mcp-server

beever-atlas: Build LLM Wiki

Name: beever-atlas: Build LLM Wiki
Author: Beever-AI

Get a conversation knowledge base with beever-atlas, for founders using large language models, with 387 GitHub stars

387 stars48 forksPythonQuality 9/10Updated 6/29/2026100% free · open source

What it does

Beever-atlas creates a conversation knowledge base for founders using large language models, allowing them to manage and query their conversational data in a structured way.

Install / run

git clone https://github.com/Beever-AI/beever-atlas.git && cd beever-atlas && pip install -r requirements.txt

When to use it

•You need to manage a large corpus of conversational data from your chatbot or language model
•You want to create a knowledge base that can be queried and updated dynamically
•You're looking for a tool to help you analyze and understand user interactions with your language model

Quick start

1Run `python app.py` to start the Beever-atlas server
2Create a new knowledge base by running `python scripts/init_db.py`
3Configure your knowledge base by editing the `config.yaml` file
4Load your conversational data into the knowledge base using the `python scripts/load_data.py` script
5Query your knowledge base using the Beever-atlas API, for example by running `curl -X GET 'http://localhost:5000/api/v1/kb'`

Ready-to-paste prompt

curl -X POST 'http://localhost:5000/api/v1/kb' -H 'Content-Type: application/json' -d '{"question": "What is the meaning of life?", "context": "philosophy"}'

Heads up: Make sure you have Python 3.8 or later installed, as Beever-atlas is not compatible with earlier versions of Python

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