Cloudflare Agents SDK vs Google Agent Development Kit

Cloudflare Agents

Agents are persistent, stateful execution environments for agentic workloads, powered by Cloudflare Durable Objects. Each agent has its own state, storage, and lifecycle — with built-in support for real-time communication, scheduling, AI model calls, MCP, workflows, and more.

Agents hibernate when idle and wake on demand. You can run millions of them — one per user, per session, per game room — each costs nothing when inactive.

npm create cloudflare@latest -- --template cloudflare/agents-starter

Or add to an existing project:

npm install agents

Read the docs — getting started, API reference, guides, and more.

Quick Example

A counter agent with persistent state, callable methods, and real-time sync to a React frontend:

// server.ts
import { Agent, routeAgentRequest, callable } from "agents";

export type CounterState = { count: number };

export class CounterAgent extends Agent<Env, CounterState> {
  initialState = { count: 0 };

  @callable()
  increment() {
    this.setState({ count: this.state.count + 1 });
    return this.state.count;
  }

  @callable()
  decrement() {
    this.setState({ count: this.state.count - 1 });
    return this.state.count;
  }
}

export default {
  async fetch(request: Request, env: Env, ctx: ExecutionContext) {
    return (
      (await routeAgentRequest(request, env)) ??
      new Response("Not found", { status: 404 })
    );
  }
};

// client.tsx
import { useAgent } from "agents/react";
import { useState } from "react";
import type { CounterAgent, CounterState } from "./server";

function Counter() {
  const [count, setCount] = useState(0);

  const agent = useAgent<CounterAgent, CounterState>({
    agent: "CounterAgent",
    onStateUpdate: (state) => setCount(state.count)
  });

  return (
    <div>
      <span>{count}</span>
      <button onClick={() => agent.stub.increment()}>+</button>
      <button onClick={() => agent.stub.decrement()}>-</button>
    </div>
  );
}

State changes sync to all connected clients automatically. Call methods like they're local functions.

The agent is a Durable Object, so it needs a binding and a SQLite migration in wrangler.jsonc:

{
  "name": "counter",
  "main": "server.ts",
  "compatibility_date": "2026-06-11",
  "compatibility_flags": ["nodejs_compat"],
  "durable_objects": {
    "bindings": [{ "name": "CounterAgent", "class_name": "CounterAgent" }]
  },
  "migrations": [{ "tag": "v1", "new_sqlite_classes": ["CounterAgent"] }]
}

Features

Packages

AI-chat modules used to live in agents/ai-chat-agent, agents/chat, agents/ai-react, and agents/ai-types. Those entry points still re-export, but they're deprecated — import from @cloudflare/ai-chat directly. New chat-from-scratch projects should look at @cloudflare/think.

Examples

The examples/ directory has 30+ self-contained demos. A non-exhaustive tour:

• Showcase — playground/ is the kitchen-sink app: state, callable methods, scheduling, chat, tools, MCP, workflows, email, voice — all in one UI
• Chat & assistants — agents-as-tools/, computer-skills/, r2-skills/, workspace-chat/, resumable-stream-chat/, structured-input/, dynamic-tools/, multi-ai-chat/, context-overflow-recovery/
• MCP — mcp/, mcp-client/, mcp-server/, mcp-worker/, mcp-worker-authenticated/, mcp-elicitation-mrtr/, mcp-elicitation/, mcp-rpc-transport/, webmcp/, webmcp-react/
• Code Mode & sandboxes — codemode/, codemode-mcp/, codemode-mcp-openapi/, dynamic-workers/, dynamic-workers-playground/, worker-bundler-playground/
• Voice — voice-agent/ is the unified voice pipeline example with Workers AI, AssemblyAI, Telnyx, and ElevenLabs STT; voice-input/ covers dictation; telnyx-voice-agent/ covers phone transport; elevenlabs-starter/ covers broader ElevenLabs media APIs
• Workflows & approvals — workflows/, a2a/
• Auth, payments, comms — auth-agent/, cross-domain/, x402/, x402-mcp/, email-agent/, github-webhook/, push-notifications/
• Game & misc — tictactoe/, ai-chat/

Examples using the OpenAI Agents SDK live in openai-sdk/. Work-in-progress experiments live in experimental/ (no stability guarantees).

Run any example locally:

cd examples/playground
npm start

Documentation

• Full docs on developers.cloudflare.com
• docs/ directory in this repo (synced upstream)
• Anthropic Patterns guide — sequential, routing, parallel, orchestrator, evaluator
• Human-in-the-Loop guide — approval workflows with pause/resume
• design/ — architecture and design decision records (chat API, sub-agents, agent tools, workspace, voice, browser tools, retries, and more)

Repository Structure

Development

Node 24+ required. pnpm workspaces with Nx for task orchestration, caching, and affected detection.

pnpm install                 # install all workspaces
pnpm run build               # build all packages (Nx, cached, dependency-ordered)
pnpm run check               # sherif + export checks + oxfmt + oxlint + typecheck
pnpm run test                # vitest + vitest-pool-workers (Workers runtime)
pnpm run test:react          # Playwright-based React hook tests
pnpm exec nx affected -t build # build only what changed
pnpm exec nx affected -t test  # test only what changed

Changes to packages/ need a changeset:

pnpm exec changeset

See AGENTS.md for deeper contributor guidance.

Contributing

We are not accepting external pull requests at this time — the SDK is evolving quickly and we want to keep the surface area manageable. That said, we'd love to hear from you:

• Bug reports & feature requests — open an issue
• Questions & ideas — start a discussion

License

MIT

Agent Development Kit (ADK) 2.0

An open-source, code-first Python framework for building, evaluating, and deploying sophisticated AI agents with flexibility and control.

Important Links: Docs, Samples & ADK Web.

