create-agent-team

# 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`