deeplearning-ai-agentic-research-agent

To orchestrate multi-agent AI research workflows, this Skill coordinates a planner, researcher, writer, and editor agent sequentially. It automates end-to-end task execution by assigning specific research, drafting, and editing phases to each specialized agent.

Yes, you can use arXiv and Wikipedia for automated literature review within a FastAPI application. This Skill integrates these sources alongside Tavily web search, feeding content directly into the researcher agent for literature review and summarization.

The best way to manage AI agent task state using Postgres and a REST API is to persist workflow progress in a database. This Skill uses Postgres-backed task state management, allowing you to start research tasks and track their progress via REST endpoints.

Yes, this multi-agent research workflow supports Docker deployment with a web UI. It provides containerized deployment for the FastAPI application and Postgres database, featuring a web interface for task submission and monitoring ongoing research workflows.

To generate cohesive research reports automatically, a writer agent synthesizes findings from the researcher agent's Tavily web search, arXiv papers, and Wikipedia summaries. An editor agent then refines the draft to produce a final cohesive report.