LangGraph
LangChain / langchain.com
Open source stateful multi-agent orchestration framework from the LangChain team with graph-based control flow, cycles, persistent state, and human-in-the-loop checkpoints for complex AI agents.
Pricing
Free
Free plan
Yes
Category
Developer Tools
Platforms
2
Free plan
Yes
API access
Yes
Open source
Yes
Platforms
2
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What is LangGraph?
LangGraph is an open source framework from the LangChain team addressing the limitations of linear LLM chains for complex agent workflows. Rather than sequential pipelines, LangGraph defines agents as nodes in a graph with explicit control flow — cycles, branches, conditional routing, and persistent state. Cycles allow agents to iterate until a goal is achieved. Persistent state means agent context survives across separate interactions for truly multi-session agents. Human-in-the-loop checkpoints pause execution at designated points for human review before continuing. Built on LangChain, LangGraph benefits from extensive model integrations and tool ecosystems.
How LangGraph works
LangGraph runs as llm application framework software built around text and code workflows. Users typically start with a prompt, upload, or connected data source, and the underlying model handles the heavy lifting before returning a result you can refine or export. It's available on python and typescript, with API access for teams that want to embed it into their own products.
Watch LangGraph in action
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What makes it worth shortlisting
The capabilities that matter most for teams evaluating LangGraph.
Graph-based control flow
Define agents as nodes in a directed graph with explicit edges, cycles, and conditional routing for complex workflow logic.
Persistent state
Agent state and conversation history preserved across separate interactions for true multi-session continuity.
Human-in-the-loop checkpoints
Pause agent execution at designated points for human review and approval before continuing autonomous action.
Best use cases
Who should use it
Pros
- Graph-based control flow enables iterative agent behaviour unavailable in simple chains
- Persistent state enables truly stateful multi-session agents
- Human-in-the-loop checkpoints essential for production agent safety
- Deep LangChain ecosystem integration for developers already on that stack
Cons
- Steeper learning curve than CrewAI for developers new to graph-based thinking
- Complexity overkill for simple single-turn AI tasks
- LangChain dependency may be limiting for teams using other frameworks
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Is it worth the price?
Open source and free. LangSmith Platform from $39/month for deployment. No separate LangGraph charge.
Model
Open Source
Starting price
Free
Free trial
No
Tools like LangGraph
CrewAI is more accessible for common multi-agent patterns. Microsoft AutoGen provides flexible multi-agent without LangChain dependency. OpenHands provides autonomous coding without framework complexity.
LangGraph vs CrewAI
A side-by-side look at the closest alternative in this category.
LangGraph
LangChain
CrewAI
CrewAI
Open source and free. LangSmith Platform from $39/month for deployment. No separate LangGraph charge.
Open source free. CrewAI+ cloud $29/month Starter, $99/month Teams, Enterprise custom.
Open source: data handling per configured LLM providers. LangSmith deployment: review LangChain data handling policy.
Open source: data handling per configured LLM providers. CrewAI+ cloud: review CrewAI's data handling policy.
Data handling entirely through configured AI provider APIs. LangGraph itself does not transmit data. LangSmith deployment subject to LangChain's privacy policy.
Data handling depends on configured LLM providers. CrewAI+ cloud processes workflow data on CrewAI's infrastructure.
- Graph-based control flow enables iterative agent behaviour unavailable in simple chains
- Persistent state enables truly stateful multi-session agents
- Human-in-the-loop checkpoints essential for production agent safety
- Deep LangChain ecosystem integration for developers already on that stack
- Most popular open source multi-agent framework with millions of downloads
- Intuitive agent role and goal definition
- Supports any LLM provider including local models
- CrewAI+ cloud reduces infrastructure management for production
- Steeper learning curve than CrewAI for developers new to graph-based thinking
- Complexity overkill for simple single-turn AI tasks
- LangChain dependency may be limiting for teams using other frameworks
- Multi-agent workflows add complexity vs single-agent approaches
- Debugging complex agent interactions requires patience
- CrewAI+ cloud adds cost for production deployment
Technical & deployment info
Key facts about model providers, platforms, and team support.
Model Provider
OpenAI, Anthropic, Agnostic
Platforms
Python, TypeScript
Deployment
Open Source, SaaS (LangSmith deployment)
Integrations
LangChain, OpenAI, Anthropic, Azure OpenAI, All LLM providers, LangSmith
Team Collaboration
No
Launch Year
2024
Security & privacy
Compliance signals and data-handling notes as reported by the vendor.
Open source: data handling per configured LLM providers. LangSmith deployment: review LangChain data handling policy.
Data handling entirely through configured AI provider APIs. LangGraph itself does not transmit data. LangSmith deployment subject to LangChain's privacy policy.
What users are saying
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Common questions about LangGraph
Editorial Verdict
Should you use LangGraph?
LangGraph is the best framework for complex stateful agents requiring iterative behaviour, persistent memory, and human-in-the-loop control. CrewAI is simpler for common multi-agent patterns.
Last verified July 24, 2026.