About Axiom - A Docs Expert Agent
Axiom is a documentation-focused agent for AI frameworks, libraries, and tools, helping users plan projects through natural-language conversation. It retrieves information from multiple documentation sources through MCP Docs Server and supports Gemini models, image analysis, and configurable model settings.
Key features
- 01Interactive chat interface
- 02Access to multiple documentation sources
- 03Multiple Gemini models
- 04Image processing and analysis
- 05Configurable model settings
- 06Docker deployment support
Use cases
- 01Plan AI agent projects using framework docs
- 02Ask about building RAG systems or chatbots
- 03Use images and graphs to inform code generation
- 04Add documentation sources through configuration
MCP support
Axiom uses MCP Docs Server to access documentation, retrieving content based on each source's llms.txt.
Details
From the project READMEAxiom - A Docs Expert Agent
Axiom is AI Agent specialized in modern AI frameworks, libraries and tools. It can assist in creating AI Agents, RAG systems, chatbots, authentication mechanisms, and even full-stack development. It is built with LangGraph, MCP Docs Server, Chainlit and Gemini Models, designed to help users create different projects using natural language instructions.

Features
- 🤖 Interactive chat interface
- 📚 Access to multiple documentation sources
- 🦾 Support for multiple Gemini models
- 🎨 Support for image processing and analysis
- 📈 Use images and graphs to create production-ready code
- 🛠️ Customizable model settings (temperature, model version)
- 🌐 Docker support for containerized deployment
Documentation Sources
Axiom used llms.txt of the given documentations and fetches content based on the URLs in llms.txt.
The agent has access to following documentations:
- LangGraph Python
- CrewAI
- Model Context Protocol (MCP)
- Chainlit
- FastHTML
- Supabase
- Pinecone
- Composio
- Mem0
- Zep
- Stripe
- Resend
- Upstash
- Netlify
- Clerk Auth
- Stack Auth
Prerequisites
- UV package manager
- Python 3.11+
- Google Gemini API Key
- Docker (optional): If you intend to use the
Dockerfile, you'll need Docker installed.
Installation
- Clone the repository:
git clone https://github.com/aasherkamal216/Axiom.git
cd Axiom
- Create and Activate Virtual Environment:
uv venv
.venv\Scripts\activate # For Windows
source .venv/bin/activate # for Mac
- Install dependencies:
uv sync
- Set up environment variables:
cp .env.example .env
Add your API keys and other credentials in .env file.
Note
If you want to disable authentication, you can remove
chainlit.yamlfile. Also remove the Oauth Callback function fromsrc/axiom/app.py.
Usage
Run the application:
- First run the MCP Doc server:
uv run mcpdoc --yaml docs_config.yaml --transport sse --port 8082 --host localhost
- Then run chainlit interface:
uv run chainlit run app.py -w
The application will be available at http://localhost:8000.
Building the Docker image (Optional)
Alternatively, you can use Docker to run the application:
docker build -t axiom .
docker run -p 7860:7860 -p 8082:8082 axiom
Adding More Docs
You can add more documentations in docs_config.yaml file. Any documentation with a llms.txt file can be added to the list.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.