<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> Nanocoder is a local-first, privacy-focused CLI coding agent bringing AI-assisted coding to local models or controlled APIs. It targets developers seeking control over AI tools and data, offering a community-driven alternative to corporate solutions. The primary benefit is enhanced privacy and flexibility via on-device AI processing.

How It Works

This terminal application connects to AI providers via OpenAI-compatible endpoints, prioritizing on-device processing. Its Model Context Protocol (MCP) server system enables integration with tools for file operations, GitHub management, and custom scripts. Configuration via agents.config.json allows per-project customization of AI models and tools.

Quick Start & Requirements

Install via npm: npm install -g @motesoftware/nanocoder. Requires Node.js 18+. Development involves cloning, npm install, and npm run build/npm run start. Configuration needs agents.config.json for AI providers and MCP servers.

Highlighted Details

• Multi-Provider Support: Integrates with OpenAI-compatible APIs, including local (Ollama, LM Studio) and cloud (OpenRouter) options, with smart fallback.
• Advanced Tool System: Uses MCP servers for dynamic tool loading (file ops, commands, GitHub) with optional execution approval.
• Custom Command System: Defines reusable prompts via Markdown files with templating, parameterization, and namespace organization, supporting command discovery via autocomplete.
• Enhanced User Experience: Features smart autocomplete, prompt history, configurable logging, colorized output, and session persistence.

Maintenance & Community

Nanocoder emphasizes community contributions to code, docs, and features, supported by a Discord server. Detailed contribution guidelines are available.

Licensing & Compatibility

The README does not specify the project's license, a critical omission for assessing commercial use or derivative works.

Limitations & Caveats

The project is developed by a "small team," potentially indicating a higher bus factor. Performance depends on user hardware and chosen local LLMs. No specific alpha status or critical bugs are noted.