gitllama 0.7.1

AI-powered git automation tool - clone, branch, change, commit, push with intelligence

GitLlama 🦙

A git automation tool that uses AI to analyze repositories and make code changes. GitLlama clones a repository, analyzes the codebase, selects an appropriate branch, and makes iterative improvements.

Core Design: Multiple Choice vs Open Response

GitLlama's AI decision-making is built on a dual approach:

• Multiple Choice Queries: For deterministic decisions (branch selection, file operations, validation checks)
• Open Response Queries: For creative tasks (code generation, commit messages, analysis)

This architecture ensures reliable decision-making while maintaining flexibility for complex tasks.

Installation

pip install gitllama

Prerequisites

GitLlama requires Ollama for AI features:

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Start Ollama server
ollama serve

# Pull a model
ollama pull gemma3:4b

Usage

Basic usage:

gitllama https://github.com/user/repo.git

With custom model:

gitllama https://github.com/user/repo.git --model llama3:8b

With specific branch:

gitllama https://github.com/user/repo.git --branch feature/my-improvement

Verbose mode:

gitllama https://github.com/user/repo.git --verbose

How It Works

1. Repository Analysis

GitLlama analyzes the repository using hierarchical summarization:

• Scans all text files and documentation
• Groups files into chunks that fit the AI's context window
• Analyzes each chunk independently
• Merges summaries hierarchically
• Produces structured insights about the project

2. Branch Selection

The AI makes branch decisions using multiple choice queries:

• Analyzes existing branches
• Scores reuse potential
• Decides: REUSE or CREATE
• Selects branch type: feature, fix, docs, or chore

3. File Modification

Iterative development with validation:

• AI selects files to modify (multiple choice)
• Generates content (open response)
• Validates changes (multiple choice)
• Continues until satisfied

4. Commit and Push

• Generates commit message (open response)
• Commits changes
• Pushes to remote repository

AI Query Interface

The dual query system provides structure where needed:

# Multiple choice for decisions
result = ai.choice(
    question="Should we reuse an existing branch?",
    options=["REUSE", "CREATE"],
    context="Current branch: main"
)

# Open response for content
result = ai.open(
    prompt="Generate a Python configuration file",
    context="Project type: web application"
)

Architecture

gitllama/
├── cli.py                 # Command-line interface
├── git_operations.py      # Git automation
├── ai_coordinator.py      # AI workflow coordination
├── ai_query.py           # Multiple choice / open response interface
├── project_analyzer.py    # Repository analysis
├── branch_analyzer.py     # Branch selection logic
├── file_modifier.py       # File modification workflow
├── response_parser.py     # Response parsing and code extraction
├── report_generator.py    # HTML report generation
└── ollama_client.py      # Ollama API client

Key Components:

• AIQuery: Dual interface for structured choices and open responses
• ProjectAnalyzer: Hierarchical analysis of repository structure
• BranchAnalyzer: Branch selection using multiple choice decisions
• FileModifier: Iterative file modification with validation
• ResponseParser: Extracts clean code from AI responses

Reports

GitLlama generates HTML reports with:

• Timeline of AI decisions
• Branch selection rationale
• File modification details
• API usage statistics
• Context window tracking

Reports are saved to gitllama_reports/ directory.

Compatible Models

Works with any Ollama model:

• gemma3:4b - Fast and efficient (default)
• llama3.2:1b - Ultra-fast for simple tasks
• codellama:7b - Optimized for code
• mistral:7b - General purpose
• gemma2:2b - Very fast

What Gets Analyzed

• Source code (Python, JavaScript, Java, Go, Rust, etc.)
• Configuration files (JSON, YAML, TOML)
• Documentation (Markdown, README)
• Build files (Dockerfile, package.json)
• Scripts (Shell, Batch)

Performance

• Small repos (<100 files): ~30 seconds
• Medium repos (100-500 files): 1-2 minutes
• Large repos (500+ files): 2-5 minutes

Development

git clone https://github.com/your-org/gitllama.git
cd gitllama
pip install -e ".[dev]"

# Run tests
pytest

Troubleshooting

Ollama not available?

# Check if Ollama is running
curl http://localhost:11434/api/tags

# Start Ollama
ollama serve

Context window too small?

# Use a model with larger context
gitllama repo.git --model mistral:7b

Analysis taking too long?

# Use a smaller model
gitllama repo.git --model llama3.2:1b

License

GPL v3 - see LICENSE file

Contributing

Contributions welcome! The modular architecture makes it easy to extend.

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Note: GitLlama requires git credentials configured for pushing changes. Ensure you have appropriate repository access before use.