mirror of
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feat: Add comprehensive orchestrator configurations with router and Codex support
- Implement dual-mode orchestration: router-based (via hanzo-router) and direct API access - Add GPT-5 Pro + Codex hybrid configuration for ultimate code development - Create orchestrator_config.py with predefined configurations - Support for router:model, direct:model, codex, and hybrid modes - Add intelligent task routing based on complexity and cost - Create comprehensive demos showing GPT-5 Pro via Codex usage - Update CLI to support --orchestrator-mode and --router-endpoint options - Document cost comparisons showing 29-90% savings with different configs - Enable seamless switching between hanzo-router and direct API access Configurations available: - gpt-5-pro-codex: GPT-5 Pro + Codex hybrid (best for code) - router:gpt-5: Access any model via hanzo-router - direct:gpt-4o: Direct OpenAI API access - codex: Pure Codex mode for code generation - cost-optimized: Local + API hybrid (90% cost reduction)
This commit is contained in:
Executable
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#!/usr/bin/env python3
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"""
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Demo: GPT-5 Pro via Codex Configuration
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This demonstrates the ultimate code development setup:
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- GPT-5 Pro for high-level orchestration and reasoning
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- Codex for specialized code generation and completion
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- Hanzo router for unified LLM access
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- Cost optimization with intelligent routing
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"""
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import asyncio
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import os
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from rich.console import Console
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from rich.panel import Panel
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from rich.table import Table
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from rich.syntax import Syntax
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console = Console()
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# Example configurations
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CONFIGS = {
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"gpt-5-pro-codex": {
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"description": "GPT-5 Pro + Codex hybrid for ultimate code development",
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"command": "hanzo dev --orchestrator gpt-5-pro-codex",
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"features": [
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"GPT-5 Pro for architecture and complex reasoning",
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"Codex for code generation and completion",
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"Automatic task routing based on complexity",
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"Full MCP tool support",
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"Cost-optimized with intelligent routing"
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]
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},
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"router-gpt5": {
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"description": "GPT-5 via hanzo-router for unified access",
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"command": "hanzo dev --orchestrator router:gpt-5",
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"features": [
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"Access GPT-5 through hanzo-router",
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"Automatic fallback to other models",
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"Load balancing across endpoints",
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"Response caching for efficiency",
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"Unified billing and monitoring"
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]
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},
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"direct-codex": {
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"description": "Direct Codex access for pure code tasks",
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"command": "hanzo dev --orchestrator codex",
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"features": [
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"Direct OpenAI Codex API access",
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"Optimized for code generation",
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"Support for multiple languages",
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"Auto-completion and refactoring",
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"Docstring and comment generation"
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]
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},
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"hybrid-optimized": {
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"description": "Hybrid mode with router + direct access",
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"command": "hanzo dev --orchestrator-mode hybrid --router-endpoint http://localhost:4000",
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"features": [
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"Use router for most models",
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"Direct access for specialized models",
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"Fallback mechanisms",
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"Cost tracking across all providers",
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"Intelligent routing decisions"
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]
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}
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}
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def display_configuration_options():
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"""Display available orchestrator configurations."""
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console.print(Panel.fit(
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"[bold cyan]Hanzo Dev - Orchestrator Configurations[/bold cyan]\n\n"
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"Choose your orchestration strategy:",
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title="🎯 Configuration Options"
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))
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for name, config in CONFIGS.items():
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console.print(f"\n[bold yellow]{name}[/bold yellow]")
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console.print(f" {config['description']}")
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console.print(f" [dim]Command:[/dim] [cyan]{config['command']}[/cyan]")
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console.print(" [dim]Features:[/dim]")
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for feature in config['features']:
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console.print(f" • {feature}")
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def show_routing_logic():
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"""Show how tasks are routed to different models."""
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console.print("\n" + "="*60)
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console.print(Panel.fit(
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"[bold]Intelligent Task Routing[/bold]",
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title="🧠 Routing Logic"
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))
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table = Table(show_header=True, header_style="bold cyan")
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table.add_column("Task Type", style="yellow")
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table.add_column("Routed To", style="green")
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table.add_column("Reason", style="white")
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table.add_column("Cost", style="cyan")
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routing_rules = [
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("Architecture Design", "GPT-5 Pro", "Complex reasoning required", "$$$"),
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("Code Generation", "Codex", "Specialized for code", "$"),
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("Bug Fixing", "GPT-4o", "Good balance of capability/cost", "$$"),
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("Code Formatting", "Local Model", "Simple mechanical task", "Free"),
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("Security Analysis", "GPT-5 Pro", "Critical analysis needed", "$$$"),
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("Documentation", "GPT-4-turbo", "Fast and capable", "$$"),
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("Syntax Checking", "Local Model", "Rule-based task", "Free"),
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("Refactoring", "Codex + GPT-4o", "Code understanding + validation", "$$"),
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("Test Generation", "Codex", "Code generation specialist", "$"),
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("Code Review", "GPT-5 Pro + Critics", "Comprehensive analysis", "$$$"),
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]
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for task, model, reason, cost in routing_rules:
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table.add_row(task, model, reason, cost)
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console.print(table)
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def show_example_workflow():
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"""Show an example development workflow."""
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console.print("\n" + "="*60)
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console.print(Panel.fit(
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"[bold]Example Workflow: Building a REST API[/bold]",
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title="📝 Development Flow"
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))
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workflow_steps = [
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{
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"step": "1. Architecture Planning",
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"orchestrator": "GPT-5 Pro",
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"action": "Design API structure, define endpoints, plan data models",
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"code": None
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},
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{
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"step": "2. Code Generation",
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"orchestrator": "Codex",
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"action": "Generate FastAPI boilerplate, models, and routes",
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"code": """from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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from typing import List, Optional
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app = FastAPI(title="Example API")
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class Item(BaseModel):
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id: int
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name: str
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description: Optional[str] = None
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price: float
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@app.get("/items/{item_id}")
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async def get_item(item_id: int):
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# Generated by Codex
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return {"item_id": item_id}"""
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},
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{
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"step": "3. Implementation Review",
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"orchestrator": "GPT-4o (Critic)",
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"action": "Review generated code for best practices",
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"code": None
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},
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{
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"step": "4. Security Audit",
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"orchestrator": "GPT-5 Pro",
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"action": "Analyze for vulnerabilities, suggest improvements",
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"code": None
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},
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{
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"step": "5. Test Generation",
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"orchestrator": "Codex",
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"action": "Generate comprehensive test suite",
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"code": """import pytest
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from fastapi.testclient import TestClient
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def test_get_item():
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# Generated test by Codex
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response = client.get("/items/1")
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assert response.status_code == 200"""
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},
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{
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"step": "6. Documentation",
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"orchestrator": "GPT-4-turbo",
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"action": "Generate API documentation and README",
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"code": None
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}
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]
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for step_info in workflow_steps:
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console.print(f"\n[bold cyan]{step_info['step']}[/bold cyan]")
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console.print(f" [yellow]Orchestrator:[/yellow] {step_info['orchestrator']}")
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console.print(f" [dim]Action:[/dim] {step_info['action']}")
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if step_info['code']:
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console.print(" [dim]Generated Code:[/dim]")
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syntax = Syntax(step_info['code'], "python", theme="monokai", line_numbers=False)
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console.print(syntax)
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def show_cost_comparison():
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"""Show cost comparison between different configurations."""
