LangChain
Python · MIT
LangChain is the open-source agent engineering platform that unifies model IO, tool calling, RAG, memory and observability under one composable framework.
agent-frameworkragorchestrationllmpython
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Overview
LangChain vs LangGraph: two paradigms from the same family
LangChain is the general LLM orchestration framework that started with chain abstractions (LCEL / prompt / model / output parser). LangGraph is a graph state-machine abstraction from the same team, focused on stateful multi-agent plus cycles plus explicit control flow. We compare abstraction level, control flow, state management, ecosystem, and typical scenarios.
Projects Compared
LangGraph
Python · MIT
LangGraph is a framework for building controllable, debuggable, long-running stateful agents, expressing agent state and control flow as a graph.
agent-frameworkstatefulgraphorchestrationpython
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Feature Comparison
| Best for | LangChain | LangGraph |
|---|---|---|
| Abstraction level | Chain abstraction (LCEL): prompt | model | parser piped together. Component-based, easy to pick up. | Graph abstraction (StateGraph): nodes + edges + state. Allows cycles, conditional branches, explicit state management. |
| Control flow | LCEL defaults to linear plus operator-overloading style implicit control flow. Loops and branches require custom RunnableLambda. | Explicit nodes plus edges: loops, branches, state read/write, sub-graphs. The control flow IS the graph. |
| State management | Short-term memory uses LangChain's built-in Memory (ConversationalBufferMemory etc.). Long-term persistence requires integrating a DB yourself. | Built-in state schema plus checkpointing (InMemorySaver / SqliteSaver / PostgresSaver etc.). Agents can pause / resume / time-travel. |
| Ecosystem and integrations | LangChain ecosystem: 700+ integrations, LangSmith observability, LangGraph Studio / LangServe deployment. LangChain works without LangGraph. | LangGraph ecosystem: relies on LangChain's integrations but StateGraph is the core. Can be mixed with LangChain components. |
| Typical use cases | Linear / single-step LLM workflows: RAG pipelines, prompt experiments, tool calling, output parsing. Common in production deployments. | Complex multi-agent / stateful workflows: agents reviewing each other in loops, time-travel debugging, long-running human-in-the-loop flows. |
GitHub Stats
| Metric | LangChain | LangGraph |
|---|---|---|
| Stars | 143.9k | 39.4k |
| Forks | 24.0k | 6.6k |
| Language | Python | Python |
| License | MIT | MIT |
| Last commit | August 11, 2026 | August 10, 2026 |
Which one should you choose?
Choose based on your primary workflow, language ecosystem, and integration needs. Review each project's documentation and recent GitHub activity before adopting it in production.
Frequently asked questions
Should I learn LangChain and LangGraph together?
Start with LangChain LCEL basics (chain / prompt / model / parser), then add LangGraph for stateful scenarios. LCEL covers ~80% of LLM workflows; the remaining 20% (loops / state persistence / time-travel) is where LangGraph earns its keep.
Has LangGraph replaced LangChain?
No. LangChain is the upper-layer chains plus integrations; LangGraph is the graph state machine. They are complementary. The official LangGraph repo is still part of the LangChain monorepo. In production they are often combined: LangGraph for workflow, LangChain for components.
Is LangGraph good for multi-agent?
Yes. StateGraph plus checkpointing is the standard multi-agent setup; CrewAI / AutoGen / OpenHands and many other frameworks rely on LangGraph for orchestration. Time-travel debugging is a multi-agent pain point, and LangGraph has it built in.
Should a new project pick LCEL or LangGraph?
Pick LCEL for RAG or simple tool calling — lighter. Pick LangGraph if you need multi-agent / loops / state persistence / long-running human approval. You can also start with LCEL and migrate to LangGraph when needed; migration cost is low.