---
title: 'Architecture Overview'
description: 'Understand RunAgent system architecture and how it works'
icon: 'diagram'
---

import NeedHelp from '/snippets/need-help.mdx';

<Info>
  **Prerequisites**: Understanding of [Core Concepts](/explanation/core-concepts) and basic knowledge of distributed systems
</Info>

## Overview

RunAgent is built on a modern, scalable architecture that enables Python-based AI agents to be accessed from any programming language. The system is designed for high performance, reliability, and ease of use.

## High-Level Architecture

```mermaid
graph TB
    subgraph "Client Applications"
        PY[Python SDK]
        JS[JavaScript SDK]
        GO[Go SDK]
        RS[Rust SDK]
    end
    
    subgraph "RunAgent Platform"
        LB[Load Balancer]
        API[REST API Gateway]
        WS[WebSocket Gateway]
        AG[Agent Manager]
        VM[Firecracker VMs]
    end
    
    subgraph "Python Agents"
        A1[Agent 1]
        A2[Agent 2]
        A3[Agent N]
    end
    
    subgraph "Infrastructure"
        DB[(Database)]
        CACHE[(Cache)]
        MON[Monitoring]
        LOG[Logging]
    end
    
    PY --> LB
    JS --> LB
    GO --> LB
    RS --> LB
    
    LB --> API
    LB --> WS
    
    API --> AG
    WS --> AG
    
    AG --> VM
    VM --> A1
    VM --> A2
    VM --> A3
    
    AG --> DB
    AG --> CACHE
    AG --> MON
    AG --> LOG
```

## Core Components

### 1. **Client SDKs**

RunAgent provides native SDKs for multiple programming languages:

- **Python SDK**: Full-featured with sync/async support
- **JavaScript SDK**: TypeScript support with async/await
- **Go SDK**: Idiomatic Go with context-aware operations
- **Rust SDK**: High-performance with zero-cost abstractions

Each SDK provides:
- **Authentication**: API key management
- **Request/Response**: Synchronous and asynchronous calls
- **Streaming**: Real-time response streaming
- **Error Handling**: Language-appropriate error types
- **Retry Logic**: Automatic retry with backoff

### 2. **API Gateway**

The API Gateway handles:

- **Request Routing**: Routes requests to appropriate agents
- **Authentication**: Validates API keys and permissions
- **Rate Limiting**: Enforces rate limits per client
- **Load Balancing**: Distributes load across agent instances
- **Protocol Translation**: Converts between HTTP and WebSocket

### 3. **Agent Manager**

The Agent Manager is responsible for:

- **Agent Lifecycle**: Starting, stopping, and monitoring agents
- **Configuration Management**: Loading and validating configurations
- **Health Monitoring**: Checking agent health and availability
- **Scaling**: Automatically scaling agents based on demand
- **Resource Management**: Allocating and managing resources

### 4. **Firecracker MicroVMs**

RunAgent uses Firecracker for secure, isolated agent execution:

- **Security**: Each agent runs in its own microVM
- **Isolation**: Complete process and resource isolation
- **Performance**: Near-native performance with minimal overhead
- **Scalability**: Fast boot times and efficient resource usage
- **Sandboxing**: Secure execution environment

## Data Flow

### 1. **Request Processing**

```mermaid
sequenceDiagram
    participant C as Client SDK
    participant LB as Load Balancer
    participant API as API Gateway
    participant AM as Agent Manager
    participant VM as Firecracker VM
    participant A as Python Agent
    
    C->>LB: HTTP/WebSocket Request
    LB->>API: Route Request
    API->>AM: Validate & Route
    AM->>VM: Execute Agent
    VM->>A: Call Entrypoint
    A->>VM: Return Response
    VM->>AM: Stream Response
    AM->>API: Forward Response
    API->>LB: Send Response
    LB->>C: Return to Client
```

### 2. **Streaming Response**

```mermaid
sequenceDiagram
    participant C as Client SDK
    participant WS as WebSocket Gateway
    participant AM as Agent Manager
    participant VM as Firecracker VM
    participant A as Python Agent
    
    C->>WS: WebSocket Connection
    WS->>AM: Route Stream Request
    AM->>VM: Execute Streaming Agent
    VM->>A: Call Streaming Entrypoint
    
    loop Stream Response
        A->>VM: Yield Chunk
        VM->>AM: Forward Chunk
        AM->>WS: Stream Chunk
        WS->>C: Send Chunk
    end
    
    A->>VM: Stream Complete
    VM->>AM: Stream End
    AM->>WS: Close Stream
    WS->>C: Close Connection
```

