build-research-agent

# Build Research Agent Team You are an expert at building AG2 research workflows. When the user wants to build a research agent: ## 1. Choose the Right Approach Ask the user: - Simple single-query search? → Two agents + DuckDuckGoSearchTool - Multi-query parallel research? → Two agents + QuickResearchTool - Deep multi-step research? → Multi-agent team with AutoPattern ## 2. Simple Research Agent (Two Agents + Search Tool) ```python import os from typing import Annotated from autogen import ConversableAgent, LLMConfig from autogen.tools.experimental import DuckDuckGoSearchTool llm_config = LLMConfig( {"model": "gpt-4o-mini", "api_key": os.environ["OPENAI_API_KEY"]} ) assistant = ConversableAgent( name="researcher", system_message="You are a research assistant. Use the search tool to find information, then synthesize a clear answer.", llm_config=llm_config, human_input_mode="NEVER", ) user = ConversableAgent( name="user", llm_config=False, human_input_mode="NEVER", is_termination_msg=lambda msg: True, # End after first response ) # Register pre-built search tool search = DuckDuckGoSearchTool() search.register_for_llm(assistant) search.register_for_execution(user) result = await user.a_run(assistant, message="What are the latest trends in AI agents?") await result.process() ``` Requires: `pip install ag2[openai,duckduckgo_search]` ## 3. Parallel Research Agent (QuickResearchTool) ```python import os from autogen import ConversableAgent, LLMConfig from autogen.tools.experimental import QuickResearchTool llm_config = LLMConfig( {"model": "gpt-4o-mini", "api_key": os.environ["OPENAI_API_KEY"]} ) assistant = ConversableAgent( name="researcher", system_message="You are a research assistant. Break down the user's question into multiple search queries and use the research tool to find comprehensive answers.", llm_config=llm_config, human_input_mode="NEVER", ) user = ConversableAgent( name="user", llm_config=False, human_input_mode="NEVER", is_termination_msg=lambda msg: True, ) # QuickResearchTool searches multiple queries in parallel using Tavily + crawl4ai research = QuickResearchTool( llm_config=llm_config, tavily_api_key=os.environ["TAVILY_API_KEY"], num_results_per_query=3, ) research.register_for_llm(assistant) research.register_for_execution(user) result = await user.a_run(assistant, message="Compare React, Vue, and Svelte for 2025") await result.process() ``` Requires: `pip install ag2[openai,quick-research]` ## 4. Multi-Agent Research Team (Group Chat) ```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 from autogen.tools.experimental import DuckDuckGoSearchTool llm_config = LLMConfig( {"model": "gpt-4o-mini", "api_key": os.environ["OPENAI_API_KEY"]} ) planner = ConversableAgent( name="planner", system_message="You break research tasks into specific questions. Output a numbered list of questions to investigate.", llm_config=llm_config, human_input_mode="NEVER", description="Plans research by breaking down topics into questions. Call first.", ) researcher = ConversableAgent( name="researcher", system_message="You search for information to answer specific questions. Use the search tool for each question.", llm_config=llm_config, human_input_mode="NEVER", description="Searches the web for answers. Call after planner has listed questions.", ) writer = ConversableAgent( name="writer", system_message="You synthesize research findings into a clear, well-structured report. When done, end with TERMINATE.", llm_config=llm_config, human_input_mode="NEVER", description="Writes the final report from gathered research. Call after researcher has findings.", is_termination_msg=lambda msg: "TERMINATE" in msg.get("content", ""), ) user = ConversableAgent(name="user", human_input_mode="NEVER", llm_config=False) # Register search on the researcher search = DuckDuckGoSearchTool() search.register_for_llm(researcher) search.register_for_execution(researcher) result = run_group_chat( pattern=AutoPattern( initial_agent=planner, agents=[planner, researcher, writer], user_agent=user, group_manager_args={"llm_config": llm_config}, ), messages="Research the current state of quantum computing.", max_rounds=15, ) ``` ## 5. Rules - Always install the right extras: `ag2[duckduckgo_search]`, `ag2[quick-research]`, `ag2[tavily]` - DuckDuckGoSearchTool needs no API key — best for getting started - QuickResearchTool needs a Tavily API key but gives much richer results - For group chat research teams, register search on the researcher agent specifically - Set `max_rounds` to prevent infinite loops - Include a termination condition in the writer/final agent