claude-agent-server

🥈Silver

Run Claude Agent in a sandbox, control it via websocket. Operations teams use this to automate workflows, integrate with internal tools, and manage AI interactions in real-time. Connects to TypeScript-based systems and supports bidirectional communication.

556530Updated 2w ago

Intermediate30min to implementautomation

Saves ~180 min per use

Quick InstallView Source

git clone https://github.com/dzhng/claude-agent-server.git

Works with:

Claude

Overview

About This Skill

https://github.com/dzhng/claude-agent-server

How to Use

[{"step":"Set up the Claude Agent Server in your TypeScript environment by installing the SDK: `npm install @claude-ai/server-sdk ws`. Configure the server with your sandbox requirements and port settings.","tip":"Use the `sandbox: true` option for isolated execution of untrusted code. Set `maxConcurrentAgents` based on your system's CPU/memory limits."},{"step":"Establish a WebSocket connection to your internal tools or systems. Use the TypeScript WebSocket client to handle bidirectional communication and real-time updates.","tip":"Implement heartbeat messages to detect connection drops. Use exponential backoff for reconnection attempts."},{"step":"Register automation tasks with the agent server by defining the workflow steps (fetch, transform, load, etc.). Include error handling configurations and fallback mechanisms.","tip":"Break complex workflows into smaller tasks. Use the `errorHandling` block to specify retry logic and notification rules."},{"step":"Start the server and monitor execution via the provided logging endpoint and monitoring dashboard. Use the real-time status updates to track progress and system health.","tip":"Integrate with tools like Grafana or Datadog for visualization. Set up alerts for error rates or resource thresholds."},{"step":"Scale horizontally by deploying multiple agent instances behind a load balancer. Use the server's built-in metrics to optimize resource allocation.","tip":"Monitor agent concurrency to prevent resource exhaustion. Consider using Kubernetes for orchestration in production environments."}]

Use Cases

Deploy an interactive AI assistant in a controlled environment for testing purposes.

Integrate Claude Agent into existing applications to enhance user interaction with AI.

Customize server behavior to tailor AI responses for specific use cases.

Run local tests on AI interactions before deploying to production environments.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/dzhng/claude-agent-server

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Deploy a Claude Agent Server instance to automate [WORKFLOW_NAME]. Configure the agent to connect via WebSocket to [INTERNAL_TOOL_OR_SYSTEM] and execute the following tasks: [TASK_LIST]. Ensure the agent handles errors by logging them to [LOGGING_ENDPOINT] and provides real-time status updates to [MONITORING_DASHBOARD]. Use the TypeScript SDK for integration.

Example Output

```typescript
// Example deployment script for Claude Agent Server
import { ClaudeAgentServer } from '@claude-ai/server-sdk';
import { WebSocketClient } from 'ws';

// Initialize the agent server
const agentServer = new ClaudeAgentServer({
  port: 8080,
  sandbox: true,
  maxConcurrentAgents: 5,
  logger: {
    endpoint: 'https://logs.internal.company.com/api/v1/traces',
    level: 'debug'
  }
});

// Connect to internal tooling via WebSocket
const wsClient = new WebSocketClient('ws://internal-tools.company.com/claude-agent');

// Define workflow automation tasks
agentServer.registerTask('data-pipeline', {
  steps: [
    { action: 'fetch', source: 's3://raw-data-bucket/daily', target: '/tmp/raw' },
    { action: 'transform', script: 'transform.py', inputs: ['/tmp/raw'] },
    { action: 'load', destination: 'postgres://analytics-db.company.com/warehouse' }
  ],
  errorHandling: {
    retryAttempts: 3,
    fallback: 'notify-team@company.com'
  }
});

// Start the server
agentServer.start().then(() => {
  console.log('Claude Agent Server running on port 8080');
  wsClient.connect();
});

// Real-time status updates
agentServer.on('status', (data) => {
  console.log(`[${new Date().toISOString()}] Agent Status:`, data);
  wsClient.send(JSON.stringify({
    type: 'agent-status',
    payload: data
  }));
});
```

**Execution Log:**
- 2024-05-15T14:32:10Z: Agent Server initialized with 5 concurrent agents
- 2024-05-15T14:32:11Z: WebSocket connection established to internal-tools.company.com
- 2024-05-15T14:32:15Z: Task 'data-pipeline' started (ID: task_12345)
- 2024-05-15T14:32:17Z: Step 1/3: Downloaded 2.4GB from s3://raw-data-bucket/daily
- 2024-05-15T14:32:22Z: Step 2/3: Transformation completed (98% success rate)
- 2024-05-15T14:32:25Z: Step 3/3: Loaded 1.8M records to analytics-db.company.com
- 2024-05-15T14:32:25Z: Task 'data-pipeline' completed successfully

**Monitoring Dashboard Output:**
```json
{
  "agent_id": "claude-agent-001",
  "status": "idle",
  "last_task": "data-pipeline",
  "resource_usage": {
    "cpu": "12%",
    "memory": "450MB",
    "disk": "2.1GB"
  },
  "uptime": "2h 15m"
}
```