LangGraph: Complete Platform Profile
LangGraph is an open-source framework for building stateful, multi-actor applications with large language models. Released by LangChain Inc. in early 2024, it represents a fundamental rethinking of how complex AI agents should be structured. Where traditional LangChain chains execute in a linear sequence, LangGraph models agent workflows as directed graphs — enabling loops, branching, parallel execution, and persistent state between steps.
The project emerged from a practical problem: LangChain's original agent abstractions were not expressive enough for production-grade agents. Real-world agentic systems need to handle errors and retry, involve human review before proceeding, coordinate multiple specialized sub-agents, and maintain state across long-running tasks that may span many LLM calls. LangGraph was built specifically to address these requirements.
This profile covers what LangGraph actually provides, who should use it, and how it fits into the broader agent framework landscape.
Browse the AI agent profiles directory to compare LangGraph against other leading frameworks.
Overview#
LangGraph treats an agentic workflow as a state machine expressed as a graph. Nodes in the graph are Python functions (or LLM calls) that transform the shared state. Edges define how control flows between nodes — either unconditionally or conditionally based on the current state. The framework maintains a persistent state object that gets updated at each node and is available to every subsequent node in the workflow.
This graph-based model is powerful for several reasons. First, it naturally supports cycles — an agent can loop back to an earlier step based on the outcome of a tool call. Second, it enables human-in-the-loop interrupts: you can pause execution at any node and wait for human input before continuing. Third, it supports checkpointing — the state can be persisted to a database, allowing long-running agents to survive process restarts.
LangGraph builds directly on LangChain abstractions (models, tools, messages) so teams already using LangChain can adopt it incrementally. It is not a standalone framework in the way that CrewAI or AutoGen are — it is best understood as a stateful orchestration layer on top of LangChain primitives.
For a practical walkthrough, see the LangGraph multi-agent tutorial.
Core Features#
State Graph Architecture#
The central abstraction in LangGraph is the StateGraph. You define a TypedDict or Pydantic model that represents the agent's state, then add nodes and edges to define how that state evolves. Each node receives the current state, performs some computation (which may include LLM calls or tool execution), and returns a partial state update. LangGraph merges the update into the current state and determines the next node to execute.
This architecture cleanly separates the state schema from the execution logic, making it much easier to reason about what an agent "knows" at any given point in a workflow.
Conditional Routing and Branching#
Edges in LangGraph can be conditional — a Python function examines the current state and returns the name of the next node to execute. This enables sophisticated routing logic: route to a human review node if confidence is low, route to a retry node if a tool call failed, route to a summarization node when a loop counter exceeds a threshold. This conditional routing is how LangGraph implements the decision logic described in the agent loop pattern.
Persistence and Checkpointing#
LangGraph has a built-in checkpointer interface that saves state snapshots to persistent storage after each node executes. The default implementation uses in-memory storage; production deployments use SqliteSaver or PostgresSaver. Checkpointing enables three critical capabilities:
- Long-running agents: A workflow can pause and resume, surviving server restarts
- Human-in-the-loop: Pause the graph, present state to a human, resume after approval
- Time travel: Roll back to any previous checkpoint for debugging or replaying with different inputs
Multi-Agent Coordination#
LangGraph supports several patterns for coordinating multiple agents. In the supervisor pattern, a supervisor agent receives the user's request and routes to specialized sub-agents (a research agent, a writing agent, a code agent). In the collaborative pattern, multiple agents work in parallel on different aspects of a problem and a synthesis node combines their outputs. Sub-agents are themselves LangGraph graphs, making the system compositional.
This multi-agent capability directly addresses the limitations of simpler chain-based approaches where all reasoning happens inside a single LLM call. See the tool use glossary entry for how individual agents interact with external systems.
Streaming and Real-Time Updates#
LangGraph provides fine-grained streaming support. You can stream individual tokens from LLM calls, stream node-level state updates, or stream custom events emitted from within nodes. This is important for building responsive UIs where users see partial results as the agent works rather than waiting for the entire workflow to complete.
LangGraph Platform (Cloud)#
LangChain Inc. offers LangGraph Platform as a managed execution environment. It handles deployment, scaling, and monitoring of LangGraph applications via a REST API. This is the commercial product the company is building its business around, targeting enterprises that want production agent infrastructure without managing it themselves.
Pricing and Plans#
LangGraph the open-source library is MIT licensed and free. You can run LangGraph agents on your own infrastructure at no cost.
