Google Agent Development Kit
Google's open-source framework for building production-grade AI agents across Python, TypeScript, Go, Java, and Kotlin.
ADK 2.0 is a compelling open-source option for teams building multi-agent systems, especially on Google Cloud. Graph workflows and collaborative agents solve real orchestration challenges, and the multi-language support is a differentiator. Skip it if you rely on LangChain's massive plugin ecosystem or plan to avoid Google infrastructure entirely.
Verified 1m ago · liveness 74/100 · cite: rightaichoice.com/tools/google-adk
- Enterprise teams building multi-agent systems with complex orchestration needs
- Developers seeking a production-ready framework with built-in evaluation and monitoring
- Projects requiring deterministic graph-based workflows alongside adaptive AI reasoning
- Teams already invested in Google Cloud infrastructure (Cloud Run, GKE, Apigee)
- Teams requiring extensive community-driven plugin ecosystems (e.g., LangChain plugins)
- Use cases centered exclusively on non-Google models without routing flexibility
- Lightweight or single-agent applications with minimal infrastructure needs
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Skip Google ADK if you need a massive plugin ecosystem or plan to avoid Google infrastructure—you'll find more flexibility in LangChain or other frameworks.
Using Google Cloud resources (Cloud Run, GKE, Vertex AI) incurs standard cloud charges—ADK itself is free, but you'll pay for compute, storage, and model API calls.
ADK is completely free (MIT license), making it a cost-effective choice for startups and enterprises alike. Compared to commercial agent platforms like LangSmith (which charges per seat), ADK has no subscription fees. You only pay for underlying cloud and model usage.
In short
Google Agent Development Kit — Google's open-source framework for building production-grade AI agents across Python, TypeScript, Go, Java, and Kotlin. Best for Enterprise teams building multi-agent systems with complex orchestration needs, Developers seeking a production-ready framework with built-in evaluation and monitoring, Projects requiring deterministic graph-based workflows alongside adaptive AI reasoning. Free to use.
What's new in Google Agent Development Kit
Checked 8 days agoAcross the latest 1 update: 1 feature update.
What people actually say about Google Agent Development Kit — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
48 mentions across 4 sources (Hacker News, YouTube, Stack Overflow, Lemmy) · researched Aug 6, 2026.
- +Open-source (MIT) and free to use
- +Multi-language SDKs: Python, TypeScript, Go, Java, Kotlin
- +Easy to get started with basic agents
- +Great integration with Google Cloud (Cloud Run, GKE)
- +New graph workflows (2.0) for deterministic logic
- −Less control than LangGraph or plain Python
- −Documentation for advanced deployment is thin
- −Ecosystem smaller than LangChain's
- −Graph workflows feel like a LangGraph copy
- −Learning curve for multi-agent orchestration
- • Cloud deployment may incur Google Cloud costs (Cloud Run, GKE)
- • Potential costs for using Vertex AI or other paid Google services
Viability Score
How well maintained and how widely used is Google Agent Development Kit? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Multi-language SDKs: Python, TypeScript, Go, Java, Kotlin
- Graph workflows for deterministic logic with AI reasoning
- Collaborative agents for multi-agent coordination
- Agent team configuration and orchestration
- Support for Gemini, Gemma, Claude models
- Model routing through Ollama, vLLM, LiteLLM, LiteRT-LM
- Google Search grounding integration
- Apigee AI Gateway deployment option
- Built-in logging, metrics, and traces
- Agents CLI for scaffolding, testing, evaluation, deployment
- Web interface and visual builder
- REST API for agent control
- MCP and A2A protocol support
- Session management and context caching
- Deployment to Cloud Run and GKE
About Google Agent Development Kit
Google Agent Development Kit (ADK) is an MIT-licensed, open-source framework designed to move AI agents from prototype to production. It targets developers and engineering teams who need reliable, scalable agents, and it now supports five languages: Python, TypeScript, Go, Java, and Kotlin. ADK 2.0, now generally available, introduces graph workflows that weave deterministic logic with adaptive AI reasoning, plus collaborative agents for multi-agent coordination—capabilities that directly address orchestration pain points. The framework supports a wide range of models, including Gemini, Gemma, and Claude, with flexible routing through Ollama, vLLM, LiteLLM, and LiteRT-LM. This means you can build agents that aren't locked into a single model provider. ADK also includes an Agents CLI for scaffolding, testing, evaluating, and deploying agents, alongside a web interface, visual builder, and REST API for control and management. Observability is built in with logging, metrics, and traces, and deployment targets include Cloud Run and GKE, with Apigee AI Gateway as an option for enterprise gateway needs. Grounding integrates with Google Search, and the framework supports MCP and A2A protocols for interoperability with external tools and agents. Session management and context caching round out the production-focused feature set. Compared to frameworks like LangChain, ADK offers a more focused, Google-backed path to production with tighter Google Cloud integration and built-in evaluation and safety tooling. It's a strong choice for teams already invested in Google infrastructure, or those who want a multi-language, open-source framework with enterprise-grade orchestration and observability from day one.
