Mita Code

A local-first, terminal-native agentic coding assistant that runs LLMs entirely on your machine via Ollama. No API keys. No cloud. No telemetry.

Features

• 100% Local — All inference runs on your hardware via Ollama. Your code never leaves your machine.
• Agentic Tool Loop — Read/write files, run shell commands, git operations — with confirmation for destructive actions.
• Hardware-Aware Model Recommendations — Detects your RAM, VRAM, and GPU to recommend models that will actually run well.
• MCP Plugin System — Compatible with the existing Model Context Protocol ecosystem (stdio and SSE transport).
• Skills — Reusable, parameterized prompt templates stored as Markdown files (e.g., /commit, /review).
• Layered Memory — MITA.md files at global, project, and directory scope are automatically injected into context.
• Layered Config — TOML configuration cascades from global to project level.
• Hooks — Lifecycle shell commands that fire on events like file writes or tool calls.
• Codebase Indexing — Local vector search (LanceDB + Tree-sitter) for RAG over your codebase.
• Unix Philosophy — Composable, pipeable, scriptable.

Requirements

• Python 3.11+
• Ollama installed and running

Installation

pipx install mita-code

Or for development:

git clone https://github.com/jtdub/mita-code.git
cd mita-code
poetry install

Quick Start

# Start Ollama (if not already running)
ollama serve

# Pull a coding model
mita models pull qwen2.5-coder:7b

# Start an interactive session
mita chat

# Or ask a single question
mita ask "explain the auth module in this project"

Usage

Interactive Chat

mita chat                           # Start agentic chat session
mita chat --model deepseek-coder-v2:16b  # Use a specific model
mita chat --no-tools                # Pure chat, no tool execution

Single-Shot Prompts

mita ask "refactor this function to use async"
cat error.log | mita ask "what went wrong?"

Model Management

mita models recommend               # See what fits your hardware
mita models list                     # List installed models
mita models pull qwen2.5-coder:14b  # Pull a model
mita models default qwen2.5-coder:14b  # Set as default

Memory

mita memory show                     # View all active memory
mita memory add "Always use pytest" --project  # Add project-level memory
mita memory edit                     # Edit nearest MITA.md

Codebase Indexing

mita index build                     # Index the current project
mita index search "database connection"  # Search the index

Skills

mita skills list                     # List available skills
# In chat, use /skill_name to invoke:
# mita> /commit
# mita> /review

Plugins (MCP)

mita plugins add filesystem --command "npx @modelcontextprotocol/server-filesystem ."
mita plugins list                    # List plugins and their tools

Configuration

mita config show                     # Show merged configuration
mita config edit --global            # Edit global config
mita config set model.default "qwen2.5-coder:14b"

Diagnostics

mita doctor                          # Check Ollama, models, config health

Configuration

Global config lives at ~/.config/mita/config.toml. Project-level overrides go in .mita/settings.toml.

[model]
default = "qwen2.5-coder:7b"
temperature = 0.1

[tools]
auto_approve = ["file_read", "glob", "grep"]
confirm_destructive = true

[index]
enabled = true
top_k = 10

See PLANNING.md for the full configuration schema.

Memory System

Mita uses layered MITA.md files that are automatically discovered and injected into context:

Scope	Location	Purpose
Global	~/.config/mita/MITA.md	Preferences across all projects
Project	<project_root>/MITA.md	Project-specific conventions
Directory	<subdir>/MITA.md	Directory-specific context

Higher-specificity files take priority. Each file is capped at 200 lines.

Tech Stack

Component	Library
CLI	Typer
Terminal UI	Rich
LLM Runtime	Ollama
LLM Client	LiteLLM
Structured Output	Instructor
Vector Store	LanceDB
Code Parsing	Tree-sitter
Config	TOML (stdlib tomllib)
Plugins	MCP

Contributing

See PLANNING.md for the full project plan, architecture, and build phases.

Full documentation is available at mita-code.readthedocs.io.

License

Apache 2.0 — see LICENSE.