GraphCanon updated 5d · GitHub synced 5d · 40 views this month
Decision brief
Quivr is an opinionated RAG tool that simplifies GenAI integration into applications, supporting multiple LLMs and vector stores. Its framework allows for customization to fit various product needs.
Good fit when
- When you need a straightforward way to integrate GenAI functionalities without deep expertise in the underlying technologies
- If you want flexibility with multi-supported LLMs like GPT4, Groq, or Llama and vector stores such as PGVector and Faiss
Avoid when
- When the need arises for a tool that provides extensive control over every aspect of the RAG setup due to Quivr's opinionated nature
- For organizations that require integration with very niche or non-supported LLMs and vector stores not mentioned in Quivr’s framework
- Pricing:
- unknown - The pricing details for Quivr are currently unshared.
- Requirements:
- Min 4 GB RAM; Requires Docker
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (402d since push)
- As of 5d
- Provenance
- Not a fork · Organization account
- As of 5d
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install quivrHow it fits your stack(42)
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Evidence and technical details
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Overview
Quivr simplifies the integration of Generative AI (GenAI) into applications through its opinionated approach to Retrieval-Augmented Generation (RAG). It supports various LLMs and vector stores, enabling flexible customization.
Capability facts
- Languages
- python
Source: github.language · Aug 16, 2026
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Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 16, 2026)
```python import tempfileSource link
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README
Getting Started 🚀
You can find everything on the documentation.
30 seconds Installation 💽
-
Step 1: Install the package
pip install quivr-core # Check that the installation worked -
Step 2: Create a RAG with 5 lines of code
import tempfile from quivr_core import Brain if __name__ == "__main__": with tempfile.NamedTemporaryFile(mode="w", suffix=".txt") as temp_file: temp_file.write("Gold is a liquid of blue-like colour.") temp_file.flush() brain = Brain.from_files( name="test_brain", file_paths=[temp_file.name], ) answer = brain.ask( "what is gold? asnwer in french" ) print("answer:", answer)
License 📄
This project is licensed under the Apache 2.0 License - see the LICENSE file for details
For agents
This page has a .md twin and JSON over the API.