Langgraph
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About Langgraph
LangGraph is a low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents. It focuses on agent orchestration capabilities such as durable execution, streaming, human-in-the-loop, and persistence. LangGraph allows mixing deterministic steps with LLM-driven agentic steps in a single graph, enabling fine-grained control over where AI is applied. It is designed to work with or without LangChain components, though it commonly integrates with LangChain for models and tools. Trusted by companies including Klarna, Uber, and J.P. Morgan, LangGraph provides scalable infrastructure for production-ready agent systems.
Key Features
Low-level orchestration framework for agent runtime
Durable execution with persistence across failures
Human-in-the-loop inspection and state modification
Comprehensive memory (short-term working memory and long-term across sessions)
Streaming support for real-time agent interactions
Seamless debugging and tracing via LangSmith
Production-ready deployment with scalable infrastructure
Ability to mix deterministic steps with LLM-driven agentic steps
Pros & Cons
Pros
- Low-level control enables highly customized agent behavior
- Durable execution ensures agents can resume after failures
- Integration with LangChain ecosystem for models, tools, and tracing
- Streaming and human-in-the-loop for interactive and safe agent workflows
- Backed by production usage at major companies like Klarna and Uber
Cons
- Not beginner-friendly; requires familiarity with agent components and orchestration concepts
- Lower-level abstraction compared to pre-built agent frameworks; may be overkill for simple use cases
- Relies on external services (LangSmith) for full debugging and deployment capabilities
- Documentation recommends starting with LangChain's agents for new users
Best For
Building long-running, stateful AI agentsEnterprise agent workflows requiring reliability and persistenceComplex agent orchestration with human oversightFintech applications needing auditable and deterministic agent logicMulti-step agent pipelines combining rule-based and AI-driven steps
FAQ
What is LangGraph?
LangGraph is a low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents. It focuses on durable execution, streaming, human-in-the-loop, and persistence.
How does LangGraph differ from LangChain?
LangChain provides abstractions and integrations for models, tools, and agent loops, while LangGraph is the orchestration runtime focused on durable execution, streaming, human-in-the-loop, and persistence. LangGraph can be used with or without LangChain.
What are the core benefits of LangGraph?
Key benefits include persistence (agents survive failures), human-in-the-loop (state inspection and modification), comprehensive memory (short-term and long-term), debugging via LangSmith, and production-ready deployment.
Can I use LangGraph without LangChain?
Yes. While LangGraph commonly integrates with LangChain components, it does not require LangChain. You can use it with any models and tools.
Who uses LangGraph?
LangGraph is trusted by companies shaping the future of agents, including Klarna, Uber, and J.P. Morgan.