Why this comparison matters
If you've spent any time building with LLMs in the last year, you've hit the same fork in the road: do you reach for LangChain as your application framework, or do you drop down into LangGraph to orchestrate a stateful agent graph? The two are often discussed as alternatives, but they're really different layers of the same stack — and picking the wrong one wastes weeks.
LangChain is the broad framework for building LLM-powered applications: model wrappers, chains, retrieval, tools, and a sprawling integrations layer. LangGraph is a lower-level orchestration library specifically for stateful, multi-step agent workflows — think nodes, edges, persisted state, interrupts, and time travel. Both come from the same team. Both are free and open source. Both push you toward LangSmith for production observability.
The real question isn't "which is better" — it's "which layer do I need for the agent I'm actually building."
Feature comparison
| Feature | LangGraph | LangChain |
|---|---|---|
| Primary purpose | Stateful agent orchestration | General LLM app framework |
| Abstraction level | Low-level (graph primitives) | Higher-level (chains, agents, tools) |
| State management | First-class, persistent | Limited, mostly ephemeral |
| Graph-based workflows | Yes (core feature) | Via LangGraph integration |
| Model integrations | Inherits from LangChain | Extensive (OpenAI, Anthropic, Google, etc.) |
| Streaming | Yes | Yes |
| Interrupts & time travel | Yes | No (native) |
| Memory persistence | Built-in checkpointers | Memory modules, less robust |
| Subgraphs / composition | Yes | Chains compose, less structured |
| Learning curve | Steep | Moderate to steep |
| Best fit | Production agents | RAG, chatbots, tool-using apps |
| Our rating | 7.8 / 10 | 8.2 / 10 |
Pricing comparison
Pricing is effectively identical, which makes sense — same team, same business model.
Open source tier (free)
Both LangGraph and LangChain are MIT-licensed and free to use. You get the full framework, all source code, and community support. No usage caps, no telemetry gates, no "call us" features hidden behind a paywall at this layer. You pay for model API calls (OpenAI, Anthropic, etc.) but the frameworks themselves cost nothing.
LangSmith (custom pricing)
The commercial layer is LangSmith — observability, tracing, evaluation, deployment tooling, and the LangGraph Studio UI. Pricing isn't published; it's a sales conversation. For solo builders and small teams, you can usually run on the free LangSmith tier (limited traces per month) and only upgrade once you have real production volume. For enterprise, expect a quote based on seat count and trace volume.
Bottom line: pricing is not a differentiator between these two. Pick based on architecture fit.
Use case scenarios
Pick LangChain when…
- You're building a RAG app or chatbot. Document loaders, splitters, vector store integrations, and retrieval chains are LangChain's bread and butter. LangChain gets you to a working prototype faster here.
- You need broad model and tool integrations. The integration surface area is enormous — hundreds of model providers, vector DBs, document loaders, and tool wrappers. If you're stitching together third-party services, LangChain saves real time.
- Your agent is mostly linear. Prompt → tool call → response. Simple ReAct loops. Single-turn or short multi-turn. You don't need a graph; you need a chain.
- You're prototyping or learning. The higher-level abstractions (chains, agents, output parsers) let you get something working in an afternoon. You can always drop down to LangGraph later if you outgrow it.
Pick LangGraph when…
- You're building a real production agent. Long-running workflows, human-in-the-loop approvals, branching logic, retries, error recovery — these are exactly what LangGraph is designed for.
- State matters and needs to persist. Checkpointers let you pause an agent run for hours or days and resume from exactly where you left off. LangChain's memory abstractions don't come close to this.
- You need interrupts or time travel. Inspecting agent state mid-run, rewinding to a previous decision point, or injecting human input at specific nodes — these are LangGraph primitives. You'd build them by hand in LangChain, badly.
- Your workflow has cycles or complex branching. Multi-agent systems, planner-executor patterns, agents that can call sub-agents — these collapse cleanly into graph nodes and edges. Trying to express them as LangChain chains gets ugly fast.
- You want fine-grained streaming. Streaming intermediate node outputs, not just final tokens, is a LangGraph strength.
Pick both
This is the under-discussed answer. In practice, most serious production stacks use LangChain for the integration layer (model wrappers, retrievers, tools) and LangGraph for the orchestration layer (the actual agent workflow). The frameworks are designed to compose. If you're building anything non-trivial, you'll likely end up using both — and that's fine.
The verdict
For prototyping and broad LLM apps: LangChain wins. Faster time to first working demo. Easier mental model. More examples to copy. If you're learning, or you're building something that's mostly retrieval-plus-prompting, start here.
For production agents with real state: LangGraph wins. The persistence, interrupts, and graph primitives aren't nice-to-haves once you ship — they're load-bearing. Trying to retrofit them onto LangChain chains later is more painful than learning LangGraph upfront.
For most serious builders: use both. LangChain for the integration breadth, LangGraph for the orchestration depth. The same team builds them. They're designed to compose. The framing of "LangGraph vs LangChain" is mostly a search-query artifact — the practical answer is "LangGraph on top of LangChain."
If you're picking exactly one because you have to: LangChain for the first month, LangGraph the moment your agent grows a second decision point. That's the rule we'd follow ourselves.