LangGraph

What is LangGraph?

LangGraph is a low-level orchestration framework and runtime from LangChain for building, managing and deploying long-running, stateful agents. You describe an agent as a graph of nodes and edges, mixing deterministic hand-coded steps with LLM-driven steps so that parts of the logic stay predictable and auditable. The library focuses on agent orchestration rather than abstracting prompts or architecture, and provides durable execution, streaming, persistence and human-in-the-loop controls. It installs with pip install -U langgraph, and you do not need LangChain to use it, although LangChain components are used throughout the docs for models and tools. For production, the separate LangGraph Platform and LangSmith Deployment offer one-click deployment, task queues, background runs, checkpointers, memory, and LangGraph Studio for visualizing and debugging agents, in cloud, hybrid or self-hosted setups. The docs name Klarna, Uber and J.P. Morgan among its users. The library is open source, and no prices are listed for the library itself.

Use cases

• Build stateful multi-step agents as graphs
• Mix deterministic steps with LLM steps
• Add human approval to agent workflows
• Run long-running agents in the background
• Persist agent memory across sessions
• Debug agent behavior in LangGraph Studio

Who it's for

• AI engineers
• Backend developers
• Platform teams
• Enterprise AI teams

Why use LangGraph

• Fine-grained control lets you decide exactly where deterministic code ends and model-driven behavior begins.
• Durable execution and persistence suit agents that run for a long time or need to resume.
• Human-in-the-loop support makes manual oversight part of the workflow.
• It does not impose prompt or architecture abstractions, so existing code and models fit in.
• Optional platform and Studio tooling covers deployment, scaling and debugging when you need it.

LangGraph features

• Graph-based agent definition with nodes, edges and state
• Durable execution for long-running workflows
• Streaming of tokens and intermediate states
• Human-in-the-loop interrupts and endpoints
• Persistence through checkpointers and a memory store
• Works with or without LangChain components
• LangGraph Studio for visualizing and debugging agents
• Task queue, background runs and webhooks on the platform
• Cloud, hybrid and self-hosted deployment options
• Built-in handling for double-texting and bursty request loads

LangGraph integrations

• LangChain
• LangSmith
• LangGraph Studio

How to get started with LangGraph

1. Install the library with pip install -U langgraph or uv add langgraph.
2. Define a StateGraph, add nodes for model calls and tools, and connect them with edges.
3. Compile the graph and invoke it, adding checkpointers, streaming or interrupts as needed.
4. Trace runs in LangSmith and deploy to LangGraph Platform or your own infrastructure.

LangGraph limitations

• LangGraph is low-level, so you assemble models, tools and prompts yourself
• Beginners are pointed to higher-level LangChain agents for prebuilt architectures
• Self-hosted and BYOC LangSmith options require the Enterprise plan
• Self-hosted deployments need manual upgrades and scaling

LangGraph pricing

LangGraph is open source, so you can use and self-host it for free.

Prices can change and may vary by country or exclude tax. Check the official pricing page.

LangGraph alternatives

The main LangGraph alternatives listed on Debuly are CrewAI, AutoGen, Semantic Kernel, OpenAI Agents SDK, LangChain Deep Agents and LightAgent. The table compares pricing and platforms; order is A–Z.

1. 

   AutoGen

   Microsoft's MIT-licensed Python and .NET framework for multi-agent AI apps, now archived; new projects are pointed to Microsoft Agent Framework.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit AutoGen (official site)
2. 

   CrewAI

   Multi-agent framework and platform for building AI agent crews in Python or a visual editor, then deploying and monitoring them.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit CrewAI (official site)
3. 

   LangChain Deep Agents

   Open-source LangChain agent harness: long-term memory, a virtual filesystem, subagents and planning included

   Developer platformsAutomation and agentsCoding
   Open sourceVisit LangChain Deep Agents (official site)
4. 

   LightAgent

   Compact open-source Python framework for building agents with tools, memory, MCP, tracing, guardrails and multi-step workflows.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit LightAgent (official site)
5. 

   OpenAI Agents SDK

   Open-source Python framework for building multi-agent workflows with tools, handoffs, guardrails, tracing, sandboxes and voice agents.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit OpenAI Agents SDK (official site)
6. 

   Semantic Kernel

   Microsoft's open-source SDK for building AI agents and multi-agent systems in Python, .NET and Java, now succeeded by Microsoft Agent Framework.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit Semantic Kernel (official site)

LangGraph FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

• Logo: from docs.langchain.com · fetched 23 Oct 2026 · Vendor trademark, used only to identify the product; removed on request
• Cover: none collected yet, so a generated tile is shown.

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