LangChain vs LlamaIndex
LangChain and LlamaIndex are both Python AI frameworks — but they solve different primary problems. Use LangChain for agents and chains; use LlamaIndex when retrieval is the core requirement.
Quick answer: Use LangChain when building AI agents, multi-step chains and tool-calling workflows. Use LlamaIndex when your primary requirement is document ingestion, retrieval and RAG — it has deeper primitives for indexing strategies and retrieval optimisation.
What is the difference?
LangChain is a Python/JavaScript framework for building LLM-powered applications — chains, agents, tools and memory. LlamaIndex (formerly GPT Index) specialises in data ingestion, indexing and retrieval for RAG systems, with deeper native support for document loading, chunking and retrieval strategies.
Feature-by-feature comparison
LangChain vs LlamaIndex across the dimensions that matter most.
| Feature | LangChain | LlamaIndex |
|---|---|---|
| Primary use case | Agent orchestration, multi-step chains, tool use, conversational AI. | Data ingestion, indexing, retrieval, RAG pipeline construction. |
| Agent support | Core feature — ReAct agents, LangGraph for stateful multi-agent workflows. | Available — but less feature-rich than LangChain for complex agent patterns. |
| RAG / retrieval depth | Good — LCEL chains support retrieval, but indexing primitives are simpler. | Excellent — native node parsers, chunking strategies, retrieval modes, query engines. |
| Document loaders | 100+ loaders — PDF, web, database, S3, Notion, and more. | 100+ loaders — similar breadth, with deeper metadata and node extraction. |
| Vector store integrations | Pinecone, Weaviate, Chroma, pgvector, Qdrant — broad coverage. | Same major stores — Pinecone, Weaviate, Chroma, pgvector, Qdrant. |
| Python support | Full-featured Python library — the primary implementation. | Full-featured Python library — the primary implementation. |
| JavaScript support | LangChain.js — well-maintained JavaScript port. | LlamaIndex.TS — maintained but less feature-complete than the Python version. |
| Observability | LangSmith — first-party tracing, evaluation and testing platform. | LlamaTrace / Arize Phoenix integrations — tracing available but less integrated. |
When to choose each
Choose LangChain when:
- You are building an AI agent that uses tools — web search, code execution, API calls.
- Your system has multiple chained LLM calls with conditional logic.
- You need stateful multi-agent workflows with LangGraph.
- You want a production-grade observability layer via LangSmith.
- You need a broad orchestration framework that handles both retrieval and agents.
Choose LlamaIndex when:
- Your core requirement is document Q&A — ingesting PDFs, web pages or enterprise data.
- You need advanced retrieval strategies — hybrid search, re-ranking, recursive retrieval.
- You are building an enterprise knowledge base or internal search tool.
- You need fine-grained control over chunking, metadata filtering and node relationships.
- You want optimised retrieval over structured and unstructured data simultaneously.
Cost comparison
LangChain
Open source (MIT). LangSmith observability platform: free Developer tier; Plus at $39/month; Enterprise pricing on request.
LlamaIndex
Open source (MIT). LlamaCloud (managed ingestion and retrieval): free tier available; paid plans for production scale. No mandatory paid tier for self-hosted use.
Both frameworks add minimal overhead above the underlying LLM API latency. LlamaIndex retrieval pipelines can be more optimised for RAG-specific workloads. LangChain agent loops add latency proportional to tool call count — architecture design matters more than framework choice for performance.
Both are open-source Python libraries with no data collection by default. LangSmith (LangChain's observability platform) processes trace data — review its data handling policy for sensitive workloads. LlamaIndex's LlamaCloud has equivalent cloud data handling terms. Self-hosted implementations have full data sovereignty.
Common use cases
Common questions
Frequently asked questions about LangChain vs LlamaIndex.
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Every business has different requirements
Integration, security and scalability constraints vary by organisation. The right choice depends on your existing stack, team size, compliance requirements and the specific workflow you are trying to automate or build.
Talk to our engineering team. We will assess your situation and recommend the approach that fits — not the one that sounds most impressive.
Reviewed by the Ascii-Core Engineering Team — specialists in AI engineering, workflow automation, product development and enterprise software architecture. Content reviewed regularly to reflect current technologies and implementation practices. · Updated June 2026