The best RAG frameworks and libraries for Python developers, compared.
Building a RAG (Retrieval-Augmented Generation) pipeline means choosing between orchestration frameworks like LangChain and LlamaIndex, or building a custom pipeline with direct SDK calls. Each approach has real tradeoffs in complexity, performance, and flexibility.
#1Custom FastAPI + pgvector Pipeline
This kitA lightweight, custom RAG pipeline built directly on FastAPI with pgvector for vector storage and direct OpenAI/Anthropic SDK calls. No framework abstractions — you own every line of code.
Pros
- Full control over chunking, embedding, retrieval, and prompt construction
- Minimal dependencies — no framework lock-in or version churn
- FastAPI AI Kit includes this pipeline pre-built and production-ready
Cons
- No pre-built integrations for exotic data sources (Confluence, Notion, etc.)
- Requires understanding the RAG pipeline at each stage
- Building advanced features (re-ranking, hybrid search) requires more manual work
Best for: Teams who want production-grade RAG without framework complexity.
#2LangChain
The most popular LLM orchestration framework. Provides abstractions for chains, agents, document loaders, vector stores, and retrieval strategies.
Pros
- Largest ecosystem of pre-built integrations and document loaders
- Community support and extensive documentation
- Supports complex multi-step agent workflows beyond simple RAG
Cons
- Heavy abstraction layers that obscure what's actually happening
- Frequent breaking changes between versions
- Performance overhead from the abstraction layer
Best for: Teams building complex agent workflows who need LangChain's pre-built integrations.
#3LlamaIndex
A data framework for LLM applications focused on data ingestion, indexing, and retrieval. More data-centric than LangChain.
Pros
- Purpose-built for RAG — data ingestion and indexing are first-class features
- Better data connector ecosystem than LangChain for structured data
- More focused scope means fewer unnecessary abstractions
Cons
- Smaller community than LangChain
- Still adds abstraction overhead compared to direct SDK calls
- Can be opinionated about index structures
Best for: Teams whose RAG pipeline needs sophisticated data ingestion from many sources.
#4Haystack
An end-to-end NLP framework by deepset, supporting RAG pipelines, question answering, and document search with a pipeline-based architecture.
Pros
- Clean pipeline architecture with composable components
- Strong evaluation and testing tools for RAG quality
- Good support for hybrid search (keyword + semantic)
Cons
- Smaller ecosystem and community than LangChain
- Heavier setup for simple use cases
- Less flexible for non-RAG LLM applications
Best for: Teams who need robust evaluation tools and hybrid search capabilities.
Our take
For most production RAG APIs, a custom pipeline on FastAPI + pgvector provides the best balance of control, performance, and simplicity. FastAPI AI Kit ships this pipeline pre-built. Reach for LangChain or LlamaIndex when you need their specific integrations — but be aware of the abstraction cost.
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