EXPLAINER

LangGraph

frameworkfreshness: volatilesources not yet verified

LangGraph is a library for building stateful, multi-step agent workflows as graphs of nodes and edges, with persistence, human-in-the-loop checkpoints and streaming.

Short answer: LangGraph models an agent as a graph with shared state: nodes do work, edges (including conditional ones) decide what happens next, and checkpoints enable pause/resume and human approval. It gives control over loops that free-running agents lack.

What is it?

LangGraph lets you draw your agent's process as a flowchart in code: steps, decisions and loops, with a shared notebook of state. Because you define the flow, it is easier to add approvals, retries and reliable resumption than in a free-form agent.

Also known as: langgraph agents, stateful agents graph, agent graph framework, langgraph checkpoint, langgraph human in the loop, langgraph vs langchain.

Technical definition

Core concepts commonly described: state schema, nodes (functions/agents), edges and conditional routing, reducers for state updates, persistence via checkpointers, interrupts for human-in-the-loop, and streaming. It is developed by the LangChain team but can be used without LangChain components. APIs and product tiers evolve; check current docs.

Entity type: framework · Freshness class: volatile.

When should I use it?

Choose when you need explicit control, persistence and human-in-the-loop in agent flows.

Implementation patterns

  • Router node plus specialist nodes.
  • Plan-execute-verify loops with retry limits.
  • Interrupt before sensitive tool calls for approval.
  • Persist state per thread for resumable conversations.

Technologies and frameworks

Commonly associated: LangGraph (Python/JS), checkpointer backends, LangSmith tracing. Names are examples of what exists, not recommendations, and projects change quickly.

Practical guide

  1. Sketch the workflow on paper.
  2. Define the state schema.
  3. Implement nodes as testable functions.
  4. Add checkpointing and approvals.
  5. Evaluate end-to-end (How to Evaluate AI Systems and Outputs).

Common failure modes

  • Unbounded loops without recursion/step limits.
  • State schema growth bloating context.
  • Tangled graphs hard to test.
  • Coupling business logic to graph internals.

Troubleshooting

  • Graph loops forever — Set recursion limits and explicit termination conditions.
  • State too large — Store references to large data; summarise.

Contrasted with

  • LangChain, Practically — Broader component library; LangGraph focuses on orchestration.
  • CrewAI — CrewAI is role-based and higher level; LangGraph is lower-level graph control.

Questions this page answers

  • What is LangGraph?
  • LangGraph vs LangChain: what is the difference?
  • How do I add human approval in a LangGraph agent?
  • How does LangGraph persistence work?

Typical goals: build controllable multi-step agents; add approvals and persistence to an agent.

Sources and verification

Status: not independently verified. This entry was authored on 2026-10-06 from general technical knowledge, without live checking of the sources below. Treat every source as a lead to confirm (identifier, title, authorship, current version) before publication or citation.

Freshness class: volatile — Products, versions, protocols, benchmarks, leaderboards, regulations and dates. Verify against the primary source before relying on specifics; re-review every 3 months.

Time-sensitive claims to verify before relying on them:

  • API surface, hosted-platform offerings and versions.
  • Relationship to LangChain packages.

Canonical sources (unverified leads):

  • LangGraph documentation — langchain-ai.github.io/langgraph (verify current URL)
  • LangGraph repository — github.com/langchain-ai/langgraph