Cenrax

Cenrax / researchswarm

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An agentic system that reads research papers (local PDFs or arXiv), generates implementation plans, writes code, and reviews it — all orchestrated by a central Director agent built on the Claude Agent SDK.

17
6
100% credibility
Found Mar 28, 2026 at 17 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Research Swarm is an AI system that reads research papers, creates implementation plans, generates and tests code, and reviews it for accuracy, with user approvals at each stage.

How It Works

1
🖥️ Discover Research Swarm

You find this helpful tool online that turns science papers into real working projects.

2
📚 Gather Your Papers

You collect your favorite research papers as PDFs or note their online IDs and place them in a simple folder.

3
🎯 Share Your Goal

You tell the tool what you want to build, like a smart image recognizer or text analyzer.

4
📖 It Reads and Summarizes

The smart assistant reads your papers, creates easy-to-understand summaries, and asks if you want to use them.

5
✅ Approve the Plan

You review the clear step-by-step building plan it makes and say yes to move forward.

6
💻 Watch Code Come Alive

It writes the complete code, tests each part safely, and shows you the ready-to-use files.

7
👀 Final Check and Tweak

It carefully reviews the code to match the papers perfectly and asks if you need any changes.

🚀 Your Project is Ready

You now have working code straight from the latest research, ready to run and experiment with.

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Star Growth

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AI-Generated Review

What is researchswarm?

ResearchSwarm is a Python-based agentic system that automates turning research papers into working code: feed it local PDFs or arXiv IDs plus an objective like "build a transformer classifier," and a central director agent orchestrates reading, planning, coding, and reviewing using Claude Agent SDK with Opus and Sonnet models. It spits out summaries, step-by-step plans, runnable code with tests, and review reports in organized output folders, with human approval gates at each stage to keep things on track. Think agentic workflows that bridge academic papers to production prototypes without manual transcription.

Why is it gaining traction?

It stands out as an agentic system framework tailored for research-to-code, unlike generic github agentic copilot tools—here, specialized agents handle paper parsing (even equations), sandboxed execution, and paper-fidelity checks, all customizable via skills without touching code. Developers dig the CLI simplicity (python main.py --objective "..."), arXiv MCP integration for fresh papers, and slash commands for Claude Code sessions, making agentic github coding feel like a guided pipeline rather than chaotic prompts. Early traction comes from its focus on vertical AI agents for ML implementation, echoing agentic systems anthropic patterns.

Who should use this?

ML engineers prototyping paper ideas, like reimplementing attention mechanisms or classifiers without weeks of reading. Researchers validating hypotheses via quick code gens. Teams in agentic systems labs building github agentic workflows for vertical AI agents in industries like healthcare or finance.

Verdict

Worth forking for research-heavy workflows—solid docs, tests, and customization make it a practical agentic system evaluation starter despite 17 stars and 1.0% credibility signaling early maturity. Try it if you hit paper-to-code bottlenecks; skip for production unless you harden it.

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