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Agent framework🔴Developer
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Google ADK

Google’s development kit for constructing, evaluating, and deploying modular AI agents.

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In Plain English

Google’s development kit for constructing, evaluating, and deploying modular AI agents.

OverviewFeaturesPricingGetting StartedUse CasesLimitationsFAQAlternatives

Overview

Google Agent Development Kit is google’s development kit for constructing, evaluating, and deploying modular AI agents. The product is most relevant to builders and business teams that want a focused system rather than assembling every component themselves. Its reported capabilities include agent composition, tool integrations, evaluation support, deployment workflows. Those capabilities suggest a practical workflow in which a team can start with a bounded problem, connect the data or services it already uses, review early results, and expand automation only after quality and governance expectations are clear.

Practical use cases include build gemini agents, create multi-agent systems, test agent behavior. Buyers should evaluate the product with representative work, including difficult examples and failure cases, instead of relying only on a polished demonstration. Important evaluation criteria include output accuracy, setup effort, permissions, data retention, export options, auditability, latency, and the ability for a human to correct or override the system. For a production rollout, teams should also test access controls, vendor support, integration limits, and how usage grows as more users or workloads are added.

Google Agent Development Kit is presented here as MCP-compatible in the client role, which can make it useful in environments where agents need a standard way to discover or invoke external capabilities. Because the vendor pages could not be reached during this automated run, that MCP classification should be checked against current vendor documentation before procurement or production architecture decisions.

Pricing could not be verified from the vendor website in this run because outbound page fetches returned no usable HTML. Accordingly, this profile does not invent plan names or dollar amounts: the pricingTiers array is intentionally empty and the record is flagged for manual verification. Before purchase, confirm current plan boundaries, included usage, overage charges, contract minimums, trial availability, and enterprise security terms directly with the vendor. The same caution applies to the feature list, which is a concise discovery summary and should be reconciled with live product documentation. This profile is therefore useful for catalog discovery and initial comparison, but it is not a substitute for a current quote, security review, or hands-on proof of concept.

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Editorial Review

**Best multi-agent framework for Google Cloud ecosystems.** Google ADK's built-in evaluation tools, local debugging UI, and native MCP support provide a more structured development experience than LangChain or CrewAI. With four official SDKs (Python, TypeScript, Go, Java), it is one of the most polyglot agent frameworks available. The primary trade-off is a smaller third-party ecosystem due to the framework's youth (launched April 2025) and the strongest feature integration being tied to Gemini and Google Cloud services. Teams already invested in Google Cloud will find ADK's Vertex AI Agent Engine deployment path and native Workspace integrations compelling, while teams on other clouds should weigh the Gemini-optimized features they may not fully leverage.

Key Features

Multi-Agent Orchestration with Workflow Primitives+

Build hierarchical agent teams using sequential, parallel, and loop workflow agents alongside LLM agents and custom agents. Agents can delegate to sub-agents through agent routing, and the new Python 2.0 Beta introduces agent teams for coordinated multi-agent execution with shared state and task delegation patterns.

Built-in Evaluation Framework+

Test agent performance with criteria-based scoring, user simulation, environment simulation, custom metrics, and an optimization module for iterative improvement. Score trajectory accuracy (correct steps taken), tool usage quality, and final response relevance — capabilities that most competing frameworks require third-party integrations to achieve.

Native MCP Server Integration+

ADK natively supports the Model Context Protocol, allowing agents to consume any MCP-compatible tool server without custom integration code. Combined with OpenAPI tool generation and traditional function tools with action confirmations, ADK provides one of the most flexible tooling ecosystems among agent frameworks.

Local Web Debugging UI Plus CLI and API Server+

Ships with three runtime modes: a web-based UI for visual debugging of agent interactions and tool calls, a CLI for terminal-driven workflows, and an API server for production integrations. The web UI provides real-time inspection of agent decision-making, making it significantly easier to debug multi-agent coordination than log-based approaches.

Multi-Language SDKs and Multi-Model Support+

Available in four official SDKs — Python 2.0 Beta, TypeScript 1.0 (new in 2026), Go, and Java — all maintained under github.com/google. Supports Gemini, Gemma, Claude, Ollama, vLLM, LiteLLM, LiteRT-LM, and Apigee AI Gateway for flexible model selection across providers.

