Get a conversation knowledge base with beever-atlas, for founders using large language models, with 387 GitHub stars
intermediateโฑ 30 minutes๐ต Free + LLM API costs
432 stars48 forksPythonQuality 9/10Updated 7/20/2026100% free ยท open source
What it is
Build a conversation knowledge base using a large language model.
What you can make with it
A personalized, in-app help assistant for your users, answering frequently asked questions.
How it helps
Beever-atlas helps founders create a custom knowledge base for their product, freeing up time to focus on development and support.
Real use case example
"A founder, Alex, wants to help her users with common queries. She sets up beever-atlas in 30 minutes and asks it to answer questions about their product. After a week, she adds 20 new answers and sees a 25% reduction in support tickets."
If you're new
Pick this up when you're new to AI tools and want to get started with building custom knowledge bases.
If you're senior
Use this when you're looking for a simple, scalable way to create a knowledge base for a complex product.
Common confusion cleared up
Beever-atlas uses a pre-existing large language model, but you can customize its knowledge base and interactions.
Best inside these AI tools
Claude DesktopClaude CodeGemini Code AssistGPT (ChatGPT)
Pairs with
Claude DesktopGeminiNotion databaseDiscord-bot
Why we list it on WorkflowStacks: This skill is best-in-class for building a knowledge base using large language models.
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.
โขYou need to manage a large corpus of conversational data from your chatbot or language model
โขYou want to create a knowledge base that can be queried and updated dynamically
โขYou'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
Saves to your device
Topics
adk-google
agents
discord-bot
fastapi
gemini
google-adk
knowledge-base
large-language-models
llm
mattermost
mcp
mcp-server
microsoft-teams
open-source
python
rag
react
slack-bot
wiki
What's inside โ free to inspect
No purchase needed
Read the entire source before you build โ unlike paid marketplaces that hide it behind a buy button.