LangChain vs LlamaIndex – Which Framework to Choose

Different Focuses

LangChain: general framework for building LLM apps. Broad scope.

LlamaIndex: specialized for data ingestion and retrieval. Deep in one area.

Often used together.

LangChain Strengths

Chains: composing multiple LLM calls.

Agents: LLMs that use tools.

Memory: conversation history.

Integrations: 100+ LLMs, vector DBs, tools.

Great for complex workflows.

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LangChain Weaknesses

Complex abstraction.

Rapid API changes – versioning painful.

Overkill for simple cases.

Learning curve steep.

LlamaIndex Strengths

Data ingestion: parse PDFs, websites, databases.

Chunking strategies: semantic, sentence, custom.

Index types: vector, tree, list, graph.

Query engines: sophisticated retrieval.

LlamaIndex Weaknesses

Less versatile than LangChain.

Smaller ecosystem for non-RAG use cases.

Overlap with LangChain creates confusion.

Use LangChain When

Building agents (LLM using tools).

Complex multi-step workflows.

Need many integrations.

Building chatbots with memory.

Use LlamaIndex When

Building RAG systems.

Complex document processing.

Need advanced retrieval strategies.

Data-heavy applications.

Or Just Direct API Calls

Simple use cases don’t need framework.

OpenAI/Anthropic SDK often enough.

Frameworks add complexity.

For MVPs: often skip both.

Performance Considerations

Framework overhead: minimal for basic use.

Complex chains: multiple LLM calls compound.

Async support in both – use it.

Cost Implications

Frameworks don’t directly cost.

But they encourage more LLM calls.

Monitor token usage carefully.

Cache aggressively.

Language Support

LangChain: Python + JavaScript.

LlamaIndex: Python primary, JS growing.

Choose based on team’s language.

Our Approach

Simple RAG: direct API + pgvector.

Complex RAG: LlamaIndex.

Agents/workflows: LangChain.

Combining: LlamaIndex for data + LangChain for logic.

Based on Real Projects

This guide is based on our work with:

• chat-AI
• RepuAI

Further Reading

If this guide helped you, you might also want to read our comprehensive guide on AI Solutions.