Karpathy Agentic ML Engineer
Automated ML engineer powered by Claude Agent SDK and Google ADK, leveraging Scientific Agent Skills to train state-of-the-art models.
- Source repo
- K-Dense-AI/karpathy
- Stars
- ★ 1.6k
- Last updated
- 2mo ago
- License
- MIT
- Primary language
- Python
- FA score
- 37/100 · Major gaps
At a glance
- How it runs
- Works with
- Platform-specificClaude CodeCodex (Partial support)
- Cost
- Free software; you pay for model usage
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- Data scientists exploring AutoML quickly using an agent to automate model training.
- Not a fit if
- Users unwilling to pay for external API keys like OpenRouter
- Teams needing advanced end-to-end ML, who should use the paid K-Dense platform
- Source review
- 37/100 · Major gaps 2 safety controls not found
What does this agent do, and when should you use it?
Karpathy is an agentic Machine Learning Engineer built on the Claude Agent SDK and Google ADK, demonstrating how to automate ML model training. It leverages Scientific Agent Skills (130+ ready-to-use skills) to provide specialized workflows. The startup script start.py sets up a sandbox environment with pre-installed ML packages (PyTorch, transformers, scikit-learn) and scientific skills, then launches an ADK web interface (localhost:8000) where users can select the karpathy agent and interact via chat. This is a simple implementation showcasing the power of Scientific Agent Skills for ML. The project is not affiliated with Andrej Karpathy; the name is a tribute.
Karpathy sets up a sandbox environment via start.py, which copies .env configuration (requires OPENROUTER_API_KEY), clones Scientific Agent Skills into the sandbox, and starts the ADK web interface. Users visit http://localhost:8000, select the karpathy agent, and issue tasks through conversation. The agent uses the sandbox environment and skills to process files (datasets, scripts) placed by the user, train models, and outputs results to the sandbox directory. Manual setup can be done via python -m karpathy.utils and the interface can be started via adk web.
- Data scientists exploring AutoML quickly using an agent to automate model training.
- Researchers needing reproducible agent-skill workflows for scientific analysis, integrated into existing environments.
- Developers building their own agents with ADK, using Karpathy as a reference example.
- Teams wanting to train models on various providers via OpenRouter, configuring AGENT_MODEL.
- Educational demonstrations of combining Claude Agent SDK and Google ADK for machine learning.
How do you install or deploy this agent?
Clone the repository: git clone https://github.com/K-Dense-AI/karpathy.git. cd into the directory, install dependencies using uv: uv sync. Create a .env file with OPENROUTER_API_KEY and AGENT_MODEL. Requires Python 3.13+, uv, and Claude Code installed and authenticated.
How do you use this agent?
Run python start.py to create the sandbox and start the ADK web interface. Open http://localhost:8000, select the karpathy agent, and converse. For manual setup, run python -m karpathy.utils first, then adk web. Place datasets or scripts in the sandbox directory for the agent to access.
What are this agent's strengths and limitations?
- Leverages Scientific Agent Skills with 130+ curated skills, saving time and effort.
- Automated sandbox setup with common ML libraries (PyTorch, transformers), reducing configuration friction.
- Integrated with ADK framework, providing a web interface for easy interaction.
- Serves as a practical example of integrating Claude Agent SDK with Google ADK.
- Requires OPENROUTER_API_KEY, creating dependency on external API and potential costs.
- Agent capabilities are limited; advanced ML workflows require the commercial K-Dense platform.
- Tied to a specific tech stack (Python 3.13, uv, Claude Code), raising setup complexity.
- Project is not affiliated with Andrej Karpathy, which could cause confusion.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Karpathy Agentic ML Engineer This agent | 37 · Major gaps | CLIFree + model costs | ★ 1.6k | 2mo ago | Python | Claude Code |
| optim-agent | 65 · Some gaps | Library / SDKFree + model costs | ★ 800 | 2mo ago | Python | Codex · Claude Code |
| Gyoshu Research Lab | 39 · Major gaps | Agent plugin / skillFree + model costs | ★ 243 | 8mo ago | TypeScript | Claude Code |
| Metaflow | 77 · Good | Library / SDKFree | ★ 10k | 3d ago | Python | — |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence: README requires user to provide OPENROUTER_API_KEY and copies it to sandbox, but no least privilege principle is stated; start.py automatically creates sandbox and copies .env without user confirmation; data flow transparency is limited, only stating outputs are in sandbox directory; sensitive data handling is not explicitly addressed; dependencies are numerous but no security audit is provided; external effects include starting web interface and creating directories, but no rollback mechanism is described; source attribution is clear (MIT license, copyright K-Dense). Deductions: lack of user confirmation, rollback mechanism, and insufficient evidence for sensitive data handling and dependency security.
Evidence: README and pyproject.toml are consistent in describing an agentic ML Engineer; dependency versions are pinned but no lock file is provided; failure messages are not documented. Deductions: dependency availability not verified, failure messages missing.
Evidence: Target audience is ML engineers and researchers, scenarios are clear; capability boundaries are partially described (training ML models) but limitations are not detailed; trigger is manual start via start.py, precision is moderate; environment requirements are explicit (Python 3.13, uv, Claude Code). Deductions: capability boundaries and trigger precision are insufficiently described.
Evidence: README has clear structure with installation, quick start, manual usage; install notes are detailed; naming is stable (karpathy); examples are few, no FAQ; known limitations only mention non-affiliation; MIT license is explicit; version 0.1.0 but no changelog; maintenance responsibility is not clear. Deductions: insufficient examples and FAQ, no changelog, unclear maintenance responsibility.
Evidence: Output is files in sandbox directory, usability is moderate; marginal value lies in providing agentic ML workflow but depends on external services; cost-benefit is not addressed. Deductions: output usability not verified, cost-benefit lacks evidence.
Evidence: Claims in README (e.g., works with Cursor) are not supported by evidence; no independent sources; facts and inferences are not clearly separated. Deductions: claims lack traceability, no cross-validation.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- The repository lacks user confirmation mechanisms; automatic copying of .env files may pose security risks.
- Dependencies are numerous and no security audit is provided; users should verify dependency security themselves.
- No rollback mechanism is provided; if the agent makes errors, recovery may be difficult.
- Claims in README (e.g., works with multiple tools) are unverified; users should rely on them with caution.