## Security Architecture

### 1. **Multi-Layer Security**

```mermaid
graph TB
    subgraph "Security Layers"
        AUTH[Authentication]
        AUTHZ[Authorization]
        RATE[Rate Limiting]
        VALID[Input Validation]
        SANDBOX[Sandboxing]
        ISOLATION[Process Isolation]
    end
    
    subgraph "Client Request"
        REQ[HTTP/WebSocket Request]
    end
    
    subgraph "Agent Execution"
        AGENT[Python Agent]
    end
    
    REQ --> AUTH
    AUTH --> AUTHZ
    AUTHZ --> RATE
    RATE --> VALID
    VALID --> SANDBOX
    SANDBOX --> ISOLATION
    ISOLATION --> AGENT
```

### 2. **Firecracker Security Model**

- **MicroVM Isolation**: Each agent runs in its own microVM
- **Resource Limits**: CPU, memory, and network constraints
- **File System**: Restricted file system access
- **Network**: Controlled network access
- **Process**: Complete process isolation

## Scalability Architecture

### 1. **Horizontal Scaling**

```mermaid
graph TB
    subgraph "Load Balancer"
        LB[Load Balancer]
    end
    
    subgraph "Agent Instances"
        A1[Agent Instance 1]
        A2[Agent Instance 2]
        A3[Agent Instance 3]
        AN[Agent Instance N]
    end
    
    subgraph "Auto-scaling"
        AS[Auto-scaler]
        METRICS[Metrics Collector]
    end
    
    LB --> A1
    LB --> A2
    LB --> A3
    LB --> AN
    
    METRICS --> AS
    AS --> A1
    AS --> A2
    AS --> A3
    AS --> AN
```

### 2. **Auto-scaling Triggers**

- **CPU Usage**: Scale up when CPU usage exceeds threshold
- **Memory Usage**: Scale up when memory usage is high
- **Request Rate**: Scale up when request rate increases
- **Response Time**: Scale up when response times are slow
- **Queue Length**: Scale up when request queue is long

## Performance Architecture

### 1. **Caching Strategy**

```mermaid
graph TB
    subgraph "Client Side"
        CLIENT[Client SDK]
        CLIENT_CACHE[Client Cache]
    end
    
    subgraph "API Gateway"
        API[API Gateway]
        API_CACHE[API Cache]
    end
    
    subgraph "Agent Manager"
        AM[Agent Manager]
        AM_CACHE[Agent Cache]
    end
    
    CLIENT --> CLIENT_CACHE
    CLIENT_CACHE --> API
    API --> API_CACHE
    API_CACHE --> AM
    AM --> AM_CACHE
    AM_CACHE --> AGENT[Python Agent]
```

### 2. **Performance Optimizations**

- **Connection Pooling**: Reuse connections for efficiency
- **Request Batching**: Batch multiple requests together
- **Response Compression**: Compress responses to reduce bandwidth
- **CDN Integration**: Use CDN for static content
- **Database Optimization**: Optimize database queries and indexes

## Monitoring and Observability

### 1. **Monitoring Stack**

```mermaid
graph TB
    subgraph "Applications"
        AGENT[Python Agent]
        API[API Gateway]
        AM[Agent Manager]
    end
    
    subgraph "Metrics Collection"
        PROMETHEUS[Prometheus]
        GRAFANA[Grafana]
    end
    
    subgraph "Logging"
        ELASTIC[Elasticsearch]
        KIBANA[Kibana]
    end
    
    subgraph "Tracing"
        JAEGER[Jaeger]
        ZIPKIN[Zipkin]
    end
    
    AGENT --> PROMETHEUS
    API --> PROMETHEUS
    AM --> PROMETHEUS
    
    AGENT --> ELASTIC
    API --> ELASTIC
    AM --> ELASTIC
    
    AGENT --> JAEGER
    API --> JAEGER
    AM --> JAEGER
```

### 2. **Key Metrics**

- **Request Rate**: Requests per second
- **Response Time**: Average and P95 response times
- **Error Rate**: Percentage of failed requests
- **Resource Usage**: CPU, memory, and network usage
- **Agent Health**: Agent availability and performance

