Does it support persistent storage for agent memory?

Absolutely. It leverages Cloudflare bindings like D1 for SQL, KV for key-value storage, and R2 for object storage to manage agent state and memory.

Can I use this for real-time AI applications?

Yes, the skill includes specialized integration guides and templates for both WebSockets and Server-Sent Events (SSE) to support real-time streaming.

What is the role of MCP in this skill?

The Model Context Protocol (MCP) integration allows your agents to connect with a standardized ecosystem of tools and data sources with minimal configuration.

Is browser automation possible with these agents?

Yes, the skill provides specific patterns and templates for browser automation, allowing agents to interact with web content directly.

What LLMs does the Cloudflare Agents skill support?

It supports all major providers including Anthropic, OpenAI, and Google Gemini, as well as Cloudflare's native AI models.

Cloudflare AI Agents

Name: Cloudflare AI Agents
Author: secondsky

bysecondsky

•

云基础设施

Builds and deploys production-ready AI agents on Cloudflare Workers using the Model Context Protocol and native platform bindings.

关于

This skill empowers developers to architect sophisticated AI agents within the Cloudflare ecosystem, leveraging Workers, D1, KV, and R2 for a serverless, scalable infrastructure. It provides comprehensive patterns for tool integration, multi-LLM support including Anthropic and OpenAI, and specialized features like browser automation, RAG implementation, and real-time communication via WebSockets or SSE. Whether you are building human-in-the-loop workflows or scheduled background agents, this skill provides the templates and architectural guidance needed to deploy high-performance AI services at the edge.

主要功能

Support for multiple LLM providers including Anthropic, OpenAI, and Google Gemini
Advanced patterns for RAG, browser automation, and human-in-the-loop workflows
Seamless integration with Cloudflare D1, KV, R2, and Durable Objects
Native Model Context Protocol (MCP) support for standardized tool use
Built-in templates for streaming chat, scheduled tasks, and state synchronization
21 GitHub stars

使用场景

Deploying autonomous serverless agents that interact with edge databases and storage
Implementing real-time, streaming AI chat interfaces with WebSocket and SSE support
Creating complex multi-agent workflows with state management and browser automation

关于

主要功能

Support for multiple LLM providers including Anthropic, OpenAI, and Google Gemini
Advanced patterns for RAG, browser automation, and human-in-the-loop workflows
Seamless integration with Cloudflare D1, KV, R2, and Durable Objects
Native Model Context Protocol (MCP) support for standardized tool use
Built-in templates for streaming chat, scheduled tasks, and state synchronization
21 GitHub stars

使用场景

Deploying autonomous serverless agents that interact with edge databases and storage
Implementing real-time, streaming AI chat interfaces with WebSocket and SSE support
Creating complex multi-agent workflows with state management and browser automation