Compare the Top AI Agent Frameworks that integrate with LangGraph as of August 2026

This a list of AI Agent Frameworks that integrate with LangGraph. Use the filters on the left to add additional filters for products that have integrations with LangGraph. View the products that work with LangGraph in the table below.

What are AI Agent Frameworks for LangGraph?

AI agent frameworks are software development platforms, SDKs, and libraries designed to build, orchestrate, and manage autonomous or semi-autonomous artificial intelligence agents. They provide foundational components such as reasoning engines, memory systems, action planning, tool integrations, and lifecycle control so developers can create intelligent agents without building every capability from scratch. These frameworks often include standardized interfaces, debugging tools, simulation environments, and performance monitoring to support robust agent development and deployment. Many AI agent frameworks integrate with existing machine learning models, APIs, and external systems to enable agents to interact with real-world data and services. By abstracting complex agent behaviors into reusable patterns and tools, AI agent frameworks accelerate innovation and help teams deploy reliable, scalable intelligent systems. Compare and read user reviews of the best AI Agent Frameworks for LangGraph currently available using the table below. This list is updated regularly.

  • 1
    LangChain

    LangChain

    LangChain

    LangChain is a powerful, composable framework designed for building, running, and managing applications powered by large language models (LLMs). It offers an array of tools for creating context-aware, reasoning applications, allowing businesses to leverage their own data and APIs to enhance functionality. LangChain’s suite includes LangGraph for orchestrating agent-driven workflows, and LangSmith for agent observability and performance management. Whether you're building prototypes or scaling full applications, LangChain offers the flexibility and tools needed to optimize the LLM lifecycle, with seamless integrations and fault-tolerant scalability.
  • 2
    AG-UI

    AG-UI

    AG-UI

    AG-UI is an open, lightweight, event-based protocol that standardizes how AI agents connect to user-facing applications. Built for simplicity and flexibility, it enables seamless integration between AI agents, real-time user context, and user interfaces. AG-UI is designed for agent-human interaction: during agent executions, backends emit events compatible with standard AG-UI event types, and agent backends can accept simple AG-UI-compatible inputs as arguments. It works with any event transport, including SSE, WebSockets, webhooks, and other streaming systems, while providing a flexible middleware layer that ensures compatibility across diverse environments. AG-UI brings agents into user-facing applications and complements the wider agentic protocol stack: MCP gives agents tools, A2A allows agents to communicate with other agents, and AG-UI connects agents directly to the user interface.
    Starting Price: Free
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