As AI systems grow more complex, developers need ways to orchestrate multi-step reasoning, branching logic, and collaboration between multiple agents. LangGraph is an open-source framework built on top of LangChain that enables stateful, multi-agent applications powered by large language models.
LangGraph models AI workflows as graphs. Nodes represent computation steps -- LLM calls, tool usage, custom logic -- and edges define the flow between them. This graph-based approach gives fine-grained control over both flow and state, making it possible to build agents that go well beyond simple prompt-response interactions.
Key Features of LangGraph
Graph-Based Workflows: Application logic is represented as a directed graph, giving developers explicit control over sequencing and branching. Complex decision trees, loops, and conditional paths are straightforward to model.
Stateful Execution: Unlike stateless chains, LangGraph maintains persistent state across steps. Agents can remember context, track progress, and build on previous reasoning throughout a workflow.
Multi-Agent Orchestration: Coordinating multiple AI agents within a single application is a first-class concern. Agents collaborate, hand off tasks, and share state, enabling sophisticated multi-agent architectures.
Human-in-the-Loop Support: Built-in mechanisms for pausing execution and waiting for human input or approval. Critical for applications where human oversight is required.
Streaming and Real-Time Output: Intermediate results can be streamed, letting users see agent progress in real time rather than waiting for the full workflow to complete.
Built-in Persistence: Checkpointing and state persistence mean workflows can be paused, resumed, and recovered -- making them resilient to failures and suitable for long-running processes.
Use Cases for LangGraph
LangGraph is adopted for building advanced AI applications:
- Conversational AI Agents: Chatbots and assistants that maintain context, use tools, and make decisions based on complex logic.
- RAG Pipelines: Retrieval-Augmented Generation workflows with advanced retrieval strategies, query rewriting, and iterative answer refinement.
- Autonomous Research Agents: Agents that plan research tasks, search multiple sources, synthesize findings, and produce comprehensive reports.
- Code Generation and Review: AI-powered dev tools that generate, review, test, and iterate on code across multiple files and repositories.
- Customer Support Automation: Multi-agent systems where specialized agents handle different aspects of customer inquiries, escalating to humans when needed.
How Is LangGraph Different From LangChain?
LangChain provides the foundational building blocks for working with LLMs -- prompt templates, model integrations, output parsers, tool connectors. LangGraph builds on top of LangChain to add explicit graph-based orchestration and state management.
LangChain's chains and agents work well for linear or simple branching workflows. LangGraph excels when you need cycles, conditional branching, persistent state, or coordination between multiple agents. Think of LangChain as the toolkit for individual AI operations; LangGraph as the framework for composing them into complex, stateful applications.
Getting Started
LangGraph is available as an open-source Python and JavaScript library. It integrates seamlessly with the LangChain ecosystem -- its extensive LLM integrations, tool connectors, and retrieval components. Whether you're building a simple conversational agent or a complex multi-agent system, LangGraph provides the structure and control to bring it to production.