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agent frameworks for RAG/agent devs

LangChain vs LlamaIndex: which is better for RAG/agent devs?

TL;DR for RAG/agent devs

LangChain shines with its huge ecosystem of tools and patterns, while LlamaIndex excels at data-centric retrieval and simpler RAG pipelines.

Key Differences

Feature LangChain LlamaIndex
Ecosystem size & plugins LangChain approach LlamaIndex approach
Data connector breadth LangChain approach LlamaIndex approach
Chains & agents vs index abstractions LangChain approach LlamaIndex approach
Learning curve Steeper, complex Gentler, accessible
Event-driven workflows LangChain approach LlamaIndex approach
Performance overhead LangChain approach LlamaIndex approach

Pricing Snapshot

Both are open-source; enterprise support & cloud hosting vary (2025‑10‑13)

Last reviewed: 2025-10-13

LangChain

Choose LangChain if:

  • You want maximum flexibility & plugins
  • You build complex agents & workflows
  • You value community examples

Pros

  • + Large ecosystem & plugins
  • + Modular chains, agents & tools
  • + Memory & callback support
  • + LLM-agnostic & open-source
  • + Vibrant community

Cons

  • - Complex to learn & configure
  • - Can be heavyweight
  • - Potential overkill for simple tasks
  • - Rapidly evolving API

LlamaIndex

Choose LlamaIndex if:

  • You focus on RAG & data ingestion
  • You need rich connectors & parsers
  • You prefer simpler pipelines

Pros

  • + Rich data connectors via LlamaHub
  • + Multiple index types (vector/tree/list/keyword)
  • + Query & chat engines
  • + Advanced parsing with LlamaParse
  • + Supports agents & workflows

Cons

  • - Smaller community
  • - Complex customization for advanced use cases
  • - Less plug‑and‑play than LangChain
  • - Rapid changes in API

Also Consider

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