Z Agents Framework

Zara is built on a modular, extensible AI framework that enables autonomous agents to perform complex tasks while maintaining personality and context awareness. The framework provides a foundation for deploying purpose-built AI agents in web3.

System Architecture

Core Components

1. Memory Management System

class MemoryManager:
    """Manages agent memory across different storage types."""
    
    def __init__(self):
        self.short_term = RedisCache()
        self.long_term = QdrantVectorStore()
        self.state = PostgresStateStore()
    
    async def store_context(self, context: Context):
        """Store context in appropriate memory store."""
        if context.is_temporary:
            await self.short_term.store(context)
        else:
            await self.long_term.store(context)
    
    async def retrieve_context(self, query: str) -> List[Context]:
        """Retrieve relevant context using semantic search."""
        short_term = await self.short_term.search(query)
        long_term = await self.long_term.search(query)
        return self.merge_contexts(short_term, long_term)

2. Task Orchestration

class TaskOrchestrator:
    """Manages task execution and agent coordination."""
    
    async def schedule_task(self, task: Task):
        """Schedule a task for execution."""
        priority = self.calculate_priority(task)
        agent = await self.select_agent(task)
        
        return await self.queue.put({
            "task_id": task.id,
            "agent_id": agent.id,
            "priority": priority,
            "payload": task.payload
        })
    
    async def coordinate_agents(self, workflow: Workflow):
        """Coordinate multiple agents for complex tasks."""
        dag = self.build_task_dag(workflow)
        return await self.execute_dag(dag)

3. Plugin System

class PluginManager:
    """Manages plugin lifecycle and integration."""
    
    async def load_plugin(self, plugin_config: Dict):
        """Load and initialize a plugin."""
        plugin = await self.registry.get_plugin(plugin_config["name"])
        instance = plugin(plugin_config)
        await instance.initialize()
        return instance
    
    async def execute_plugin_task(self, plugin: Plugin, task: Task):
        """Execute a task using a plugin."""
        context = await self.get_context(task)
        result = await plugin.execute(task, context)
        await self.store_result(result)

Z Agents Framework

1. Agent Configuration

# agent_config.yaml
name: "Trading Analysis Agent"
version: "1.0.0"
description: "Analyzes trading opportunities and provides insights"

personality:
  base: "analytical"
  traits:
    - "precise"
    - "data-driven"
    - "risk-aware"

capabilities:
  - name: "technical_analysis"
    config:
      indicators: ["RSI", "MACD", "BB"]
  - name: "sentiment_analysis"
    config:
      sources: ["twitter", "telegram", "discord"]

runtime:
  type: "persistent"
  scaling:
    min_instances: 1
    max_instances: 5
    target_cpu_utilization: 75

plugins:
  - name: "dex_screener"
    version: "1.2.0"
  - name: "twitter_api"
    version: "2.0.0"

memory:
  short_term:
    type: "redis"
    ttl: 3600
  long_term:
    type: "qdrant"
    collection: "trading_memory"

2. Agent Runtime

class AgentRuntime:
    """Manages agent lifecycle and execution."""
    
    async def initialize_agent(self, config: AgentConfig):
        """Initialize agent with configuration."""
        self.personality = await self.load_personality(config.personality)
        self.capabilities = await self.load_capabilities(config.capabilities)
        self.plugins = await self.load_plugins(config.plugins)
        
        return Agent(
            personality=self.personality,
            capabilities=self.capabilities,
            plugins=self.plugins
        )
    
    async def execute_task(self, agent: Agent, task: Task):
        """Execute task with agent."""
        context = await self.get_context(task)
        response = await agent.process(task, context)
        await self.store_response(response)

3. Plugin Interface

class BasePlugin:
    """Base class for plugins."""
    
    async def initialize(self):
        """Initialize plugin resources."""
        pass
    
    async def execute(self, task: Task, context: Context) -> Result:
        """Execute plugin-specific task."""
        raise NotImplementedError
    
    async def cleanup(self):
        """Cleanup plugin resources."""
        pass

class TradingPlugin(BasePlugin):
    """Plugin for trading analysis."""
    
    async def execute(self, task: Task, context: Context) -> Result:
        """Execute trading analysis task."""
        data = await self.fetch_market_data(task.symbol)
        analysis = await self.analyze_data(data)
        return self.format_result(analysis)

Deployment Architecture

graph TD
    A[Dev.Fun Platform] --> B[Agent Registry]
    B --> C[Kubernetes Cluster]
    
    C --> D[Agent Pods]
    C --> E[Service Mesh]
    C --> F[Monitoring]
    
    D --> G[Agent Container]
    G --> H[Agent Runtime]
    H --> I[Plugins]
    
    E --> J[Service Discovery]
    E --> K[Load Balancing]
    
    F --> L[Prometheus]
    F --> M[Grafana]

Memory Store Architecture

graph TD
    A[Memory Manager] --> B[Short-term Memory]
    A --> C[Long-term Memory]
    
    B --> D[Redis Cache]
    C --> E[Qdrant Vector DB]
    
    D --> F[Recent Interactions]
    D --> G[Temporary Context]
    
    E --> H[Historical Data]
    E --> I[Learned Patterns]
    
    subgraph "Context Types"
    J[User Context]
    K[Market Context]
    L[Social Context]
    end
    
    F --> J
    F --> K
    F --> L

Scaling and Performance

1. Horizontal Scaling

# kubernetes/agent-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: trading-agent
spec:
  replicas: 3
  selector:
    matchLabels:
      app: trading-agent
  template:
    metadata:
      labels:
        app: trading-agent
    spec:
      containers:
      - name: agent
        image: zara/trading-agent:latest
        resources:
          requests:
            cpu: "500m"
            memory: "1Gi"
          limits:
            cpu: "2"
            memory: "4Gi"

2. Performance Monitoring

class PerformanceMonitor:
    """Monitor agent performance metrics."""
    
    async def collect_metrics(self):
        """Collect performance metrics."""
        metrics = {
            "response_time": self.calculate_response_time(),
            "memory_usage": self.get_memory_usage(),
            "task_throughput": self.get_task_throughput(),
            "error_rate": self.get_error_rate()
        }
        await self.store_metrics(metrics)

Security and Access Control

1. Authentication

2. Data Protection

Next Steps

1. Implement custom plugins

2. Enhance memory management

3. Add new agent capabilities

4. Scale deployment

5. Monitor performance