# 🎯 Vibe Code Mode Python Backend: Quick Start Summary

## 📦 What You Have

I've created a **complete migration blueprint** from Rust Goose to Python Vibe Code Mode, consisting of:

### 1. **Architecture Guide** (`VIBE_CODE_MODE_PYTHON_ARCHITECTURE.md`)
   - **Complete system design** with all components explained
   - Pydantic schemas for all data models
   - Full service layer implementation code
   - Repository patterns
   - Core orchestrator logic
   - FastAPI route handlers
   - **Best for**: Understanding the complete architecture & detailed implementation

### 2. **Implementation Guide** (`VIBE_CODE_IMPLEMENTATION_GUIDE.md`)
   - **Step-by-step setup instructions** (30-minute quickstart)
   - Complete working code for:
     - Configuration management
     - Built-in tools (shell, file I/O, JSON)
     - Sandbox runtime (JavaScript/TypeScript)
     - Service implementations
     - API routes
     - Docker deployment
   - Testing examples
   - Performance optimization tips
   - **Best for**: Hands-on implementation & getting code running quickly

### 3. **Migration Checklist** (`MIGRATION_CHECKLIST.md`)
   - **8-phase roadmap** with detailed tasks
   - Feature parity comparison (Rust ↔ Python)
   - Dynamic LLM configuration guide
   - Common issues & solutions
   - Success criteria
   - **Best for**: Planning your migration & tracking progress

### 4. **Migration Automation Script** (`migrate_from_rust.py`)
   - **Automatic analyzer** for Rust Goose codebase
   - Extracts structures, functions, providers
   - **Generates Python equivalents**:
     - Pydantic models from Rust structs
     - Service stubs
     - Provider adapters
   - Creates project structure
   - **Best for**: Fast-tracking code generation from existing Rust code

---

## 🚀 Getting Started (Next 30 Minutes)

### Step 1: Clone/Create Python Project
```bash
# Option A: Create from scratch
mkdir vibe-code-backend && cd vibe-code-backend

# Option B: Use migration script to auto-generate
python migrate_from_rust.py /path/to/goose-codebase \
    --output vibe-code-backend \
    --generate-project
```

### Step 2: Set Up Environment
```bash
# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install fastapi uvicorn pydantic anthropic openai

# Ensure Node.js tools available
npm install -g typescript esbuild
which tsc  # Should show path to tsc
```

### Step 3: Copy Starter Code
From **Implementation Guide**, copy these files to your project:

```
vibe-code-backend/
├── config/
│   ├── settings.py        # From Implementation Guide Section 1
│   └── llm_config.py      # User LLM configuration
├── models/
│   ├── tool_model.py      # From Architecture Guide Section 3.2
│   ├── execution_model.py
│   └── llm_config_model.py
├── schemas/
│   ├── tool_schemas.py    # From Architecture Guide Section 3.1
│   ├── code_execution.py
│   └── llm_request.py
├── services/
│   ├── code_generation.py     # From Architecture Guide Section 3.3
│   ├── code_compilation.py
│   ├── code_sandbox.py
│   └── tool_execution.py
├── core/
│   ├── tool_registry.py       # From Architecture Guide Section 3.5
│   ├── vibe_code_executor.py
│   └── llm_provider.py        # From Architecture Guide Section 3.5
├── api/routes/
│   ├── code_execution.py      # From Implementation Guide Section 6
│   └── tools.py
├── tools/
│   └── builtin_tools.py       # From Implementation Guide Section 2
├── main.py                     # From Implementation Guide Section 7
└── requirements.txt            # Provided
```

### Step 4: Run the Backend
```bash
# Start FastAPI server
python main.py

# Should show:
# 🚀 Initializing Vibe Code Mode Backend...
# 📦 Registering built-in tools...
# ✅ Backend initialized successfully
# INFO: Uvicorn running on http://0.0.0.0:8000

# Test API
curl http://localhost:8000/health
# Response: {"status":"ok","service":"vibe-code-backend","initialized":true}
```

