fusefable 0.1.1

Fuse multiple AI models and judge the best answer for coding

Fuse Fable

🌐 Languages: English · ไทย (Thai)

Fan out a coding prompt to many AI models in parallel, then let a judge pick the single best answer — total latency ≈ x2 (slowest model + one judge pass), not x7–x15.

Works three ways: as a CLI, as an MCP server (connect Cursor / VS Code / Claude), and as a subagent / pipe (callable by other tools and scripts).

Install

pip install fusefable            # core
pip install "fusefable[mcp]"     # if you want the MCP server

From source:

git clone https://github.com/proultrax9/fusefable.git
cd fusefable
pip install -e ".[mcp]"

Setup (first run)

fusefable config

• Choose AI Gateway → one key for everything, then it asks "how many models?" and prompts for each one.
  • Known gateways (base URL auto-filled): openrouter, groq, together, fireworks, deepinfra, novita, hyperbolic, aimlapi, portkey, deepseek, openai — any other works too, just type its base URL.
• Or Single providers → it asks how many, then the API kind of each:
  • openai_compat — any OpenAI-compatible endpoint (you provide the base URL)
  • anthropic — Anthropic native (/v1/messages, base URL auto-filled)
  • google — Google Gemini native (generateContent, base URL auto-filled)

Set your API key as an environment variable named as the wizard asks:

export OPENROUTER_API_KEY=sk-...      # macOS/Linux
setx OPENROUTER_API_KEY "sk-..."      # Windows (open a new terminal afterwards)

Config is stored at ~/.fusefable/config.yaml.

1) Use as a CLI

fusefable ask "Write a quicksort function in Python"
fusefable ask --show-all "..."                   # show every answer + judge reason
fusefable ask --models gpt-5,qwen3-coder "..."   # restrict to specific models
fusefable ask --cheap "..."                      # use cheap_models from config
ff ask "..."                                      # short alias

2) Use as a subagent / in a pipe

fusefable ask --quiet "..."                # print only the answer (no headers)
echo "Explain this code" | fusefable ask --quiet   # read the prompt from stdin
cat bug.py | fusefable ask -q "Find the bug in this code"
fusefable ask --json "..."                 # JSON output: answer, chosen_model, reason, cost, candidates

--json is ideal for scripts/agents that parse the result; --quiet is ideal for piping.

3) Use as an MCP server (Cursor / VS Code / Claude / other agents)

Run as an MCP server over stdio:

fusefable mcp

Exposes a tool fuse_ask(question, models?, cheap?) for any MCP client.

Cursor

~/.cursor/mcp.json (or Settings → MCP):

{
  "mcpServers": {
    "fusefable": {
      "command": "fusefable",
      "args": ["mcp"],
      "env": { "OPENROUTER_API_KEY": "sk-..." }
    }
  }
}

VS Code (Copilot / MCP-compatible extension)

.vscode/mcp.json in your project:

{
  "servers": {
    "fusefable": {
      "command": "fusefable",
      "args": ["mcp"],
      "env": { "OPENROUTER_API_KEY": "sk-..." }
    }
  }
}

Claude Desktop

claude_desktop_config.json:

{
  "mcpServers": {
    "fusefable": {
      "command": "fusefable",
      "args": ["mcp"],
      "env": { "OPENROUTER_API_KEY": "sk-..." }
    }
  }
}

Requires pip install "fusefable[mcp]" and a completed fusefable config. If fusefable isn't on the app's PATH, use a full path such as python -m fusefable.cli.

How it works

1. Fan-out — every model is called concurrently via asyncio (total time = slowest model).
2. Any model that times out or fails is dropped — it never slows the whole run.
3. Judge — model names are anonymized (Answer A/B/C...) and a judge model picks the best.
4. Returns the best answer plus an estimated cost.

Development

pip install -e ".[dev,mcp]"
pytest -q

License

MIT