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Prad Nukala 6406208b35 shell: fish functions -> ~/.local/bin executables + bash/zsh ports; manage ~/.agents

- 16 cross-shell utilities converted to sh/bash chezmoi executables:
  ai c2p cp-appid extract_agents fab gdc gh-sonr-org-migrate ghg-clone
  ghn gho git-summary launch_omp_livediff_overlay lc-elir mdcp mdp pj
  (ghg-clone not named ghg: unrelated cargo binary owns that name)
- cwd/history helpers stay per-shell: yy c cd clip-last + ghg/ghn/gho
  cd-wrappers in bash/functions/ and zsh/functions/, loaded by rc files
- fish keeps only shell-state functions; converted ones now hit PATH
- cp-appid ignore gating moved to .local/bin path
- dot_agents/skills: 82 skills materialized from dotstate storage so
  dot_claude skill symlinks resolve on fresh bootstrap; dotstate
  profile/symlinks.json released .agents/skills .bashrc .zshrc

2026-08-10 15:15:06 -04:00

2.7 KiB

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Cloudflare Agents SDK

Cloudflare Agents SDK enables building AI-powered agents on Durable Objects with state, WebSockets, SQL, scheduling, and AI integration.

Core Value

Build stateful, globally distributed AI agents with persistent memory, real-time connections, scheduled tasks, and async workflows.

When to Use

Persistent state + memory required
Real-time WebSocket connections
Long-running workflows (minutes/hours)
Chat interfaces with AI models
Scheduled/recurring tasks with state
DB queries with agent state

What Type of Agent?

Use Case	Class	Key Features
AI chat interface	`AIChatAgent`	Auto-streaming, tools, message history, resumable
MCP tool provider	`Agent` + MCP	Expose tools to AI systems
Custom logic/routing	`Agent`	Full control, WebSockets, email, SQL
Real-time collaboration	`Agent`	WebSocket state, broadcasts
Email processing	`Agent`	`onEmail()` handler

Quick Start

AI Chat Agent:

import { AIChatAgent } from "agents";
import { openai } from "@ai-sdk/openai";

export class ChatAgent extends AIChatAgent<Env> {
  async onChatMessage(onFinish) {
    return this.streamText({
      model: openai("gpt-4"),
      messages: this.messages,
      onFinish,
    });
  }
}

Base Agent:

import { Agent } from "agents";

export class MyAgent extends Agent<Env> {
  onStart() {
    this.sql`CREATE TABLE IF NOT EXISTS users (id TEXT PRIMARY KEY)`;
  }
  
  async onRequest(request: Request) {
    return Response.json({ state: this.state });
  }
}

Reading Order

Task	Files to Read
Quick start	README only
Build chat agent	README → api.md (AIChatAgent) → patterns.md
Setup project	README → configuration.md
Add React frontend	README → api.md (Client Hooks) → patterns.md
Build MCP server	api.md (MCP) → patterns.md
Background tasks	api.md (Scheduling, Task Queue) → patterns.md
Debug issues	gotchas.md

Package Entry Points

Import	Purpose
`agents`	Server-side Agent classes, lifecycle
`agents/react`	`useAgent()` hook for WebSocket connections
`agents/ai-react`	`useAgentChat()` hook for AI chat UIs

In This Reference

configuration.md - SDK setup, wrangler config, routing
api.md - Agent classes, lifecycle, client hooks
patterns.md - Common workflows, best practices
gotchas.md - Common issues, limits

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