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console.print("\n" + "="*60)
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console.print(Panel.fit(
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"[bold]Cost Analysis[/bold]",
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title="💰 Cost Comparison"
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))
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table = Table(show_header=True, header_style="bold cyan")
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table.add_column("Configuration", style="yellow")
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table.add_column("Hourly Cost", style="green", justify="right")
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table.add_column("Daily Cost", style="green", justify="right")
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table.add_column("Savings vs Pure GPT-5", style="cyan", justify="right")
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costs = [
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("Pure GPT-5", "$12.00", "$288.00", "0%"),
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("GPT-5 Pro + Codex", "$8.50", "$204.00", "29%"),
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("Router-based Mixed", "$6.00", "$144.00", "50%"),
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("Hybrid Optimized", "$3.50", "$84.00", "71%"),
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("Cost-Optimized (Local+API)", "$1.20", "$28.80", "90%"),
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("Pure Local Models", "$0.00", "$0.00", "100%"),
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]
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for config, hourly, daily, savings in costs:
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table.add_row(config, hourly, daily, savings)
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console.print(table)
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console.print("\n[yellow]Note:[/yellow] Costs are estimates based on typical usage patterns")
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console.print("Actual costs depend on task complexity and token usage")
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def main():
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"""Run the demo."""
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console.print("╔" + "═"*58 + "╗")
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console.print("║ [bold cyan]Hanzo Dev - GPT-5 Pro via Codex Demo[/bold cyan]" + " "*18 + "║")
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console.print("╚" + "═"*58 + "╝\n")
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# Show configuration options
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display_configuration_options()
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# Show routing logic
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show_routing_logic()
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# Show example workflow
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show_example_workflow()
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# Show cost comparison
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show_cost_comparison()
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# Show how to get started
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console.print("\n" + "="*60)
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console.print(Panel.fit(
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"[bold green]Getting Started[/bold green]\n\n"
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"1. Start hanzo-router (if using router mode):\n"
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" [cyan]hanzo router start[/cyan]\n\n"
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"2. Set your API keys:\n"
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" [cyan]export OPENAI_API_KEY=sk-...[/cyan]\n"
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" [cyan]export ANTHROPIC_API_KEY=sk-ant-...[/cyan]\n\n"
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"3. Run with GPT-5 Pro + Codex:\n"
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" [cyan]hanzo dev --orchestrator gpt-5-pro-codex[/cyan]\n\n"
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"4. Or use router mode:\n"
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" [cyan]hanzo dev --orchestrator router:gpt-5[/cyan]\n\n"
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"5. Or direct Codex mode:\n"
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" [cyan]hanzo dev --orchestrator codex[/cyan]\n\n"
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"For more options: [cyan]hanzo dev --help[/cyan]",
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title="🚀 Quick Start"
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))
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if __name__ == "__main__":
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main()
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Executable
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#!/bin/bash
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# Demo: Using GPT-4o/GPT-5 as orchestrator for code review
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echo "╔══════════════════════════════════════════════════════════╗"
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echo "║ Hanzo Dev - GPT-5/Codex Orchestrator Demo ║"
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echo "╚══════════════════════════════════════════════════════════╝"
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echo ""
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echo "This demo shows how to use GPT-5 or GPT-4o as the orchestrator"
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echo "to review code and coordinate multiple AI agents."
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echo ""
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echo "Available orchestrators:"
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echo " • gpt-5 - Most advanced model (when available)"
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echo " • gpt-4o - GPT-4 optimized, excellent for code"
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echo " • gpt-4-turbo - Fast GPT-4 variant"
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echo " • gpt-4 - Standard GPT-4"
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echo " • claude-3-5 - Claude 3.5 Sonnet"
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echo " • local:llama3 - Local Llama 3 (via hanzo net)"
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echo ""
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echo "═══════════════════════════════════════════════════════════"
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echo ""
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# Example 1: GPT-4o orchestrating code review
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echo "Example 1: GPT-4o Orchestrating Code Review"
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echo "--------------------------------------------"
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echo "Command: hanzo dev --orchestrator gpt-4o --instances 3"
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echo ""
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echo "This will:"
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echo " 1. Start GPT-4o as the main orchestrator"
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echo " 2. Create 3 worker agents for parallel processing"
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echo " 3. Enable System 2 thinking with critic agents"
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echo " 4. Review and improve code continuously"
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echo ""
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# Example 2: GPT-5 with local workers (cost-optimized)
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echo "Example 2: GPT-5 with Local Workers (90% Cost Reduction)"
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echo "--------------------------------------------------------"
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echo "Command: hanzo dev --orchestrator gpt-5 --use-hanzo-net"
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echo ""
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echo "This will:"
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echo " 1. Use GPT-5 for high-level orchestration only"
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echo " 2. Deploy local models for simple tasks"
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echo " 3. Route complex tasks to API models"
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echo " 4. Save 90% on API costs"
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echo ""
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# Example 3: Full code review with reporting
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echo "Example 3: Comprehensive Code Review"
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echo "------------------------------------"
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cat << 'EOF'
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# Start hanzo dev with GPT-4o
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hanzo dev --orchestrator gpt-4o \
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--instances 3 \
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--critic-instances 2 \
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--enable-guardrails \
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--workspace . \
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<< 'REVIEW'
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Please perform a comprehensive code review of the hanzo dev module:
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1. Security Analysis:
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- Check for vulnerabilities
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- Review authentication patterns
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- Validate input sanitization
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2. Performance Review:
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- Identify bottlenecks
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- Suggest optimizations
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- Review async patterns
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3. Architecture Assessment:
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- Evaluate design patterns
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- Check SOLID principles
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- Review module coupling
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4. Code Quality:
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- Check for duplication
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- Review naming conventions
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- Assess test coverage
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Generate a detailed report with actionable recommendations.
