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	LangChain (ChatOllama / ChatOpenAI)
Agent orchestration	LangGraph
Vector Store	LanceDB
Code Parsing	Tree-sitter
Config	TOML (stdlib tomllib)
Plugins	MCP via langchain-mcp-adapters

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.