nanocoder

A beautiful local-first coding agent running in your terminal - built by the community for the community ⚒

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Nanocoder is a local-first CLI coding agent that supports multiple AI providers with tool support for file operations and command execution. It focuses on privacy and control, allowing users to code locally with AI tools. The tool is designed to bring the power of agentic coding tools to local models or controlled APIs like OpenRouter, promoting community-led development and inclusive collaboration in the AI coding space.

README:

Nanocoder

A local-first CLI coding agent that brings the power of agentic coding tools like Claude Code and Gemini CLI to local models or controlled APIs like OpenRouter. Built with privacy and control in mind, Nanocoder supports multiple AI providers with tool support for file operations and command execution.

FAQs
Installation
- For Users (Recommended)
- For Development
Configuration
Features
Community

FAQs

What is Nanocoder?

Nanocoder is a local-first CLI coding agent that brings the power of agentic coding tools like Claude Code and Gemini CLI to local models or controlled APIs like OpenRouter. Built with privacy and control in mind, Nanocoder supports any AI provider that has an OpenAI compatible end-point, tool and non-tool calling models.

How is this different to OpenCode?

This comes down to philosophy. OpenCode is a great tool, but it's owned and managed by a venture-backed company that restricts community and open-source involvement to the outskirts. With Nanocoder, the focus is on building a true community-led project where anyone can contribute openly and directly. We believe AI is tool powerful to be in the hands of big corporations and everyone should have access.

We also strongly believe in the "local-first" approach, where your data, models, and processing stay on your machine whenever possible to ensure maximum privacy and user control. Beyond that, we're actively pushing to develop advancements and frameworks for small, local models to be effective at coding locally.

Not everyone will agree with this philosophy, and that's okay. We believe in fostering an inclusive community that's focused on open collaboration and privacy-first AI coding tools.

I want to be involved, how do I start?

Firstly, we would love for you to be involved. You can get started contributing to Nanocoder in several ways, check out the Community section of this README.

Installation

For Users (Recommended)

Install globally and use anywhere:

npm install -g @motesoftware/nanocoder

Then run in any directory:

nanocoder

For Development

If you want to contribute or modify Nanocoder:

Prerequisites:

Node.js 18+
npm

Setup:

Clone and install dependencies:

git clone [repo-url]
cd nanocoder
npm install

Build the project:

npm run build

Run locally:

npm run start

Or build and run in one command:

npm run dev

Configuration

AI Provider Setup

Nanocoder supports any OpenAI-compatible API through a unified provider configuration. Create agents.config.json in your working directory (where you run nanocoder):

{
	"nanocoder": {
		"providers": [
			{
				"name": "Ollama",
				"baseUrl": "http://localhost:11434/v1",
				"models": ["qwen2.5-coder:14b", "llama3.2"]
			},
			{
				"name": "OpenRouter",
				"baseUrl": "https://openrouter.ai/api/v1",
				"apiKey": "your-openrouter-api-key",
				"models": ["openai/gpt-4o-mini", "anthropic/claude-3-haiku"]
			},
			{
				"name": "LM Studio",
				"baseUrl": "http://localhost:1234/v1",
				"models": ["local-model"]
			}
		]
	}
}

Common Provider Examples:

Ollama (Local):
- First run: ollama pull qwen2.5-coder:14b
- Use: "baseUrl": "http://localhost:11434/v1"
OpenRouter (Cloud):
- Use: "baseUrl": "https://openrouter.ai/api/v1"
- Requires: "apiKey": "your-api-key"
LM Studio: "baseUrl": "http://localhost:1234/v1"
vLLM: "baseUrl": "http://localhost:8000/v1"
LocalAI: "baseUrl": "http://localhost:8080/v1"
llama.cpp server: "baseUrl": "http://localhost:8080/v1"
OpenAI: "baseUrl": "https://api.openai.com/v1"

Provider Configuration:

name: Display name used in /provider command
baseUrl: OpenAI-compatible API endpoint
apiKey: API key (optional for local servers)
models: Available model list for /model command

MCP (Model Context Protocol) Servers

Nanocoder supports connecting to MCP servers to extend its capabilities with additional tools. Configure MCP servers in your agents.config.json:

{
	"nanocoder": {
		"mcpServers": [
			{
				"name": "filesystem",
				"command": "npx",
				"args": [
					"@modelcontextprotocol/server-filesystem",
					"/path/to/allowed/directory"
				]
			},
			{
				"name": "github",
				"command": "npx",
				"args": ["@modelcontextprotocol/server-github"],
				"env": {
					"GITHUB_TOKEN": "your-github-token"
				}
			},
			{
				"name": "custom-server",
				"command": "python",
				"args": ["path/to/server.py"],
				"env": {
					"API_KEY": "your-api-key"
				}
			}
		]
	}
}

When MCP servers are configured, Nanocoder will:

Automatically connect to all configured servers on startup
Make all server tools available to the AI model
Show connected servers and their tools with the /mcp command

Popular MCP servers:

Filesystem: Enhanced file operations
GitHub: Repository management
Brave Search: Web search capabilities
Memory: Persistent context storage
View more MCP servers

Note: The agents.config.json file should be placed in the directory where you run Nanocoder, allowing for project-by-project configuration with different models or API keys per repository.

