Ronnyx

Ronnyx

A self-hosted AI assistant runtime. Define your tools, connect your services, and interact through an HTTP API or CLI -- all from a single YAML file.

Ronnyx runs as a persistent service with session memory and automatic tool dispatch. It supports both external tool servers (via MCP) and custom Python functions, so you can mix off-the-shelf integrations with your own logic.

Features

• Connect external tool servers or write your own Python tools
• Session-aware chat API with multi-turn conversation history
• Single YAML config with environment variable resolution
• Built-in CLI client for interactive use
• Async LangGraph agent with automatic tool routing

Quick Start

git clone https://github.com/baranylcn/ronnyx
cd ronnyx
python -m venv venv
source venv/bin/activate   # macOS / Linux
venv\Scripts\activate      # Windows
pip install -e .

Copy and edit the config file:

cp ronnyx.yaml.example ronnyx.yaml

Set your API keys in .env or directly in ronnyx.yaml:

OPENAI_API_KEY=sk-...
GITHUB_TOKEN=ghp_...

Start the server:

ronnyx-serve

In a separate terminal, start the CLI:

ronnyx-chat

Configuration

All configuration lives in ronnyx.yaml. Environment variables referenced with ${VAR} are resolved from the shell environment or a .env file.

llm:
  model: gpt-4o
  api_key: ${OPENAI_API_KEY}

context:
  github_username: ${GITHUB_USERNAME}

servers:
  github:
    transport: stdio
    command: npx
    args: ["-y", "@modelcontextprotocol/server-github"]
    env:
      GITHUB_TOKEN: ${GITHUB_TOKEN}

custom_tools:
  - function: my_tools.list_repos
    name: list_repos
    description: "List GitHub repos for a user with sorting"

Sections

llm -- Model and API key. Any OpenAI-compatible model works.

context -- Key-value pairs injected into the system prompt. Use this to give the assistant information it cannot retrieve from tools (username, timezone, preferences).

servers -- External tool servers using the Model Context Protocol. Each entry spawns a subprocess (stdio) or connects to a remote endpoint (SSE). All tools exposed by the server become available to the agent automatically.

custom_tools -- Python functions loaded alongside server tools. Point to any callable or StructuredTool instance using its dotted import path. Use this when you need custom logic or when an external server doesn't cover your use case.

API

POST /api/chat

Send a message and receive a response within a session.

curl -X POST http://localhost:8000/api/chat \
  -H "Content-Type: application/json" \
  -d '{"session_id": "1", "message": "How many open issues do I have?"}'

{
  "session_id": "1",
  "reply": "You have 3 open issues in your repository."
}

Each session maintains its own conversation history, so follow-up questions work naturally.

GET /api/tools

List all loaded tools.

curl http://localhost:8000/api/tools

GET /

Health check.

CLI

The CLI connects to the running server over HTTP.

ronnyx-chat
ronnyx-chat --base-url http://localhost:8000/api/chat --session-id 42

You > How many open issues do I have?
Ronnyx > You have 3 open issues in your repository.

The server must be running before starting the CLI.

Testing

pytest -q

All tests run offline without API keys or external servers.

Contributing

Contributions are welcome. See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines. Pull requests should be focused, clearly scoped, and include tests.

License

MIT. See [LICENSE.md](LICENSE.md).

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: baranylcn
• Source: baranylcn/ronnyx
• License: MIT

Install and usage instructions live in the source repository linked above.