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from langgraph.graph.message import add_messages
from langgraph.func import entrypoint
from langchain_core.messages import BaseMessage
from langchain_core.runnables import RunnableConfig
from .tasks import call_model, get_structued_output, manage_memories, call_tool, get_recent_memories
@entrypoint()
def agent(messages: list[BaseMessage], config: RunnableConfig):
tool_calls_count = 0
tool_names_called = set()
answer = None
links = []
# Fetch Recent User Information
recent_memories = get_recent_memories().result()
llm_future = call_model(messages, memories=recent_memories)
memories_future = manage_memories(messages[-1].content)
# Now, wait for both to complete
llm_response = llm_future.result()
memories = memories_future.result()
while True:
if not llm_response.tool_calls:
break
# Execute tools
tool_results_future = []
for tool_call in llm_response.tool_calls:
tool_names_called.add(tool_call["name"])
tool_results_future.append(call_tool(tool_call))
tool_results = [fut.result() for fut in tool_results_future]
tool_calls_count += len(tool_results)
# Append to message list
messages = add_messages(messages, [llm_response, *tool_results])
# Call model again
llm_response = call_model(messages, memories=recent_memories).result()
# Check if any tools other than search_memories were called
other_tools_called = any(name != "search_memories" for name in tool_names_called)
if tool_calls_count > 0 and other_tools_called:
# Structure the final output
structured_output = (
get_structued_output(llm_response.content).result().model_dump()
)
answer = structured_output["text"]
links = [str(link) for link in structured_output.get("links", [])]
else:
answer = llm_response.content
return {
"answer": answer,
"links": links,
"messages": messages + [llm_response]
}
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