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Last updated: September 20, 2026
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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
uvorpipand install key libraries:langgraph,openai,tavily-python(for web search), andpydanticfor data validation. - Store your API keys (OpenAI, Tavily, etc.) in a
.envfile and load them withpython-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.
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Editor’s Pick: Beginner-friendly no-code agent builder for automating content curation and research workflows.
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