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LangGraph

LangGraph AI Agent
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Overview

An open-source AI framework for building stateful, multi-actor applications with large language models (LLMs).

LangGraph is an open-source library designed to facilitate the development of stateful, multi-actor applications utilizing large language models (LLMs). It enables the creation of complex agent and multi-agent workflows, supporting features such as cycles, controllability, and persistence. LangGraph allows developers to define flows involving cycles, essential for most agentic architectures, and provides fine-grained control over both the flow and state of applications. Additionally, it includes built-in persistence, enabling advanced human-in-the-loop and memory features, making it suitable for creating reliable, fault-tolerant agent-based systems.

Product overview

Capabilities, pricing and fit

LangGraph is best understood as an agent-development framework. An open-source AI framework for building stateful, multi-actor applications with large language models (LLMs).

Information as of July 30, 2026

What it can do

  • Agent application development

    An open-source AI framework for building stateful, multi-actor applications with large language models (LLMs).[1]

Where it fits best

  • Developing complex AI agents with stateful workflows.[1]

Pricing and availability

Access: Source repository[1]

Sources (1)
  1. GitHub - langchain-ai/langgraph: Build resilient agents. · GitHubGitHub · checked 2026-07-30

Autonomy level

85/100

Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.

Reasoning: LangGraph enables high autonomy through cyclic workflows and stateful execution that allow agents to dynamically adapt to evolving conditions without predefined linear paths. Its support for multi-agent collaboration, dynamic decision-making (conditional branching), and persistent context management permits agents to iteratively process tasks, eval...

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Some of the use cases of LangGraph:

  • Developing complex AI agents with stateful workflows.
  • Implementing multi-agent systems with controllable interactions.
  • Creating applications requiring iterative decision-making processes.
  • Enhancing AI agents with persistence and human-in-the-loop capabilities.

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Popularity level: 82%

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AI Agent Store · Specialist AI agents

For custom ai agent development consultancies

Your AI agent for stateful multi-agent client applications.

Exploring LangGraph? For custom AI agent development consultancies, your own AI agent can scope client outcomes, build and evaluate agents, and deliver and maintain client releases. Start with a setup tailored to this work.

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