LangGraph

Documentation Index

Fetch the complete documentation index at: https://docs.idun-group.com/llms.txt

Use this file to discover all available pages before exploring further.

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Code

from typing import Annotated, TypedDict

from langchain_google_genai import ChatGoogleGenerativeAI
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages


class State(TypedDict):
    messages: Annotated[list, add_messages]


llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash")


def chatbot(state: State):
    return {"messages": [llm.invoke(state["messages"])]}


graph = StateGraph(State)
graph.add_node("chatbot", chatbot)
graph.add_edge(START, "chatbot")
graph.add_edge("chatbot", END)

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Config

server:
  api:
    port: 8000

agent:
  type: LANGGRAPH
  config:
    name: "my-langgraph-agent"
    graph_definition: "./agent.py:graph"
    checkpointer:
      type: sqlite
      db_url: "sqlite:///conversations.db"

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How it works

1. Load. graph_definition is parsed as <file_path>:<variable_name>. The adapter resolves it as a file path first (relative or absolute), then falls back to a Python module import path (my_pkg.agent:graph). Both work.
2. Validate. The exported variable must be a StateGraph. A CompiledStateGraph is accepted: the engine extracts .builder and recompiles with the engine-managed checkpointer and store, preserving any interrupt_before and interrupt_after you supplied to .compile(). A warning is logged when this path is taken.
3. Compile. The engine compiles the StateGraph with the checkpointer configured in config.yaml (memory, sqlite, or postgres) and wraps the result in LangGraphAGUIAgent from CopilotKit for AG-UI event streaming.
4. Serve. Chat requests POST to /agent/run with the AG-UI streaming protocol. The engine emits typed events (RUN_STARTED, TEXT_MESSAGE_CONTENT, TOOL_CALL_START, TOOL_CALL_ARGS, TOOL_CALL_END, STEP_STARTED, STEP_FINISHED, RUN_FINISHED) as SSE data: lines.

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Notes

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Next steps