LangChain / LangGraph
Agent and graph runtime ecosystem for building tool-using, stateful, and observable LLM applications.
Best for
Deployment targets
Source boundary
LangChain documentation positions agents around model, tools, graph runtime, tracing, and evaluation workflows.
| Source | Type | Verified | Citation |
|---|---|---|---|
| LangChain docs | docs | 2026-09-15 | Official documentation for LangChain and related agent workflows. |
| LangChain GitHub | github | 2026-09-15 | Public repository for LangChain packages. |
Relationships
| Type | Source | Target | Confidence |
|---|---|---|---|
| supports | langchain-langgraph | tracing | 0.84 |
Compatibility
| Type | Target | Status | Evidence |
|---|---|---|---|
| runtime_deployment | serverless-container-runtime | supported | LangChain and LangGraph applications commonly run in server, container, and workflow runtime deployments. |
How should teams choose between OpenAI Agents SDK and LangChain / LangGraph?
Choose based on workflow shape, integration breadth, and operational requirements. OpenAI Agents SDK is a focused fit for OpenAI-native agent orchestration, while LangChain / LangGraph is stronger when graph runtime and broad integrations are central.
When should teams choose LangChain / LangGraph over LlamaIndex?
Choose LangChain / LangGraph when graph-style control flow and broad integrations dominate. Choose LlamaIndex when document context, private data, and retrieval-first workflows are the center of the system.
What should a research-agent stack verify before implementation?
Verify retrieval sources, browser automation boundaries, citation handling, and whether the selected framework can keep source provenance visible across multi-step synthesis.