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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.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/mkurman/zorai --skill autogen명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
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.
SOC 직업 분류 기준
SKILL.md 표시 중
| 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)