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