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What you can do with Carrel

Carrel is a self-hosted knowledge base that parses, indexes, and searches your documents so AI agents can retrieve source passages, images, and related facts through an API.

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FengZhenfei/carrel on GitHub

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What do you want to do?

4 use cases

  • Let a coding agent search your product docs

    Deploy Carrel locally and give Claude Code or Codex a search skill over your own document folders.

    View prompt
    Using the Carrel repo at https://github.com/FengZhenfei/carrel, set up the carrel-search skill for this agent and query the search endpoint at http://127.0.0.1:9810/search with a question about <topic>. Return the answer with the source passages cited. Verify that the response contains source passages before answering.

  • Answer contract questions with cited passages

    Index a folder of contracts and let an agent pull the clauses it needs before it answers.

    View prompt
    Use the Carrel search API in the FengZhenfei/carrel repo at http://127.0.0.1:9810/search to find the clauses about <contract term> across the enabled contract folder. Report each answer with the source passage it came from. Verify that every claim in the answer appears in a returned passage.

  • Trace decisions and conflicts across meeting notes

    Build a knowledge graph over meeting notes to follow relationships, sources, and conflicting facts.

    View prompt
    Using the FengZhenfei/carrel search API at http://127.0.0.1:9810/search, ask about how <decision topic> changed across the meeting notes, and follow graph relationships when more evidence is needed. Return a dated timeline with sources and mark any conflicting facts. Verify that each dated item links to a source passage.

  • Keep a research library current as files change

    Schedule Carrel to scan a research folder so new papers and notes show up in agent answers.

    View prompt
    Use the FengZhenfei/carrel repo to search the research library at http://127.0.0.1:9810/search for <topic>. Report whether results include a document added in the last week. Verify the returned sources are dated or named after the new file before reporting success.

How to use Carrel

  1. Check you have what it needs: Linux, macOS with Docker Desktop, or Windows through WSL2 (native Windows is not supported); Python 3.12+ and Docker 25+ with Compose v2; A text embedding endpoint and a vision-language endpoint, either local or hosted.
  2. Open FengZhenfei/carrel on GitHub and read the README and license.
  3. Run git clone https://github.com/FengZhenfei/carrel.git and then cd carrel.
  4. Run ./deploy.sh to start the services and create the configuration files.
  5. Edit config/knowledge-base.env to set your embedding and vision model URLs, model names, and API keys.
  6. Put your documents in runtime/mirror/<folder>/, open http://127.0.0.1:9800, and enable that folder.

These steps come from our top use case, Let a coding agent search your product docs, which also has a copy-paste agent prompt.

Carrel alternatives · Best AI repos for Research

About this repo

  • Who it is forDevelopers and small teams who want a private, self-hosted retrieval service that Claude Code, Codex, or another agent can query over their own document folders.
  • Why it is hot
  • LanguagePython
  • Sourcegithub.com/FengZhenfei/carrel · Back to Hot repos

Requirements

  • Linux, macOS with Docker Desktop, or Windows through WSL2 (native Windows is not supported)
  • Python 3.12+ and Docker 25+ with Compose v2
  • A text embedding endpoint and a vision-language endpoint, either local or hosted
  • A chat model if you want knowledge graphs
  • Optional: an NVIDIA GPU with NVIDIA Container Toolkit for the local model stack
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Questions about Carrel

What is Carrel?

Carrel is a self-hosted knowledge base that parses, indexes, and searches your documents so AI agents can retrieve source passages, images, and related facts through an API. It is an open-source project on GitHub: https://github.com/FengZhenfei/carrel.

Who is Carrel for?

Developers and small teams who want a private, self-hosted retrieval service that Claude Code, Codex, or another agent can query over their own document folders.

What can I do with Carrel?

4 use cases so far, for example: Let a coding agent search your product docs; Answer contract questions with cited passages; Trace decisions and conflicts across meeting notes. Each has steps and a copy-paste agent prompt.

What do I need to run Carrel?

Linux, macOS with Docker Desktop, or Windows through WSL2 (native Windows is not supported); Python 3.12+ and Docker 25+ with Compose v2; A text embedding endpoint and a vision-language endpoint, either local or hosted; A chat model if you want knowledge graphs.

What are alternatives to Carrel?

Other hot AI repos in Research: Scientific Agent Skills, Speaker Mem R1, DSH Council, Evo Bot, AI Engineering Course.

Is Carrel safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.