ff-taskforce 0.1.0

Multi-LLM roundtable for diverse perspective gathering

taskforce

AI agent-oriented multi-LLM roundtable library.

Multiple top-tier LLMs (GPT, Grok, Claude, Gemini) are queried in parallel with the same agenda, and the collected opinions are classified into common / divergent / unique perspectives, returned as a structured IdeaPool.

This package is designed for AI agents, not for direct human use. The primary interface is the MCP wrapper (roundtable_discuss tool), which allows agents to invoke a roundtable discussion as a tool call. A Python API is also available for programmatic integration.

Quick Start

1. Install

pip install taskforce

For MCP server support:

pip install taskforce[mcp]

2. Set environment variables

At least two provider API keys are required (one will be excluded as the caller). XAI_API_KEY is always required (used by the summarizer).

OPENAI_API_KEY=sk-...
XAI_API_KEY=xai-...
ANTHROPIC_API_KEY=sk-ant-...
GEMINI_API_KEY=AI...

3. Use as MCP tool (recommended for agents)

Add to your MCP server config:

TASKFORCE_CALLER_PROVIDER is the provider of the agent that will call this tool. The matching provider's model is excluded from the panel -- querying the same model that is already reasoning adds no diversity. For example, if Claude Code is the caller, set it to "anthropic" so Claude is excluded from the panel.

{
  "mcpServers": {
    "taskforce": {
      "command": "python",
      "args": ["-m", "taskforce.mcp_wrapper"],
      "env": {
        "TASKFORCE_CALLER_PROVIDER": "anthropic"
      }
    }
  }
}

The agent can then call the roundtable_discuss tool with agenda and context parameters.

4. Use as Python library

from taskforce import Taskforce

tf = Taskforce(caller_provider="anthropic")
pool = tf.discuss(
    agenda="Evaluate the trade-offs of approach A vs B",
    context="<detailed context here>"
)

# pool.common    -- list[str]: points most models agree on
# pool.divergent -- list[DivergentPoint]: topics with differing positions
# pool.unique    -- list[UniquePoint]: points raised by only one model

Important Notes

• Paid API calls. Every discuss() invocation calls multiple LLM APIs in parallel. Agents should confirm with the user before calling.
• caller_provider exclusion. The model from the same provider as the calling agent is excluded from the panel to maximize perspective diversity.
• XAI_API_KEY is mandatory. The summarizer (grok-4-1-fast-non-reasoning) always uses the XAI key.
• Rich context matters. Input tokens are cheap. Provide as much context as possible -- specifications, constraints, background, decisions already made -- so the panel can give concrete, actionable opinions instead of generic advice.

API

Taskforce(caller_provider, dotenv_path=None)

• caller_provider (str): The LLM provider of the calling agent (e.g. "anthropic", "openai"). That provider's model is excluded from the panel.
• dotenv_path (str | None): Path to .env file. Defaults to auto-discovery.

Taskforce.discuss(agenda, context="") -> IdeaPool

Synchronous wrapper. Queries the panel, summarizes, and returns an IdeaPool.

Taskforce.discuss_async(agenda, context="") -> IdeaPool

Async version for use in async contexts.

IdeaPool

Field	Type	Description
agenda	str	The original agenda
common	list[str]	Points most models agree on
divergent	list[DivergentPoint]	Topics with differing positions (topic, positions: dict[model, position])
unique	list[UniquePoint]	Points from a single model (point, source_model)
total_cost	float	Total API cost (USD)
total_tokens	int	Total tokens consumed

License

MIT