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AI/TLDR

Quivr

Opinionated RAG core that turns your files into a queryable second brain

GitHub★ 39.5kWebsitecore.quivr.com
RAG Frameworks & PlatformsOpen source
Language
Python
$pip install quivr-core

Overview

Quivr is an open-source framework for building Retrieval-Augmented Generation (RAG) applications. It lets you ingest your own files and then ask questions about them in plain language, so you can build a personal assistant or a 'second brain' on top of your documents.

The project ships an opinionated, ready-made RAG pipeline through the quivr-core Python package. It handles the retrieval and answering for you, so you can focus on your product instead of wiring up the plumbing. Quivr works with many LLM providers and supports a wide range of file formats.

What it does

  • Opinionated RAG pipeline that is fast and efficient out of the box, so you do not have to assemble retrieval logic yourself
  • Works with any LLM, including OpenAI, Anthropic, Mistral, and local models served through Ollama
  • Handles any file, such as PDF, TXT, and Markdown, and lets you plug in your own parsers
  • Customizable RAG: add internet search, add tools, and tune behavior through a YAML workflow config
  • Configurable retrieval with reranking and adjustable history, token limits, and temperature settings
  • Integrates with Megaparse for file ingestion before querying with Quivr

Getting started

Quivr needs Python 3.10 or newer. You install the quivr-core package and can build a working RAG in just a few lines of code.

Install the package

Install quivr-core from PyPI using pip.

bashbash
pip install quivr-core

Create a brain from your files

Build a Brain from one or more files and ask it a question. Quivr handles ingestion and retrieval for you.

pythonpython
from quivr_core import Brain

brain = Brain.from_files(
    name="test_brain",
    file_paths=["./my_first_doc.pdf", "./my_second_doc.txt"],
)

answer = brain.ask("what is gold?")
print("answer:", answer)

Set your API key

Add your LLM provider API key to your environment. Quivr supports Anthropic, OpenAI, and Mistral, plus local models through Ollama.

pythonpython
import os
os.environ["OPENAI_API_KEY"] = "myopenai_apikey"

Customize retrieval with a YAML workflow

Define a workflow in a YAML file and load it with RetrievalConfig to control reranking, history, token limits, and temperature, then pass it when you ask a question.

pythonpython
from quivr_core.config import RetrievalConfig

retrieval_config = RetrievalConfig.from_yaml("./basic_rag_workflow.yaml")
answer = brain.ask("your question", retrieval_config=retrieval_config)

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

When to use it

  • Build a personal 'second brain' assistant that answers questions from your own notes and documents
  • Add a document question-answering feature to a product without building the RAG pipeline from scratch
  • Experiment with different retrieval strategies by editing a YAML config instead of changing code
  • Run RAG over private files with a local LLM through Ollama to keep data on your own machine

How Quivr compares

Quivr alongside other open-source rag frameworks & platforms tools AI/TLDR tracks, ranked by GitHub stars.

ToolStarsWhat it does
Dify★ 156kAn open-source platform with a visual workflow builder for creating LLM and RAG applications without writing much code.
graphify★ 119kTurns a folder of code, docs, PDFs and images into a local knowledge graph with tree-sitter AST parsing and Leiden communities — queryable by agents over MCP, no vector store.
RAGFlow★ 90.9kA RAG engine built around deep document understanding that turns complex files into a grounded, citation-backed question-answering layer.
Pathway★ 62.3kA Python framework with a Rust streaming engine that keeps ETL, real-time analytics and RAG pipelines continuously up to date as source data changes.
Context7★ 62.2kContext7 pulls current, version-specific documentation and code examples for any library and feeds them into your LLM, available as a CLI skill or an MCP server.
LightRAG★ 39.7kA graph-based RAG system that builds an entity-and-relationship knowledge graph for fast retrieval and easy incremental updates.
Quivr★ 39.5kOpinionated RAG core that turns your files into a queryable second brain
Langchain-Chatchat★ 38.6kAn offline-deployable RAG and agent application built on LangChain that answers questions from a local knowledge base using open-source LLMs, embedding models and vector stores.