agentic-research-agent-fastapi

Automate multi-step research workflows via FastAPI with PostgreSQL task tracking.

2|1|Updated May 16, 2026

Search Tags:#planning #research #postgresql #fastapi #tools #automate

Ready to run

npx skills add https://github.com/Aradotso/ai-agent-skills --skill agentic-research-agent-fastapi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agentic-research-agent-fastapi
Source: https://github.com/Aradotso/ai-agent-skills/tree/main/skills/agentic-research-agent-fastapi
Command: npx skills add https://github.com/Aradotso/ai-agent-skills --skill agentic-research-agent-fastapi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates end-to-end, multi-step research workflows by coordinating planning, execution, and reflective iteration through a FastAPI service backed by PostgreSQL, enabling scalable collaboration between agents and tools like Tavily, arXiv, and Wikipedia.

Core Features & Use Cases

Planning agent decomposes prompts into structured subtasks for reliable task breakdown.
Executor agent runs research tasks, gathers results with external tools, and integrates outputs.
Reflection/iteration loop refines findings and improves outcomes across cycles.
PostgreSQL-backed task tracking and a lightweight web UI to trigger and monitor workflows.

Quick Start

Start the Agentic Research Agent service in your environment and issue a request to generate a multi-step research workflow.

Frequently Asked Questions about agentic-research-agent-fastapi

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate multi-step research workflows with planning and reflection?▼

Automate multi-step research workflows using a FastAPI service that decomposes prompts into subtasks, executes them with external tools, and iteratively refines results through a reflection loop backed by PostgreSQL task tracking.

What is the best way to structure research tasks for reliable execution?▼

Structure research tasks using a planning agent that decomposes complex prompts into structured subtasks, enabling an executor agent to reliably run research and gather results with tools like Tavily, arXiv, and Wikipedia.

Do I need Docker and an OpenAI-compatible LLM to run automated research agents?▼

Yes, Docker is required for reproducible deployment runs, and an OpenAI-compatible LLM is required to power the agentic reasoning. The system operates as a FastAPI service with PostgreSQL-backed task tracking.

Can I use arXiv and Wikipedia tools with a FastAPI research agent?▼

Yes, the FastAPI research agent integrates with arXiv and Wikipedia, alongside Tavily. The executor agent uses these external tools to gather results, while PostgreSQL tracks the workflow status and outputs.

How does a reflection loop improve research outcomes across multiple cycles?▼

A reflection loop improves research outcomes by iteratively evaluating and refining gathered findings after the executor agent completes its tasks. This cycle ensures the multi-step workflow corrects errors and enhances quality across multiple iterations.

How do I monitor research task progress when using a FastAPI agent?▼

Monitor research task progress through a lightweight web UI and PostgreSQL-backed task tracking. This setup allows you to trigger workflows and observe the planning, execution, and reflection stages in real time.