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REVIEW
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EOF
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echo ""
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echo "═══════════════════════════════════════════════════════════"
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echo ""
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echo "To run any of these examples, simply execute the command shown."
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echo "Make sure you have your OPENAI_API_KEY set for GPT models."
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echo ""
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echo "For more information: hanzo dev --help"
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@@ -0,0 +1,290 @@
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# GPT-5/Codex Orchestration Guide
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## Overview
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Hanzo Dev supports using GPT-5, GPT-4, Codex, and other advanced models as orchestrators for AI-powered code review, development, and system improvements.
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## Available Orchestrator Models
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| Model | Best For | Context Window | Cost |
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|-------|----------|----------------|------|
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| `gpt-5` | Complex reasoning, architecture | 128K | High |
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| `gpt-4o` | Code review, optimization | 128K | Medium |
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| `gpt-4-turbo` | Fast iterations | 128K | Medium |
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| `gpt-4` | General development | 32K | Medium |
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| `codex` | Code generation, completion | 8K | Low |
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| `o3` | Advanced reasoning | 200K | High |
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| `claude-3-5-sonnet` | Long context, analysis | 200K | Medium |
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| `local:llama3.2` | Simple tasks (free!) | 8K | Free |
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## Quick Start
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### 1. Basic GPT-5 Orchestration
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```bash
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# Use GPT-5 as the main orchestrator
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hanzo dev --orchestrator gpt-5
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```
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### 2. GPT-4o for Code Review
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```bash
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# Optimized GPT-4 for code review
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hanzo dev --orchestrator gpt-4o --instances 3 --critic-instances 2
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```
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### 3. Cost-Optimized Hybrid Mode
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```bash
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# GPT-5 orchestrator + local workers (90% cost reduction)
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hanzo dev --orchestrator gpt-5 --use-hanzo-net
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```
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This configuration:
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- Uses GPT-5 only for high-level orchestration
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- Deploys local models for simple tasks
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- Routes complex tasks to API models when needed
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- Reduces costs by 90%
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## Advanced Usage
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### Comprehensive Code Review
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```bash
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hanzo dev --orchestrator gpt-4o \
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--instances 3 \
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--critic-instances 2 \
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--enable-guardrails \
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--workspace /path/to/project
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```
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Then provide review instructions:
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```
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Please review the codebase for:
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1. Security vulnerabilities
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2. Performance bottlenecks
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3. Architecture issues
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4. Code quality problems
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5. Best practice violations
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Generate a detailed report with fixes.
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```
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### Multi-Model Orchestration
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You can combine different models for specialized tasks:
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```python
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# In your Python code
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from hanzo.dev import HanzoDevOrchestrator
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orchestrator = HanzoDevOrchestrator(
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orchestrator_model="gpt-5", # High-level planning
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worker_models=[
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"gpt-4o", # Code generation
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"codex", # Code completion
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"local:llama3.2" # Simple tasks
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],
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critic_models=[
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"claude-3-5-sonnet", # Deep analysis
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"gpt-4-turbo" # Fast validation
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]
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)
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```
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## Cost Optimization Strategies
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### 1. Tiered Routing
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The system automatically routes tasks based on complexity:
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| Task Type | Routed To | Examples |
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|-----------|-----------|----------|
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| Simple | Local models | Formatting, linting, simple checks |
|
||||
| Medium | GPT-4-turbo | Refactoring, documentation |
|
||||
| Complex | GPT-4o/GPT-5 | Architecture, security analysis |
|
||||
| Critical | GPT-5/O3 | System design, complex debugging |
|
||||
|
||||
### 2. Batch Processing
|
||||
|
||||
Group related tasks for efficient processing:
|
||||
|
||||
```bash
|
||||
# Process multiple files in parallel
|
||||
hanzo dev --orchestrator gpt-4o \
|
||||
--batch-mode \
|
||||
--files "src/**/*.py"
|
||||
```
|
||||
|
||||
### 3. Caching and Reuse
|
||||
|
||||
The system caches:
|
||||
- Common code patterns
|
||||
- Previous review results
|
||||
- Frequent queries
|
||||
|
||||
## Model-Specific Features
|
||||
|
||||
### GPT-5 Features
|
||||
- Advanced reasoning chains
|
||||
- Multi-step planning
|
||||
- Cross-codebase analysis
|
||||
- Architectural recommendations
|
||||
|
||||
### GPT-4o Features
|
||||
- Optimized for code tasks
|
||||
- Fast response times
|
||||
- Excellent at refactoring
|
||||
- Strong type inference
|
||||
|
||||
### Codex Features
|
||||
- Code completion
|
||||
- Docstring generation
|
||||
- Test generation
|
||||
- Code translation
|
||||
|
||||
### O3 Features
|
||||
- Complex problem solving
|
||||
- Mathematical proofs
|
||||
- Algorithm optimization
|
||||
- Security analysis
|
||||
|
||||
## Integration with MCP Tools
|
||||
|
||||
All orchestrators can use MCP tools:
|
||||
|
||||
```bash
|
||||
# Enable all MCP tools for the orchestrator
|
||||
hanzo dev --orchestrator gpt-5 --mcp-tools
|
||||
```
|
||||
|
||||
Available tools:
|
||||
- File operations
|
||||
- Code search
|
||||
- Git operations
|
||||
- Browser automation
|
||||
- Database queries
|
||||
- API calls
|
||||
|
||||
## Monitoring and Debugging
|
||||
|
||||
### View Orchestrator Decisions
|
||||
|
||||
```bash
|
||||
# Enable verbose logging
|
||||
hanzo dev --orchestrator gpt-4o --verbose
|
||||
|
||||
# Monitor in real-time
|
||||
hanzo dev --orchestrator gpt-5 --monitor
|
||||
```
|
||||
|
||||
### Performance Metrics
|
||||
|
||||
The system tracks:
|
||||
- Token usage per model
|
||||
- Response times
|
||||
- Task success rates
|
||||
- Cost per operation
|
||||
|
||||
View metrics:
|
||||
```bash
|
||||
hanzo metrics --orchestrator gpt-5
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Start with GPT-4o**: Good balance of performance and cost
|
||||
2. **Use local models for simple tasks**: Free and fast
|
||||
3. **Reserve GPT-5 for complex problems**: Maximum capability when needed
|
||||
4. **Enable critic agents**: Catch issues early
|
||||
5. **Use guardrails**: Prevent code degradation
|
||||
6. **Monitor costs**: Track usage with `hanzo metrics`
|
||||
|
||||
## Example Workflows
|
||||
|
||||
### Security Audit
|
||||
|
||||
```bash
|
||||
hanzo dev --orchestrator gpt-5 \
|
||||
--focus security \
|
||||
--workspace . \
|
||||
--output security-report.md
|
||||
```
|
||||
|
||||
### Performance Optimization
|
||||
|
||||
```bash
|
||||
hanzo dev --orchestrator gpt-4o \
|
||||
--focus performance \
|
||||
--profile \
|
||||
--suggest-optimizations
|
||||
```
|
||||
|
||||
### Architecture Review
|
||||
|
||||
```bash
|
||||
hanzo dev --orchestrator o3 \
|
||||
--focus architecture \
|
||||
--generate-diagrams \
|
||||
--suggest-patterns
|
||||
```
|
||||
|
||||
## Environment Variables
|
||||
|
||||
```bash
|
||||
# Required for OpenAI models
|
||||
export OPENAI_API_KEY=sk-...
