LangChain is designed for building standard AI agents quickly with a higher-level API.
LangGraph provides granular control over state, workflows, persistence, approvals, and long-running agents.
You can use both together, starting with LangChain and moving to custom LangGraph workflows as complexity grows.
The choice between LangChain and LangGraph no longer comes down to two separate tools that solve the same problem. The two projects now sit at different levels of the same agent stack. LangChain offers a higher-level path for common AI agents, while LangGraph gives developers tighter control over state, flow, persistence, and complex agent behavior. LangChain agents also run on LangGraph, so the two can work together inside one system.
LangChain focuses on the core agent loop: a model receives a task, calls tools, gets tool results, and returns an answer. Its create_agent API gives developers a direct path to this setup. LangChain also offers model abstractions, tool support, middleware, and integrations across providers. This structure can reduce the amount of code needed for a standard agent.
The 1.0 release also changed the shape of the project. LangChain reduced its package surface and moved older features into langchain-classic. The current 1.x line puts more focus on the agent loop and middleware. LangChain 1.0 also requires Python 3.10 or newer.
Recent releases show active work across the framework. The GitHub release page lists LangChain Core 1.4.6, along with updates to create_agent, middleware types, structured output fallbacks, provider tools, and human-in-the-loop behavior.
LangGraph takes a lower-level approach. It lets developers define an agent workflow as a graph with nodes, state, and clear transitions. That structure fits systems where an agent must follow several steps, pause for approval, resume later, or mix fixed code with model-based decisions. LangChain describes LangGraph as a framework and runtime for highly custom agents and long-running production systems.
State forms a major part of the LangGraph model. A workflow can keep data across steps and use persistence for longer tasks. Human review can also sit inside the workflow. This matters for cases such as financial operations, support actions, research tasks, or other systems where a model should not make every decision without a checkpoint.
Recent LangGraph releases show continued work on this runtime layer. The current GitHub release page lists LangGraph 1.2.11, with updates such as trace_policy, checkpoint packages, SQLite support, PostgreSQL support, and related SDK changes.
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LangChain makes the common path shorter. LangGraph makes the full path more explicit. A simple support agent may need a model, several tools, and a clear response. LangChain can handle that pattern with less custom workflow code.
A more complex system may need a fixed sequence, several decision points, stored state, retries, approval steps, and a clear route after each action. LangGraph gives each part a place inside the workflow. That extra control also adds more design work, so the lower-level approach can require more effort from the development team.
The distinction does not mean LangChain and LangGraph must compete. LangChain 1.0 runs its agents on LangGraph. A team can start with create_agent and later place that agent inside a custom LangGraph workflow when the product needs more control. LangChain describes this path as a way to move from a high-level agent to a custom graph without a full framework switch.
Production requirements often decide the framework level. A short agent task with little state can fit the LangChain model well. A long business process needs stronger workflow control. LangGraph targets long-running agents, stateful tasks, human review, multi-agent workflows, and cases where teams need tighter control over latency or cost.
The deployment story has also matured. LangGraph Platform launched for long-running and stateful agents, then received a new name: LangSmith Deployment. LangChain announced that name change in October 2025. The platform supports deployment and management for LangGraph applications.
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LangChain suits a standard agent with a clear model-tool-response cycle. It offers a higher-level API and keeps the initial architecture easier to manage. LangGraph suits a system where workflow state, persistence, approvals, custom routes, or long tasks matter from the start.
The strongest part of the current ecosystem comes from the connection between both frameworks. LangChain does not lock an agent inside a separate runtime. Its agents use LangGraph underneath, which gives a project a path from a simple agent to a more controlled workflow.
That makes the choice less about picking one winner and more about choosing the right level of control. LangChain handles the common agent pattern. LangGraph handles the custom workflow layer. For many projects, that distinction can guide the architecture before complexity turns into technical debt.
1. What is the main difference between LangChain and LangGraph
LangChain provides a higher-level approach for building common AI agents, while LangGraph focuses on explicit workflow orchestration, state, persistence, and complex agent behavior.
2. Is LangGraph built on LangChain?
Not exactly. LangChain agents run on LangGraph, allowing LangChain to provide a higher-level agent experience while LangGraph handles the underlying runtime and workflow capabilities.
3. When should I use LangChain?
Use LangChain when you need a relatively straightforward model-tool-response agent and want to minimize custom workflow code.
4. When should I use LangGraph?
LangGraph is useful when your application requires persistent state, multi-step workflows, human approval, retries, custom routing, or long-running agent processes.
5. Can LangChain and LangGraph be used together?
Yes. You can build an agent with LangChain and incorporate it into a LangGraph workflow when your application needs greater control and orchestration.