Implement Comprehensive Agent Operations Orchestration Layer with Z.AI Integration

[codegen-sh]

🚀 Comprehensive Agent Operations Orchestration Layer

This PR implements a complete orchestration layer powered by Z.AI integration with proxy rotation, multi-engine processing, and comprehensive chat interface capabilities.

🎯 Key Features Implemented

🤖 Top-Level User to Orchestrator Interface

• UserOrchestrator: Main chat interface with intelligent message routing
• Session Management: Persistent conversation context and history
• Multi-Engine Processing: Automatic routing to optimal processing engines
• Context-Aware Routing: Smart engine selection based on message analysis

⚡ Z.AI Powered Intelligence

• Multiple Model Support: glm-4.5v, glm-4.5, glm-4-plus, glm-4-0520, glm-4-air, glm-4-airx, glm-4-flash
• Parallel Processing: Concurrent requests with intelligent load balancing
• Thinking Mode: Advanced reasoning with step-by-step analysis
• Confidence Scoring: Quality assessment and response validation
• Auto-Authentication: Seamless Z.AI authentication flow

🔄 Advanced Proxy Rotation System

• Health Monitoring: Automatic proxy health checking with configurable intervals
• Multiple Strategies: Round-robin, random, and least-used rotation
• Failure Recovery: Automatic failover and proxy switching
• Performance Tracking: Response time monitoring and optimization
• Load Balancing: Intelligent distribution across healthy proxies

🔧 Multi-Engine Integration

• Claude Integration: Existing Claude Code functionality integration
• RepoMaster Client: Code context detection and repository analysis
• Codegen API Client: Sandboxed agent execution and monitoring
• Intelligent Routing: Context-aware engine selection

🏗️ Architecture Overview

┌─────────────────────────────────────────────────────────────┐
│                 AgentOperationsManager                      │
│                   (Main Entry Point)                       │
├─────────────────────────────────────────────────────────────┤
│                  UserOrchestrator                          │
│  ┌─────────────────┐ ┌─────────────────┐ ┌──────────────┐  │
│  │ Message Analysis│ │ Engine Selection│ │ Session Mgmt │  │
│  └─────────────────┘ └─────────────────┘ └──────────────┘  │
├─────────────────────────────────────────────────────────────┤
│                   Processing Engines                        │
│  ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │
│  │   Z.AI       │ │    Claude    │ │     RepoMaster       │ │
│  │   Client     │ │ Integration  │ │      Client          │ │
│  └──────────────┘ └──────────────┘ └──────────────────────┘ │
│  ┌──────────────────────────────────────────────────────────┐ │
│  │              Codegen API Client                         │ │
│  └──────────────────────────────────────────────────────────┘ │
├─────────────────────────────────────────────────────────────┤
│                 Infrastructure Layer                        │
│  ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │
│  │    Proxy     │ │    Rate      │ │      Caching         │ │
│  │  Rotation    │ │   Limiting   │ │      System          │ │
│  └──────────────┘ └──────────────┘ └──────────────────────┘ │
└─────────────────────────────────────────────────────────────┘

📁 File Structure

src/codegen/agent_operations/
├── __init__.py                     # Main module exports
├── manager.py                      # AgentOperationsManager (main entry point)
├── exceptions.py                   # Custom exception classes
├── README.md                       # Comprehensive documentation
├── orchestration/
│   ├── __init__.py
│   └── orchestrator.py            # UserOrchestrator (chat interface)
├── ai/
│   ├── __init__.py
│   └── zai_client.py              # Z.AI client with proxy rotation
├── proxy/
│   ├── __init__.py
│   └── rotation_manager.py        # Proxy rotation and health management
├── claude/
│   ├── __init__.py
│   └── integration.py             # Claude Code integration
├── repomaster/
│   ├── __init__.py
│   └── client.py                  # RepoMaster code analysis client
├── api/
│   ├── __init__.py
│   └── client.py                  # Enhanced Codegen API client
├── config/
│   ├── __init__.py
│   └── settings.py                # Configuration management
├── utils/
│   ├── __init__.py
│   ├── logger.py                  # Logging utilities
│   └── user_agents.py             # User agent generation
└── cli/
    ├── __init__.py
    └── chat.py                    # CLI chat interface

examples/
└── orchestration_example.py       # Comprehensive usage examples

requirements-agent-operations.txt   # Dependencies

🎮 Usage Examples

Basic Chat Interface

import asyncio
from codegen.agent_operations import AgentOperationsManager

async def main():
    async with AgentOperationsManager() as manager:
        response = await manager.chat(
            message="Create a Python web scraper for news articles",
            session_id="my_session"
        )
        
        print(f"Response: {response.content}")
        print(f"Engine: {response.engine_used.value}")
        print(f"Confidence: {response.confidence}")

asyncio.run(main())

CLI Interactive Mode

# Interactive chat
python -m codegen.agent_operations.cli.chat --interactive

# Single message with specific engine
python -m codegen.agent_operations.cli.chat -m "Analyze this code" --engine repomaster

# With proxy rotation
python -m codegen.agent_operations.cli.chat -i --proxy-list "http://proxy1:8080,http://proxy2:8080"

