AI Agents
Langchain
The agent engineering platform
★ 147.4k starsMIT
View guide →// agents · langchain-ai/langgraph
Langgraph
Langgraph is an open-source ai agents project by langchain-ai (langchain-ai/langgraph on GitHub). It has 42,672 GitHub stars and 7,236 forks, is released under the MIT license, and was last updated on GitHub on 3 October 2026.
Build resilient agents
★ 42.7k stars⑂ 7.2k forksMITupdated 2026-10
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
so here's a placeholder. The guide below is complete regardless.
// project facts
Project facts
- Repository
- langchain-ai/langgraph
- GitHub stars
- 42,672
- Forks
- 7,236
- License
- MIT
- Last updated on GitHub
- 3 October 2026
- Category
- AI Agents
- Listing
- Full install guide
- Official site
- Official site
// how it works
How it works
- Pick a framework
Application frameworks, agent runtimes, or voice stacks — choose by whether you are building apps, automations, or realtime voice.
- Install the SDK
Python or TypeScript packages install with one command; most include quickstart templates.
- Define tools and memory
Give the agent functions it can call and a memory layer so it stays coherent across sessions.
- Run and observe
Launch the agent, watch its tool calls and reasoning traces, and iterate on prompts and tools.
// features
Why people love it
Tool use & function calling
Agents act on the world: APIs, databases, browsers, and shell commands.
Multi-agent orchestration
Crews and graphs of specialized agents collaborating on complex tasks.
Memory systems
Short and long-term memory so agents improve across sessions.
Observability
Traces of every decision and tool call — essential for debugging.
Voice & realtime
Low-latency stacks for conversational voice agents.
Model agnostic
Swap underlying LLMs without rewriting your agent logic.
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// run it
Run it yourself
- Step 1 — Clone the repo
Download the source and move into the project folder.
git clone https://github.com/langchain-ai/langgraph.git cd langgraph - Step 2 — Create an environment
Isolate dependencies so nothing clashes with your other projects.
python -m venv .venv source .venv/bin/activate # Windows: .venv\Scripts\activate - Step 3 — Install and launch
Install the requirements, then follow the README's launch command (it varies per project).
pip install -r requirements.txt
// use cases
What you can build
- Research assistants that browse and summarize.
- Coding agents that edit, test, and PR.
- Support agents with real tool access.
- Voice agents for calls and meetings.
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// faq
Questions, answered
Framework or finished agent?
Frameworks build your own; some repos here ship ready-made agents you can run directly.
Do agents need big models?
Tool-calling quality matters more than raw size — strong mid-size models work well.
Who maintains Langgraph?
It is an open-source project at langchain-ai/langgraph with 7,236 forks. Check the repo's contributors and recent commits to see how active maintenance is.
How do I keep agents safe?
Sandbox tool access, confirm destructive actions, and review traces — treat agents like junior employees with supervision.
What does it cost to run?
Self-hosted frameworks are free; you pay only for model inference (local or API).
Where do I start?
A quickstart template in the framework's docs — most get a first agent running in minutes.
// keep exploring
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View guide →// alternatives
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The most-starred other AI Agents projects in the LibreAI catalogue — the realistic shortlist if Langgraph is not the right fit.
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// more in agents
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Get Langgraph
Free and open — grab the source, star the repo, and start creating.