goatcode 1.1.0

Production-grade deterministic coding agent with real architecture

GOATCODE - Production-Grade Coding Agent

A deterministic, tool-augmented, autonomous coding agent that beats prompt-only solutions through real architecture.

🎯 The 80% That Matters

Unlike other AI coding assistants that rely on prompts alone, GOATCODE implements:

1. 🔍 File Indexing Engine - AST-based semantic code search with vector embeddings
2. 🧠 Context Injection - Automatic relevance-based retrieval with token budget management
3. 🔄 Test→Fix→Retry Loop - Iterative validation with automatic error recovery
4. 📊 Diff-Based Patching - AST-aware minimal surgical edits (no full rewrites)
5. 💰 Token Budget Manager - Dynamic context window optimization
6. 🗄️ Memory System - Resolution pattern storage and retrieval

Prompt = 20%. Architecture = 80%

🚀 Quick Start

Prerequisites

• Python 3.9+
• For local models: Ollama
• For SaaS: API key (OpenAI, Anthropic)

Installation

# Clone repository
git clone https://github.com/yourusername/goatcode.git
cd goatcode

# Install dependencies
pip install -r requirements.txt

# If using Ollama, pull a model
ollama pull llama2
ollama pull codellama

Usage

Interactive Mode (Recommended)

# With local Ollama
python -m goatcode --provider ollama --model llama2

# With OpenAI
python -m goatcode --provider openai --model gpt-4 --api-key $OPENAI_API_KEY

# With Anthropic
python -m goatcode --provider anthropic --model claude-3-opus

Batch Mode

python -m goatcode --provider ollama \
  -p "Create a Python function to parse JSON with error handling" \
  -o result.json

Example Session

🐐 GOATCODE - Production-Grade Coding Agent

Interactive Mode - Type your coding requests
Commands: /help, /exit, /status, /models
------------------------------------------------------------

📝 > Create a REST API endpoint for user authentication with JWT

🚀 Starting execution...

============================================================
Status: SUCCESS
============================================================

📊 Analysis Summary:
Creating a secure REST API endpoint for user authentication 
using JWT tokens. Will include login, registration, and 
protected route middleware.

📋 Implementation Plan:
  1. Create auth module with JWT utilities
  2. Implement login endpoint with password hashing
  3. Implement registration endpoint with validation
  4. Create authentication middleware
  5. Add comprehensive error handling

📝 Files Modified:
  [CREATE] src/auth/jwt_utils.py
  [CREATE] src/auth/endpoints.py
  [CREATE] src/auth/middleware.py
  [UPDATE] requirements.txt

✅ Validation Report:
  ✓ All validations passed
  ✓ Type checking clean
  ✓ Tests passing (12/12)

🎯 Confidence Score: 95%

============================================================

🏗️ Architecture

goatcode/
├── core/
│   └── agent.py              # Main orchestrator
├── llm/
│   └── interface.py          # Multi-provider LLM support
├── tools/
│   └── registry.py           # Tool system
├── context/
│   └── manager.py            # Context pruning & injection
├── memory/
│   └── system.py             # Pattern storage & retrieval
├── validation/
│   └── engine.py             # Test→Fix→Retry loop
└── cli/
    └── main.py               # CLI entry point

Execution Pipeline

User Request
    ↓
1. Intent Analysis (What to build?)
    ↓
2. Project Context (File structure, dependencies)
    ↓
3. Risk Analysis (Security, edge cases)
    ↓
4. Memory Lookup (Similar past solutions)
    ↓
5. Implementation Plan (Files, functions, steps)
    ↓
6. Code Generation (Complete, correct code)
    ↓
7. Validation Loop (Test → Fix → Retry)
    ↓
8. Diff Patching (Minimal surgical changes)
    ↓
Success / Report

🛠️ Available Tools

• read_file - Read file contents
• write_file - Write or overwrite files
• list_directory - List project structure
• search_project - Text-based code search
• semantic_search - Vector-based code search
• run_tests - Execute test suites
• run_linter - Code linting
• run_typecheck - Type checking
• git_diff - Show git changes
• memory_lookup - Search past solutions
• memory_store - Store resolution patterns

🤖 Supported LLM Providers

Local (Free)

• Ollama - Run models locally
  • llama2, codellama, mistral, mixtral
  • No API costs, complete privacy

SaaS (API Key Required)

• OpenAI - GPT-4, GPT-3.5-turbo
• Anthropic - Claude 3 (Opus, Sonnet, Haiku)
• Google - Gemini Pro (coming soon)

Multi-Provider with Fallback

from goatcode.llm.interface import create_multi_provider_router

router = create_multi_provider_router([
    {'provider': 'ollama', 'model': 'llama2'},
    {'provider': 'openai', 'model': 'gpt-3.5-turbo'}
])

📊 Performance Comparison

Feature	GOATCODE	Cursor	Copilot	ChatGPT
File Indexing	✅	❌	❌	❌
Context Injection	✅	⚠️	❌	❌
Test→Fix Loop	✅	❌	❌	❌
Diff Patching	✅	❌	❌	❌
Memory System	✅	❌	❌	❌
Token Budget	✅	❌	❌	❌
Local Models	✅	❌	❌	❌

🎯 Why This Wins

vs. Cursor/Copilot

• ❌ They hide the architecture → ✅ GOATCODE shows the engineering
• ❌ Prompt-only solutions → ✅ Real tool orchestration
• ❌ No verification loop → ✅ Test→Fix→Retry cycle
• ❌ No context management → ✅ Smart context injection
• ❌ Cloud-only → ✅ Local Ollama support

vs. ChatGPT

• ❌ No project context → ✅ Full project analysis
• ❌ No file operations → ✅ Complete tool system
• ❌ No validation → ✅ Automated testing
• ❌ No memory → ✅ Pattern learning

🔒 Security Features

• ✅ No hardcoded secrets
• ✅ No unsafe eval/exec
• ✅ Input validation
• ✅ Least-privilege design
• ✅ Automatic dependency checking

🧪 Development

# Install dev dependencies
pip install -r requirements-dev.txt

# Run tests
pytest tests/

# Run linter
flake8 goatcode/

# Type checking
mypy goatcode/

📈 Roadmap

• Core agent architecture
• Multi-provider LLM support
• Tool system
• FAISS vector search
• AST-aware diff patching
• Web interface
• VSCode extension
• CI/CD integration
• Team collaboration features

🤝 Contributing

This is a production-grade reference implementation. Contributions welcome:

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open Pull Request

📝 License

MIT License - See LICENSE file

🙏 Acknowledgments

• Inspired by the need for deterministic, verifiable AI coding
• Built on top of excellent open-source tools
• Community-driven development

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Built with Python 🐍 | Powered by Determinism ⚡ | Designed for Production 🚀

Prompt is 20%. This is the 80%.