## Deployment Architecture

### 1. **Cloud Deployment**

```mermaid
graph TB
    subgraph "Cloud Provider"
        subgraph "Load Balancer"
            LB[Load Balancer]
        end
        
        subgraph "API Tier"
            API1[API Gateway 1]
            API2[API Gateway 2]
        end
        
        subgraph "Agent Tier"
            AGENT1[Agent Manager 1]
            AGENT2[Agent Manager 2]
        end
        
        subgraph "VM Tier"
            VM1[Firecracker VM 1]
            VM2[Firecracker VM 2]
            VM3[Firecracker VM 3]
        end
        
        subgraph "Data Tier"
            DB[(Database)]
            CACHE[(Cache)]
        end
    end
    
    LB --> API1
    LB --> API2
    API1 --> AGENT1
    API2 --> AGENT2
    AGENT1 --> VM1
    AGENT1 --> VM2
    AGENT2 --> VM3
    AGENT1 --> DB
    AGENT2 --> CACHE
```

### 2. **Local Development**

```mermaid
graph TB
    subgraph "Local Machine"
        CLI[RunAgent CLI]
        SERVER[Local Server]
        AGENT[Python Agent]
    end
    
    CLI --> SERVER
    SERVER --> AGENT
```

## Error Handling Architecture

### 1. **Error Propagation**

```mermaid
graph TB
    subgraph "Error Sources"
        AGENT[Python Agent]
        VM[Firecracker VM]
        AM[Agent Manager]
        API[API Gateway]
    end
    
    subgraph "Error Handling"
        VALIDATE[Validation]
        RETRY[Retry Logic]
        FALLBACK[Fallback]
        LOG[Logging]
    end
    
    subgraph "Client Response"
        CLIENT[Client SDK]
    end
    
    AGENT --> VM
    VM --> AM
    AM --> API
    API --> VALIDATE
    VALIDATE --> RETRY
    RETRY --> FALLBACK
    FALLBACK --> LOG
    LOG --> CLIENT
```

### 2. **Error Types**

- **Client Errors**: Authentication, validation, rate limiting
- **Server Errors**: Internal server errors, agent failures
- **Network Errors**: Connection timeouts, network issues
- **Agent Errors**: Python exceptions, function errors

## Future Architecture

### 1. **Planned Enhancements**

- **Multi-Region Deployment**: Global agent deployment
- **Edge Computing**: Deploy agents closer to users
- **GPU Support**: GPU-accelerated agent execution
- **Custom Runtimes**: Support for additional languages
- **Advanced Caching**: Intelligent response caching

### 2. **Scalability Improvements**

- **Kubernetes Integration**: Native Kubernetes support
- **Service Mesh**: Istio integration for advanced networking
- **Event-Driven Architecture**: Event-based agent communication
- **Stream Processing**: Real-time data processing capabilities

## Best Practices

### 1. **Agent Design**

- **Stateless**: Design agents to be stateless
- **Idempotent**: Make operations idempotent
- **Error Handling**: Implement proper error handling
- **Resource Management**: Manage resources efficiently
- **Monitoring**: Add appropriate logging and metrics

### 2. **Performance Optimization**

- **Caching**: Use caching for expensive operations
- **Streaming**: Use streaming for long-running operations
- **Batch Processing**: Batch multiple operations together
- **Resource Limits**: Set appropriate resource limits
- **Connection Pooling**: Use connection pooling

## Next Steps

<CardGroup cols={2}>
  <Card title="Production Considerations" icon="gauge" href="/explanation/production-considerations">
    Learn about production deployment requirements
  </Card>
  <Card title="Security" icon="shield" href="/explanation/security">
    Understand RunAgent's security model
  </Card>
  <Card title="Performance Tuning" icon="speed" href="/how-to/advanced-tasks">
    Learn advanced performance optimization techniques
  </Card>
  <Card title="Deployment" icon="cloud" href="/runagent-cloud/cloud-deployment">
    Deploy your agents to production
  </Card>
</CardGroup>

<Note>
**🎉 Excellent!** You now understand RunAgent's architecture and how it enables seamless multi-language AI agent deployment. This knowledge will help you build more effective agents and troubleshoot issues!
</Note>

<NeedHelp context="Architecture Overview" />
