Open Source · MIT Licensed · v2.0

Research Papers
Become
production_code()

ScholarDevClaw reads the latest ML research, maps improvements directly to your codebase, and generates validated patches — fully autonomous, runs locally, zero data sharing.

curl -fsSL https://ronak-iitd.github.io/ScholarDevClaw/install.sh | bash

Also via pip · View on GitHub →

Supports
Python
JavaScript
TypeScript
Go
Rust
Java
1260+ Tests Passing
MIT Open Source
15 Paper Specs
Process

Six phases.
One autonomous pipeline.

From raw research paper to production-ready pull request — ScholarDevClaw handles the entire workflow without human intervention.

01
Analyze Repository
Tree-sitter extracts a full AST from your codebase across 6+ languages. Detects frameworks, patterns, dependencies, and architecture automatically.
02
Search Research Papers
Searches arXiv (2.4M+ papers), Papers with Code, and GitHub for ML research relevant to your codebase. Extracts implementation specs from PDFs.
03
Map to Your Code
6-tier matching locates where improvements apply: exact name → fuzzy → imports → text scan → legacy → LLM semantic. Every match is confidence-scored.
04
Generate Patches
Creates validated patches using 15 CST templates (RMSNorm, FlashAttention, RoPE, SwiGLU…), 10 CST transformers, and LLM synthesis fallback.
05
Benchmark & Validate
Real subprocess benchmarks measure actual speedup and memory impact. Mandatory approval gates on low-confidence or risky validations before proceeding.
06
Open a Safe PR
Creates a clean pull request on a safe branch with full changelog, benchmark results, and confidence scores attached. Protected-branch blocking keeps you safe.
Capabilities

Built for research engineers

Production-grade tooling for teams who operationalize ML research at scale.

Repository Intelligence
Tree-sitter AST extraction across 6+ languages. Detects frameworks, dependency patterns, and architectural improvement opportunities automatically.
Python ✓
JavaScript ✓
TypeScript ✓
Go ✓
Rust ✓
Java ✓
1# AST Analysis
2from scholardevclaw import analyze
3 
4result = analyze("./my-model")
5# → 6 improvements found
6# → RMSNorm candidate: 94%
7# → FlashAttn match: 97%
Research Intelligence
Searches arXiv (2.4M+ papers), Papers with Code, and GitHub for ML research relevant to your architecture. Extracts implementation specs from PDFs automatically.
2.4M+ arXiv Papers
6-Tier Smart Matching
Exact name → Fuzzy → Imports → Text scan → Legacy → LLM semantic. Every match is confidence-scored with mandatory approval gates on risky mappings.
→ Exact name match
→ Fuzzy matching
→ Import analysis
→ Text scanning
→ LLM semantic
6 Tiers
Patch Generation
15 CST templates for the most impactful ML optimisations — RMSNorm, FlashAttention, SwiGLU, RoPE and more — plus LLM synthesis fallback.
15 Templates
Benchmark Validation
Real subprocess benchmarks measure speedup and memory impact. 1260+ tests guarantee patch quality and prevent regressions before any PR is opened.
1260+ Tests
Interactive TUI
Premium terminal interface with workflow wizard, live logs, run history, artifact viewer, and approval gates — all in one place.
$ scholardevclaw tui
Launching…
✓ Wizard ready
▸ analyze → suggest
▸ map → generate
⏸ Approval required
By the numbers

Precision at scale

Every number earned — built on real benchmarks, real tests, and real research integrations.

0
Paper Specs
Built-in research implementations
0
Tests Passing
Full integration & unit coverage
0
Languages
Python, JS, TS, Go, Rust, Java
0
Code Templates
Battle-tested CST transformers
Demo

Running in under
a minute

Install once, improve any codebase. ScholarDevClaw handles the full research-to-code pipeline autonomously.

1
Install
curl -fsSL …/install.sh | bash
2
Run the demo on nanoGPT
scholardevclaw demo
3
Launch the interactive TUI
scholardevclaw tui
4
Point at your own project
scholardevclaw analyze .
scholardevclaw — zsh
FAQ

Common questions

Have something else in mind? Reach out on GitHub — we respond quickly.

Open an Issue Start a Discussion

ScholarDevClaw is an autonomous Research-to-Code AI Agent that analyzes your codebase, finds relevant improvements from the latest ML papers, maps them to your code, and generates validated patches automatically. It supports Python, JS/TS, Go, Rust, and Java — running entirely on your local machine.

Generic AI coding assistants complete code you write. ScholarDevClaw proactively reads actual research papers, extracts implementation specs, and autonomously maps them to your specific codebase using 6-tier matching. It's built for researchers and engineers who want to operationalize ML innovations — not just autocomplete.

No. ScholarDevClaw works out of the box with its built-in knowledge base of 15 paper specs — RMSNorm, FlashAttention, RoPE, SwiGLU, and more. For advanced LLM-powered semantic matching you can optionally connect Claude, GPT, Gemini, or any other provider.

Never. ScholarDevClaw runs entirely locally. Your code is analyzed on your machine and never transmitted anywhere unless you explicitly configure an external LLM service. All analysis, mapping, and patch generation happens locally — you have full control.

Yes. ScholarDevClaw is fully open source under the MIT license. Fork it, contribute to it, or build on top of it. The source code, docs, and contribution guide are all on GitHub.

Get started

Your codebase deserves
the latest research.

Install ScholarDevClaw and run your first research-to-code pipeline in under a minute.

Install now View on GitHub