### Step 5: Test Code Execution
```bash
curl -X POST http://localhost:8000/api/code/execute \
  -H "Content-Type: application/json" \
  -d '{
    "user_prompt": "Generate code that lists files",
    "code": "import { shell } from \"developer\";\nexport default async function main() {\n  const result = await shell(\"ls -la\");\n  return result;\n}"
  }'
```

---

## 🎯 How the System Works (High Level)

### The Vibe Code Mode Flow

```
┌─────────────────────────────────────────────────────────┐
│                      USER PROMPT                        │
│  "Create LOG.md with git commits and package.json"      │
└────────────────┬────────────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────────────┐
│          1. CODE GENERATION (LLM)                       │
│  - Call user's configured LLM (Anthropic/OpenAI/etc)   │
│  - Provide available tools list                         │
│  - Get back TypeScript code                             │
└────────────────┬────────────────────────────────────────┘
                 │
                 ▼ TypeScript
┌─────────────────────────────────────────────────────────┐
│          2. COMPILATION (tsc)                           │
│  - TypeScript → JavaScript                              │
│  - Type checking                                        │
│  - Error reporting                                      │
└────────────────┬────────────────────────────────────────┘
                 │
                 ▼ JavaScript
┌─────────────────────────────────────────────────────────┐
│          3. EXECUTION (Node.js Sandbox)                 │
│  - Run JavaScript in subprocess                         │
│  - Capture stdout/stderr/logs                           │
│  - Intercept tool calls                                 │
└────────────────┬────────────────────────────────────────┘
                 │
                 ├─────────────────────────────────────┐
                 │ Tool Call (e.g., shell command)     │
                 ▼                                     │
    ┌──────────────────────────┐                     │
    │ 4. TOOL EXECUTION        │                     │
    │ - Run Python handler     │                     │
    │ - Get result             │                     │
    │ - Return to JavaScript   │                     │
    └──────────────┬───────────┘                     │
                   │◄────────────────────────────────┘
                 
                 ▼
┌─────────────────────────────────────────────────────────┐
│          5. RESPONSE                                    │
│  - Generated code                                       │
│  - Tool execution graph (DAG)                           │
│  - Logs & outputs                                       │
│  - Final result                                         │
└─────────────────────────────────────────────────────────┘
```

### Key Differences from Rust Version

| Aspect | Rust (Goose) | Python (Vibe) |
|--------|------------|--------------|
| **Backend Runtime** | Rust binary | Python + FastAPI |
| **Code Sandbox** | Deno + V8 embedded | Node.js subprocess |
| **Tool System** | Async Rust callbacks | Python async handlers |
| **LLM Config** | Hard-coded providers | User-configurable (per user) |
| **Compilation** | Custom pctx | TypeScript Compiler (tsc) |
| **Deployment** | Single binary | Docker container |

**Advantage**: Python version is **more maintainable** and **user-configurable**. Users can choose their own LLM provider!

---

## 🔄 Dynamic LLM Configuration (Key Feature!)

### How It Works

**User sets their LLM provider once:**
```json
POST /api/config/llm
{
  "provider": "anthropic",
  "model": "claude-3-sonnet-20240229",
  "api_key": "sk-ant-..."
}
```

**Then every code generation uses their choice:**
```python
# In vibe_code_executor.py
async def execute(self, request, user_id, user_llm_config):
    # user_llm_config comes from DB based on user_id
    llm = LLMProviderFactory.get_provider(user_llm_config)
    code = await self.code_gen.generate_code(..., user_llm_config)
```

