Google Agent Development Kit
Google's MIT-licensed open-source framework for building, evaluating, and deploying production AI agents in five languages.
ADK 2.0 is worth adopting if you're building multi-agent systems and want graph workflows, collaborative agents, built-in evaluation, and traces from day one — without paying for a framework license. The MIT license and five-language coverage (Python, TypeScript, Go, Java, Kotlin) genuinely lower the cost of a mixed-language team. Choose it over LangChain when deterministic orchestration and observability matter more than a huge third-party plugin catalog, and over closed agent platforms when Cloud Run, GKE, and Apigee AI Gateway already fit your estate. Skip it if you need a no-code visual builder or want your stack anchored on AWS or Azure.
Last checked 14d ago · cite: rightaichoice.com/tools/google-adk
- Enterprise engineering teams building multi-agent systems with complex orchestration
- Multi-language teams needing Python, TypeScript, Go, Java, or Kotlin agents
- Projects that need deterministic graph workflows alongside adaptive AI reasoning
- Teams already on Google Cloud (Cloud Run, GKE, Apigee)
- Teams that want a visual no-code agent builder instead of writing code
- Projects needing a large third-party plugin marketplace
- Teams anchored on AWS or Azure seeking deep native cloud integration
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Skip Google ADK if you want a no-code visual agent builder or a large third-party plugin marketplace, rather than a code-first framework you extend with your own Python, TypeScript, Go, Java, or Kotlin tools.
The framework is free under MIT, but every deployed agent runs on Cloud Run, GKE, or the managed Agent Runtime, so you pay Google Cloud compute and storage separately.
ADK itself is $0 under an MIT license and works for a solo developer or a large enterprise equally, so the real cost story is the infrastructure underneath it. Route to Gemma on Ollama or vLLM locally and your marginal cost is hardware you already own; point at Gemini, Claude, or OpenAI and you pay per token to that provider. Cloud Run and GKE bill by usage, which suits small teams that want to start cheap, while larger enterprises typically add Apigee AI Gateway and managed Agent Runtime.
In short
Google Agent Development Kit — Google's MIT-licensed open-source framework for building, evaluating, and deploying production AI agents in five languages. Best for Enterprise engineering teams building multi-agent systems with complex orchestration, Multi-language teams needing Python, TypeScript, Go, Java, or Kotlin agents, Projects that need deterministic graph workflows alongside adaptive AI reasoning. Free to use.
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.
Average across the 4 sources that answered — each source counts once, not each post.
- +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: September 2026
How we score →Key Features
- Open-source MIT-licensed framework
- SDKs for Python, TypeScript, Go, Java, and Kotlin
- Graph workflows blending deterministic code with AI reasoning
- Collaborative workflows for multi-agent coordination
- Sequential, loop, parallel, and custom template workflow patterns
- Agent routing and workflow patterns
- Model support for Gemini, Gemma, and Claude
- Model routing through OpenAI, Ollama, vLLM, LiteLLM, LiteRT-LM
- Google Search grounding
- Apigee AI Gateway and Agent Platform hosted models
- Live and Voice Agents (Python, Java) with audio and video
- Agents CLI for scaffolding, testing, evaluation, deployment
- Web Interface and Visual Builder
- Command Line and API Server run modes
- Ambient agents with resume and cancel run controls
About Google Agent Development Kit
Google Agent Development Kit (ADK) is an MIT-licensed, open-source framework for building production AI agents rather than prototypes. It ships in five languages — Python, TypeScript, Go, Java, and Kotlin — each installed from its own package (pip install google-adk, npm install @google/adk, go get google.golang.org/adk/v2, com.google.adk:google-adk, com.google.adk:google-adk-kotlin-core). ADK 2.0, now generally available, adds graph workflows that weave deterministic code with adaptive AI reasoning, and collaborative workflows for multi-agent coordination, alongside sequential, loop, parallel, and custom template workflow patterns. It suits developers and platform teams who want orchestration and observability to be first-class rather than bolted on. Model choice is flexible: Gemini, Gemma, and Claude, with routing through OpenAI, Ollama, vLLM, LiteLLM, and LiteRT-LM, plus Gemma hosted on Agent Platform and Apigee AI Gateway. The Agents CLI scaffolds, builds, tests, evaluates, and deploys agents from your existing AI-enabled editor, and you can run them through a Web Interface, Visual Builder, Command Line, API Server, or as ambient agents with resume and cancel controls. Deployment targets Cloud Run and GKE, or the managed Agent Runtime. Built-in observability covers logging, metrics, and traces, with evaluation criteria, user and environment simulation, and custom metrics. Components include custom function tools, MCP tools, OpenAPI tools, action confirmations, artifacts, callbacks, plugins, sessions with rewind and migration, state, events, memory, context compression, and model context caching. A2A protocol support lets agents expose and consume each other, and Google Search grounding is built in.
