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AI agent

LangGraph

Open-source Python framework from LangChain for building long-running, stateful agents as graphs. Its docs list durable execution, human-in-the-loop interrupts, memory and optional deployment on LangSmith.

Maintainer
LangChain, Inc. (langchain-ai organisation on GitHub)
Licence
MIT (LICENSE file: "MIT License", Copyright (c) 2024 LangChain, Inc.)
Last release
langgraph 1.2.12, released 2026-09-21 (the repository also publishes langgraph-cli, langgraph-sdk and checkpoint packages as separate releases; the releases page marks the langgraph-cli tag cli==0.4.32.dev0, 2026-09-23, as Latest)
Last verified Jump to what it can access ↓

What it can access

An agent can take actions, not only answer questions, so what it is allowed to do on your behalf matters most. This is what the listing states, based on the sources below. Anavem does not rate it safe or unsafe: check it against what you plan to use it for.

Permissions it asks for

  • Runs inside your own Python application; you define the tools your agent uses (the quickstart uses the @tool decorator and model.bind_tools)
  • Interrupts can pause a run before a tool call so a person can review or edit it; they need a checkpointer and a thread ID
  • Checkpointers persist agent state; the docs say production use should have a persistent, database-backed checkpointer
  • Optional tracing to LangSmith when you set LANGSMITH_TRACING=true and an API key

Data it can reach

Docs state: LangGraph is a library that runs wherever you run your Python code, and what an agent can reach depends on the tools you give it. The overview docs state it can be used without LangChain. Persisted state goes to the checkpointer database you configure. LangSmith tracing is described as opt-in via environment variables. LangSmith Deployment options listed in the docs: Cloud (managed by LangChain on AWS and GCP), self-hosted with control plane (your Kubernetes cluster), hybrid (LangChain-managed control plane, your data plane) and standalone server (Docker, Compose or Kubernetes with your own PostgreSQL, Redis and a LangSmith license). (README, docs.langchain.com, 2026-10-03)

How it is installed or connected

From the repository README and the LangGraph overview docs (https://docs.langchain.com/oss/python/langgraph/overview), accessed 2026-10-03:

pip install -U langgraph

The LangGraph quickstart (https://docs.langchain.com/oss/python/langgraph/quickstart) sets an ANTHROPIC_API_KEY environment variable in the shell or a .env file for its example model. A JavaScript/TypeScript library, LangGraph.js, is linked from the README.

Limitations

  • The overview calls LangGraph "very low-level, and focused entirely on agent orchestration": you write Python code, define tools and choose a model provider; for prebuilt architectures it points to LangChain agents
  • Interrupts and durable resume require a checkpointer and a thread ID in the run configuration
  • Hosted deployment goes through LangSmith Deployment; the docs list plan requirements (Plus plan or above for Cloud, Enterprise plan for self-hosted with control plane, your own license for standalone)
  • The overview states LangGraph "does not abstract prompts or architecture"

Not sure what to look for? Read what to check before installing.

Quick answers

Who maintains this AI agent?
LangChain, Inc. (langchain-ai organisation on GitHub).
What can it access?
It asks for: Runs inside your own Python application; you define the tools your agent uses (the quickstart uses the @tool decorator and model.bind_tools), Interrupts can pause a run before a tool call so a person can review or edit it; they need a checkpointer and a thread ID, Checkpointers persist agent state; the docs say production use should have a persistent, database-backed checkpointer, Optional tracing to LangSmith when you set LANGSMITH_TRACING=true and an API key. Docs state: LangGraph is a library that runs wherever you run your Python code, and what an agent can reach depends on the tools you give it. The overview docs state it can be used without LangChain. Persisted state goes to the checkpointer database you configure. LangSmith tracing is described as opt-in via environment variables. LangSmith Deployment options listed in the docs: Cloud (managed by LangChain on AWS and GCP), self-hosted with control plane (your Kubernetes cluster), hybrid (LangChain-managed control plane, your data plane) and standalone server (Docker, Compose or Kubernetes with your own PostgreSQL, Redis and a LangSmith license). (README, docs.langchain.com, 2026-10-03). We do not label anything safe or unsafe; read the official sources before you install.
What licence does it use?
MIT (LICENSE file: "MIT License", Copyright (c) 2024 LangChain, Inc.). Check the terms if you plan to use it commercially.
What are its limitations?
The overview calls LangGraph "very low-level, and focused entirely on agent orchestration": you write Python code, define tools and choose a model provider; for prebuilt architectures it points to LangChain agents. Interrupts and durable resume require a checkpointer and a thread ID in the run configuration. Hosted deployment goes through LangSmith Deployment; the docs list plan requirements (Plus plan or above for Cloud, Enterprise plan for self-hosted with control plane, your own license for standalone). The overview states LangGraph "does not abstract prompts or architecture".
When was it last released?
langgraph 1.2.12, released 2026-09-21 (the repository also publishes langgraph-cli, langgraph-sdk and checkpoint packages as separate releases; the releases page marks the langgraph-cli tag cli==0.4.32.dev0, 2026-09-23, as Latest). Verified Oct 3, 2026.

Sources

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