# AceAI Design Document

**Ace AI - Agent framework that delivers**

## Vision

AceAI aims to be a lightweight, powerful agent framework that bridges the gap between AI reasoning and real-world tool execution. By integrating with MCP (Model Context Protocol), AceAI provides agents with standardized access to tools while maintaining simplicity and flexibility.

## Core Architecture

### 1. Agent Layer (Reasoning & Orchestration)
The agent layer handles high-level reasoning, planning, and decision-making:

```python
class Agent:
    - name: str
    - capabilities: List[str] 
    - mcp_clients: List[MCPClient]
    - memory: AgentMemory
    - planner: TaskPlanner
    
    def process(request: str) -> AgentResponse
    def execute_task(task: Task) -> TaskResult
    def reflect(result: TaskResult) -> None
```

### 2. Task Layer (Work Units)
Tasks represent discrete units of work that agents can execute:

```python
class Task:
    - id: str
    - description: str
    - type: TaskType (SIMPLE, COMPLEX, PIPELINE)
    - dependencies: List[Task]
    - tools_required: List[str]
    
class Pipeline:
    - tasks: List[Task]
    - execution_strategy: ExecutionStrategy
```

### 3. MCP Integration Layer (Tool Access)
This layer provides standardized access to tools through MCP servers:

```python
class MCPClient:
    - server_url: str
    - available_tools: List[Tool]
    
    def call_tool(tool_name: str, params: dict) -> ToolResult
    def list_tools() -> List[Tool]
    def get_tool_schema(tool_name: str) -> ToolSchema

class ToolRegistry:
    - mcp_clients: Dict[str, MCPClient]
    
    def register_mcp_server(name: str, client: MCPClient)
    def discover_tools() -> List[Tool]
    def execute_tool(tool_name: str, params: dict) -> ToolResult
```

### 4. Memory & Context Layer
Agents need memory to maintain context across interactions:

```python
class AgentMemory:
    - short_term: Dict[str, Any]  # Current session
    - long_term: PersistentStore   # Cross-session
    - working: WorkingMemory       # Current task context
    
    def store(key: str, value: Any, scope: MemoryScope)
    def retrieve(key: str, scope: MemoryScope) -> Any
    def clear(scope: MemoryScope)
```

## Lihil-MCP Integration Strategy

### 1. Web Development Agent Capabilities
Integrate `lihil-mcp` to give agents web development superpowers:

```python
# Agent with Lihil web capabilities
web_agent = Agent(name="WebDevAgent")
web_agent.add_mcp_server("lihil", LihilMCPClient("http://localhost:8000"))

# Agent can now:
# - Create API endpoints
# - Generate web UIs  
# - Handle database operations
# - Manage web server lifecycle
```

### 2. Specific Integration Points

#### A. Route Generation Agent
```python
class RouteAgent(Agent):
    """Specialized agent for creating web API routes"""
    
    def create_endpoint(self, spec: EndpointSpec) -> RouteResult:
        # Use Lihil-MCP to generate route code
        task = Task(
            description=f"Create {spec.method} endpoint at {spec.path}",
            tools_required=["lihil.create_route", "lihil.validate_route"]
        )
        return self.execute_task(task)
```

#### B. Full-Stack Development Pipeline
```python
class FullStackPipeline(Pipeline):
    """End-to-end web application development"""
    
    def __init__(self):
        super().__init__([
            Task("analyze_requirements", tools=["lihil.analyze_spec"]),
            Task("design_api", tools=["lihil.design_endpoints"]),
            Task("generate_backend", tools=["lihil.create_routes"]),
            Task("create_frontend", tools=["lihil.generate_ui"]),
            Task("setup_database", tools=["lihil.setup_db"]),
            Task("deploy_app", tools=["lihil.deploy"])
        ])
```

### 3. Tool Ecosystem
Lihil-MCP would provide these tools to AceAI agents:

```yaml
Web Framework Tools:
  - lihil.create_route: Create new API endpoints
  - lihil.generate_model: Generate data models
  - lihil.setup_middleware: Configure middleware
  - lihil.create_auth: Setup authentication
  - lihil.generate_docs: Create API documentation

Development Tools:
  - lihil.run_tests: Execute test suite
  - lihil.start_server: Launch development server
  - lihil.hot_reload: Enable live reloading
  - lihil.debug_endpoint: Debug specific routes

Deployment Tools:
  - lihil.build_app: Build for production
  - lihil.deploy_service: Deploy to cloud
  - lihil.setup_monitoring: Configure observability
```

