Deep Research Agent, an autonomous research AI agent

Repository: https://github.com/tarun7r/deep-research-agent

Most "research" agents just summarise the top 3 web search results. I wanted something better. I wanted an agent that could plan, verify, and synthesize information like a human analyst.

How it works (The Architecture): Instead of a single LLM loop, this system orchestrates four specialised agents:

1. The Planner: Analyzes the topic and generates a strategic research plan.

2. The Searcher: An autonomous agent that dynamically decides what to query and when to extract deep content.

3. The Synthesizer: Aggregates findings, prioritizing sources based on credibility scores.

4. The Writer: Drafts the final report with proper citations (APA/MLA/IEEE) and self-corrects if sections are too short.

The "Secret Sauce": Credibility Scoring One of the biggest challenges with AI research is hallucinations. To solve this, I implemented an automated scoring system. It evaluates sources (0-100) based on domain authority (.edu, .gov) and academic patterns before the LLM ever summarizes them

Built With: Python, LangGraph & LangChain, Google Gemini API, Chainlit

I’ve attached a demo video below showing the agents in action as they tackle a complex topic from scratch.

Check out the code, star the repo, and contribute