create-agent-team
Scaffold a complete AG2 multi-agent team with agents, tools, group chat configuration, and an entry point. Use when the user wants to create a new multi-agent workflow from scratch.
ソース情報
- リポジトリ
- ag2ai/cursor-plugin
- ソースの最終更新活動
- 2026年3月12日 03:00
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ソースの指示 · 読み取り専用プレビュー- name
- create-agent-team
- description
- Scaffold a complete AG2 multi-agent team with agents, tools, group chat configuration, and an entry point. Use when the user wants to create a new multi-agent workflow from scratch.
# Create AG2 Agent Team
You are an expert AG2 framework developer. The user wants to scaffold a multi-agent team. Follow these steps:
## 1. Understand the Task
Ask the user (if not already clear):
- What is the overall goal of the agent team?
- What distinct roles are needed? (e.g., researcher, coder, reviewer)
- Should agents communicate dynamically (AutoPattern) or in sequence (RoundRobinPattern)?
- What tools/capabilities do the agents need?
## 2. Generate the Code
Create a Python file with this structure:
```python
import os
from typing import Annotated
from autogen import ConversableAgent, LLMConfig
from autogen.agentchat import run_group_chat
from autogen.agentchat.group.patterns import AutoPattern
# 1. LLM Configuration
llm_config = LLMConfig(
{"model": "gpt-4o-mini", "api_key": os.environ["OPENAI_API_KEY"]},
)
# 2. Define Agents
# - Give each agent a clear name (no whitespace), system_message, and description
# - description is used by AutoPattern's LLM to select the next speaker
# - Set human_input_mode appropriately
user = ConversableAgent(
name="user",
human_input_mode="NEVER",
llm_config=False,
)
# ... define role-specific agents ...
# 3. Register Tools (if needed)
# - Use @agent.register_for_llm() and @agent.register_for_execution() decorators
# - Or use Tool/Toolkit classes
# - For pre-built tools: from autogen.tools.experimental import DuckDuckGoSearchTool
# 4. Orchestration — choose pattern based on workflow:
# - AutoPattern: LLM picks next speaker (best for dynamic collaboration)
# - RoundRobinPattern: sequential pipeline (each agent adds to output)
# - DefaultPattern: pure handoff-driven (you define all transitions)
result = run_group_chat(
pattern=AutoPattern(
initial_agent=first_agent,
agents=[agent1, agent2, agent3],
user_agent=user,
group_manager_args={"llm_config": llm_config},
),
messages="Describe the task here.",
max_rounds=15,
)
# 5. Results
result.process()
print(result.summary)
```
## 3. For Handoff-Driven Workflows
When agents should explicitly route to each other:
```python
from autogen.agentchat.group import (
OnCondition, AgentTarget, TerminateTarget, StringLLMCondition,
)
from autogen.agentchat.group.patterns import DefaultPattern
# Define handoffs
researcher.handoffs.add_llm_condition(
OnCondition(
target=AgentTarget(analyst),
condition=StringLLMCondition(prompt="Research is complete and ready for analysis"),
)
)
analyst.handoffs.set_after_work(TerminateTarget())
result = run_group_chat(
pattern=DefaultPattern(
initial_agent=researcher,
agents=[researcher, analyst],
user_agent=user,
),
messages="Start researching...",
max_rounds=20,
)
```
## 4. Rules to Follow
- Import from `autogen`, not `ag2`
- Always use `LLMConfig()` class, never raw dicts for llm_config
- Every agent must have a unique name and a meaningful `description`
- Pair every `register_for_llm()` with a `register_for_execution()`
- Set `human_input_mode="NEVER"` for fully autonomous agents
- Use type annotations with `Annotated[type, "description"]` for all tool parameters
- Do NOT add tools to the group manager's llm_config
- Always set `max_rounds` to prevent infinite loops
- When using `AutoPattern`, always provide `llm_config` in `group_manager_args`
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