## Implementation Phases

### Phase 1: Core Agent Framework (v0.2.0)
- [ ] Basic Agent class with memory
- [ ] Task and Pipeline abstractions
- [ ] Simple MCP client integration
- [ ] Tool registry and discovery

### Phase 2: Lihil Integration (v0.3.0)
- [ ] Lihil-MCP client wrapper
- [ ] Web development agent templates
- [ ] Route generation capabilities
- [ ] Basic UI generation

### Phase 3: Advanced Features (v0.4.0)
- [ ] Multi-agent collaboration
- [ ] Advanced planning algorithms
- [ ] Learning from execution feedback
- [ ] Tool composition and chaining

### Phase 4: Production Ready (v1.0.0)
- [ ] Enterprise security features
- [ ] Scalable deployment options
- [ ] Rich monitoring and observability
- [ ] Plugin ecosystem

## Code Structure

```
aceai/
├── __init__.py              # Public API
├── core/
│   ├── agent.py            # Core Agent class
│   ├── task.py             # Task and Pipeline classes
│   ├── memory.py           # Memory management
│   └── planner.py          # Task planning logic
├── mcp/
│   ├── client.py           # MCP client implementation
│   ├── registry.py         # Tool registry
│   └── integrations/
│       ├── lihil.py        # Lihil-MCP integration
│       └── standard.py     # Standard MCP tools
├── agents/
│   ├── web_dev.py          # Web development agents
│   ├── data.py             # Data processing agents
│   └── general.py          # General purpose agents
└── utils/
    ├── logging.py          # Logging utilities
    ├── config.py           # Configuration management
    └── validation.py       # Input validation
```

## Example Usage Scenarios

### 1. Rapid API Development
```python
from aceai import WebDevAgent
from aceai.mcp.integrations import LihilMCP

# Create agent with Lihil capabilities
agent = WebDevAgent(name="APIBuilder")
agent.add_mcp_server(LihilMCP("http://localhost:8000"))

# Build a complete API from requirements
api_spec = """
Create a user management API with:
- User registration and login
- Profile management
- Password reset functionality
- JWT authentication
"""

result = agent.build_api(api_spec)
print(f"API created at: {result.endpoint_url}")
```

### 2. Collaborative Development
```python
from aceai import Agent, Pipeline

# Create specialized agents
backend_agent = Agent("BackendDev", capabilities=["api", "database"])
frontend_agent = Agent("FrontendDev", capabilities=["ui", "react"])
devops_agent = Agent("DevOps", capabilities=["deployment", "monitoring"])

# Create collaborative pipeline
pipeline = Pipeline([
    Task("design_architecture", agent=backend_agent),
    Task("implement_api", agent=backend_agent),
    Task("create_ui", agent=frontend_agent),
    Task("deploy_app", agent=devops_agent)
])

app = pipeline.execute("Build a todo app with real-time updates")
```

## Key Design Principles

1. **Simplicity First**: Easy to get started with minimal boilerplate
2. **MCP Integration**: Leverage standardized tool access protocols  
3. **Composability**: Agents, tasks, and tools should compose naturally
4. **Extensibility**: Plugin architecture for adding new capabilities
5. **Production Ready**: Built for real-world deployment scenarios
6. **Developer Experience**: Rich debugging and monitoring capabilities

## Success Metrics

- **Time to First Working Agent**: < 5 minutes
- **Tool Integration Effort**: < 1 hour for new MCP server
- **Agent Development Speed**: 10x faster than manual coding
- **Community Adoption**: Active plugin ecosystem
- **Enterprise Readiness**: Used in production environments

## Future Vision

AceAI + Lihil-MCP represents a new paradigm where:
- Agents can rapidly prototype and deploy web applications
- Developers focus on high-level requirements instead of implementation details
- AI agents collaborate to build complex software systems
- The barrier between idea and deployed application disappears

This integration positions AceAI as the premier framework for AI-driven software development, with Lihil providing the robust web development foundation that agents need to build real applications.