Dragon CLI

Chat with local GGUF LLMs (100% CPU, via llama-cpp-python), with optional MCP (Model Context Protocol) tool-calling and a SQLite-backed conversation history.

Install

pip install -e .
dragon-cli-install-llm

This exposes a dragon-cli command. llama-cpp-python is intentionally kept out of the core dependencies, since it ships as C++ source on PyPI for many platforms — a plain pip install would try to compile it locally and fail without a C/C++ toolchain (Visual Studio Build Tools on Windows, gcc on Linux, Xcode command line tools on macOS).

dragon-cli-install-llm detects your OS/architecture (Windows, Linux, or macOS) and installs a prebuilt CPU wheel instead, so a compiler usually isn't needed at all. If no prebuilt wheel is available for your exact platform/Python combination, it prints the toolchain to install and the fallback command to run.

If you already have a working C/C++ toolchain and would rather build from source yourself, skip the installer and use the extra instead: pip install -e ".[llm]".

You can also run it without installing:

python dragon_cli.py
# or
python -m dragon_cli

Usage

dragon-cli                       # model menu + chat
dragon-cli --model 2             # skip the model menu
dragon-cli --list                # just list the model catalog
dragon-cli --no-mcp              # disable MCP connectors
dragon-cli --resume 3            # resume saved conversation #3
dragon-cli --list-conversations  # list saved conversations and exit
dragon-cli --db path/to/other.db # use a different conversation database
dragon-cli --serve               # serve the model over HTTP instead of chatting
dragon-cli --serve --port 8080   # custom port (default: 8000)
dragon-cli --serve --host 0.0.0.0  # expose beyond localhost (e.g. to your LAN) — opt-in only

HTTP server (--serve)

dragon-cli --serve loads a model and exposes it over a minimal OpenAI-compatible HTTP API instead of starting the interactive chat. It's built on FastAPI, so interactive docs come for free:

• GET / — landing page with usage examples
• GET /docs — Swagger UI
• GET /redoc — ReDoc
• GET /openapi.json — raw OpenAPI schema
• GET /v1/models — lists the currently loaded model
• POST /v1/chat/completions — same shape as OpenAI's endpoint, streaming ("stream": true, server-sent events) and non-streaming both supported, request body validated (422 with a clear error on malformed input)

Point any OpenAI-compatible client/SDK at http://127.0.0.1:8000/v1 (adjust host/port to match --host/--port) and it works without extra glue code. This mode does not expose MCP tool-calling — it's the model only, kept intentionally simple and predictable. It also binds to 127.0.0.1 by default (only reachable from your own machine); pass --host 0.0.0.0 explicitly if you want it reachable from other devices on your network.

In-chat commands

Every message is autosaved to SQLite (dragon_cli.db by default) as soon as it's sent, so nothing is lost if the process crashes.

MCP connectors

On first run, an example mcp_servers.json is created in the working directory. Edit it to point at your MCP servers and restart to connect.

Project layout

src/dragon_cli/
├── cli.py            # argparse entry point
├── app.py             # model selection + orchestration
├── setup_llm.py        # cross-platform llama-cpp-python installer
├── server.py            # OpenAI-compatible HTTP server (--serve)
├── banner.py            # ASCII banner
├── config.py             # shared paths/colors
├── models/            # model catalog, download, loading
├── mcp/                # MCP server manager + tool-call parsing
├── storage/            # SQLite conversation store
└── chat/                # chat session, streaming, slash commands