User Preferences

Nanocoder automatically saves your preferences to remember your choices across sessions. Preferences are stored in ~/.nanocoder-preferences.json in your home directory.

What gets saved automatically:

Last provider used: The AI provider you last selected (by name from your configuration)
Last model per provider: Your preferred model for each provider
Session continuity: Automatically switches back to your preferred provider/model when restarting

How it works:

When you switch providers with /provider, your choice is saved
When you switch models with /model, the selection is saved for that specific provider
Next time you start Nanocoder, it will use your last provider and model
Each provider remembers its own preferred model independently

Manual management:

View current preferences: The file is human-readable JSON
Reset preferences: Delete ~/.nanocoder-preferences.json to start fresh
No manual editing needed: Use the /provider and /model commands instead

Commands

Built-in Commands

/help - Show available commands
/clear - Clear chat history
/model - Switch between available models
/provider - Switch between configured AI providers
/mcp - Show connected MCP servers and their tools
/debug - Toggle logging levels (silent/normal/verbose)
/custom-commands - List all custom commands
/exit - Exit the application
!command - Execute bash commands directly without leaving Nanocoder (output becomes context for the LLM)

Custom Commands

Nanocoder supports custom commands defined as markdown files in the .nanocoder/commands directory. Like agents.config.json, this directory is created per codebase, allowing you to create reusable prompts with parameters and organize them by category specific to each project.

Example custom command (.nanocoder/commands/test.md):

---
description: 'Generate comprehensive unit tests for the specified component'
aliases: ['testing', 'spec']
parameters:
  - name: 'component'
    description: 'The component or function to test'
    required: true
---

Generate comprehensive unit tests for {{component}}. Include:

- Happy path scenarios
- Edge cases and error handling
- Mock dependencies where appropriate
- Clear test descriptions

Usage: /test component="UserService"

Features:

YAML frontmatter for metadata (description, aliases, parameters)
Template variable substitution with {{parameter}} syntax
Namespace support through directories (e.g., /refactor:dry)
Autocomplete integration for command discovery
Parameter validation and prompting

Pre-installed Commands:

/test - Generate comprehensive unit tests for components
/review - Perform thorough code reviews with suggestions
/refactor:dry - Apply DRY (Don't Repeat Yourself) principle
/refactor:solid - Apply SOLID design principles

Features

🔌 Multi-Provider Support

Universal OpenAI compatibility: Works with any OpenAI-compatible API
Local providers: Ollama, LM Studio, vLLM, LocalAI, llama.cpp
Cloud providers: OpenRouter, OpenAI, and other hosted services
Smart fallback: Automatically switches to available providers if one fails
Per-provider preferences: Remembers your preferred model for each provider
Dynamic configuration: Add any provider with just a name and endpoint

🛠️ Advanced Tool System

Built-in tools: File operations, bash command execution
MCP (Model Context Protocol) servers: Extend capabilities with any MCP-compatible tool
Dynamic tool loading: Tools are loaded on-demand from configured MCP servers
Tool approval: Optional confirmation before executing potentially destructive operations

📝 Custom Command System

Markdown-based commands: Define reusable prompts in .nanocoder/commands/
Template variables: Use {{parameter}} syntax for dynamic content
Namespace organization: Organize commands in folders (e.g., refactor/dry.md)
Autocomplete support: Tab completion for command discovery
Rich metadata: YAML frontmatter for descriptions, aliases, and parameters

🎯 Enhanced User Experience

Smart autocomplete: Tab completion for commands with real-time suggestions
Prompt history: Access and reuse previous prompts with /history
Configurable logging: Silent, normal, or verbose output levels
Colorized output: Syntax highlighting and structured display
Session persistence: Maintains context and preferences across sessions
Real-time indicators: Shows token usage, timing, and processing status

⚙️ Developer Features

TypeScript-first: Full type safety and IntelliSense support
Extensible architecture: Plugin-style system for adding new capabilities
Project-specific config: Different settings per project via agents.config.json
Debug tools: Built-in debugging commands and verbose logging
Error resilience: Graceful handling of provider failures and network issues

Community

We're a small team building Nanocoder and would love your help! Whether you're interested in contributing code, documentation, or just being part of our community, there are several ways to get involved.

If you want to contribute:

Read our detailed CONTRIBUTING.md guide for information on development setup, coding standards, and how to submit your changes.

If you want to be part of our community:

Join our Discord server to connect with other users, ask questions, share ideas, and get help: Join our Discord server

Whether you're interested in:

Adding support for new AI providers
Improving tool functionality
Enhancing the user experience
Writing documentation
Reporting bugs or suggesting features
Just learning about local-first AI coding tools

All contributions and community participation are welcome!

For Tasks:

Click tags to check more tools for each tasks

code locally execute commands manage ai providers configure mcp servers create custom commands

For Jobs:

software developer ai engineer data scientist devops engineer technical writer

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nanocoder

README:

Nanocoder

Table of Contents

FAQs

What is Nanocoder?

How is this different to OpenCode?

I want to be involved, how do I start?

Installation

For Users (Recommended)

For Development

Configuration

AI Provider Setup

MCP (Model Context Protocol) Servers

User Preferences

Commands

Built-in Commands

Custom Commands

Features

🔌 Multi-Provider Support

🛠️ Advanced Tool System

📝 Custom Command System

🎯 Enhanced User Experience

⚙️ Developer Features

Community

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