LangGraph Platform pricing:
- Developer: Free tier with limited executions — suitable for evaluation
- Plus: Consumption-based pricing, approximately $0.01 per execution step
- Enterprise: Custom pricing with SLA, on-premise deployment, dedicated support
For most development and mid-scale production use, the self-hosted open-source version is the practical choice. LangGraph Platform becomes relevant when teams want to avoid building their own agent infrastructure (queuing, persistence layer, API gateway).
LangSmith (observability) is a separate product with its own pricing but is effectively required for debugging non-trivial LangGraph applications.
Strengths#
Genuine state management. Unlike frameworks that bolt persistence onto chains as an afterthought, LangGraph was designed from the start around persistent state. The checkpointer interface is clean, well-documented, and pluggable.
Human-in-the-loop first-class. The ability to pause a running graph, collect human input, and resume is built into the core primitives rather than being a workaround. This is critical for production systems in regulated industries or high-stakes domains.
Expressive control flow. Conditional edges and cycle support allow you to express complex agent logic — retry loops, escalation paths, parallel fan-out — in a structured way that is readable and debuggable. See chain-of-thought for the underlying reasoning patterns these workflows enable.
LangChain ecosystem compatibility. Existing LangChain tools, retrievers, and integrations work in LangGraph without modification. Teams with existing LangChain codebases adopt LangGraph incrementally.
Production focus. The framework's design priorities — persistence, streaming, monitoring, deterministic control flow — reflect the lessons learned from building production agents rather than demos.
Limitations#
Steep learning curve. The graph mental model is more abstract than simple chains. Developers new to state machines or dataflow programming will spend significant time understanding how state updates propagate and how edge routing works.
Younger ecosystem. Compared to LangChain's thousands of tutorials and examples, LangGraph has fewer resources. Community support is good but thinner at the edges.
LangChain dependency. LangGraph is not a standalone framework. Teams that have chosen to avoid LangChain (perhaps due to its abstraction complexity) cannot use LangGraph without pulling in the LangChain dependency tree.
Verbosity for simple cases. Defining a state schema, instantiating a StateGraph, adding nodes, adding edges, and compiling — a LangGraph workflow takes more boilerplate to set up than an equivalent sequential chain. For simple linear workflows, the overhead is not justified.
Ideal Use Cases#
LangGraph excels in:
- Multi-step research agents: Agents that search, read, reason, and iterate multiple times before producing a final answer
- Code generation pipelines: Write code, run tests, observe errors, fix code — a workflow requiring loops and conditional routing based on test results
- Document review workflows: Automated first-pass review with human approval gates before proceeding
- Customer service automation: Complex ticket routing across multiple specialized agents with escalation to human agents when confidence is low
- Long-running background agents: Tasks that execute over minutes or hours, require checkpointing, and may be interrupted and resumed
If your agent needs to "try something, check if it worked, and try again differently if not," LangGraph is the right tool.
Getting Started#
- Install dependencies:
pip install langgraph langchain-openai - Define your state schema as a TypedDict
- Create a
StateGraph, add nodes as Python functions, add edges - Compile the graph with a checkpointer
- Stream execution with
graph.stream(input_state)
The official LangGraph documentation includes a "ReAct agent from scratch" tutorial that is the best starting point. After the basics, study the human-in-the-loop and multi-agent examples. The LangGraph multi-agent tutorial on this site walks through building a practical research assistant.
How It Compares#
LangGraph vs CrewAI: CrewAI uses a role-based mental model — you define agents with roles and assign them tasks. LangGraph uses a graph mental model — you define state and control flow explicitly. CrewAI is faster to get started for common multi-agent patterns; LangGraph is more flexible for custom workflows. See the CrewAI directory entry for more.
LangGraph vs AutoGen: AutoGen agents communicate through conversational messages; coordination emerges from the conversation. LangGraph coordination is explicit — you control the graph. LangGraph is more deterministic; AutoGen is more flexible but harder to control.
LangGraph vs standard LangChain: Use standard LangChain for linear, stateless workflows. Use LangGraph when you need persistent state, loops, human interrupts, or multi-agent coordination. They are complementary, not competing.
Bottom Line#
LangGraph is the most technically sophisticated open-source framework for building production-grade AI agents. Its graph-based state machine model, combined with first-class support for persistence and human-in-the-loop, makes it the right choice for complex, real-world agent workflows. The trade-off is a steeper learning curve and more setup overhead than simpler alternatives. Teams building demos or simple chatbots should start with standard LangChain. Teams building production agents that need to be reliable, resumable, and controllable should invest in LangGraph.
Best for: Engineering teams building production multi-step agents that require persistent state, human review gates, or complex conditional control flow.