Pricing Plans

Free

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Getting Started with Google ADK

  1. 1**Install ADK**: `pip install google-agent-dev-kit` and set up Python development environment with required dependencies
  2. 2**Configure models**: Set up Gemini API access or configure LiteLLM for your preferred models (Claude, GPT-4, etc.)
  3. 3**Explore local UI**: Run the built-in web interface to understand agent debugging and interaction patterns before building custom agents
  4. 4**Build simple agent**: Start with a single-agent example using built-in tools (Google Search, code execution) to learn the framework patterns
  5. 5**Plan production deployment**: Evaluate Vertex AI Agent Engine for managed deployment or containerization for other cloud platforms
Ready to start? Try Google ADK →

Best Use Cases

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Build Gemini agents

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Create multi-agent systems

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Test agent behavior

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Google ADK doesn't handle well:

  • ⚠Python SDK is still in 2.0 Beta as of 2026 — APIs may shift before stable release
  • ⚠Smaller third-party plugin ecosystem than LangChain due to ~1-year framework age
  • ⚠Some advanced features (built-in Google Search, grounding) only work with Gemini models
  • ⚠Vertex AI Agent Engine deployment incurs Google Cloud costs in addition to LLM API fees
  • ⚠Opinionated workflow patterns may not fit highly dynamic or unstructured agent designs

Pros & Cons

✓ Pros

  • ✓Code-first abstractions support versioning and testable agent behavior
  • ✓Evaluation and deployment workflows address more than prompt prototyping
  • ✓Natural fit for teams already using Gemini and Google Cloud

✗ Cons

  • ✗Requires software engineering skills and operational ownership
  • ✗End-to-end cost depends on separate model and cloud services
  • ✗Deep Google integration may reduce portability to other stacks

Frequently Asked Questions

How does Google ADK compare to LangChain and LangGraph?+

ADK is more opinionated and ships with a built-in evaluation framework, local debugging UI, and structured workflow agent types (sequential, parallel, loop) out of the box, whereas LangChain/LangGraph are more flexible and modular with a larger third-party ecosystem. ADK also provides four official language SDKs (Python, TypeScript, Go, Java) versus LangChain's Python and JavaScript. Choose ADK for structured multi-agent development with Google Cloud integration; choose LangChain/LangGraph for maximum flexibility and community ecosystem breadth.

Can I use ADK with non-Google models like Claude or GPT-4?+

Yes. ADK supports any LLM through LiteLLM integration, and the documentation explicitly lists Anthropic Claude, Ollama, vLLM, LiteRT-LM, and Apigee AI Gateway as supported model providers alongside Gemini and Gemma. However, some Gemini-optimized features like built-in Google Search grounding are only available when using Google models.

Does ADK require Google Cloud to run?+

No. ADK runs entirely locally for development and includes a local web UI, CLI, and API server runtime. You can also deploy to any container platform including Cloud Run, GKE, or non-Google clouds via standard Docker containers. Google Cloud and Vertex AI Agent Engine are optional managed deployment targets, not requirements.

What languages does ADK support and what's new in 2026?+

ADK ships in four official SDKs: Python (currently 2.0 Beta with workflow agents and agent teams), TypeScript 1.0 (newly released in 2026 — making ADK one of the few enterprise agent frameworks with first-party TypeScript support), Go, and Java. The Python 2.0 Beta introduces new workflow primitives, ambient agents, resumable runs, and cancelable execution.

What evaluation capabilities does ADK provide?+

ADK ships with a comprehensive evaluation framework that includes criteria-based scoring, user simulation (synthetic user interactions), environment simulation (mocked tools and external systems), and custom metrics. It also offers an optimization module for iterative improvement, allowing teams to systematically test and refine agent quality before production deployment.
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What's New in 2026

ADK TypeScript 1.0 is now available as of 2026, opening the framework to JavaScript/Node.js teams alongside the existing Python, Go, and Java SDKs. ADK Python 2.0 Beta also launched with new workflow primitives, agent teams, ambient agents, resumable runs, and cancelable execution — significantly expanding the framework's orchestration capabilities.

Alternatives to Google ADK

CrewAI

AI Agents

Open-source Python framework for orchestrating role-playing, autonomous AI agents that collaborate as a 'crew' to complete complex tasks.

LangGraph

AI agent framework

LangGraph is LangChain's open-source framework for building stateful, durable, multi-agent workflows in Python and JavaScript with graph-based control flow.

Microsoft AutoGen

Multi-Agent Builders

Microsoft's open-source framework for building multi-agent AI systems with asynchronous, event-driven architecture.

Strands Agents

AI Agent Builders

AWS open-source SDK for building AI agents in Python and TypeScript with model-driven tool orchestration, multi-provider LLM support, and native AWS deployment options.

OpenAI Agents SDK

AI Agent Builders

OpenAI Agents SDK is an open-source Python framework for building agentic apps with handoffs, guardrails, sessions, tracing, MCP tools, sandbox agents, and realtime voice agents.

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User Reviews

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Quick Info

Category

Agent framework

Website

google.github.io/adk-docs
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