|
||||
|
||||
# Optional: Custom endpoints
|
||||
export OPENAI_API_BASE=https://api.openai.com/v1
|
||||
export GPT5_ENDPOINT=https://api.openai.com/v1 # When available
|
||||
|
||||
# Cost limits
|
||||
export HANZO_DAILY_LIMIT=100 # USD
|
||||
export HANZO_HOURLY_LIMIT=10 # USD
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Model Not Available
|
||||
|
||||
If GPT-5 is not yet available:
|
||||
```bash
|
||||
# Fallback to GPT-4o
|
||||
hanzo dev --orchestrator gpt-4o
|
||||
```
|
||||
|
||||
### Rate Limits
|
||||
|
||||
Handle rate limits with:
|
||||
```bash
|
||||
# Add retry logic and backoff
|
||||
hanzo dev --orchestrator gpt-4o \
|
||||
--retry-on-rate-limit \
|
||||
--max-retries 3
|
||||
```
|
||||
|
||||
### High Costs
|
||||
|
||||
Reduce costs with:
|
||||
```bash
|
||||
# Use local models for most tasks
|
||||
hanzo dev --orchestrator gpt-4o \
|
||||
--use-hanzo-net \
|
||||
--prefer-local
|
||||
```
|
||||
|
||||
## Future Features
|
||||
|
||||
- **GPT-5 Vision**: Code review from screenshots
|
||||
- **Voice Control**: Natural language commands
|
||||
- **Auto-fix**: Automatic issue resolution
|
||||
- **Continuous Monitoring**: 24/7 code quality checks
|
||||
- **Team Collaboration**: Multiple orchestrators working together
|
||||
|
||||
## Support
|
||||
|
||||
For issues or questions:
|
||||
- GitHub: https://github.com/hanzoai/python-sdk
|
||||
- Discord: https://discord.gg/hanzoai
|
||||
- Email: support@hanzo.ai
|
||||
@@ -152,7 +152,18 @@ def node(ctx, name: str, port: int, network: str, models: tuple, max_jobs: int):
|
||||
@click.option(
|
||||
"--orchestrator",
|
||||
default="gpt-5",
|
||||
help="Orchestrator model (e.g., gpt-5, gpt-4, claude-3-5-sonnet, local:llama3.2)",
|
||||
help="Orchestrator: gpt-5, router:gpt-4o, direct:claude, codex, gpt-5-pro-codex, cost-optimized",
|
||||
)
|
||||
@click.option(
|
||||
"--orchestrator-mode",
|
||||
type=click.Choice(["router", "direct", "codex", "hybrid", "local"]),
|
||||
default=None,
|
||||
help="Force orchestrator mode (router via hanzo-router, direct API, codex, hybrid, local)",
|
||||
)
|
||||
@click.option(
|
||||
"--router-endpoint",
|
||||
default=None,
|
||||
help="Hanzo router endpoint (default: http://localhost:4000)",
|
||||
)
|
||||
@click.option("--claude-path", help="Path to Claude Code executable")
|
||||
@click.option("--monitor", is_flag=True, help="Start in monitor mode")
|
||||
@@ -184,6 +195,8 @@ def dev(
|
||||
ctx,
|
||||
workspace: str,
|
||||
orchestrator: str,
|
||||
orchestrator_mode: str,
|
||||
router_endpoint: str,
|
||||
claude_path: str,
|
||||
monitor: bool,
|
||||
repl: bool,
|
||||
@@ -218,15 +231,39 @@ def dev(
|
||||
hanzo dev --monitor # Auto-monitor and restart mode
|
||||
"""
|
||||
from .dev import run_dev_orchestrator
|
||||
from .orchestrator_config import get_orchestrator_config, OrchestratorMode
|
||||
|
||||
# Get orchestrator configuration
|
||||
orch_config = get_orchestrator_config(orchestrator)
|
||||
|
||||
# Override mode if specified
|
||||
if orchestrator_mode:
|
||||
orch_config.mode = OrchestratorMode(orchestrator_mode)
|
||||
|
||||
# Override router endpoint if specified
|
||||
if router_endpoint and orch_config.router:
|
||||
orch_config.router.endpoint = router_endpoint
|
||||
|
||||
# Auto-enable hanzo net if using local orchestrator
|
||||
if orchestrator.startswith("local:"):
|
||||
if orchestrator.startswith("local:") or orch_config.mode == OrchestratorMode.LOCAL:
|
||||
use_hanzo_net = True
|
||||
|
||||
# Show configuration
|
||||
console.print(f"[bold cyan]Orchestrator Configuration[/bold cyan]")
|
||||
console.print(f" Mode: {orch_config.mode.value}")
|
||||
console.print(f" Primary Model: {orch_config.primary_model}")
|
||||
if orch_config.router:
|
||||
console.print(f" Router Endpoint: {orch_config.router.endpoint}")
|
||||
if orch_config.codex:
|
||||
console.print(f" Codex Model: {orch_config.codex.model}")
|
||||
console.print(f" Cost Optimization: {'Enabled' if orch_config.enable_cost_optimization else 'Disabled'}")
|
||||
console.print()
|
||||
|
||||
asyncio.run(
|
||||
run_dev_orchestrator(
|
||||
workspace=workspace,
|
||||
orchestrator_model=orchestrator,
|
||||
orchestrator_config=orch_config, # Pass the config
|
||||
claude_path=claude_path,
|
||||
monitor=monitor,
|
||||
repl=repl or not monitor, # Default to REPL if not monitoring
|
||||
|
||||
@@ -832,14 +832,16 @@ async def run_dev_orchestrator(**kwargs):
|
||||
"""Run the Hanzo Dev orchestrator with multi-agent networking.
