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cool-play-agent-teams

Role-driven multi-agent orchestration framework built with strong typing and tool-only collaboration flow. Runtime model execution uses pydantic_ai with OpenAI-compatible endpoints.

Evaluation Snapshot

Recent SWE-bench snapshots are archived under docs/evaluations/swebench/. Current snapshots cover only the first 100 items from SWE-bench Verified, not the full benchmark.

Using glm-5,Temperature: 0.7,Top P:0.95.

Mode Benchmark Pass Rate Passed Failed Mean Duration Input Tokens Cached Input Output Tokens Requests Tool Calls Report
Normal SWE-bench Verified 100 72.0% 72 28 369.2s 60,265,198 58,214,976 451,537 2,432 2,484 HTML
Orchestration SWE-bench Verified 100 73.0% 73 27 704.2s 103,016,077 95,659,776 1,886,195 6,026 7,171 HTML

Highlights:

  • Orchestration currently reaches 73/100 on SWE-bench Verified 100, with 96 runs finishing in completed state and 4 ending in failed.
  • Normal mode currently reaches 72/100 on SWE-bench Verified 100, with 97 runs finishing in completed state and 3 ending in failed.
  • Token usage is reported directly in the table so model IO and tool activity can be compared without deriving cost assumptions.

Web Interface

Agent Teams Web Interface

Start the server with uv run agent-teams server start and open http://127.0.0.1:8000 in your browser. Use uv run agent-teams server restart to restart the managed server, and uv run agent-teams server stop --force to force stop it. The web UI now includes a language toggle beside the settings button so you can switch between English and Simplified Chinese in-page.

Frontend assets are now decoupled under frontend/dist and served by the backend.

Quick start

1) Install dependencies

Use the setup script for your platform, install from PyPI, or install directly with uv.

Windows:

.\setup.bat

Linux/macOS:

sh setup.sh

Install from PyPI:

pip install cool-play-agent-teams

Direct install:

uv sync --extra dev

2) help

agent-teams --help

# for evals
agent-teams-evals --help

Metadata

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