mlcode

AI coding agent for machine learning tasks

Create, execute, and evaluate Jupyter notebooks, iterate on model architectures, and automate ML workflows directly from your terminal.

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What is mlcode?

mlcode is an AI coding agent tailored for machine learning workflows. Operating inside your terminal or notebook workspace, it helps you:

• Create, edit, & run Jupyter notebooks for data exploration, model training, and evaluation.
• Iterate on ML models — tweak hyperparameters, benchmark metrics, and refactor training pipelines.
• Automate experiment loops — inspect stack traces, fix code errors, and log results.
• Maintain session history — durable session trees with branching, resume, and compaction.

tau_coding  →  tau_agent  →  tau_ai

• tau_ai: Model provider streaming layer (OpenAI, Anthropic, OpenRouter, local models).
• tau_agent: Portable agent brain (messages, tools, events, session tree).
• tau_coding: CLI application, Textual interactive TUI, file/shell tools, and resources.

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Quickstart

Installation

mlcode requires Python 3.12 or newer.

From PyPI:

# Using uv (recommended)
uv tool install mlcode-ai

# Using pip
pip install mlcode-ai

For local development:

uv run mlcode

Usage

Run mlcode from your ML project directory:

cd my-ml-project
mlcode

Then prompt mlcode in the interactive TUI:

build a baseline PyTorch model for CIFAR-10 in a notebook and evaluate accuracy

Non-interactive print mode for scripts:

mlcode -p "summarize dataset structure in data/"
mlcode --cwd /path/to/project -p "run notebook and output evaluation metrics"

Connect a Model Provider

Start mlcode and authenticate your AI provider with /login:

/login
/login openai
/model

Supported providers include OpenAI, Anthropic, OpenRouter, OpenAI Codex, Hugging Face, and local OpenAI-compatible endpoints (Ollama, vLLM, DeepSeek).

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Capabilities

• Interactive TUI & Print Mode: Full terminal interface powered by Textual or non-interactive CLI.
• Coding & Execution Tools: read, write, edit, and bash execution.
• Notebook & ML Automation: Work with scripts, notebooks, and experiment loops.
• Durable Sessions: Append-only JSONL session histories with tree branching (/tree) and recovery.
• Custom Skills & Instructions: Support for project-level instructions (AGENTS.md) and custom skills.

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License

Released under the MIT License.