Autonomous Research Agent

Autonomous Research Agent

Autonomous Research Agent is a multi-agent AI research system designed to transform complex questions into structured, evidence-grounded research reports using real web sources and Large Language Models.

The system uses a specialized agent workflow where the Planner Agent breaks the user's question into research objectives and subquestions, the Researcher Agent performs real web searches and collects relevant sources and evidence, and the Analyst Agent synthesizes and compares the gathered information. The Verifier Agent then validates important claims against the collected evidence, while the Writer Agent generates the final structured report with traceable citations.

The system is built with LLMs, LangGraph, Google Search, evidence grounding, Pydantic, and FastAPI, with a modular architecture supporting conditional agent routing, retries, error handling, evaluation, and scalable workflows.

The project also includes a modern AI research dashboard where users can submit research questions, monitor the progress of each specialized agent, explore retrieved sources and evidence, and view the final verified research report.

Key Features:

Multi-Agent AI Architecture

Real LLM Integration

Real-Time Web Research

Automated Research Planning

Evidence Collection & Grounding

AI-Powered Analysis

Claim Verification

Citation-Based Research Reports

LangGraph Workflow Orchestration

FastAPI Backend

Interactive AI Research Dashboard

Error Handling & Retry Mechanisms

Automated Testing & Evaluation

Docker Support

Core Workflow:

Question → Planning → Research → Analysis → Verification → Report

The goal is to provide an autonomous research workflow that can investigate complex topics, validate information across multiple sources, and produce structured, evidence-backed reports with transparent citations.