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AutoGen (Microsoft) — multi-agent conversation framework. Agent-to-agent chat, code generation & execution, tool use, group chat, and human-in-the-loop. Build collaborative AI systems with specialized agents.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/mkurman/zorai --skill autogen命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Use when provisioning Prime Intellect GPU compute, managing pods/disks/sandboxes, running hosted RL training via prime lab, installing or publishing RL environments, or exposing local services via Prime Tunnel. Covers the `prime` CLI (PyPI: prime) for all Prime Intellect platform operations.
Shared Python package for Science Skills, currently containing http_client -- a unified HTTP client with rate limiting, retries, and exponential backoff. Not a standalone agent skill. Do not invoke directly.
Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.
基于 SOC 职业分类
正在显示 SKILL.md
| name | autogen |
| description | AutoGen (Microsoft) — multi-agent conversation framework. Agent-to-agent chat, code generation & execution, tool use, group chat, and human-in-the-loop. Build collaborative AI systems with specialized agents. |
| tags | ["autogen","multi-agent","conversation","microsoft","llm","agent-framework","zorai"] |
AutoGen (Microsoft) enables multi-agent conversations where specialized LLM agents collaborate, write and execute code, use tools, and solve problems together. Supports group chat, human-in-the-loop, and flexible agent topologies.
uv pip install pyautogen
import autogen
config_list = [{"model": "gpt-4", "api_key": "sk-your-key"}]
assistant = autogen.AssistantAgent(
name="coder",
llm_config={"config_list": config_list},
)
user = autogen.UserProxyAgent(
name="user",
human_input_mode="NEVER",
code_execution_config={"work_dir": "coding", "use_docker": False},
)
user.initiate_chat(
assistant,
message="Write a Python function to calculate Fibonacci numbers.",
)
from autogen import GroupChat, GroupChatManager
groupchat = GroupChat(agents=[engineer, critic, executor], messages=[], max_round=10)
manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config)