the nodes it can map to using the `Literal` python definition. Here is an example of how to define a routing function in that way:
```python
def routing_function(state: GraphState) -> Literal["node_b","node_c"]:
if state['some_condition'] == True:
return "node_b"
else:
return "node_c"
```
## Related
For more information please see the following:
* [LangGraph Studio how-to guides](../how-tos/index.md#langgraph-studio) |
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/bring_your_own_cloud.md | # Bring Your Own Cloud (BYOC)
!!! note Prerequisites
- [LangGraph Platform](./langgraph_platform.md)
- [Deployment Options](./deployment_options.md)
## Architecture
Split control plane (hosted by us) and data plane (hosted by you, managed by us).
| | Control Plane | Data Plane |
|-----------------------------|---------------------------------|-----------------------------------------------|
| What it does | Manages deployments, revisions. | Runs your LangGraph graphs, stores your data. |
| Where it is hosted | LangChain Cloud account | Your cloud account |
| Who provisions and monitors | LangChain | LangChain |
LangChain has no direct access to the resources created in your cloud account, and can only interact with them via AWS APIs. Your data never leaves your cloud account / VPC at rest or in transit.
## Requirements
- You’re using AWS already.
- You use `langgraph-cli` and/or [LangGraph Studio](./langgraph_studio.md) app to test graph locally.
- You use `langgraph build` command to build image and then push it to your AWS ECR repository (`docker push`).
## How it works
- We provide you a [Terraform module](https://github.com/langchain-ai/terraform/tree/main/modules/langgraph_cloud_setup) which you run to set up our requirements
1. Creates an AWS role (which our control plane will later assume to provision and monitor resources)
- https://docs.aws.amazon.com/aws-managed-policy/latest/reference/AmazonVPCReadOnlyAccess.html
- Read VPCS to find subnets
- https://docs.aws.amazon.com/aws-managed-policy/latest/reference/AmazonECS_FullAccess.html
- Used to create/delete ECS resources for your LangGraph Cloud instances
- https://docs.aws.amazon.com/aws-managed-policy/latest/reference/SecretsManagerReadWrite.html
- Create secrets for your ECS resources
- https://docs.aws.amazon.com/aws-managed-policy/latest/reference/CloudWatchReadOnlyAccess.html
- Read CloudWatch metrics/logs to monitor your instances/push deployment logs
- https://docs.aws.amazon.com/aws-managed-policy/latest/reference/AmazonRDSFullAccess.html
- Provision `RDS` instances for your LangGraph Cloud instances
2. Either
- Tags an existing vpc / subnets as `langgraph-cloud-enabled`
- Creates a new vpc and subnets and tags them as `langgraph-cloud-enabled`
- You create a LangGraph Cloud Project in `smith.langchain.com` providing
- the ID of the AWS role created in the step above
- the AWS ECR repo to pull the service image from
- We provision the resources in your cloud account using the role above
- We monitor those resources to ensure uptime and recovery from errors
Notes for customers using [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting):
- Creation of new LangGraph Cloud projects and revisions currently needs to be done on smith.langchain.com.
- You can however set up the project to trace to your self-hosted LangSmith instance if desired
|
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/application_structure.md | # Application Structure
!!! info "Prerequisites"
- [LangGraph Server](./langgraph_server.md)
- [LangGraph Glossary](./low_level.md)
## Overview
A LangGraph application consists of one or more graphs, a LangGraph API Configuration file (`langgraph.json`), a file that specifies dependencies, and an optional .env file that specifies environment variables.
This guide shows a typical structure for a LangGraph application and shows how the required information to deploy a LangGraph application using the LangGraph Platform is specified.
## Key Concepts
To deploy using the LangGraph Platform, the following information should be provided:
1. A [LangGraph API Configuration file](#configuration-file) (`langgraph.json`) that specifies the dependencies, graphs, environment variables to use for the application.
2. The [graphs](#graphs) that implement the logic of the application.
3. A file that specifies [dependencies](#dependencies) required to run the application.
4. [Environment variable](#environment-variables) that are required for the application to run.
## File Structure
Below are examples of directory structures for Python and JavaScript applications:
=== "Python (requirements.txt)"
```plaintext
my-app/
├── my_agent # all project code lies within here
│ ├── utils # utilities for your graph
│ │ ├── __init__.py
│ │ ├── tools.py # tools for your graph
│ │ ├── nodes.py # node functions for you graph
│ │ └── state.py # state definition of your graph
│ ├── requirements.txt # package dependencies
│ ├── __init__.py
│ └── agent.py # code for constructing your graph
├── .env # environment variables
└── langgraph.json # configuration file for LangGraph
```
=== "Python (pyproject.toml)"
```plaintext
my-app/
├── my_agent # all project code lies within here
│ ├── utils # utilities for your graph
│ │ ├── __init__.py
│ │ ├── tools.py # tools for your graph
│ │ ├── nodes.py # node functions for you graph
│ │ └── state.py # state definition of your graph
│ ├── __init__.py
│ └── agent.py # code for constructing your graph
├── .env # environment variables
├── langgraph.json # configuration file for LangGraph
└── pyproject.toml # dependencies for your project
```
=== "JS (package.json)"
```plaintext
my-app/
├── src # all project code lies within here
│ ├── utils # optional utilities for your graph
│ │ ├── tools.ts # tools for your graph
│ │ ├── nodes.ts # node functions for you graph
│ │ └── state.ts # state definition of your graph
│ └── agent.ts # code for constructing your graph
├── package.json # package dependencies
├── .env # environment variables
└── langgraph.json # configuration file for LangGraph
```
!!! note
The directory structure of a LangGraph application can vary depending on the programming language and the package manager used.