|
||||
|
||||
This is the main entry point from the CLI that sets up:
|
||||
1. Configurable orchestrator (GPT-5, GPT-4, Claude, etc.)
|
||||
1. Configurable orchestrator (GPT-5, GPT-4, Claude, Codex, etc.)
|
||||
2. Multiple worker agents (Claude instances for implementation)
|
||||
3. Critic agents for System 2 thinking
|
||||
4. MCP tool networking between instances
|
||||
5. Code quality guardrails
|
||||
6. Router-based or direct model access
|
||||
"""
|
||||
workspace = kwargs.get("workspace", "~/.hanzo/dev")
|
||||
orchestrator_model = kwargs.get("orchestrator_model", "gpt-5")
|
||||
orchestrator_config = kwargs.get("orchestrator_config", None) # New config object
|
||||
claude_path = kwargs.get("claude_path")
|
||||
monitor = kwargs.get("monitor", False)
|
||||
repl = kwargs.get("repl", True)
|
||||
|
||||
@@ -0,0 +1,317 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Orchestrator Configuration for Hanzo Dev
|
||||
|
||||
Supports multiple orchestration modes:
|
||||
1. Router-based: Use hanzo-router to access any LLM
|
||||
2. Direct model: Direct API access to specific models
|
||||
3. Codex mode: Specialized code-focused orchestration
|
||||
4. Hybrid: Combine router and direct access
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
from enum import Enum
|
||||
from typing import Dict, List, Optional, Any
|
||||
import os
|
||||
|
||||
|
||||
class OrchestratorMode(Enum):
|
||||
"""Orchestration modes."""
|
||||
ROUTER = "router" # Via hanzo-router (unified gateway)
|
||||
DIRECT = "direct" # Direct model API access
|
||||
CODEX = "codex" # Codex-specific mode
|
||||
HYBRID = "hybrid" # Router + direct combination
|
||||
LOCAL = "local" # Local models only
|
||||
|
||||
|
||||
class ModelProvider(Enum):
|
||||
"""Model providers."""
|
||||
OPENAI = "openai"
|
||||
ANTHROPIC = "anthropic"
|
||||
GOOGLE = "google"
|
||||
MISTRAL = "mistral"
|
||||
LOCAL = "local"
|
||||
ROUTER = "router" # Via hanzo-router
|
||||
CODEX = "codex" # OpenAI Codex
|
||||
|
||||
|
||||
@dataclass
|
||||
class ModelConfig:
|
||||
"""Configuration for a specific model."""
|
||||
name: str # Model name (e.g., "gpt-5", "gpt-4o")
|
||||
provider: ModelProvider # Provider type
|
||||
endpoint: Optional[str] = None # Custom endpoint
|
||||
api_key: Optional[str] = None # API key (if not using env)
|
||||
context_window: int = 8192 # Context window size
|
||||
max_output: int = 4096 # Max output tokens
|
||||
temperature: float = 0.7 # Temperature setting
|
||||
capabilities: List[str] = field(default_factory=list) # Model capabilities
|
||||
cost_per_1k_input: float = 0.01 # Cost per 1K input tokens
|
||||
cost_per_1k_output: float = 0.03 # Cost per 1K output tokens
|
||||
supports_tools: bool = True # Supports function calling
|
||||
supports_vision: bool = False # Supports image input
|
||||
supports_streaming: bool = True # Supports streaming responses
|
||||
|
||||
|
||||
@dataclass
|
||||
class RouterConfig:
|
||||
"""Configuration for hanzo-router."""
|
||||
endpoint: str = "http://localhost:4000" # Router endpoint
|
||||
api_key: Optional[str] = None # Router API key
|
||||
model_preferences: List[str] = field(default_factory=list) # Preferred models
|
||||
fallback_models: List[str] = field(default_factory=list) # Fallback models
|
||||
load_balancing: bool = True # Enable load balancing
|
||||
cache_enabled: bool = True # Enable response caching
|
||||
retry_on_failure: bool = True # Retry failed requests
|
||||
max_retries: int = 3 # Max retry attempts
|
||||
|
||||
|
||||
@dataclass
|
||||
class CodexConfig:
|
||||
"""Configuration for Codex mode."""
|
||||
model: str = "code-davinci-002" # Codex model
|
||||
mode: str = "code-review" # Mode: code-review, generation, completion
|
||||
languages: List[str] = field(default_factory=lambda: ["python", "typescript", "go"])
|
||||
max_tokens: int = 8000 # Max tokens for Codex
|
||||
stop_sequences: List[str] = field(default_factory=list) # Stop sequences
|
||||
enable_comments: bool = True # Generate with comments
|
||||
enable_docstrings: bool = True # Generate docstrings
|
||||
enable_type_hints: bool = True # Generate type hints
|
||||
|
||||
|
||||
@dataclass
|
||||
class OrchestratorConfig:
|
||||
"""Complete orchestrator configuration."""
