modal_agent_design.md

AutoGPT-like Agent Design with Modal and MCO

Core Agent Architecture

1. Agent Components

• LLM Interface: Claude API via Modal for reasoning and planning
• Memory System: Persistent state tracking across steps
• Tool System: Code interpreter, file operations, web access
• MCP Client: Integration with MCO MCP server
• Execution Engine: Runs tools and processes results

2. Agent Capabilities

• Code Generation: Create and modify code files
• Code Execution: Run code in sandbox environment
• File Management: Create, read, update, delete files
• Web Access: Search and retrieve information
• Self-Reflection: Evaluate progress against goals
• Planning: Break down tasks into steps

Modal Implementation

1. Modal Setup

import modal

# Define the Modal app
app = modal.App("mco-autogpt-agent")

# Base image with required dependencies
image = modal.Image.debian_slim().pip_install(
    "anthropic",
    "requests",
    "python-dotenv",
    "beautifulsoup4",
    "numpy",
    "pandas",
    "matplotlib"
)

# Mount local directory for file persistence
volume = modal.Volume.from_name("mco-agent-volume")

2. Agent Function

@app.function(
    image=image,
    volumes={"/data": volume},
    timeout=600,
    keep_warm=1
)
def run_agent(task_description, mco_orchestration_id=None):
    # Initialize agent
    agent = AutoGPTAgent(
        task=task_description,
        orchestration_id=mco_orchestration_id
    )
    
    # Run agent loop
    return agent.run()

3. Tool Functions

@app.function(image=image, volumes={"/data": volume})
def execute_code(code, language="python"):
    # Set up sandbox environment
    # Execute code safely
    # Return results
    pass

@app.function(image=image)
def search_web(query):
    # Perform web search
    # Parse and return results
    pass

@app.function(image=image, volumes={"/data": volume})
def file_operation(operation, path, content=None):
    # Handle file operations (read, write, list, etc.)
    pass

MCO Integration

1. MCP Client

class MCPClient:
    def __init__(self, orchestration_id=None):
        self.orchestration_id = orchestration_id
        
    def start_orchestration(self, config):
        # Call MCO start_orchestration tool
        # Return orchestration ID
        pass
        
    def get_next_directive(self):
        # Call MCO get_next_directive tool
        # Return directive
        pass
        
    def complete_step(self, step_id, result):
        # Call MCO complete_step tool
        # Return status
        pass
        
    def get_workflow_status(self):
        # Call MCO get_workflow_status tool
        # Return status
        pass

2. Orchestration Integration

class AutoGPTAgent:
    def __init__(self, task, orchestration_id=None):
        self.task = task
        self.mcp_client = MCPClient(orchestration_id)
        self.memory = []
        self.tools = {
            "execute_code": execute_code,
            "search_web": search_web,
            "file_operation": file_operation
        }
        
    def run(self):
        # If no orchestration ID, start new orchestration
        if not self.mcp_client.orchestration_id:
            config = {"task": self.task}
            self.mcp_client.start_orchestration(config)
            
        results = []
        
        # Main agent loop
        while True:
            # Get next directive from MCO
            directive = self.mcp_client.get_next_directive()
            
            if directive["type"] == "complete":
                # Workflow is complete
                break
                
            # Process directive
            result = self._process_directive(directive)
            results.append(result)
            
            # Complete step
            self.mcp_client.complete_step(directive["step_id"], result)
            
        return results

3. Directive Processing

def _process_directive(self, directive):
    # Extract information from directive
    instruction = directive["instruction"]
    context = directive["persistent_context"]
    injected = directive.get("injected_context", {})
    
    # Add to memory
    self.memory.append({
        "role": "system",
        "content": f"Directive: {instruction}\nContext: {json.dumps(context)}"
    })
    
    if injected:
        self.memory.append({
            "role": "system",
            "content": f"Additional context: {json.dumps(injected)}"
        })
    
    # Generate thinking process
    thinking = self._generate_thinking(instruction, context, injected)
    
    # Execute tools based on thinking
    result = self._execute_plan(thinking)
    
    return {
        "thinking": thinking,
        "result": result
    }

Agent Thinking Process

1. Thinking Generation

def _generate_thinking(self, instruction, context, injected):
    # Prepare prompt for Claude
    prompt = f"""
    <task>{instruction}</task>
    
    <context>
    {json.dumps(context, indent=2)}
    </context>
    
    {"<injected>" + json.dumps(injected, indent=2) + "</injected>" if injected else ""}
    
    <memory>
    {self._format_memory()}
    </memory>
    
    <available_tools>
    {self._format_tools()}
    </available_tools>
    
    <thinking>
    """
    