Behind the Verdict
ADK's strongest argument is that it treats production concerns as core, not add-ons. Graph workflows let you keep critical control flow in ordinary code and hand only the ambiguous steps to a model, which is a more honest architecture than asking an LLM to orchestrate everything. Collaborative workflows, plus sequential, loop, parallel, and custom template patterns, cover most multi-agent shapes without you inventing an orchestration layer. The Agents CLI moves an idea to a coded agent quickly inside your existing editor, and evaluation — criteria, user simulation, environment simulation, custom metrics — is the part most teams skip until it hurts. Observability via logging, metrics, and traces is documented in-framework, not left to your APM vendor. Model flexibility is real: Gemini, Gemma, and Claude are supported, and routing runs through OpenAI, Ollama, vLLM, LiteLLM, and LiteRT-LM, so you are not locked to one provider. Deployment is equally concrete — Standard deployment, Cloud Run, GKE, or the managed Agent Runtime, with test-deployed-agents guidance. Watch the trade-offs. It is code-first; there is a Visual Builder, but the framework assumes developers in one of five languages. Some capabilities, like Live and Voice Agents, are documented for Python and Java, so check your language before committing. Examples lean toward Google models and Google infrastructure, and the heaviest integrations are Google's own. If you want a vast community plugin marketplace, that is a different tool. If you want a maintainable, testable, cloud-portable agent core with a clear upgrade story — Go v1.x to v2.x has documented migration guidance — ADK is a credible default.
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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.
Install with pip install google-adk, declare an Agent with model gemini-flash-latest and the google_search tool, then run it through the Web Interface to iterate on instructions.
Outcome: A working grounded research agent in a local dev loop, with the same code deployable later via the Agents CLI.
Model the critical control flow as a graph workflow, delegate ambiguous steps to a Gemini agent, wire MCP and OpenAPI tools, and deploy to GKE with logging, metrics, and traces enabled.
Outcome: A multi-agent service running on infrastructure you already operate, with traces pointing at the exact failing step.
Use evaluation criteria, user simulation, and custom metrics to score the new agent version, then gradually route traffic using the Agents CLI test-deployed-agents flow.
Outcome: A measurable before/after comparison instead of a subjective read of a few sample prompts.
Use Cases
- Build a multi-agent pipeline where graph workflows keep critical logic deterministic and models handle the ambiguous steps.
- Prototype a planner/executor agent team locally via the dev UI, then deploy with the Agents CLI without framework changes.
- Evaluate agent versions against evaluation criteria, user simulation, and custom metrics before shipping a change.
- Wire Google Search grounding into a research agent with a single tool declaration.
- Expose one ADK agent over A2A and consume it from another agent or service.
- Ship a voice or live agent in Python or Java using the Live and Voice Agents workflows.
- Run ambient agents that can be resumed or cancelled mid-run from the runtime config.
Models Under the Hood
as of 2026-09-15
Limitations
- ADK is code-first: you need development skills in Python, TypeScript, Go, Java, or Kotlin.
- Documentation and examples lean toward Google's models and Google Cloud infrastructure, and the deepest deployment paths run through Cloud Run, GKE, or the managed Agent Runtime.
