Appofficial publisher

LangGraph

App : A product you use directly

Framework for building stateful agents with an explicit control graph.

The reference framework for serious production-grade agents; intended for developers.

Official site GitHub · 43,847 ★

Overall score

8.8 /10

Security: nothing to flag

Utility

8.5

Reliability

10.0

Security

No caveat on record

Ease

Technical · Technical

Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.

What it does, concretely

Building AI agents that hold up in production is difficult: complex workflows fail, state is lost between steps, and it is hard to intervene manually when something goes wrong. LangGraph addresses this by providing low-level infrastructure for orchestrating durable, stateful agents.

In practice, LangGraph represents agent behavior as a directed graph: each node is a processing step, each edge is a conditional transition. The framework manages short-term memory (ongoing reasoning) and long-term memory (context across sessions), and can resume execution exactly where it left off after a failure.

LangGraph is aimed at Python or JavaScript developers building agents for production: conversational agents, complex task automation, hierarchical multi-agent systems. Companies such as Klarna, Replit, and Elastic use it for real-world use cases.

It is not a tool for the general public or for non-developers. Getting started requires understanding the concept of a control graph, managing your own infrastructure, and writing code. Those looking for a visual interface or a ready-to-use agent should look elsewhere.

What it offers

·Durable execution: agents persist through failures and automatically resume exactly where they left off.
·Comprehensive memory: management of short-term working memory and long-term persistent memory across sessions.
·Human-in-the-loop control: ability to inspect and modify agent state at any point during execution.
·Native token-by-token streaming: real-time display of agent reasoning and actions.
·Multi-agent architectures: support for single, multi-agent, and hierarchical control flows within one framework.
·Debugging with LangSmith: visualization of execution paths, state transitions, and detailed runtime metrics.
·Production deployment via LangSmith Deployment: scalable infrastructure for long-running stateful workflows. (paid)
·Compatible with LangChain and usable standalone, with no mandatory dependency on other LangChain tools.

Our tips

Start with the free LangChain Academy course (Introduction to LangGraph) to understand the concepts of state, memory, and human-in-the-loop before writing your first graph.
For agents that need to plan, use subagents, or work with file systems, the Deep Agents package built on top of LangGraph offers a higher level of abstraction and can save time.
Pair LangGraph with LangSmith from the start of development: execution trace visualization makes it easier to debug unexpected behavior before going to production.
LangGraph is available in both Python and JavaScript/TypeScript (LangGraph.js): choose the version that fits your existing stack.
The framework adds no overhead to your code and is designed with streaming in mind: take advantage of native streaming to improve user experience from the design stage.

Watch out

The learning curve is real: understanding and modeling a workflow as a directed graph requires a significant initial effort for developers who are not familiar with the concept.
Scalable production deployment relies on LangSmith Deployment, a paid platform; self-hosting remains possible but requires managing the infrastructure yourself.

In practice

Price: Freemium
Commercial use: Commercial use: yes
Available on: cli, docker
Licence: MIT

Security and data

Rating: Security: nothing to flag
Your data: Data stays local
Maintenance: Active
Last release: 24 October 2026

Links and repository checked on 25 October 2026 · added on 1 January 2024

How to install LangGraph

1. Make sure Python is installed on your machine (version compatible with LangGraph dependencies). 2. Install LangGraph via pip: pip install -U langgraph 3. Refer to the official documentation at docs.langchain.com for the quickstart guide. 4. For debugging, create a LangSmith account and configure the corresponding environment variables before running your first agents. 5. For production deployment, explore LangSmith Deployment through the LangSmith interface (paid service).

pip install -U langgraph

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