QuivrHQ/quivr
quivr
Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want.
Usage guide
quivr is an open-source project around api, chatbot, chatgpt with 39,182 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in Python, useful for judging integration effort in a similar stack.
- GitHub did not detect a repository license, so commercial permission is unconfirmed. Review the repository terms and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating quivr for Python AI workflows.
- Comparing a GitHub project with 39,182 stars and current repository activity.
Pros
- quivr has visible GitHub traction with 39,182 stars. Topics: ai, api, chatbot.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- No license was detected, so usage risk needs manual review.
Production readiness
quivr should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
GitHub did not report a license, which usually requires manual legal review before production use.
quivr architecture preview
quivr's main path starts at the entry surface, runs through Agent orchestration runtime, combines OpenAI / Llama, Vector index / PostgreSQL / Files / repository context, GitHub / Discord / APIs / webhooks, and returns Grounded answers / search results.
Entry
Web / product entry
Users start from a web UI, hosted product surface, or browser-based workflow.
https://core.quivr.com
Runtime
Agent orchestration runtime
The orchestration layer plans tasks, calls tools, manages context, and decides the next action.
agent workflow
Model
OpenAI / Llama
Model calls are likely routed through OpenAI, Llama based on README and topic signals.
OpenAI, Llama
Context
Vector index / PostgreSQL / Files / repository context
Context comes from Vector index, PostgreSQL, Files / repository context, which constrains what the model or runtime can use.
Vector index, PostgreSQL, Files / repository context
Tools
GitHub / Discord / APIs / webhooks
Tool adapters let the runtime act outside the model through GitHub / Discord / APIs / webhooks.
GitHub, Discord, APIs / webhooks
Output
Grounded answers / search results
The final result is an answer or ranked result grounded in retrieved context.
answer output
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Install tutorial
Before you install
- Python runtime and an isolated virtual environment
- A clean working directory for the first test run
Check the runtime environment
quivr depends on a Python-style environment. Use venv, conda, or a container to keep dependencies isolated.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/QuivrHQ/quivr.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ pip install quivr-coreAdoption guidance and sources
Practical use cases
Knowledge-base assistant
Use it for document-grounded AI workflows where retrieval quality matters.
Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your prod
This is one of the documented reasons to evaluate quivr before choosing a stack.
Focus area: ai
This is one of the documented reasons to evaluate quivr before choosing a stack.
RAG project comparison
Compare quivr with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official quivr setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented quivr example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is quivr?
quivr is an open-source rag project. Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want.
How do I install quivr?
Start with the official README. The first detected setup step is: git clone https://github.com/QuivrHQ/quivr.git.
Is quivr beginner-friendly?
If you already know the Python ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can quivr be used commercially?
GitHub did not detect a repository license, so commercial permission is unconfirmed. Review the repository terms and any model weights, datasets, dependencies, or external services before commercial adoption.
Does quivr need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt quivr?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.