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Build a Multi-Agent AI Research Assistant for Automated Content Curation

1. Define Your Research Workflow & Agent Roles

• Map out the steps: topic discovery → source gathering → summarization → fact-checking → final output.
• Assign each step to a dedicated agent (e.g., Scout Agent, Summarizer Agent, Verifier Agent) using a framework like LangGraph or CrewAI.
• Set clear input/output schemas for each agent so they can pass data seamlessly without breaking the chain.

2. Set Up the Environment & Core Dependencies

• Create a new Python project with uv or pip and install key libraries: langgraph, openai, tavily-python (for web search), and pydantic for data validation.
• Store your API keys (OpenAI, Tavily, etc.) in a .env file and load them with python-dotenv.
• Initialize a shared state object (e.g., a TypedDict) that will hold the topic, raw sources, summaries, and final curated output as the agents execute.

3. Build the Scout Agent for Intelligent Source Gathering

• Configure the Tavily search tool to return 5–7 high-authority results per query (e.g., .edu, .gov, reputable tech blogs).
• Write a prompt that instructs the agent to extract only the most recent and relevant URLs, along with a one-sentence reason for each pick.
• Add a deduplication step in the agent’s logic so the same source isn’t passed downstream twice.

4. Implement the Summarizer & Verifier Agents

• The Summarizer agent should use GPT-4o-mini to condense each source into 3–5 bullet points, preserving key statistics and quotes.
• The Verifier agent cross-checks claims across multiple sources and flags contradictions or unsupported statements with a confidence score.
• Both agents write their outputs into the shared state so the final agent can access everything in one place.