---
description: best pract
globs: 
alwaysApply: false
---
The most robust pattern is to treat every agent node as a pure function AgentState → Command, where AgentState is an explicit, typed snapshot of everything the rest of the graph must know.
My overall confidence that the practices below will remain valid for ≥ 12 months is 85 % (expert opinion).

1 Design a single source of truth for state
Guideline	Why it matters	Key LangGraph API
Define a typed schema (TypedDict or pydantic.BaseModel) for the whole graph.	Static typing catches missing keys early and docs double as living design specs.
langchain-ai.github.io
StateGraph(YourState)
Use channel annotations such as Annotated[list[BaseMessage], operator.add] on mutable fields.	Makes accumulation (+) vs. overwrite clear and prevents accidental loss of history.
langchain-ai.github.io
messages: Annotated[list[BaseMessage], operator.add]
Keep routing out of business data—store the next hop in a dedicated field (next: Literal[...]).	Separates control-flow from payload; easier to debug and replay.
langchain-ai.github.io
next: Literal["planner", "researcher", "__end__"]

2 Pass information with Command objects
Pattern

python
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def planner(state: AgentState) -> Command[Literal["researcher", "executor", END]]:
    decision = model.invoke(...state.messages)
    return Command(
        goto = decision["next"],
        update = {
            "messages": [decision["content"]],
            "plan": decision["plan"]
        }
    )
Best-practice notes

Always update via update=… rather than mutating the state in-place. This guarantees immutability between nodes and makes time-travel/debugging deterministic.
langchain-ai.github.io

When handing off between sub-graphs, set graph=Command.PARENT or the target sub-graph’s name so orchestration stays explicit.
langchain-ai.github.io

3 Choose a message-sharing strategy early
Strategy	Pros	Cons	When to use
Shared scratch-pad (every intermediate LLM thought stored in messages)
langchain-ai.github.io
Maximum transparency; great for debugging & reflection.	Context window bloat, higher cost/time.	≤ 3 specialist agents or short tasks.
Final-result only (each agent keeps private scratch-pad, shares only its final answer)
langchain-ai.github.io
Scales to 10 + agents; small token footprint.	Harder to post-mortem; agents need local memory.	Large graphs; production workloads.

Tip: If you hide scratch-pads, store them in a per-agent key (e.g. researcher_messages) for replay or fine-tuning even if they’re not sent downstream.
langchain-ai.github.io

4 Inject only what a tool needs
When exposing sub-agents as tools under a supervisor:

python
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from langgraph.prebuilt import InjectedState

def researcher(state: Annotated[AgentState, InjectedState]):
    ...
Why: keeps tool signatures clean and prevents leaking confidential state.
Extra: If the tool must update global state, let it return a Command so the supervisor doesn’t have to guess what changed.
langchain-ai.github.io

5 Structure the graph for clarity & safety
Network ➜ every agent connects to every other (exploration, research prototypes).

Supervisor ➜ one LLM decides routing (good default for 3-7 agents).

Hierarchical ➜ teams of agents with team-level supervisors (scales past ~7 agents).
langchain-ai.github.io

Pick the simplest architecture that meets today’s needs; refactor to sub-graphs as complexity grows.

6 Operational best practices
Concern	Best practice
Tracing & observability	Attach a LangFuse run-ID to every AgentState at graph entry; emit state snapshots on node enter/exit so traces line up with LangFuse v3 spans.
Memory & persistence	Use Checkpointer for cheap disk-based snapshots or a Redis backend for high-QPS, then time-travel when an LLM stalls.
Parallel branches	Use map edges (built-in) to fan-out calls, but cap parallelism with an asyncio semaphore to avoid API rate-limits.
Vector lookup	Put retrieval results in a dedicated key (docs) so they don’t clutter messages; store only document IDs if you need to replay cheaply.

7 Evidence from the literature (why graphs work)
Peer-reviewed source	Key takeaway	Credibility (0-10)
AAAI 2024 Graph of Thoughts‎ shows arbitrary-graph reasoning beats tree/chain structures by up to 62 % on sorting tasks.
arxiv.org
Graph topology yields better exploration & feedback loops—mirrors LangGraph’s StateGraph.	9
EMNLP 2024 EPO Hierarchical LLM Agents demonstrates hierarchical agents outperform flat agents on ALFRED by >12 % and scales with preference-based training.
aclanthology.org
Validates splitting planning vs. execution agents (Supervisor + workers).	9

Non-peer-reviewed source	Why included	Credibility
Official LangGraph docs (June 2025).
langchain-ai.github.io
Primary specification of the library’s APIs and guarantees.	8

8 Minimal starter template (v 0.6.*)
python
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from typing import Annotated, Literal, Sequence, TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.types import Command
from langchain_openai import ChatOpenAI
import operator

class AgentState(TypedDict):
    messages: Annotated[Sequence[str], operator.add]
    next: Literal["planner", "researcher", "__end__"]
    plan: str | None

llm = ChatOpenAI()

def planner(state: AgentState) -> Command[Literal["researcher", END]]:
    resp = llm.invoke(...)
    return Command(
        goto = resp["next"],
        update = {"messages": [resp["content"]],
                  "plan": resp["plan"]}
    )

def researcher(state: AgentState) -> Command[Literal["planner"]]:
    resp = llm.invoke(...)
    return Command(goto="planner",
                   update={"messages": [resp["content"]]})

g = StateGraph(AgentState)
g.add_node(planner)
g.add_node(researcher)
g.add_edge(START, planner)
g.add_edge(planner, researcher)
g.add_edge(researcher, planner)
g.add_conditional_edges(planner)
graph = g.compile()
Bottom line
Use typed immutable state, route with Command, and keep private scratch-pads separate from shared context. These patterns align with both the latest LangGraph APIs and empirical results from hierarchical, graph-based agent research.