🤖 AutoGen Integration

"""
Governed Coding Assistant with AutoGen and Agent OS
====================================================

A production-ready coding assistant with:
- Kernel-level governance
- Sandboxed code execution
- Full audit trail
- Policy-based controls
"""

import os
from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager
from agent_os.integrations import autogen_kernel
from agent_os.policies import Policy, ExecutionPolicy, GroupPolicy
from agent_os.sandbox import SandboxConfig
from agent_os.audit import FlightRecorder

# =============================================================================
# 1. Configure the Flight Recorder for Auditing
# =============================================================================

recorder = FlightRecorder(
    output_path="./audit_logs/coding_assistant.jsonl",
    capture_level="detailed",  # "basic", "detailed", "full"
    include_timestamps=True,
    include_token_counts=True
)

# =============================================================================
# 2. Define Policies
# =============================================================================

# Policy for the coding assistant
coder_policy = Policy(
    name="coder_policy",
    rules=[
        # Response quality
        {"action": "generate", "require_explanation": True},
        {"action": "code_suggest", "require_comments": True},
        
        # Safety
        {"action": "generate", "blocked_topics": ["hacking", "exploits", "malware"]},
        {"action": "code_suggest", "blocked_patterns": [
            r"eval\s*\(",
            r"exec\s*\(",
            r"__import__\s*\("
        ]}
    ]
)

# Policy for code execution
exec_policy = ExecutionPolicy(
    name="safe_execution",
    allowed_languages=["python"],
    allowed_packages=["pandas", "numpy", "matplotlib", "seaborn", "requests", "json"],
    max_execution_time=30,
    max_memory_mb=256,
    max_output_size_kb=50
)

# Sandbox configuration
sandbox = SandboxConfig(
    isolation="process",
    filesystem={
        "work_dir": "./workspace",
        "write_paths": ["./workspace"],
        "blocked_paths": ["/etc", "/root", "~"]
    },
    network={"enabled": False}
)

# =============================================================================
# 3. Create and Wrap Agents
# =============================================================================

# LLM configuration
llm_config = {
    "model": "gpt-4-turbo",
    "api_key": os.environ["OPENAI_API_KEY"],
    "temperature": 0.3  # Lower temperature for coding tasks
}

# Create the coding assistant
coder = AssistantAgent(
    name="coder",
    system_message="""You are an expert Python developer. You write clean, 
    well-documented code with proper error handling. Always explain your 
    approach before writing code.""",
    llm_config=llm_config
)

# Create the code reviewer
reviewer = AssistantAgent(
    name="reviewer",
    system_message="""You are a senior code reviewer. Review code for:
    - Bugs and logic errors
    - Security vulnerabilities
    - Performance issues
    - Code style and best practices
    Provide specific, actionable feedback.""",
    llm_config=llm_config
)

# Create the user proxy with code execution
user_proxy = UserProxyAgent(
    name="user",
    human_input_mode="TERMINATE",
    max_consecutive_auto_reply=10,
    code_execution_config={
        "work_dir": "./workspace",
        "use_docker": False
    }
)

# =============================================================================
# 4. Wrap Agents with Agent OS Governance
# =============================================================================

# Wrap the coder with policy governance
governed_coder = autogen_kernel.wrap(
    coder,
    policy=coder_policy,
    audit_log=True,
    flight_recorder=recorder
)

# Wrap the reviewer
governed_reviewer = autogen_kernel.wrap(
    reviewer,
    policy=coder_policy,
    audit_log=True,
    flight_recorder=recorder
)

# Wrap the user proxy with execution controls
governed_user_proxy = autogen_kernel.wrap(
    user_proxy,
    execution_policy=exec_policy,
    sandbox=sandbox,
    flight_recorder=recorder
)

# =============================================================================
# 5. Create Governed Group Chat
# =============================================================================

# Group policy for the development team
group_policy = GroupPolicy(
    name="dev_team",
    rules=[
        # Require review after coding
        {"after_agent": "coder", "require_agent": "reviewer"},
        
        # Limit iterations
        {"max_consecutive_same_agent": 2}
    ]
)

# Create the group chat
group_chat = GroupChat(
    agents=[governed_user_proxy, governed_coder, governed_reviewer],
    messages=[],
    max_round=15
)

# Create and wrap the manager
manager = GroupChatManager(groupchat=group_chat, llm_config=llm_config)

governed_manager = autogen_kernel.wrap_group_chat(
    manager,
    policy=group_policy,
    flight_recorder=recorder,
    flow_control={
        "terminate_on": [{"keyword": "TASK_COMPLETE"}],
        "detect_loops": True
    }
)

# =============================================================================
# 6. Run the Governed Conversation
# =============================================================================

def main():
    """Run the governed coding assistant."""
    
    print("=" * 60)
    print("Governed Coding Assistant")
    print("Agent OS Kernel: Active")
    print("=" * 60)
    
    # Start the conversation
    governed_user_proxy.initiate_chat(
        governed_manager,
        message="""
        Create a Python script that:
        1. Reads a CSV file containing sales data
        2. Calculates monthly totals and growth rates
        3. Generates a summary report with visualizations
        4. Saves the report as a PDF
        
        The CSV has columns: date, product, quantity, price
        """
    )
    
    # Print audit summary
    print("\n" + "=" * 60)
    print("Audit Summary")
    print("=" * 60)
    
    summary = recorder.get_summary()
    print(f"Total messages: {summary['total_messages']}")
    print(f"Code executions: {summary['code_executions']}")
    print(f"Policy violations: {summary['policy_violations']}")
    print(f"Blocked actions: {summary['blocked_actions']}")
    print(f"Total tokens used: {summary['total_tokens']}")

if __name__ == "__main__":
    main()