LangGraph Documentation

LangGraph is LangChain Inc's low-level orchestration framework and runtime for long-running, stateful agents, and the documentation is aimed at engineers who want to mix deterministic hand-coded steps with LLM-driven ones inside a single graph. The overview establishes the four properties the framework is built around — durable execution that survives failures, human-in-the-loop inspection and modification of agent state, short- and long-term memory, and LangSmith integration for tracing — and is explicit that LangGraph can be used without LangChain itself. The Graph API pages carry the substance: StateGraph as the core class, state schemas expressed as TypedDict, dataclass or Pydantic models, and reducers that decide how each node's update merges into shared state, where the default overwrites, operator.add accumulates, and add_messages performs ID-aware message merging. Nodes are plain synchronous or asynchronous functions receiving state, config and runtime context, with optional input-keyed caching and TTL policies. Edges cover fixed transitions, conditional routing functions, the START and END constants, and parallel fan-out when a node has several outgoing edges. Dynamic control flow is handled by Send, which creates runtime edges carrying per-branch state for map-reduce patterns, and Command, which combines a state update with a goto. Further tracks cover checkpointers and persistence, interrupts, streaming and subgraphs. The repository is MIT-licensed with roughly 39.4k stars, and both Python and JavaScript/TypeScript SDKs are documented.

Topic

Agentic

Level

Intermediate

Format

Documentation

Cost

Free

Duration

Reference docs; ~6-8 hours to work through the overview, Graph API and persistence tracks

Provider

LangChain

Hands-on

Yes — code/exercises

Certificate

None

• ✓Model an agent as a StateGraph with typed state, nodes and edges
• ✓Choose reducers so parallel node updates merge instead of clobbering each other
• ✓Route dynamically with conditional edges, Send fan-out and Command objects
• ✓Persist runs with checkpointers so an agent survives a process restart
• ✓Pause a graph for human review and resume it from stored state
• ✓Stream tokens, state updates and events out of a running graph
• ✓Compose subgraphs so a large agent stays testable in pieces

• •39.4k GitHub stars, MIT licensed, and explicitly usable without adopting LangChain itself
• •Documents the runtime concerns most agent tutorials never reach: checkpointing, interrupts and durable execution
• •Parallel-safe state is treated as a first-class design problem via reducers rather than left to the reader to discover
• •Python and JavaScript/TypeScript are both covered, so one architecture can span a mixed stack
• •A production writeup from Qodo reports Postgres checkpointing took only a few lines, but also that docs on newer features were incomplete and that testing non-deterministic flows was the genuinely hard part

This page was written from 4 sources, 2 on domains other than docs.langchain.com.

1. 1.docs.langchain.com — overviewvendor
2. 2.docs.langchain.com — graph apivendor
3. 3.github.com — langgraph
4. 4.qodo.ai — why we chose langgraph to build our coding agent