Two dominant open-source frameworks for LLM applications, with overlapping but distinct sweet spots.
LlamaIndex if RAG and data ingestion are the core. LangChain (with LangGraph) if agents, workflows and tool orchestration dominate. Many teams use both.
LlamaIndex is opinionated about ingestion, indexing and retrieval — the RAG side of the stack. It's the fastest path to a production-grade retrieval pipeline.
LangChain is broader — chains, tools, agents, memory and integrations. LangGraph adds durable stateful workflows for real agentic systems.
Neither is required. Many teams outgrow both and land on a custom orchestration layer for production. Both are fine starting points.
Framework for building LLM apps with chains, agents, tools and memory. LangGraph adds durable stateful workflows.
Framework specialised in data ingestion, indexing and retrieval-augmented generation.
| Criterion | LangChain | LlamaIndex |
|---|---|---|
| RAG maturity | Solid | Best-in-class |
| Agent framework | LangGraph — production-ready | Agent Runner — growing |
| Integrations | Very broad (200+) | Focused on data + LLMs |
| Evals / observability | LangSmith | Built-in eval + Trulens |
| Learning curve | Steeper (many abstractions) | Gentler for RAG |
| Production readiness | High with LangGraph | High for RAG pipelines |
Common pattern: LlamaIndex for ingestion + retrieval, LangGraph for the agent + workflow layer that consumes the retriever. Best of both without over-committing to either.
No. For simple chatbots or single-shot generation, raw SDKs are simpler. Frameworks earn their keep when you have multi-step logic, evals or many integrations.
Both are used in production at scale in 2026. LangGraph specifically has closed the durability + observability gap that plagued earlier LangChain deployments.
For large teams shipping many AI features, a thin custom orchestration layer often wins long-term. Use these frameworks to move fast; graduate when abstractions hurt more than they help.
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