Comparing RAG Frameworks: PrivateGPT vs LangChain vs LlamaIndex vs Haystack

You need a retrieval-augmented generation (RAG) framework to build systems that feed documents or data into a language model while keeping it offline. Four tools dominate this space. They solve the same problem—connecting your data to an LLM—but they differ in what they assume you already have, how much they guide you, and how much freedom they leave you.

Here's what actually differs between them, based on verified specs from their official repositories.

The Four Frameworks at a Glance

Tool	License	Min RAM	Offline	Role
PrivateGPT	Apache-2.0	8GB RAM	Yes	Complete system
LangChain	MIT	Depends on model	Yes (with local models)	Orchestration
LlamaIndex	MIT	Depends on model	Yes (with local models)	Data retrieval
Haystack	Apache-2.0	Depends on model	Yes (with local models)	Pipeline framework

PrivateGPT: Pick This If You Want a Ready-Made System

What it does: PrivateGPT is a complete application, not a library. You give it documents; it builds a vector store, handles retrieval, and runs Q&A—all offline. No model orchestration needed from you.

Who picks it: Teams that want document QA to work on day one. Minimal integration work. You bring documents and a local LLM; PrivateGPT handles the rest.

Tradeoff: Less flexible than the others. You're using PrivateGPT's pipeline, not building your own. If you need to customize retrieval logic or chain multiple LLM steps, you'll hit walls.

Verified: Apache-2.0, 8GB RAM minimum, fully offline (no external API calls). Source: github.com/zylon-ai/private-gpt, checked 2026-09-12.

LangChain: Pick This If You Need Maximum Flexibility

What it does: Orchestration library. It chains LLM calls, manages prompts, integrates retrievers, memory, and external tools. Model-agnostic—works with any LLM, local or remote.

Who picks it: Engineers building custom multi-step workflows. Agent systems. Complex retrieval logic. You design the chain; LangChain executes it.

Tradeoff: Steeper learning curve. You write more code. You must choose and integrate your own vector database, embedding model, and LLM. RAM depends entirely on what backend you attach.

Verified: MIT, mature and very active, offline with local models. Source: github.com/langchain-ai/langchain, checked 2026-09-12.

LlamaIndex: Pick This If Retrieval Is Your Main Problem

What it does: Specializes in indexing and retrieving document context. Structures your data into a format that LLMs can consume efficiently. Lighter and more focused than LangChain.

Who picks it: Teams building RAG apps where retrieval quality matters most. You pair it with any LLM (local or remote) and run QA. Faster to prototype than LangChain for document-heavy tasks.

Tradeoff: Narrower scope. Not designed for multi-agent workflows or complex orchestration. If you need intricate prompt chaining, use LangChain instead.

Verified: MIT, mature and very active, offline with local models. Source: github.com/run-llama/llama_index, checked 2026-09-12.

Haystack: Pick This If You Want Search + QA in One Pipeline

What it does: Pipeline framework built for search and question-answering. Declares retrieval logic as a graph, not imperative code. Components connect in a DAG (directed acyclic graph).

Who picks it: Teams with IR (information retrieval) backgrounds. Enterprises that already use Elasticsearch or similar. Want declarative pipeline definition, not code.

Tradeoff: Smaller community than LangChain or LlamaIndex. Steeper learning curve if you're unfamiliar with pipeline DAGs. Less third-party integration compared to LangChain.

Verified: Apache-2.0, mature and active, offline with local models. Source: github.com/deepset-ai/haystack, checked 2026-09-12.

Decision Checklist

• Need a working app today, not a library? → PrivateGPT
• Building agents or multi-step workflows? → LangChain
• Focusing on document retrieval quality first? → LlamaIndex
• Prefer declarative pipeline DAGs over code? → Haystack
• Don't know which: start with LlamaIndex or LangChain (both have larger communities, more tutorials)

What to Check Before Starting

All four run offline. All four need a local LLM—they don't provide one. Check that your chosen LLM (Llama 2, Mistral, etc.) fits your hardware. None of these frameworks bundle a model; you provide it yourself. RAM shown is framework minimum; add your model's requirements on top.

License matters if you're building commercial software: PrivateGPT and Haystack are Apache-2.0 (permissive); LangChain and LlamaIndex are MIT (also permissive). All allow commercial use.

Originally published at Forged Goods. The ready-made version: Local-AI Stack Directory: 40 Self-Hosted LLM & Vector-DB Tools, Verified Specs.