|
||||
mode: OrchestratorMode = OrchestratorMode.ROUTER
|
||||
primary_model: str = "gpt-5" # Primary orchestrator model
|
||||
models: Dict[str, ModelConfig] = field(default_factory=dict)
|
||||
router: Optional[RouterConfig] = None
|
||||
codex: Optional[CodexConfig] = None
|
||||
worker_models: List[str] = field(default_factory=list) # Worker agent models
|
||||
critic_models: List[str] = field(default_factory=list) # Critic agent models
|
||||
local_models: List[str] = field(default_factory=list) # Local models
|
||||
enable_cost_optimization: bool = True # Enable cost optimization
|
||||
cost_threshold: float = 0.10 # Cost threshold per request
|
||||
prefer_local: bool = True # Prefer local models when possible
|
||||
enable_caching: bool = True # Cache responses
|
||||
enable_monitoring: bool = True # Monitor performance
|
||||
debug: bool = False # Debug mode
|
||||
|
||||
|
||||
# Predefined configurations
|
||||
CONFIGS = {
|
||||
"gpt-5-pro-codex": OrchestratorConfig(
|
||||
mode=OrchestratorMode.HYBRID,
|
||||
primary_model="gpt-5-pro",
|
||||
models={
|
||||
"gpt-5-pro": ModelConfig(
|
||||
name="gpt-5-pro",
|
||||
provider=ModelProvider.OPENAI,
|
||||
context_window=200000,
|
||||
capabilities=["reasoning", "code", "analysis", "vision"],
|
||||
cost_per_1k_input=0.20,
|
||||
cost_per_1k_output=0.60,
|
||||
supports_vision=True,
|
||||
),
|
||||
"codex": ModelConfig(
|
||||
name="code-davinci-002",
|
||||
provider=ModelProvider.CODEX,
|
||||
context_window=8000,
|
||||
capabilities=["code_generation", "completion", "review"],
|
||||
cost_per_1k_input=0.02,
|
||||
cost_per_1k_output=0.02,
|
||||
),
|
||||
},
|
||||
codex=CodexConfig(
|
||||
model="code-davinci-002",
|
||||
mode="code-review",
|
||||
enable_comments=True,
|
||||
enable_docstrings=True,
|
||||
enable_type_hints=True,
|
||||
),
|
||||
worker_models=["codex", "gpt-4o"],
|
||||
critic_models=["gpt-5-pro"],
|
||||
enable_cost_optimization=True,
|
||||
),
|
||||
|
||||
"router-based": OrchestratorConfig(
|
||||
mode=OrchestratorMode.ROUTER,
|
||||
primary_model="router:gpt-5",
|
||||
router=RouterConfig(
|
||||
endpoint=os.getenv("HANZO_ROUTER_URL", "http://localhost:4000"),
|
||||
model_preferences=["gpt-5", "gpt-4o", "claude-3-5-sonnet"],
|
||||
fallback_models=["gpt-4-turbo", "gpt-3.5-turbo"],
|
||||
load_balancing=True,
|
||||
cache_enabled=True,
|
||||
),
|
||||
worker_models=["router:gpt-4o", "router:claude-3-5"],
|
||||
critic_models=["router:gpt-5"],
|
||||
enable_cost_optimization=True,
|
||||
),
|
||||
|
||||
"direct-gpt5": OrchestratorConfig(
|
||||
mode=OrchestratorMode.DIRECT,
|
||||
primary_model="gpt-5",
|
||||
models={
|
||||
"gpt-5": ModelConfig(
|
||||
name="gpt-5-latest",
|
||||
provider=ModelProvider.OPENAI,
|
||||
endpoint="https://api.openai.com/v1",
|
||||
context_window=128000,
|
||||
capabilities=["reasoning", "code", "analysis"],
|
||||
cost_per_1k_input=0.15,
|
||||
cost_per_1k_output=0.45,
|
||||
),
|
||||
},
|
||||
worker_models=["gpt-4o"],
|
||||
critic_models=["gpt-5"],
|
||||
enable_cost_optimization=False, # Use GPT-5 for everything
|
||||
),
|
||||
|
||||
"codex-focused": OrchestratorConfig(
|
||||
mode=OrchestratorMode.CODEX,
|
||||
primary_model="codex",
|
||||
codex=CodexConfig(
|
||||
model="code-davinci-002",
|
||||
mode="code-generation",
|
||||
languages=["python", "typescript", "rust", "go"],
|
||||
max_tokens=8000,
|
||||
enable_comments=True,
|
||||
),
|
||||
worker_models=["codex"],
|
||||
critic_models=["gpt-4o"], # Use GPT-4o for code review
|
||||
enable_cost_optimization=True,
|
||||
),
|
||||
|
||||
"cost-optimized": OrchestratorConfig(
|
||||
mode=OrchestratorMode.HYBRID,
|
||||
primary_model="local:llama3.2",
|
||||
models={
|
||||
"local:llama3.2": ModelConfig(
|
||||
name="llama-3.2-3b",
|
||||
provider=ModelProvider.LOCAL,
|
||||
context_window=8192,
|
||||
capabilities=["basic_reasoning", "simple_tasks"],
|
||||
cost_per_1k_input=0.0,
|
||||
cost_per_1k_output=0.0,
|
||||
),
|
||||
},
|
||||
router=RouterConfig(
|
||||
endpoint="http://localhost:4000",
|
||||
model_preferences=["gpt-4o", "claude-3-5"],
|
||||
fallback_models=["gpt-3.5-turbo"],
|
||||
),
|
||||
worker_models=["local:llama3.2", "local:qwen2.5"],
|
||||
critic_models=["router:gpt-4o"], # Only use API for critical review
|
||||
local_models=["llama3.2", "qwen2.5", "mistral"],
|
||||
enable_cost_optimization=True,
|
||||
prefer_local=True,
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
def get_orchestrator_config(name: str) -> OrchestratorConfig:
|
||||
"""Get a predefined orchestrator configuration.
|
||||
|
||||
Args:
|
||||
name: Configuration name or model spec
|
||||
|
||||
Returns:
|
||||
OrchestratorConfig instance
|
||||
"""
|
||||
# Check predefined configs
|
||||
if name in CONFIGS:
|
||||
return CONFIGS[name]
|
||||
|
||||
# Parse model spec (e.g., "router:gpt-5", "direct:gpt-4o", "codex")
|
||||
if ":" in name:
|
||||
mode, model = name.split(":", 1)
|
||||
if mode == "router":
|
||||
return OrchestratorConfig(
|
||||
mode=OrchestratorMode.ROUTER,
|
||||
primary_model=f"router:{model}",
|
||||
router=RouterConfig(
|
||||
model_preferences=[model],
|
||||
),
|
||||
)
|
||||
elif mode == "direct":
|
||||
return OrchestratorConfig(
|
||||
mode=OrchestratorMode.DIRECT,
|
||||
primary_model=model,
|
||||
)
|
||||
elif mode == "local":
|
||||
return OrchestratorConfig(
|
||||
mode=OrchestratorMode.LOCAL,
|
||||
primary_model=f"local:{model}",
|
||||
local_models=[model],
|
||||
enable_cost_optimization=True,
|
||||
prefer_local=True,
|
||||
)
|
||||
|
||||
# Default to router mode with specified model
|
||||
return OrchestratorConfig(
|
||||
mode=OrchestratorMode.ROUTER,
|
||||
primary_model=name,
|
||||
router=RouterConfig(
|
||||
model_preferences=[name],
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
def list_available_configs() -> List[str]:
|
||||
"""List available orchestrator configurations."""
|
||||
return list(CONFIGS.keys()) + [
|
||||
"router:<model>",
|
||||
"direct:<model>",
|
||||
"local:<model>",
|
||||
"codex",
|
||||
]
|
||||
|
||||
|
||||
# Export configuration builder
|
||||
def build_custom_config(
|
||||
mode: str = "router",
|
||||
primary_model: str = "gpt-5",
|
||||
use_router: bool = True,
|
||||
use_codex: bool = False,
|
||||
worker_models: Optional[List[str]] = None,
|
||||
critic_models: Optional[List[str]] = None,
|
||||
enable_cost_optimization: bool = True,
|
||||
router_endpoint: Optional[str] = None,
|
||||
) -> OrchestratorConfig:
|
||||
"""Build a custom orchestrator configuration.
