langgraph▌

Directed graph framework for building stateful, multi-step agent workflows with fine-grained control. \n \n StateGraph with typed state schemas, reducers for accumulating lists/values, and nodes that return partial state updates \n Static edges for fixed flow, conditional edges for branching, and Command for combining state updates with dynamic routing \n Send API for fan-out parallelism to worker nodes with result aggregation via reducers \n Invoke for single execution and stream modes (values,

Production-grade framework for building stateful, multi-actor AI applications with explicit graph structure. \n \n Supports graph construction with StateGraph, conditional routing, cycles, and branching for complex agent workflows \n Includes state management with reducers, checkpointers for persistence, and human-in-the-loop patterns \n Handles tool integration, streaming, and async execution across multiple agents sharing state \n Requires Python 3.9+, langgraph package, and LLM API access (Op

LangGraph builds stateful, multi-actor agent applications using a graph-based architecture:

Recommendation: Use TypedDict for most cases. Use Pydantic when you need validation or complex nested structures.

Role: LangGraph Agent Architect

Catches common bugs in LangGraph state management, graph structure, and async patterns. \n \n Identifies 20+ critical issues including state mutations, missing reducers, invalid conditional edge returns, and checkpointer configuration errors \n Covers state schema problems like improper use of add_messages , full-state returns, and Pydantic models without annotations \n Detects graph structure issues: missing entry points, unreachable nodes, incomplete conditional paths, and undeclared Command d

Durable graph execution with thread-scoped checkpoints, state history, and cross-thread long-term memory. \n \n Three checkpointer options: InMemorySaver for testing, SqliteSaver for local development, PostgresSaver for production; always pass thread_id in config to enable persistence \n Browse and replay from past checkpoints using get_state_history() , fork execution by updating state at a past point, or manually modify state before resuming \n Store API provides cross-thread memory for user p

Pause graph execution for human review, approval, or validation, then resume with their input. \n \n Requires three components: a checkpointer (InMemorySaver or PostgresSaver), a thread ID in config, and JSON-serializable interrupt payloads \n interrupt(value) pauses and surfaces data; Command(resume=value) resumes and returns that value to the paused node \n All code before interrupt() re-executes on resume, so side effects must be idempotent (use upsert, not insert) \n Supports approval workfl

Access LangGraph documentation to build stateful agents and multi-agent workflows. \n \n Fetches official LangGraph Python docs covering state machines, graph-based agent design, and human-in-the-loop patterns \n Prioritizes relevant documentation by query type: implementation guides for how-to questions, concept pages for theory, tutorials for end-to-end examples, and API references for technical details \n Automatically selects 2–4 most relevant documentation URLs and retrieves their content t