localcode

PyPI License Python Platform

A coding agent that runs a local model on your Mac.
No cloud inference, no API key, no account.

A localcode turn: two reads, one edit, then pytest reporting 5 passed

localcode runs an open-weight model on your Mac and uses it to read, edit and test your code. Your prompts and your files stay on your machine. The only thing it downloads is the model weights, once per model.

Install

pip install -U localcode      # or: uv pip install -U localcode

The inference server ships inside the package. Nothing is compiled or cloned on your machine.

Run

cd your-project
localcode

On first launch localcode recommends a model for your Mac's memory. Pick one, wait for the download, and start typing.

> Implement the retry decorator in retry.py so every test in test_retry.py passes. Then run: pytest -q

Docs: mjwsolo.github.io/localcode

What it does

  • Reads and edits files in your project
  • Runs your tests, builds, Git and shell commands, and asks before anything risky
  • Searches code by name, content or structure
  • Scaffolds and launches apps, then checks that they respond
  • Remembers the task across messages

Requirements

  • Mac with Apple Silicon
  • 16 GB unified memory or more
  • Python 3.10 or newer
  • About 12 GB of free disk for the smallest model

Models

localcode recommends a model by your Mac's memory and marks it with a star. You choose; nothing is selected for you. Every model runs on binaries shipped in the package.

Model Weights Quant Active params Min RAM
Gemma 4 12B 7.4 GB UD-Q4_K_XL 12B (dense) 16 GB
Qwen 3.6 35B-A3B 10.7 GB UD-IQ2_M 3.0B (MoE) 24 GB
Gemma 4 26B-A4B 11.2 GB UD-IQ3_S 3.8B (MoE) 24 GB
DiffusionGemma 26B-A4B 15.7 GB Q4_K_M 4B (diffusion MoE) 32 GB
Muse Glimmer 30B 15.9 GB UD-Q4_K_XL 30B (dense, vision) 32 GB
Qwen 3.8 27B 17.9 GB UD-Q4_K_XL 27B (dense) 36 GB
North-Mini-Code 30B-A3B 17.9 GB UD-Q4_K_M 3B (MoE) 36 GB
Gemma 4 12B (full) 23.8 GB BF16 12B (dense) 48 GB
Gemma 4 26B-A4B 28.0 GB UD-Q8_K_XL 3.8B (MoE) 64 GB
Qwen 3.6 35B-A3B 38.5 GB UD-Q8_K_XL 3.0B (MoE) 96 GB

Min RAM is the memory at which localcode will recommend the model. You can pick a heavier one by hand. DiffusionGemma is a research model that is never recommended automatically.

Measured on a MacBook Pro (M5 Max, 128 GB) with Qwen 3.6 35B-A3B UD-IQ2_M at a 131072-token context: about 89 tokens/s generation, about 1174 tokens/s prompt processing, and 12 to 15 seconds for a typical four-tool-call task.

Network

Inference is local. Three features use the network: model downloads, the web_search and web_fetch tools, and any MCP servers you add. See Network Boundary for the full list.

Why local?

Powerful, personal AI should work for everyone, on any device, anywhere. That means running it locally. localcode is a first step.

Sponsors

To sponsor localcode, reach out.

Contributing

See CONTRIBUTING.md.

License

Apache 2.0. See LICENSE.

Project links

Data verified by PyPI on Aug 24, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (0.3.71)

1 maintainer

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Avatar for thecchef from gravatar.com thecchef

Credits

Author: LocalCode contributors

GitHub Statistics

Data verified by PyPI on Aug 24, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python >=3.10

Provides Extra

voice

Tags

ai cli coding-assistant gemma local-first tui

Classifiers

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