ADK in other languages: ADK Java, ADK Kotlin, ADK Go & ADK Typescript.

Agent Development Kit (ADK) is a flexible and modular framework that applies
software development principles to AI agent creation. It is designed to
simplify building, deploying, and orchestrating agent workflows, from simple
tasks to complex systems. While optimized for Gemini, ADK is model-agnostic,
deployment-agnostic, and compatible with other frameworks.

✨ Key Features

• Workflow Runtime: A graph-based execution engine for composing
  deterministic execution flows for agentic apps, with support for routing,
  fan-out/fan-in, loops, retry, state management, dynamic nodes,
  human-in-the-loop, and nested workflows.

• Task API: Structured agent-to-agent delegation with multi-turn task
  mode, single-turn controlled output, mixed delegation patterns,
  human-in-the-loop, and task agents as workflow nodes.

• Modular Multi-Agent Systems: Design scalable applications by composing
  multiple specialized agents into flexible hierarchies.

• Rich Tool Ecosystem: Utilize pre-built tools, custom functions,
  OpenAPI specs, MCP tools or integrate existing tools to give agents diverse
  capabilities, all for tight integration with the Google ecosystem.

• Code-First Development: Define agent logic, tools, and orchestration
  directly in Python for ultimate flexibility, testability, and versioning.

• Agent Config: Build agents without code. Check out the
  Agent Config feature.

• Tool Confirmation: A tool confirmation flow (HITL) that can guard tool execution with explicit confirmation and custom input.

• Deploy Anywhere: Easily containerize and deploy agents on Cloud Run or
  scale seamlessly with Agent Runtime on Gemini Enterprise Agent Platform
  (formerly Vertex AI Agent Engine).

🚀 Installation

Stable Release (Recommended)

You can install the latest stable version of ADK using pip:

pip install google-adk

Requirements: Python 3.11+.

For transitive dependency protection, we recommend installing with our companion
constraints files (for python 3.11 to 3.15).

Choose the constraints file matching your Python version:

## For example, for Python 3.11
curl -o constraints-3.11.txt https://raw.githubusercontent.com/google/adk-python/main/constraints-3.11.txt
pip install google-adk -c constraints-3.11.txt
rm constraints-3.11.txt

To install optional integrations, you can use the following command:

pip install "google-adk[extensions]"

The release cadence is roughly bi-weekly.

Development Version

Bug fixes and new features are merged into the main branch on GitHub first. If you need access to changes that haven't been included in an official PyPI release yet, you can install directly from the main branch:

pip install git+https://github.com/google/adk-python.git@main

Note: The development version is built directly from the latest code commits. While it includes the newest fixes and features, it may also contain experimental changes or bugs not present in the stable release. Use it primarily for testing upcoming changes or accessing critical fixes before they are officially released.

Quick Start

Beginner Note: ADK applications are built using two main classes:
Agent (defines an AI's instructions, tools, and behavior) and
Workflow (orchestrates agents and tasks in a graph-based flow).

Agent

from google.adk import Agent

root_agent = Agent(
    name="greeting_agent",
    model="gemini-3.5-flash",
    instruction="You are a helpful assistant. Greet the user warmly.",
)

Workflow

from google.adk import Agent, Workflow

generate_fruit_agent = Agent(
    name="generate_fruit_agent",
    instruction="Return the name of a random fruit. Return only the name.",
)

generate_benefit_agent = Agent(
    name="generate_benefit_agent",
    instruction="Tell me a health benefit about the specified fruit.",
)

root_agent = Workflow(
    name="root_agent",
    edges=[("START", generate_fruit_agent, generate_benefit_agent)],
)

Run Locally

## Interactive CLI
adk run path/to/my_agent

## Web UI (supports multi-agent directories or pointing directly to a single agent folder)
adk web path/to/agents_dir

Development UI

A built-in development UI to help you test, evaluate, debug, and showcase your agent(s).

Evaluate Agents

Run the bundled evaluation sample from the repo root:

adk eval \
    contributing/samples/evaluation/home_automation_agent \
    contributing/samples/evaluation/basic_criteria/home_automation.evalset.json \
    --config_file_path contributing/samples/evaluation/basic_criteria/eval_config.json

🚀 Deployment Options

Deploying the Agent Locally with Docker Container:

adk deploy docker --with_ui <agent-folder>

Deploying the Agent in Google Cloud (Cloud Run)

adk deploy cloud_run --with_ui <agent-folder>

You may set the following environment variables in adk command, or in a .env file instead.

adk deploy cloud_run --with_ui --env GOOGLE_GENAI_USE_ENTERPRISE=1 <agent-folder>

📚 Documentation

• Getting Started: https://adk.dev/
• Guides: See
  docs/guides/
  for task-oriented walkthroughs of agents, tools, events, plugins, and
  workflows.
• Samples: See
  contributing/samples/
  for runnable example agents.

🤝 Contributing

We welcome contributions from the community! Whether it's bug reports, feature requests, documentation improvements, or code contributions, please see our:

• General contribution guideline and flow.
• Code Contributing Guidelines to get started.

Community Repo

We have the adk-python-community repo that is home to a growing ecosystem of community-contributed tools, third-party
service integrations, and deployment scripts that extend the core capabilities
of the ADK.

Vibe Coding

If you want to develop an agent via vibe coding, the llms.txt and llms-full.txt files published on the ADK documentation site can be used as context for an LLM. The former is a summarized version, while the latter contains the full documentation for LLMs with a large context window.

Community Events

• [Completed] ADK's 1st community meeting on Wednesday, October 15, 2025. Remember to join our group to get access to the recording, and deck.

📄 License

This project is licensed under the Apache 2.0 License — see the
LICENSE file for details.

Happy Agent Building!