LangGraphOpen-source framework for building stateful, multi-actor LLM applications with graph-based workflows.
Overview
Key features
- Graph-based agent orchestration
- Built-in state management and memory
- Multi-actor and multi-agent support
- Streaming and async execution
- Checkpointing for pause and resume
- Compatible with major LLM providers
Pricing
- Model
- Freemium
- Category
- Large Language Models (LLMs)
- Rating
- 4.8 / 5 (5)
Use cases
Build Multi-Agent Collaboration Systems
Orchestrate multiple specialized agents that communicate and hand off tasks through graph-defined workflows, enabling complex problem-solving across roles like researcher, planner, and executor.
Long-Running Stateful Agents
Develop agents that maintain memory and persistent state across sessions, using checkpointing to pause, resume, and recover workflows without losing context.
Human-in-the-Loop Approval Flows
Insert human review checkpoints into LLM workflows for sensitive decisions, allowing reviewers to approve, edit, or reject agent actions before execution continues.
Complex Branching LLM Pipelines
Implement workflows with cycles, conditional branching, and retries that go beyond linear chains, giving developers fine-grained control over tool use and model routing.
Pros & Cons
Pros
- Fine-grained control over agent flow
- Supports cycles and complex branching
- Stateful execution with persistence
- Human-in-the-loop checkpoints
- Integrates with LangChain ecosystem
Cons
- Steeper learning curve than simple chains
- Requires understanding of graph concepts
- Documentation can lag rapid releases
- Primarily code-first, no visual builder
Reviews
Average from 5 ratings.
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Skeptical, then convinced
I went in skeptical — most tools in this space overpromise. It actually delivers on multi-actor and multi-agent support, and fine-grained control over agent flow caught me off guard. Documentation can lag rapid releases is why this isn't a perfect score, still, I'd recommend giving it a real trial.
Years in this space
I've evaluated a lot of these over the years. What stands out here is graph-based agent orchestration — handled better than most — and integrates with LangChain ecosystem. Worth the time if this is your use case.
Years in this space
I've evaluated a lot of these over the years. What stands out here is multi-actor and multi-agent support — handled better than most — and fine-grained control over agent flow. Documentation can lag rapid releases is my one real gripe. Worth the time if this is your use case.
Solid for our team
We rolled this out across the team last quarter and integrates with LangChain ecosystem. Built-in state management and memory fits neatly into how we already work, and multi-actor and multi-agent support removed a step we used to do by hand. Steeper learning curve than simple chains, which is the main caveat, but it has held up under daily use.
Skeptical, then convinced
I went in skeptical — most tools in this space overpromise. It actually delivers on streaming and async execution, and stateful execution with persistence caught me off guard. Steeper learning curve than simple chains is why this isn't a perfect score, still, I'd recommend giving it a real trial.
Q&A
Is LangGraph open source? Is it free?
Yes. LangGraph is an MIT-licensed open-source library and is free to use.
Asked by Timur Nazarov · May 1, 2026
Does LangGraph impact the performance of my app?
LangGraph will not add any overhead to your code and is specifically designed with streaming workflows in mind.
Asked by Aisha Khan · Apr 7, 2026
How is LangGraph different from other agent frameworks?
Other agentic frameworks can work for simple, generic tasks but fall short for complex tasks bespoke to a company’s needs. LangGraph provides a more expressive framework to handle companies’ unique tasks without restricting users to a single black-box cognitive architecture.
Asked by Tunde Balogun · Apr 3, 2026
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