- Some capabilities are language-limited — Live and Voice Agents are documented for Python and Java only — so verify your chosen language before standardizing.
- The framework is distributed across separate per-language repositories and release-note pages, which means version status (for example Go v1.x versus v2.x) has to be checked per language.
- There is no large third-party plugin ecosystem of the kind LangChain offers; extensibility centers on function tools, MCP tools, OpenAPI tools, callbacks, and plugins you write yourself.
as of 2026-09-14
Verification history
We have re-verified Google Agent Development Kit 18 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
Showing the 6 most recent of 18 verification passes.
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
Any developer or engineering team that can run agents on its own infrastructure and wants the framework at no license cost.
What this tier adds
Free entry point: the MIT license covers graph workflows, collaborative agents, all five language SDKs, and the Agents CLI.
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 itself is $0 under an MIT license and works for a solo developer or a large enterprise equally, so the real cost story is the infrastructure underneath it. Route to Gemma on Ollama or vLLM locally and your marginal cost is hardware you already own; point at Gemini, Claude, or OpenAI and you pay per token to that provider. Cloud Run and GKE bill by usage, which suits small teams that want to start cheap, while larger enterprises typically add Apigee AI Gateway and managed Agent Runtime.
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.
Solo developers reach a running agent in roughly 15–30 minutes: install the package for your language, write one Agent declaration, and launch the dev Web Interface. A platform engineer wiring graph workflows, MCP or OpenAPI tools, and traces into an existing GKE or Cloud Run estate should budget half a day to first deploy. Teams adopting evaluation and Gemini model routing usually need a few
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 a hand-rolled Python agent loop: replace the orchestration code with graph workflows and collaborative workflows, keeping your existing tool functions as ADK function tools.
- →From LangChain: port chains to sequential, loop, or parallel workflow patterns and keep provider access via the LiteLLM or OpenAI routing options.
- →From ADK Go v1.x: follow the ADK 2.0 Go v1.x to v2.x migration guidance and the new workflow package.
- →From a single-vendor SDK: keep your prompts as instructions and route the same agent to Gemma or Claude without rewriting the agent definition.
- →From a hosted agent platform: run the Agents CLI against your existing repository and deploy to Cloud Run or GKE using the Standard deployment path.
- ↗To LangChain: re-express graph and collaborative workflows as chains or graphs and rebuild evaluation tooling separately.
- ↗To a managed agent platform: export agent definitions as config and rebuild tool wiring on the target platform's tool model.
- ↗To a single-vendor SDK: inline your instruction strings and tool declarations, dropping the A2A and MCP abstractions.
- ↗To a no-code builder: document your agent team and workflow patterns, then recreate them visually without the code-level control.
- ↗To another open-source framework: session, state, and memory handling must be rebuilt, since ADK's are framework-native.
Integrations
Resources & Guides
- Resourcegoogle.github.io
Agent Development Kit (ADK)
Build powerful multi-agent systems with Agent Development Kit (ADK)
- Resourcegoogle.github.io
Build Agents
Helpful link from google.github.io
- Resourcegoogle.github.io
Run Agents
Helpful link from google.github.io
- Resourcegoogle.github.io
Components
Helpful link from google.github.io
- Resourcegoogle.github.io
Agent Development Kit (ADK)
Build powerful multi-agent systems with Agent Development Kit (ADK)
- API Referencegoogle.github.io
Reference
Methods, params, types from google.github.io
Tutorials & Learning
YouTube returned 6 videos for “Google Agent Development Kit”, and we withheld 6: 6 did not mention Google Agent Development Kit. We are showing none, because we could not prove any of them are about Google Agent Development Kit.
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.
Agenta
Open-source workspace for building, evaluating, and deploying AI agents you talk to in Slack, Telegram, or a browser chat
Haystack
Open-source Python framework for building inspectable RAG pipelines and production agents, installable via pip.
AutoGen
Microsoft's open-source framework for building conversational and event-driven AI agents in Python and .NET.
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