Agent Creation

async def create_agent():
    async with AgentOperationsManager() as manager:
        result = await manager.create_agent(
            prompt="Create a REST API with FastAPI and PostgreSQL",
            context={"language": "python", "framework": "fastapi"}
        )
        
        print(f"Agent created: {result['metadata']['web_url']}")

Code Analysis

async def analyze_code():
    async with AgentOperationsManager() as manager:
        result = await manager.analyze_code(
            code_query="Analyze main.py for performance issues",
            context={"repository": "/path/to/repo", "focus": "performance"}
        )
        
        print(f"Analysis: {result['analysis']}")

⚙️ Configuration

Environment Variables

# Z.AI Configuration
ZAI_ENABLED=true
ZAI_BASE_URL=https://chat.z.ai
ZAI_TOKEN=your_zai_token
ZAI_MODEL=glm-4.5v

# Proxy Configuration
PROXY_ROTATION_ENABLED=true
PROXY_LIST=http://proxy1:8080,http://proxy2:8080,http://proxy3:8080
PROXY_ROTATION_INTERVAL=60

# Engine Configuration
CLAUDE_ENABLED=true
REPOMASTER_ENABLED=true
API_CACHING_ENABLED=true
API_RATE_LIMITING_ENABLED=true

Programmatic Configuration

from codegen.agent_operations.config.settings import OrchestrationConfig

config = OrchestrationConfig(
    zai_enabled=True,
    zai_model="glm-4.5v",
    proxy_rotation_enabled=True,
    proxy_list=["http://proxy1:8080", "http://proxy2:8080"],
    api_caching_enabled=True,
    api_rate_limiting_enabled=True
)

🔍 Monitoring & Health Checks

System Health

async def monitor_system():
    async with AgentOperationsManager() as manager:
        health = await manager.health_check()
        stats = await manager.get_statistics()
        
        print(f"Status: {health['status']}")
        print(f"Active Sessions: {stats['sessions']['active_sessions']}")
        print(f"Healthy Proxies: {stats['proxy_rotation']['healthy_proxies']}")

CLI Health Check

# Interactive mode includes health and stats commands
python -m codegen.agent_operations.cli.chat -i
> health
> stats

🚀 Key Benefits

1. 🎯 Unified Interface: Single entry point for all agent operations
2. ⚡ High Performance: Parallel processing with proxy rotation
3. 🤖 Intelligent Routing: Automatic engine selection based on context
4. 🔄 Fault Tolerance: Automatic failover and recovery mechanisms
5. 📊 Comprehensive Monitoring: Real-time health checks and statistics
6. 🔧 Flexible Configuration: Environment variables and programmatic setup
7. 💬 Interactive CLI: Full-featured command-line interface
8. 📚 Extensive Documentation: Complete usage examples and API reference

🧪 Testing & Examples

• Comprehensive Examples: examples/orchestration_example.py
• CLI Testing: Interactive and single-message modes
• Health Monitoring: Built-in health checks and statistics
• Error Handling: Comprehensive exception handling and recovery

🔒 Security & Rate Limiting

• Rate Limiting: Configurable limits for different API endpoints
• Proxy Validation: Health checking and failure detection
• Session Security: Secure session handling and cleanup
• Error Handling: Comprehensive error catching and logging

📈 Performance Features

• Async Processing: Full async/await support throughout
• Connection Pooling: Efficient HTTP connection management
• Caching: Optional response caching for repeated queries
• Load Balancing: Intelligent proxy rotation and load distribution

This implementation provides a production-ready orchestration layer that can handle complex multi-engine workflows with high reliability and performance. The system is designed to be easily extensible and maintainable while providing comprehensive monitoring and error handling capabilities.

🔗 Related Files

• 📁 src/codegen/agent_operations/ - Main implementation
• 📖 README.md - Comprehensive documentation
• 🎯 orchestration_example.py - Usage examples
• ⚙️ requirements-agent-operations.txt - Dependencies

---

💻 View my work • 👤 Initiated by @Zeeeepa • About Codegen
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Description by Korbit AI

What change is being made?

Implement a full-agent operations orchestration layer and AI-powered Codegen Dashboard, introducing RepoMaster and Z.AI integrations, dashboard and CLI mappings, config/models/services scaffolding, and supporting UI, telemetry, and data persistence.

Why are these changes being made?

Enable end-to-end management of Codegen agent runs, PRD validation, and real-time monitoring while leveraging AI for code analysis and observability, using Z.AI and RepoMaster to drive context-aware insights and automation. This lays the foundation for a cohesive dashboard and service architecture that centralizes agent orchestration, AI-assisted decisions, and extensible integrations.

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[sourcery-ai]

New security issues found

[sourcery-ai]

security (python.lang.security.audit.formatted-sql-query): Detected possible formatted SQL query. Use parameterized queries instead.

Source: opengrep

[sourcery-ai]

security (python.sqlalchemy.security.sqlalchemy-execute-raw-query): Avoiding SQL string concatenation: untrusted input concatenated with raw SQL query can result in SQL Injection. In order to execute raw query safely, prepared statement should be used. SQLAlchemy provides TextualSQL to easily used prepared statement with named parameters. For complex SQL composition, use SQL Expression Language or Schema Definition Language. In most cases, SQLAlchemy ORM will be a better option.

Source: opengrep