**Users can easily switch:**
- Anthropic → OpenAI
- Claude 3 Sonnet → GPT-4
- Cloud → Local (Ollama)

---

## 📊 Project Structure Overview

```
vibe-code-backend/
│
├── 🔧 Configuration
│   └── config/
│       ├── settings.py         # Environment & server config
│       └── llm_config.py       # Per-user LLM provider config
│
├── 📦 Data Models
│   ├── models/                 # Domain models
│   │   ├── tool_model.py
│   │   ├── execution_model.py
│   │   └── llm_config_model.py
│   └── schemas/                # Pydantic request/response
│       ├── tool_schemas.py
│       ├── code_execution.py
│       └── llm_request.py
│
├── 🏭 Services (Business Logic)
│   └── services/
│       ├── code_generation.py    # LLM → TypeScript
│       ├── code_compilation.py   # TypeScript → JavaScript
│       ├── code_sandbox.py       # Execute JavaScript
│       └── tool_execution.py     # Run individual tools
│
├── 💾 Data Access
│   └── repositories/
│       ├── tool_registry_repo.py
│       └── session_repo.py
│
├── 🎯 Orchestration
│   └── core/
│       ├── tool_registry.py      # Meta-tools: list_functions, get_details
│       ├── vibe_code_executor.py # Main orchestrator
│       └── llm_provider.py       # Provider factory
│
├── 🌐 API
│   ├── main.py                   # FastAPI app
│   └── api/routes/
│       ├── code_execution.py    # /api/code/execute
│       └── tools.py             # /api/tools/
│
├── 🛠️ Tools & Utilities
│   ├── tools/
│   │   └── builtin_tools.py     # shell, file I/O, etc.
│   └── utils/
│       ├── process_manager.py   # Node.js subprocess
│       ├── validators.py
│       └── errors.py
│
└── 📝 Sandbox (JavaScript)
    └── sandbox/
        ├── runtime/
        │   ├── tool_bridge.js    # IPC bridge
        │   └── sandbox_init.js   # Setup
        └── declarations/
            └── tools.d.ts        # TypeScript types
```

---

## 🎓 Reading Order (Recommended)

1. **START HERE**: This file (Overview)
2. **VIBE_CODE_MODE_PYTHON_ARCHITECTURE.md** - Understand the design
3. **VIBE_CODE_IMPLEMENTATION_GUIDE.md** - Get code running (sections 1-7)
4. **MIGRATION_CHECKLIST.md** - Plan your implementation (phases 1-8)
5. **migrate_from_rust.py** - (Optional) Auto-generate from existing Rust code

---

## 🔑 Key Points to Remember

### 1. **Models, Services, Routes Pattern**
- **Models**: Data structures (what)
- **Services**: Business logic (how)
- **Routes**: API endpoints (who can call it)

### 2. **Async/Await for Everything**
Python is single-threaded but async-capable. Always use:
```python
async def my_function():
    result = await some_async_operation()
    return result
```

### 3. **Dynamic LLM Support**
Every request includes the user's LLM config. Services **must** accept it:
```python
async def generate_code(self, request, user_llm_config):
    # user_llm_config determines which LLM to call
    llm = LLMProviderFactory.get_provider(user_llm_config)
```

### 4. **Type Safety with Pydantic**
Always use Pydantic models for validation:
```python
class CodeExecutionRequest(BaseModel):
    user_prompt: str
    code: Optional[str] = None
    timeout_seconds: int = 30
    # Automatic validation!
```

### 5. **Error Handling**
Custom exceptions make debugging easier:
```python
try:
    js_code, error = await self.compile(code)
except CompilationError as e:
    execution.add_log(f"Compilation failed: {e}", "error")
```

---

## 🧪 Quick Testing

### Test 1: Health Check
```bash
curl http://localhost:8000/health
```

### Test 2: List Available Tools
```bash
curl http://localhost:8000/api/tools/
```

### Test 3: Get Tool Details
```bash
curl http://localhost:8000/api/tools/developer.shell
```

### Test 4: Execute Simple Code
```bash
curl -X POST http://localhost:8000/api/code/execute \
  -H "Content-Type: application/json" \
  -d '{
    "user_prompt": "Echo hello",
    "code": "import { shell } from \"developer\";\nexport default async function main() { return await shell(\"echo hello\"); }"
  }'
```

---

## 📈 Performance Expectations

| Operation | Time |
|-----------|------|
| LLM call (code generation) | 1-5s |
| TypeScript compilation | ~500ms |
| JavaScript execution | ~100ms |
| Tool execution | Varies (shell: 100ms-1s) |
| **Total round-trip** | 2-7 seconds |

**Can optimize with:**
- Code caching
- Parallel tool execution
- LLM result caching
- Pre-compiled tools

---

## 🐛 Debugging Tips

### Enable Debug Logging
```python
# In config/settings.py
LOG_LEVEL: str = "DEBUG"  # Shows all logs

# Then run
python main.py
```