|
||||
|
||||
Args:
|
||||
mode: Orchestration mode (router, direct, codex, hybrid, local)
|
||||
primary_model: Primary orchestrator model
|
||||
use_router: Use hanzo-router for model access
|
||||
use_codex: Enable Codex for code tasks
|
||||
worker_models: Worker agent models
|
||||
critic_models: Critic agent models
|
||||
enable_cost_optimization: Enable cost optimization
|
||||
router_endpoint: Custom router endpoint
|
||||
|
||||
Returns:
|
||||
Custom OrchestratorConfig
|
||||
"""
|
||||
config = OrchestratorConfig(
|
||||
mode=OrchestratorMode(mode),
|
||||
primary_model=primary_model,
|
||||
worker_models=worker_models or ["gpt-4o", "claude-3-5"],
|
||||
critic_models=critic_models or ["gpt-5"],
|
||||
enable_cost_optimization=enable_cost_optimization,
|
||||
)
|
||||
|
||||
if use_router:
|
||||
config.router = RouterConfig(
|
||||
endpoint=router_endpoint or "http://localhost:4000",
|
||||
model_preferences=[primary_model],
|
||||
)
|
||||
|
||||
if use_codex:
|
||||
config.codex = CodexConfig(
|
||||
model="code-davinci-002",
|
||||
mode="code-review",
|
||||
)
|
||||
|
||||
return config
|
||||
Executable
+285
@@ -0,0 +1,285 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test GPT-5/Codex as orchestrator for comprehensive code review.
|
||||
|
||||
This script demonstrates using the most powerful models (GPT-5, GPT-4, Codex)
|
||||
as orchestrators to review all code and provide system-level improvements.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from rich.console import Console
|
||||
from rich.table import Table
|
||||
from rich.panel import Panel
|
||||
from rich.syntax import Syntax
|
||||
|
||||
console = Console()
|
||||
|
||||
class GPT5CodeReviewer:
|
||||
"""Use GPT-5/Codex for comprehensive code review and orchestration."""
|
||||
|
||||
def __init__(self, model="gpt-5", api_key=None):
|
||||
"""Initialize with specified model.
|
||||
|
||||
Args:
|
||||
model: Model to use (gpt-5, gpt-4o, gpt-4-turbo, codex, o3)
|
||||
api_key: OpenAI API key (reads from env if not provided)
|
||||
"""
|
||||
self.model = model
|
||||
self.api_key = api_key or os.getenv("OPENAI_API_KEY")
|
||||
|
||||
# Model configurations for different review scenarios
|
||||
self.model_configs = {
|
||||
"gpt-5": {
|
||||
"name": "gpt-5-latest",
|
||||
"context": 128000,
|
||||
"capabilities": ["code_review", "architecture", "security", "performance"],
|
||||
"cost_per_1k": 0.15
|
||||
},
|
||||
"gpt-4o": {
|
||||
"name": "gpt-4o",
|
||||
"context": 128000,
|
||||
"capabilities": ["code_review", "architecture", "refactoring"],
|
||||
"cost_per_1k": 0.05
|
||||
},
|
||||
"gpt-4-turbo": {
|
||||
"name": "gpt-4-turbo-preview",
|
||||
"context": 128000,
|
||||
"capabilities": ["code_review", "documentation"],
|
||||
"cost_per_1k": 0.03
|
||||
},
|
||||
"codex": {
|
||||
"name": "code-davinci-002",
|
||||
"context": 8000,
|
||||
"capabilities": ["code_generation", "completion", "review"],
|
||||
"cost_per_1k": 0.02
|
||||
},
|
||||
"o3": {
|
||||
"name": "o3-preview",
|
||||
"context": 200000,
|
||||
"capabilities": ["reasoning", "architecture", "security", "optimization"],
|
||||
"cost_per_1k": 0.20
|
||||
}
|
||||
}
|
||||
|
||||
async def review_with_hanzo_dev(self, project_path: Path):
|
||||
"""Use hanzo dev with GPT-5 orchestrator to review entire project."""
|
||||
|
||||
console.print(Panel.fit(
|
||||
f"[bold cyan]Code Review with {self.model.upper()} Orchestrator[/bold cyan]\n"
|
||||
f"Project: {project_path}",
|
||||
title="🤖 AI Code Review"
|
||||
))
|
||||
|
||||
# Start hanzo dev with GPT-5 orchestrator
|
||||
cmd = [
|
||||
"hanzo", "dev",
|
||||
"--orchestrator", self.model,
|
||||
"--instances", "3", # Main coder + 2 reviewers
|
||||
"--critic-instances", "2", # 2 critic agents
|
||||
"--enable-guardrails",
|
||||
"--workspace", str(project_path),
|
||||
"--review-mode" # Special mode for code review
|
||||
]
|
||||
|
||||
console.print(f"[yellow]Starting:[/yellow] {' '.join(cmd)}")
|
||||
|
||||
# Create review prompts for different aspects
|
||||
review_tasks = [
|
||||
{
|
||||
"aspect": "Security",
|
||||
"prompt": "Review the codebase for security vulnerabilities, focusing on:\n"
|
||||
"- Input validation\n- Authentication/authorization\n"
|
||||
"- Data sanitization\n- Secret management\n"
|
||||
"- Dependency vulnerabilities"
|
||||
},
|
||||
{
|
||||
"aspect": "Performance",
|
||||
"prompt": "Analyze performance bottlenecks and optimization opportunities:\n"
|
||||
"- Algorithm complexity\n- Database queries\n"
|
||||
"- Caching strategies\n- Async/parallel processing\n"
|
||||
"- Memory usage patterns"
|
||||
},
|
||||
{
|
||||
"aspect": "Architecture",
|
||||
"prompt": "Evaluate the system architecture and design patterns:\n"
|
||||
"- SOLID principles adherence\n- Coupling and cohesion\n"
|
||||
"- Scalability considerations\n- Design pattern usage\n"
|
||||
"- Module boundaries and interfaces"
|
||||
},
|
||||
{
|
||||
"aspect": "Code Quality",
|
||||
"prompt": "Assess overall code quality and maintainability:\n"
|
||||
"- Code duplication\n- Naming conventions\n"
|
||||
"- Documentation completeness\n- Test coverage\n"
|
||||
"- Error handling patterns"
|
||||
},
|
||||
{
|
||||
"aspect": "Best Practices",
|
||||
"prompt": "Check adherence to language-specific best practices:\n"
|
||||
"- Python: PEP 8, type hints, docstrings\n"
|
||||
"- JavaScript: ESLint rules, modern syntax\n"
|
||||
"- Error handling and logging\n- Configuration management"
|
||||
}
|
||||
]
|
||||
|
||||
# Display review plan
|
||||
table = Table(title="Review Plan", show_header=True)
|
||||
table.add_column("Aspect", style="cyan")
|
||||
table.add_column("Focus Areas", style="white")
|
||||
|
||||
for task in review_tasks:
|
||||
table.add_row(
|
||||
task["aspect"],
|
||||
task["prompt"].replace("\n", " ").replace("- ", "• ")[:80] + "..."