Behind the Verdict
ADK 2.0 hits a sweet spot for teams that need more than a simple agent library but don't want to stitch together a dozen tools. The graph workflows are a standout: they let you mix deterministic code with AI reasoning, so you can guarantee certain steps happen in order while still letting the model adapt. Collaborative agents handle the multi-agent coordination that gets messy in other frameworks, and the fact that you get this in Python, TypeScript, Go, Java, and Kotlin means your team doesn't have to learn a new language. Where ADK really shines is its production focus. Built-in logging, metrics, and traces mean you can debug and monitor agents the same way you would any other service, and the Agents CLI streamlines the path from local dev to deployment on Cloud Run or GKE. If you're already on Google Cloud, this integration is a major time-saver. But here's the catch: if you're not committed to Google infrastructure, some of that value fades. While ADK supports non-Google models and runtimes, the tightest integration is with Google Cloud services like Apigee. And if you're used to LangChain's sprawling ecosystem of plugins and community integrations, ADK has a smaller, though growing, set of partners and skills. For single-agent, lightweight applications, ADK's features may be overkill—you could get away with a simpler library. But for multi-agent systems with complex orchestration, it's worth a serious look. In practice, we'd reach for ADK when we need deterministic workflows + AI flexibility and want a framework that's designed to be operated, not just demoed. The open-source license and multi-language support lower the barrier to adoption, and the built-in evaluation and safety tooling give teams confidence to ship. Watch out for the learning curve if you're
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Real-world workflow fit
Concrete scenarios for the personas Google Agent Development Kit actually fits — and what changes day-one when you adopt it.
You need to quickly prototype a multi-agent system that handles common queries and escalates complex issues.
Outcome: Within a day, you scaffold a project with agents-cli, define a collaborative workflow with a primary support agent and a fallback specialist agent, and deploy to Cloud Run. ADK's built-in evaluation tools help you test and iterate before going live.
Your team wants to move existing AI workflows to a more scalable infrastructure while preserving determinism.
Outcome: You use ADK's graph workflows to encode business rules as deterministic steps, while letting AI handle flexible reasoning. You deploy to GKE and integrate with Apigee AI Gateway for unified access. ADK's multi-language support allows the team to use their preferred languages.
You want to build a personal research assistant that fetches live data.
Outcome: You use the Python SDK, add the Google Search tool, and run locally with the web interface. You can deploy to Cloud Run for free tier usage, and ADK handles sessions and memory, so you can focus on the assistant's logic.
Use Cases
- Build a multi-agent pipeline on Gemini and deploy to Vertex AI in one workflow.
- Prototype a planner/executor/critic agent team locally via the dev UI, then ship without framework changes.
- Evaluate agent versions against a fixed test set using the built-in harness.
- Wire Google-native tools (Drive, Calendar, Search) into an agent with minimal glue code.
Models Under the Hood
as of 2026-08-14
Limitations
- ADK's framework is smaller than LangChain's, so you'll have fewer community integrations.
- If you're not using Google Cloud, you miss out on tight integration with services like Cloud Run and Apigee.
- The framework is code-first; there's no no-code builder, so non-developers will struggle.
- Also, while it supports third-party models, the majority of documentation and examples focus on Gemini.
as of 2026-08-06
Verification history
We have re-verified Google Agent Development Kit 16 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
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Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Google Agent Development Kit tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0/mo
Ideal for
Individual developers, startups, and enterprises that want a free, open-source framework with no per-seat costs. Ideal for prototyping and production if you can manage your own infrastructure.
