Langchain-Chatchat vs Quivr

Langchain-Chatchat

RAG

Quivr

RAG

Tagline

Langchain-Chatchat

Self-hostable RAG and agent framework that wires LangChain to any local open-source LLM and a knowledge base.

Quivr

Open-source RAG framework for building custom AI assistants over your own documents in a few lines of Python.

Pricing

Langchain-Chatchat

Free· Apache-2.0 open source; self-hosted, infra costs only

Quivr

Free· Open source (pip install quivr-core); pay only for LLM/vector-store usage

Free trial

Langchain-Chatchat

Yes

Quivr

Yes

API

Langchain-Chatchat

Yes

Quivr

Yes

Platforms

Langchain-Chatchat

api

Quivr

api

Open source

Langchain-Chatchat

Yes · Apache-2.0

Quivr

Yes · Apache-2.0

GitHub stars

Langchain-Chatchat

38,666

checked 2026-09-29

Quivr

7,414

checked 2026-09-29

Last GitHub push

Langchain-Chatchat

2025-11-10

Quivr

2025-02-21

First commit

Langchain-Chatchat

2023-03

Quivr

2024-05

Model used

Langchain-Chatchat

Multi-model (GLM-4, Qwen2, Llama 3, etc. via Xinference/Ollama/LocalAI/FastChat)

Quivr

Multi-model (OpenAI, Anthropic, Mistral, Gemma)

Best for

Langchain-Chatchat

Pick Langchain-Chatchat if you need an open-source, on-prem RAG and agent scaffold that can drive local Qwen, GLM or Llama models against a private knowledge base.

Quivr

Pick Quivr if you are a Python developer who wants a lightweight, model-agnostic RAG library you can extend rather than a hosted chat-your-docs SaaS.

Not for

Langchain-Chatchat

Skip it if you want a hosted, turnkey RAG product or a polished consumer chatbot without managing Python, GPUs and a vector store yourself.

Quivr

Skip it if you want a turnkey no-code product with a polished UI, hosted vector store, and a sales team to call.

Editorial score

Langchain-Chatchat

7.4 / 10

Quivr

8.4 / 10

Use cases

Langchain-Chatchat

private-knowledge-baseoffline-ragdocument-qalocal-llm-agentsenterprise-chatbot

Quivr

document-qacustom-knowledge-baserag-pipelineinternal-assistantschat-with-pdf

Pros

Langchain-Chatchat

• Fully offline, self-hosted RAG stack with Apache-2.0 license
• Framework-agnostic: plugs into Xinference, Ollama, LocalAI, FastChat, One API
• Ships both Streamlit UI and FastAPI service with OpenAI-compatible endpoints
• Built-in agent tools (SQL chat, arXiv, Wolfram, text-to-image)
• Large community (~38k stars) and broad model coverage

Quivr

• Genuinely open source and pip-installable, no vendor lock-in
• Model-agnostic: OpenAI, Anthropic, Mistral, and Gemma supported
• Minimal boilerplate to get a working RAG assistant running
• Pairs with Megaparse for tougher PDF and document ingestion
• Customizable pipeline with tools and web search when you need more

Cons

Langchain-Chatchat

• Dependency and GPU setup is non-trivial; not a one-click install
• Documentation is Chinese-first; English coverage lags
• Release cadence has slowed since the v0.3 peak
• You still pick and operate your own vector DB and model server

Quivr

• Python library, not a hosted product or UI
• You manage infra, vector store, and evals yourself
• Documentation site is sparse compared to larger RAG frameworks
• LLM and embedding costs are on you

Website

Langchain-Chatchat

github.com

Quivr

core.quivr.com

Editorial score: rule-based, 0–10, from AI-assisted profile inputs (see /methodology) — not a user rating; “—” means unscored. “Not listed” means we have no record of it, not that it is absent. GitHub figures and prices carry the date they were checked or captured; prices are shown as published, unconverted.