Is the infrastructure scalable?

Absolutely. By leveraging Cloudflare Workers and Durable Objects, your agents can scale to millions of independent, globally distributed instances automatically.

When should I use Agents SDK instead of Vercel AI SDK?

Use Agents SDK when your application requires bidirectional WebSockets, persistent state stored in SQLite, or complex multi-agent coordination. Use AI SDK for simpler request-response chat interfaces.

Can I build real-time applications with this skill?

Yes, it is designed specifically for real-time use cases using WebSockets and Server-Sent Events (SSE) for low-latency communication between the client and the agent.

Does it support the Model Context Protocol (MCP)?

Yes, it includes guidance for building stateful MCP servers that allow AI models to interact with external tools and data sources securely.

How does this skill handle data persistence?

It guides you through configuring Durable Objects and SQLite migrations, allowing each agent instance to store up to 1GB of data locally with built-in persistence.

Cloudflare AI Agents

Name: Cloudflare AI Agents
Author: jezweb

byjezweb

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117

クラウドインフラストラクチャ

Simplifies building stateful, real-time AI agents on Cloudflare Workers using the Agents SDK and Durable Objects.

概要

This skill provides expert guidance for architecting and deploying autonomous agents on Cloudflare's serverless platform. It helps developers navigate the choice between the lightweight Vercel AI SDK and the infrastructure-heavy Cloudflare Agents SDK, offering implementation patterns for WebSockets, persistent SQLite state, multi-agent orchestration, and scheduled tasks. By automating the complexities of Durable Object bindings and migration configurations, it enables the creation of globally unique, stateful micro-servers capable of handling long-running workflows and real-time bidirectional communication.

主な機能

117 GitHub stars
Persistent SQLite state management up to 1GB per agent
Built-in task scheduling and cron-based agent workflows
Multi-agent orchestration and inter-agent communication
WebSocket bidirectional real-time communication management
Model Context Protocol (MCP) server implementation patterns

ユースケース

Developing autonomous background agents with long-term memory
Building real-time collaborative AI chat systems
Implementing human-in-the-loop approval workflows for AI actions

概要

主な機能

117 GitHub stars
Persistent SQLite state management up to 1GB per agent
Built-in task scheduling and cron-based agent workflows
Multi-agent orchestration and inter-agent communication
WebSocket bidirectional real-time communication management
Model Context Protocol (MCP) server implementation patterns

ユースケース

Developing autonomous background agents with long-term memory
Building real-time collaborative AI chat systems
Implementing human-in-the-loop approval workflows for AI actions