What this tier adds
This is the only tier, offering the full framework under MIT license, including all SDKs, tools, and features. There is no paid upgrade, so costs only arise from underlying cloud usage.
Where the pricing makes sense
The company stage and team size where Google Agent Development Kit's pricing actually pencils out — and where peers do it cheaper.
ADK is completely free (MIT license), making it a cost-effective choice for startups and enterprises alike. Compared to commercial agent platforms like LangSmith (which charges per seat), ADK has no subscription fees. You only pay for underlying cloud and model usage.
Setup time & first value
How long it actually takes to get something useful out of Google Agent Development Kit — broken out by persona, not the marketing-page minute.
For developers familiar with Python or TypeScript, you can have your first agent running in under an hour—just install the SDK and follow the quickstart. Multi-language setups may take longer if you need to configure each SDK. The agents CLI tool can scaffold a project in minutes, and deployment to Cloud Run is straightforward.
Switching to or from Google Agent Development Kit
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangChain: You can port agents by rewriting the orchestration logic in ADK's Python or TypeScript SDK. The built-in evaluation tools can replace LangSmith's, and deployment to Cloud Run is simpler than managing
- →From a custom solution: If you have a monolithic agent, you can break it into ADK's multi-agent or graph workflow patterns, using the CLI to scaffold the project structure.
- ↗To LangChain: If you need a larger community ecosystem or support for many non-Google integrations, you can migrate to LangChain. The core concepts are similar, so you'll reuse your prompt engineering and tool
- ↗To a managed platform: If you want to avoid managing infrastructure, you can move to a paid agent platform like Vertex AI Agent Builder, which uses ADK under the hood.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Google Agent Development Kit
Common stack mates teams adopt alongside Google Agent Development Kit, with the specific reason each pairing earns its keep.
Phidata
Open-source Python framework for building and running private, production-ready multi-agent systems.
Semantic Kernel
Microsoft's open-source SDK for building production-grade AI agents with plugins and orchestration.
INK Editor
Open-source agent OS for building, securing, and scaling production AI agents.
Featured Head-to-Head Comparisons
Crewai vs Google Adk
If you're an enterprise team already invested in Google Cloud and need deterministic graph-based workflows with multi-language SDKs, Google ADK 2.0's free open-source framework is a powerful choice. For organizations prioritizing discovery of automation opportunities, governance (RBAC, audit trails, PII redaction), and human-in-the-loop approvals, CrewAI's enterprise platform is the better fit despite custom pricing. Freelancers or small teams on a tight budget may prefer ADK's free offering.
Google Adk vs Langchain
Choose LangChain if you need robust observability and evaluation for complex agents, especially if you're already using LangChain frameworks. Choose Google ADK if you're building multi-agent systems on Google Cloud and want a free, open-source framework with deterministic graph workflows.
Google Adk vs N8n
If you're a developer who wants a lightweight, code-first framework tightly integrated with Google Cloud and multi-model routing, ADK 2.0 is your pick—especially now with graph workflows. But if you need a visual canvas, 500+ integrations out of the box, and fine-grained control over execution costs, n8n gives you that flexibility with self-hosting and human-in-the-loop guardrails. Choose n8n for ops-heavy automation with a GUI; choose ADK for pure code-based agent orchestration.
Google Adk vs Langgraph
For enterprise teams already on Google Cloud needing deterministic multi-agent orchestration with multi-language SDKs, Google ADK is the clear pick. LangGraph wins when you need deep control over state, loops, and human-in-the-loop workflows. If you value low-level primitives and prompt caching (per latest updates), LangGraph edges ahead. Both are free, so choose based on required control vs. integrated cloud tooling.
Autogen vs Google Adk
If you need flexible multi-agent experimentation with any LLM, choose AutoGen. For production-grade enterprise deployments with deterministic logic, multi-language SDKs, and Google Cloud integration, Google ADK is the stronger choice, especially with ADK 2.0's graph workflows and Kotlin support.
Alternatives to Google Agent Development Kit
View allPhidata
Open-source Python framework for building and running private, production-ready multi-agent systems.
Semantic Kernel
Microsoft's open-source SDK for building production-grade AI agents with plugins and orchestration.
INK Editor
Open-source agent OS for building, securing, and scaling production AI agents.
Frequently Asked Questions
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