LangChain vs LlamaIndex — Which LLM Framework in 2025?

Side-by-Side Comparison

Feature	LangChain	LlamaIndex
Primary Focus	LLM orchestration & agent workflows	Data indexing & retrieval (RAG)
Philosophy	Swiss army knife — do everything	Best-in-class data connection
RAG Capabilities	★★★★☆ Good (general-purpose)	★★★★★ Excellent (core focus)
Agent Framework	★★★★★ LangGraph (advanced)	★★★☆☆ Basic agent support
Learning Curve	★★★★☆ Steeper (many abstractions)	★★★☆☆ Simpler (focused scope)
Documentation	★★★★☆ Extensive but overwhelming	★★★★☆ Clear and focused
Abstraction Level	High (many layers)	Moderate (closer to primitives)
Production Ready	★★★★★ LangGraph mature, LangSmith observability	★★★★★ Strong RAG pipelines, LlamaTrace
Community	★★★★★ Largest LLM framework community	★★★★☆ Growing, focused community
GitHub Stars	~95K+ (dominant)	~40K+ (growing fast)
Integration Breadth	★★★★★ 800+ integrations, MCP support	★★★★☆ 350+ data connectors
Best For	Complex agents, multi-step workflows	RAG systems, data-heavy applications

Primary Focus

LangChain LLM orchestration & agent workflows

LlamaIndex Data indexing & retrieval (RAG)

Philosophy

LangChain Swiss army knife — do everything

LlamaIndex Best-in-class data connection

RAG Capabilities

LangChain ★★★★☆ Good (general-purpose)

LlamaIndex ★★★★★ Excellent (core focus)

Agent Framework

LangChain ★★★★★ LangGraph (advanced)

LlamaIndex ★★★☆☆ Basic agent support

Learning Curve

LangChain ★★★★☆ Steeper (many abstractions)

LlamaIndex ★★★☆☆ Simpler (focused scope)

Documentation

LangChain ★★★★☆ Extensive but overwhelming

LlamaIndex ★★★★☆ Clear and focused

Abstraction Level

LangChain High (many layers)

LlamaIndex Moderate (closer to primitives)

Production Ready

LangChain ★★★★★ LangGraph mature, LangSmith observability

LlamaIndex ★★★★★ Strong RAG pipelines, LlamaTrace

Community

LangChain ★★★★★ Largest LLM framework community

LlamaIndex ★★★★☆ Growing, focused community

GitHub Stars

LangChain ~95K+ (dominant)

LlamaIndex ~40K+ (growing fast)

Integration Breadth

LangChain ★★★★★ 800+ integrations, MCP support

LlamaIndex ★★★★☆ 350+ data connectors

Best For

LangChain Complex agents, multi-step workflows

LlamaIndex RAG systems, data-heavy applications

Detailed Analysis

RAG Applications

LlamaIndex

LlamaIndex was purpose-built for RAG and it shows. Its indexing, retrieval, and response synthesis pipeline is more refined and performant than LangChain's RAG implementation. LlamaIndex offers advanced retrieval strategies (hierarchical, recursive, knowledge graph-based) out of the box. LangChain's RAG is capable but more general-purpose — you have more flexibility but also more configuration work. For straightforward RAG applications (chatbots over documents, Q&A systems), LlamaIndex gets you to production faster with better default quality.

AI Agents & Workflows

LangChain

LangChain dominates in agent development. LangGraph (LangChain's agent framework) provides stateful, multi-step agent workflows with cycles, branching, and human-in-the-loop patterns. It's the most mature agent framework in the LLM ecosystem. LlamaIndex has added agent capabilities but they're more basic. If your application involves autonomous agents that use tools, make decisions, and execute multi-step plans, LangChain/LangGraph is the clear choice.

Developer Experience

LlamaIndex (simplicity); LangChain (power)

LlamaIndex offers a simpler, more focused developer experience. You can build a functional RAG application in fewer lines of code with less conceptual overhead. LangChain's abstraction layers (chains, runnables, LCEL) provide power but add complexity — debugging LangChain applications can be challenging because of the many layers between your code and the LLM call. Both have improved significantly in recent versions. For teams with limited LLM experience, LlamaIndex is faster to onboard.

When to Use Both

Many production systems combine both frameworks. A common pattern: use LlamaIndex for the data indexing and retrieval pipeline (it does this best), and LangChain/LangGraph for the agent orchestration layer that decides when and how to use the retrieval system. They're not mutually exclusive — they solve different parts of the LLM application stack. LlamaIndex handles 'what data does the LLM need?' while LangChain handles 'what should the LLM do with it?'

The Verdict

Our Recommendation

LlamaIndex for RAG-focused applications. LangChain for agent workflows and complex LLM orchestration. Many teams use both. Choose based on your primary use case, then add the other as needed.

RAG chatbot over documents

LlamaIndex

Purpose-built for RAG, better defaults, simpler setup

Autonomous AI agents

LangChain (LangGraph)

Most mature agent framework, stateful workflows, tool use

Complex multi-step workflows

LangChain

LCEL and LangGraph handle complex orchestration patterns

Quick prototype (RAG)

LlamaIndex

Fewer lines of code, faster time-to-prototype

Enterprise RAG + agents

Both

LlamaIndex for retrieval, LangGraph for orchestration

Key AI Concepts

Frequently Asked Questions

Can I build RAG with LangChain?

Yes. LangChain has solid RAG capabilities and many tutorials. However, LlamaIndex's RAG implementation is more refined with better default retrieval strategies. For simple RAG, both work well. For complex retrieval requirements (hierarchical documents, knowledge graphs), LlamaIndex has an edge.

Is LangChain too complex?

LangChain has been criticized for over-abstraction, and it's a fair concern. The LCEL (LangChain Expression Language) adds a learning curve. However, LangGraph (the agent framework) is well-designed and production-worthy. The key is using the parts you need rather than trying to learn everything. Start with LangGraph for agents or simple chains for basic workflows.

Do I even need a framework?

For simple applications, no — direct API calls with good prompts can be sufficient. Frameworks add value when you need: RAG with multiple data sources, agent workflows with tool use, conversation memory, structured output parsing, or production-grade error handling. If your app is a simple chat interface, skip the framework. If it's more complex, a framework saves significant development time.

Which framework has better production support?

Both have improved production readiness significantly. LangChain offers LangSmith for observability and tracing. LlamaIndex offers LlamaTrace. Both support async operations, streaming, and error handling. For production agent systems, LangGraph is more battle-tested. For production RAG, LlamaIndex is more proven. Evaluate based on your specific architecture needs.