|
||||
)
|
||||
|
||||
console.print(table)
|
||||
|
||||
# Execute reviews
|
||||
results = {}
|
||||
for task in review_tasks:
|
||||
console.print(f"\n[bold]Reviewing: {task['aspect']}[/bold]")
|
||||
|
||||
# Simulate review execution (in real implementation, this would
|
||||
# call hanzo dev with the specific review prompt)
|
||||
result = await self._execute_review(task["aspect"], task["prompt"])
|
||||
results[task["aspect"]] = result
|
||||
|
||||
# Display summary
|
||||
if result.get("issues"):
|
||||
console.print(f" [red]✗ Found {len(result['issues'])} issues[/red]")
|
||||
else:
|
||||
console.print(f" [green]✓ No major issues found[/green]")
|
||||
|
||||
if result.get("suggestions"):
|
||||
console.print(f" [yellow]💡 {len(result['suggestions'])} suggestions[/yellow]")
|
||||
|
||||
return results
|
||||
|
||||
async def _execute_review(self, aspect: str, prompt: str):
|
||||
"""Execute a specific review aspect."""
|
||||
|
||||
# In a real implementation, this would:
|
||||
# 1. Send the prompt to hanzo dev
|
||||
# 2. Wait for orchestrator to coordinate agents
|
||||
# 3. Collect and format results
|
||||
|
||||
# For demonstration, return sample results
|
||||
return {
|
||||
"aspect": aspect,
|
||||
"status": "completed",
|
||||
"issues": [],
|
||||
"suggestions": [
|
||||
f"Consider improving {aspect.lower()} in module X",
|
||||
f"Optimize {aspect.lower()} patterns in service Y"
|
||||
],
|
||||
"score": 85
|
||||
}
|
||||
|
||||
def generate_review_report(self, results: dict):
|
||||
"""Generate comprehensive review report."""
|
||||
|
||||
console.print("\n" + "="*60)
|
||||
console.print(Panel.fit(
|
||||
"[bold green]Code Review Complete[/bold green]",
|
||||
title="📊 Summary Report"
|
||||
))
|
||||
|
||||
# Overall score calculation
|
||||
total_score = sum(r.get("score", 0) for r in results.values())
|
||||
avg_score = total_score / len(results) if results else 0
|
||||
|
||||
# Score interpretation
|
||||
if avg_score >= 90:
|
||||
grade = "A"
|
||||
color = "green"
|
||||
message = "Excellent code quality!"
|
||||
elif avg_score >= 80:
|
||||
grade = "B"
|
||||
color = "yellow"
|
||||
message = "Good quality with minor improvements needed"
|
||||
elif avg_score >= 70:
|
||||
grade = "C"
|
||||
color = "orange"
|
||||
message = "Acceptable but needs attention"
|
||||
else:
|
||||
grade = "D"
|
||||
color = "red"
|
||||
message = "Significant improvements required"
|
||||
|
||||
console.print(f"\n[bold]Overall Grade:[/bold] [{color}]{grade}[/{color}] ({avg_score:.1f}/100)")
|
||||
console.print(f"[italic]{message}[/italic]\n")
|
||||
|
||||
# Detailed results
|
||||
for aspect, result in results.items():
|
||||
console.print(f"[bold cyan]{aspect}:[/bold cyan]")
|
||||
console.print(f" Score: {result.get('score', 0)}/100")
|
||||
|
||||
if result.get("issues"):
|
||||
console.print(" [red]Issues:[/red]")
|
||||
for issue in result["issues"][:3]:
|
||||
console.print(f" • {issue}")
|
||||
|
||||
if result.get("suggestions"):
|
||||
console.print(" [yellow]Suggestions:[/yellow]")
|
||||
for suggestion in result["suggestions"][:3]:
|
||||
console.print(f" • {suggestion}")
|
||||
|
||||
console.print()
|
||||
|
||||
|
||||
async def main():
|
||||
"""Run GPT-5 code review demonstration."""
|
||||
|
||||
# Display available models
|
||||
console.print(Panel.fit(
|
||||
"[bold]Available Orchestrator Models[/bold]\n\n"
|
||||
"• [cyan]gpt-5[/cyan] - Most advanced, best for complex reviews\n"
|
||||
"• [cyan]gpt-4o[/cyan] - Optimized GPT-4, good balance\n"
|
||||
"• [cyan]gpt-4-turbo[/cyan] - Fast GPT-4 variant\n"
|
||||
"• [cyan]codex[/cyan] - Specialized for code\n"
|
||||
"• [cyan]o3[/cyan] - Advanced reasoning model\n",
|
||||
title="🎯 Model Selection"
|
||||
))
|
||||
|
||||
# Example: Review current project with GPT-5
|
||||
reviewer = GPT5CodeReviewer(model="gpt-5")
|
||||
project_path = Path.cwd()
|
||||
|
||||
console.print("\n[bold]Starting comprehensive code review...[/bold]\n")
|
||||
|
||||
# Run the review
|
||||
results = await reviewer.review_with_hanzo_dev(project_path)
|
||||
|
||||
# Generate report
|
||||
reviewer.generate_review_report(results)
|
||||
|
||||
# Show example commands
|
||||
console.print("\n" + "="*60)
|
||||
console.print(Panel.fit(
|
||||
"[bold]Example Commands[/bold]\n\n"
|
||||
"[cyan]# Use GPT-5 as orchestrator[/cyan]\n"
|
||||
"hanzo dev --orchestrator gpt-5\n\n"
|
||||
|
||||
"[cyan]# Use GPT-4o for balanced performance[/cyan]\n"
|
||||
"hanzo dev --orchestrator gpt-4o --instances 3\n\n"
|
||||
|
||||
"[cyan]# Use Codex for code-specific tasks[/cyan]\n"
|
||||
"hanzo dev --orchestrator codex --focus code-generation\n\n"
|
||||
|
||||
"[cyan]# Use O3 for complex reasoning[/cyan]\n"
|
||||
"hanzo dev --orchestrator o3 --enable-reasoning\n\n"
|
||||
|
||||
"[cyan]# Hybrid: Local for simple, GPT-5 for complex[/cyan]\n"
|
||||
"hanzo dev --orchestrator gpt-5 --use-hanzo-net\n",
|
||||
title="💡 Usage Examples"
|
||||
))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
Reference in New Issue
Block a user