### Inspect Generated Code
The API response includes `generated_code`:
```json
{
  "success": true,
  "generated_code": "import { shell } from ...",
  "execution_logs": [...]
}
```

### Check Tool Registration
```bash
curl http://localhost:8000/api/tools/
# Returns list of registered tools
```

### Test Compilation
Create `test.ts`:
```typescript
import { shell } from "developer";
export default async function main() {
  return await shell("echo test");
}
```

Then:
```bash
tsc test.ts --target ES2020 --lib ES2020
```

---

## 🚀 Next Steps

1. **Week 1**: Follow **Implementation Guide** sections 1-3
   - Get project structure
   - Implement models & schemas
   - Create repositories

2. **Week 2**: Follow **Implementation Guide** sections 4-5
   - Implement services
   - Add built-in tools

3. **Week 3**: Follow **Implementation Guide** sections 6-7
   - Build API routes
   - Write tests

4. **Week 4**: Follow **Implementation Guide** sections 8+
   - Deploy with Docker
   - Production hardening

---

## 💡 Pro Tips

### Tip 1: Use Dependency Injection
```python
@app.post("/api/code/execute")
async def execute_code(
    request: CodeExecutionRequest,
    executor: VibeCodeExecutor = Depends(get_executor),
):
    return await executor.execute(request, ...)
```

### Tip 2: Stream Logs in Real-Time
```python
@app.get("/execution/{id}/logs")
async def get_logs(id: str):
    async def event_generator():
        for log in execution.logs:
            yield f"data: {json.dumps(log)}\n\n"
    return StreamingResponse(event_generator(), media_type="text/event-stream")
```

### Tip 3: Cache Tool Declarations
```python
@functools.lru_cache(maxsize=1)
def get_tool_declarations(tool_count: int) -> str:
    # Generate once, reuse many times
    return self.tool_registry.generate_tool_declarations()
```

### Tip 4: Secure API Keys
```python
from cryptography.fernet import Fernet

CIPHER = Fernet(os.getenv("ENCRYPTION_KEY"))

# Store encrypted
encrypted = CIPHER.encrypt(api_key.encode())

# Decrypt when needed
decrypted = CIPHER.decrypt(encrypted).decode()
```

---

## ❓ FAQ

**Q: Can I use Python instead of Node.js for code execution?**
A: Not recommended (complexity). Node.js is standard, lightweight, and widely available. Python execution would require re-implementing the entire tool system.

**Q: How do I add custom tools?**
A: Implement in `tools/builtin_tools.py`:
```python
async def my_tool_handler(arg1: str, arg2: int) -> Dict:
    # Your implementation
    return {"result": ...}

# Register in tools.py
tools = [
    Tool(
        id="my.tool",
        handler=my_tool_handler,
        ...
    )
]
```

**Q: Can I use a different TypeScript compiler?**
A: Yes! Replace `tsc` with `esbuild`, `swc`, etc. in `code_compilation.py`. They're faster but may have different behavior.

**Q: How do I scale this?**
A: Add:
- Database (PostgreSQL)
- Job queue (Celery/RQ)
- Cache (Redis)
- Multiple workers
- Load balancer (Nginx)

**Q: Is this production-ready?**
A: After completing all 8 phases in the checklist, yes! But add: rate limiting, monitoring, audit logging, secrets management.

---

## 📞 Getting Help

1. **Architecture questions**: See `VIBE_CODE_MODE_PYTHON_ARCHITECTURE.md`
2. **Implementation issues**: Check `VIBE_CODE_IMPLEMENTATION_GUIDE.md`
3. **Progress tracking**: Use `MIGRATION_CHECKLIST.md`
4. **Code generation**: Run `migrate_from_rust.py`
5. **Type errors**: Install `mypy`: `pip install mypy && mypy .`
6. **Test failures**: Run `pytest -v` for detailed output

---

## ✨ You're Ready!

You now have:
- ✅ Complete architecture documentation
- ✅ Production-quality code templates
- ✅ Step-by-step implementation guide
- ✅ Migration automation tool
- ✅ Detailed checklist for tracking progress
- ✅ Examples for every component

**Start with the 30-minute quickstart above, then follow the Implementation Guide.**

Good luck! 🚀
