Autonomous Research Agent: From Question to Cited Report Automatically
An autonomous research agent plans, searches, evaluates and writes with minimal human input. We explain the architecture and the open-source options.
📜 Table of Contents
The workflow behind every autonomous research agent: plan, search, evaluate, write, repeat. Understanding those five stages tells you where it shines and where it will confidently hallucinate - so you know what to verify.
The Five-Stage Architecture
1. Plan: decompose the question into sub-queries (gpt-researcher, 28,855 stars, does this with a planner model). 2. Search: run web searches per sub-query. 3. Evaluate: score sources by credibility - this is where quality lives or dies. 4. Write: synthesize with citations. 5. Loop: verify gaps, re-search, refine.
Open-source options: gpt-researcher for general research, Sakana AI-Scientist (14,352 stars) for experimental research, and OpenHands (83,221 stars) when research means reading and modifying code. All run with local models for private data. The failure mode to watch: when a sub-query returns nothing useful, the agent may pad with weak sources - check every citation.
Comparison
| Stage | Job | Risk |
|---|---|---|
| Plan | Decompose question | Off-track queries |
| Search | Gather sources | Thin results |
| Evaluate | Score credibility | Weak sources pass |
| Write | Synthesize + cite | Hallucinated cites |
| Loop | Verify and refine | Runaway token use |
FAQ
Q: How long does a research run take?
A: Typically 2-10 minutes depending on sub-query count and model speed; cost scales with searches and tokens.
Q: Can it access paywalled or internal sources?
A: Only what the search tool can reach. For internal docs, wire your own retrieval into the pipeline.
Written by our editorial team; tools listed here are tested or verified against public sources. Links point to official sites or GitHub repos for reference only — no paid placements.