    # Call Claude API
    response = anthropic.completions.create(
        model="claude-3-5-sonnet",
        prompt=prompt,
        max_tokens=2000,
        stop=["</thinking>"]
    )
    
    return response.completion

2. Plan Execution

def _execute_plan(self, thinking):
    # Extract tool calls from thinking
    tool_calls = self._extract_tool_calls(thinking)
    
    results = []
    for tool_call in tool_calls:
        tool_name = tool_call["name"]
        tool_args = tool_call["args"]
        
        if tool_name in self.tools:
            # Execute tool
            result = self.tools[tool_name](**tool_args)
            results.append({
                "tool": tool_name,
                "args": tool_args,
                "result": result
            })
            
            # Add to memory
            self.memory.append({
                "role": "function",
                "name": tool_name,
                "content": json.dumps(result)
            })
    
    # Generate summary of results
    summary = self._generate_summary(results)
    
    return {
        "tool_results": results,
        "summary": summary
    }

Code Review Agent Specialization

1. Code Review Tools

@app.function(image=image)
def analyze_code(code, language):
    # Analyze code for issues
    # Return analysis results
    pass

@app.function(image=image)
def suggest_improvements(code, analysis):
    # Generate improvement suggestions
    # Return improved code
    pass

@app.function(image=image)
def run_tests(code, test_cases):
    # Run tests on code
    # Return test results
    pass

2. Code Review Workflow

def review_code(self, code_files):
    results = {}
    
    for file_path, code in code_files.items():
        # Determine language
        language = self._detect_language(file_path)
        
        # Analyze code
        analysis = analyze_code(code, language)
        
        # Generate suggestions
        suggestions = suggest_improvements(code, analysis)
        
        # Create test cases
        test_cases = self._generate_test_cases(code, language)
        
        # Run tests
        test_results = run_tests(code, test_cases)
        
        # Compile results
        results[file_path] = {
            "analysis": analysis,
            "suggestions": suggestions,
            "test_results": test_results
        }
    
    return results

MCO Workflow Creation

1. SNLP File Generation

def generate_snlp_files(self, review_type, language_focus):
    # Generate mco.core
    core_content = self._generate_core_file(review_type, language_focus)
    
    # Generate mco.sc
    sc_content = self._generate_sc_file(review_type, language_focus)
    
    # Generate mco.features
    features_content = self._generate_features_file(review_type, language_focus)
    
    # Generate mco.styles
    styles_content = self._generate_styles_file(review_type, language_focus)
    
    return {
        "mco.core": core_content,
        "mco.sc": sc_content,
        "mco.features": features_content,
        "mco.styles": styles_content
    }

2. File Content Generation

def _generate_core_file(self, review_type, language_focus):
    return f"""// MCO Core Configuration

@workflow "Code Review Assistant"
>This is an AI assistant that performs thorough code reviews for {language_focus} code with a focus on {review_type}.
>The workflow follows a structured progression to ensure comprehensive and reliable code reviews.

@description "Multi-step code review workflow with progressive revelation"
>This workflow demonstrates MCO's progressive revelation capability - core requirements stay persistent while features and styles are strategically injected at optimal moments.
>The agent should maintain focus on the current step while building upon previous work.

@version "1.0.0"

// Data Section - Persistent state throughout workflow
@data:
  language: "{language_focus}"
  review_type: "{review_type}"
  code_files: []
  issues_found: {}
  suggestions: {}
  test_results: {}
>Focus on building reliable, autonomous code review workflows that complete successfully without human intervention.
>The agent should maintain context across all steps and build upon previous work iteratively.
>Use the data variables to track state and progress throughout the workflow.

// Agents Section - Workflow execution structure
@agents:
  orchestrator:
    name: "MCO Orchestrator"
    description: "Manages workflow state and progressive revelation"
    model: "claude-3-5-sonnet"
    steps:
      - "Understand the code review requirements and scope"
      - "Analyze code structure and organization"
      - "Identify bugs, errors, and potential issues"
      - "Evaluate code quality and adherence to best practices"
      - "Generate improvement suggestions with examples"
      - "Create comprehensive review report with actionable recommendations"
"""

Deployment and Integration

1. Web Endpoint

@app.endpoint(method="POST")
def agent_endpoint(task_description, mco_config=None):
    # Start agent with task
    results = run_agent(task_description, mco_config)
    return results

2. Gradio Integration

def create_gradio_interface():
    # Create Gradio interface
    # Connect to Modal endpoint
    # Return interface
    pass

3. Deployment Script

def deploy():
    # Deploy Modal app
    app.deploy()
    
    # Create and launch Gradio interface
    interface = create_gradio_interface()
    interface.launch()

Testing and Validation

1. Unit Tests

def test_agent_components():
    # Test individual agent components
    pass

def test_mco_integration():
    # Test MCO integration
    pass

def test_tool_execution():
    # Test tool execution
    pass

2. Integration Tests

def test_end_to_end():
    # Test full agent workflow
    pass

def test_code_review():
    # Test code review functionality
    pass

def test_snlp_generation():
    # Test SNLP file generation
    pass

3. Performance Monitoring

def monitor_performance(results):
    # Track execution time
    # Monitor resource usage
    # Log errors and issues
    pass