deeplearning-ai-agentic-research

You can automate multi-step research workflows by posting a prompt to a FastAPI endpoint, where a planner agent decomposes the query into tasks and coordinates specialized agents to execute steps and produce a final report.

A multi-agent research pipeline uses a planner agent to generate a workflow, then coordinates specialized researchers and agents to execute steps using tools like Tavily, arXiv, and Wikipedia to gather data and draft results.

Research task progress is tracked via Postgres-backed state management, allowing you to poll a REST API endpoint for live updates on task execution status until the final report is produced.

Yes, the multi-agent research pipeline operates within a FastAPI service and supports single-container deployment, allowing you to kick off research tasks and monitor results locally through a REST API.

AI agents can integrate with Tavily for web search, arXiv for academic papers, and Wikipedia as a knowledge base to automate data gathering across multi-step research workflows.

The best way to orchestrate arXiv searches is using a multi-agent pipeline where a planner agent decomposes your query into tasks, then coordinates researchers to query arXiv and other tools to gather and draft findings.