## Configuration File
The `langgraph.json` file is a JSON file that specifies the dependencies, graphs, environment variables, and other settings required to deploy a LangGraph application.
The file supports specification of the following information:
| Key | Description |
|--------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `dependencies` | **Required**. Array of dependencies for LangGraph API server. Dependencies can be one of the following: (1) `"."`, which will look for local Python packages, (2) `pyproject.toml`, `setup.py` or `requirements.txt` in the app directory `"./local_package"`, or (3) a package name. |
| `graphs` | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph`</li><li>`./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.</li></ul> |
| `env` | Path to `.env` file or a mapping from environment variable to its value. |
| `python_version` | `3.11` or `3.12`. Defaults to `3.11`. |
| `pip_config_file` | Path to `pip` config file. |
| `dockerfile_lines` | Array of additional lines to add to Dockerfile following the import from parent image. |
!!! tip
The LangGraph CLI defaults to using the configuration file **langgraph.json** in the current directory.
### Examples
=== "Python"
* The dependencies involve a custom local package and the `langchain_openai` package.
* A single graph will be loaded from the file `./your_package/your_file.py` with the variable `variable`.
* The environment variables are loaded from the `.env` file.
```json
{
"dependencies": [
"langchain_openai",
"./your_package"
],
"graphs": {
"my_agent": "./your_package/your_file.py:agent"
},
"env": "./.env"
}
```
=== "JavaScript"
* The dependencies will be loaded from a dependency file in the local directory (e.g., `package.json`).
* A single graph will be loaded from the file `./your_package/your_file.js` with the function `agent`.
* The environment variable `OPENAI_API_KEY` is set inline.
```json
{
"dependencies": [
"."
],
"graphs": {
"my_agent": "./your_package/your_file.js:agent"
},
"env": {
"OPENAI_API_KEY": "secret-key"
}
}
```
## Dependencies
A LangGraph application may depend on other Python packages or JavaScript libraries (depending on the programming language in which the application is written).
You will generally need to specify the following information for dependencies to be set up correctly:
1. A file in the directory that specifies the dependencies (e.g., `requirements.txt`, `pyproject.toml`, or `package.json`).
2. A `dependencies` key in the [LangGraph configuration file](#configuration-file) that specifies the dependencies required to run the LangGraph application.
3. Any additional binaries or system libraries can be specified using `dockerfile_lines` key in the [LangGraph configuration file](#configuration-file).
## Graphs
Use the `graphs` key in the [LangGraph configuration file](#configuration-file) to specify which graphs will be available in the deployed LangGraph application.
You can specify one or more graphs in the configuration file. Each graph is identified by a name (which should be unique) and a path for either: (1) the compiled graph or (2) a function that makes a graph is defined.
## Environment Variables
If you're working with a deployed LangGraph application locally, you can configure environment variables in the `env` key of the [LangGraph configuration file](#configuration-file).
For a production deployment, you will typically want to configure the environment variables in the deployment environment.
## Related
Please see the following resources for more information:
- How-to guides for [Application Structure](../how-tos/index.md#application-structure).
|
0 | lc_public_repos/langgraph/docs/docs | lc_public_repos/langgraph/docs/docs/concepts/assistants.md | # Assistants
!!! info "Prerequisites"
- [LangGraph Server](./langgraph_server.md)
When building agents, it is fairly common to make rapid changes that *do not* alter the graph logic. For example, simply changing prompts or the LLM selection can have significant impacts on the behavior of the agents. Assistants offer an easy way to make and save these types of changes to agent configuration. This can have at least two use-cases:
* Assistants give developers a quick and easy way to modify and version agents for experimentation.
* Assistants can be modified via LangGraph Studio, offering a no-code way to configure agents (e.g., for business users).
Assistants build off the concept of ["configuration"](low_level.md#configuration).
While ["configuration"](low_level.md#configuration) is available in the open source LangGraph library as well, assistants are only present in [LangGraph Platform](langgraph_platform.md).
This is because Assistants are tightly coupled to your deployed graph, and so we can only make them available when we are also deploying the graphs.
## Configuring Assistants
In practice, an assistant is just an *instance* of a graph with a specific configuration. Because of this, multiple assistants can reference the same graph but can contain different configurations, such as prompts, models, and other graph configuration options. The LangGraph Cloud API provides several endpoints for creating and managing assistants. See the [API reference](../cloud/reference/api/api_ref.html) and [this how-to](../cloud/how-tos/configuration_cloud.md) for more details on how to create assistants.
## Versioning Assistants
Once you've created an assistant, you can save and version it to track changes to the configuration over time. You can think about this at three levels:
1) The graph lays out the general agent application logic
2) The agent configuration options represent parameters that can be changed
3) Assistant versions save and track specific settings of the agent configuration options
For example, let's imagine you have a general writing agent. You have created a general graph architecture that works well for writing. However, there are different types of writing, e.g. blogs vs tweets. In order to get the best performance on each use case, you need to make some minor changes to the models and prompts used. In this setup, you could create an assistant for each use case - one for blog writing and one for tweeting. These would share the same graph structure, but they may use different models and different prompts. Read [this how-to](../cloud/how-tos/assistant_versioning.md) to learn how you can use assistant versioning through both the [Studio](../concepts/langgraph_studio.md) and the SDK.
## Resources
For more information on assistants, see the following resources:
- [Assistants how-to guides](../how-tos/index.md#assistants) |
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