Graph-based stateful orchestration runtime for agent loops.
Gives you precise control over how your AI agents think and act step-by-step, so they handle complex business processes reliably.
LangGraph is LangChain's framework for building stateful, multi-actor applications with LLMs, modeled as directed graphs. Unlike conversational multi-agent frameworks, LangGraph gives you explicit control over the execution flow through a graph-based state machine where nodes represent computation steps and edges define transitions — including conditional routing based on state.
The fundamental abstraction is the StateGraph: you define a typed state object, add nodes that read and write to that state, and connect them with edges. Conditional edges let you branch execution based on state values, creating loops, retries, and complex branching logic that's difficult to achieve with linear chain-based approaches. This makes LangGraph particularly suited for agentic applications that need to make decisions, loop back for corrections, or handle multiple execution paths.
LangGraph's persistence layer (checkpointing) is a genuine differentiator. Every step of graph execution can be automatically saved, enabling time-travel debugging, human-in-the-loop approval workflows, and fault-tolerant execution that can resume from any checkpoint. The MemorySaver and SqliteSaver implementations work out of the box, with PostgreSQL support for production deployments.
The framework ships with several pre-built architectures: ReAct agents, plan-and-execute patterns, multi-agent supervisors, and hierarchical teams. The createreactagent function gets you a tool-calling agent in a few lines, while custom graphs give you full control. LangGraph also supports subgraphs — graphs within graphs — for composing complex systems from reusable components.
LangGraph Platform (formerly LangGraph Cloud) provides deployment infrastructure with a REST API server, task queues, cron scheduling, and streaming support. It integrates tightly with LangSmith for observability, giving you trace-level visibility into every node execution, state mutation, and LLM call.
The honest tradeoff: LangGraph has a steeper learning curve than simpler frameworks because you're building state machines, not just connecting prompts. The graph abstraction is powerful but can feel like over-engineering for simple workflows. It's best suited for teams that need precise control over agent behavior, complex branching logic, and production-grade persistence — particularly those already invested in the LangChain ecosystem.
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LangGraph is the most production-ready agent orchestration framework available, offering fine-grained control over agent state, cycles, and persistence. It demands more upfront learning than alternatives but rewards with unmatched flexibility for complex workflows.
Define execution graphs with TypedDict or Pydantic state schemas. Nodes receive and return state updates, with automatic state merging via configurable reducers (overwrite, append, or custom merge logic).
Use Case:
Building an agent that tracks conversation history, tool results, and decision state across multiple reasoning steps with type-safe state management.
Edges between nodes can be conditional, routing to different nodes based on state values. Supports static routing and dynamic routing via functions. Enables loops, retries, and multi-path execution.
Use Case:
Creating a quality control loop where an agent generates content, evaluates it, and either approves it or loops back for revision based on quality scores.
Automatic state persistence at every graph step using pluggable checkpointers (MemorySaver, SqliteSaver, PostgresSaver). Enables resuming interrupted executions, time-travel to any previous state, and branching from historical checkpoints.
Use Case:
Implementing a document review workflow where a human approves AI-generated sections, with the ability to reject and rewind to any previous draft.
Graphs can contain other graphs as nodes, enabling modular architecture where complex workflows are composed from independently developed and tested subgraphs. State can be mapped between parent and child schemas.
Use Case:
Building a multi-team agent system where each team is its own subgraph coordinated by a supervisor graph.
Pre-built patterns for supervisor-worker hierarchies, collaborative agent teams, and agent handoffs. The supervisor pattern uses one agent to delegate and route between specialized worker agents within the graph structure.
Use Case:
Creating a customer service system where a supervisor agent routes tickets to specialized agents based on the customer's issue.
Managed deployment infrastructure providing a REST API server with streaming, background task queues for long-running workflows, cron job scheduling, and integrated monitoring through LangSmith.
Use Case:
Deploying a production agent that handles concurrent user requests, runs background processing, and provides real-time streaming responses through a REST API.
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View Pricing Options →Building agentic applications that require cycles, retries, and conditional branching based on LLM reasoning outputs
Implementing human-in-the-loop approval workflows with persistent state and the ability to resume from any checkpoint
Creating multi-agent supervisor architectures where a coordinator delegates tasks to specialized worker agents
Developing complex RAG pipelines with query routing, multi-step retrieval, and adaptive response generation strategies
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We believe in transparent reviews. Here's what LangGraph doesn't handle well:
Use LangGraph when your workflow needs cycles (loops), conditional branching, persistent state, or human-in-the-loop approval. Simple linear chains don't need LangGraph. If your agent needs to make decisions about what to do next, retry on failure, or maintain state across interactions, LangGraph adds real value.
Partially. LangGraph has its own package and doesn't require LangChain's chains or retrieval abstractions. However, it depends on langchain-core for base types and message formats. You can use raw API calls within nodes, but you're still importing LangChain's foundational types.
Use PostgresSaver for production. Configure it when compiling your graph: graph.compile(checkpointer=PostgresSaver(conn_string)). Every node execution automatically persists the full state. You can resume from any checkpoint by passing its thread_id and checkpoint_id. This also enables human-in-the-loop — pause before a node, wait for approval, then resume.
Implement retry logic through conditional edges — if a node fails, route back to it or to an error handling node. With checkpointing, you can resume from the last successful step after fixing the issue. The framework itself doesn't have built-in retry decorators, but the graph structure makes retry patterns natural.
LangGraph adds minimal computational overhead — the graph execution engine is lightweight Python. The real costs are LLM calls and checkpointing I/O. MemorySaver has negligible overhead; PostgresSaver adds a few milliseconds per checkpoint. For most applications, LLM latency dominates total execution time by 100x.
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In 2026, LangGraph matured into the primary agent framework within the LangChain ecosystem. Key updates include LangGraph Platform for managed deployment, a new persistence layer for long-running agents, improved streaming support, native human-in-the-loop patterns, and a visual LangGraph Studio for debugging agent graphs. Cloud deployment options expanded significantly with LangGraph Cloud.
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