Think (Experimental)

Fetch https://developers.cloudflare.com/agents/api-reference/think/ for complete documentation.

@cloudflare/think — a higher-level chat agent class that handles the streamText loop, tool execution, and message persistence for you. You provide getModel() and getSystemPrompt(); Think handles the rest.

npm install @cloudflare/think

Minimal Agent

import { Think } from '@cloudflare/think';
import { createWorkersAI } from 'workers-ai-provider';
import { routeAgentRequest } from 'agents';

export class MyAgent extends Think<Env> {
	getModel() {
		return createWorkersAI({ binding: this.env.AI })('@cf/meta/llama-4-scout-17b-16e-instruct');
	}

	getSystemPrompt() {
		return 'You are a helpful assistant.';
	}
}

export default {
	fetch: (req, env) => routeAgentRequest(req, env)
};

Wrangler Config

{
	"compatibility_flags": ["nodejs_compat", "experimental"],
	"durable_objects": {
		"bindings": [{ "name": "MyAgent", "class_name": "MyAgent" }]
	},
	"migrations": [{ "tag": "v1", "new_sqlite_classes": ["MyAgent"] }],
	"ai": { "binding": "AI" }
}

Note: Think requires the experimental compatibility flag.

Custom Tools

import { tool } from 'ai';
import { z } from 'zod';

export class MyAgent extends Think<Env> {
	getTools() {
		return {
			getWeather: tool({
				description: 'Get weather',
				parameters: z.object({ city: z.string() }),
				execute: async ({ city }) => `72°F in ${city}`
			})
		};
	}
}

Lifecycle Hooks

Hook	When	Use for
`configureSession()`	Agent starts	Set up memory, context providers
`beforeTurn(ctx)`	Before each LLM call	Per-turn model/tools/system prompt; return `TurnConfig`
`onChunk(chunk)`	Each streaming chunk	Progress tracking
`onChatResponse(result)`	After LLM turn completes	Chaining, follow-up `saveMessages`
`onChatError(error)`	On LLM error	Error handling

async beforeTurn(ctx: TurnContext): Promise<TurnConfig> {
  if (ctx.continuation) {
    return { model: cheaperModel };
  }
  return {};
}

Sub-Agents

const child = this.subAgent(SpecialistAgent, 'specialist-1');
await child.chat('Analyze this data...', (chunk) => {
	// stream callback
});

Client

Same React hooks as AIChatAgent:

const agent = useAgent({ agent: 'MyAgent', name: 'session-1' });
const { messages, input, handleInputChange, handleSubmit } = useAgentChat({ agent });

Think vs AIChatAgent

	Think	AIChatAgent
`streamText` loop	Built-in	You write it
Tool execution	Automatic	You wire it
Customization	Override hooks	Full control in `onChatMessage`
Built-in tools	Workspace, execute, browser	None
Compatibility flag	Requires `experimental`	Standard

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