Google Agent Development Kit
FreeOpen-source framework to build, debug, and deploy production-grade AI agents.
By Tanmay Verma, Founder · Last verified 20 Jun 2026
4.9k views
Added 4/21/2026
69/100MonitorIn short
Google Agent Development Kit — Open-source framework to build, debug, and deploy production-grade AI agents. 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.
Affiliate disclosure: We earn a commission when you use our links. Editorial picks are independent. How we choose.
Is Google Agent Development Kit actually worth it?
LiveSee what real users actually say. We scan live discussions, reviews and complaints across the web and hand you an honest verdict — in under a minute.
3 free scans · no card needed · downloadable report
Editorial Verdict
Best for
Enterprise teams building multi-agent systems with complex orchestration needsDevelopers seeking a production-ready framework with built-in evaluation and monitoringProjects requiring deterministic graph-based workflows alongside adaptive AI reasoningTeams already invested in Google Cloud infrastructure (Cloud Run, GKE, Apigee)Hackathons and rapid prototyping with multi-language support
Not ideal for
Teams requiring extensive community-driven plugin ecosystems (e.g., LangChain plugins)Use cases centered on non-Google models exclusively (though ADK supports third-party models)Lightweight or single-agent applications with minimal infrastructure needsDevelopers preferring a lower-level, fully customizable framework without opinionated patternsMulti-cloud deployments without additional configuration for non-GCP integrations
ADK is a strong choice for teams needing a production-ready, multi-language agent framework backed by Google. Its graph workflows and enterprise integrations make it stand out, but the developer community is still growing. Consider ADK if you need scalability and reliability over community-driven ecosystems like LangChain.
Last verified: June 2026
Behind the Verdict
ADK excels for enterprise teams building complex multi-agent systems where reliability and deterministic workflows are critical. Its graph-based workflows in ADK 2.0 allow you to mix code with AI reasoning, a unique feature for production deployments. The multi-language support (Python, TS, Go, Java, Kotlin) reduces onboarding friction for diverse teams. However, the ecosystem around third-party plugins is less mature than LangChain or CrewAI. If your use case is lightweight or you need a vast library of community tools, ADK may feel restrictive. For teams already on Google Cloud, the native integrations with Cloud Run, GKE, and Apigee make deployment seamless. ADK is open-source (MIT), so you avoid vendor lock-in, but the most polished integrations are with Google services. Expect a learning curve for advanced features like graph workflows and MCP/A2A protocols. Real-world caveat: documentation is improving but still catching up to the rapid releases; plan for some trial and error. Verdict: best for serious production agents, not for quick prototypes.
Skip Google Agent Development Kit if Skip Google ADK if you need a no-code agent builder or aren't using Google Cloud for deployment.
Latest from Google Agent Development Kit
We're gathering recent updates for Google Agent Development Kit from changelogs, press, Hacker News, and social. Check back in a day or two.
Viability Score
69/100
MonitorHow likely is Google Agent Development Kit to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
55
funding runway
40
website health
90
wrapper dependency
100
Last calculated: June 2026
How we score →About Google Agent Development Kit
Google Agent Development Kit (ADK) is an open-source framework for building, debugging, and deploying production-grade AI agents at enterprise scale. It supports multiple languages including Python, TypeScript, Go, Java, and Kotlin, making it accessible to a wide range of developers. ADK focuses on reliability and scalability, offering features like multi-agent orchestration, graph-based workflows, and streaming capabilities. With its ecosystem integrations, ADK connects agents to various AI models (Gemini, Gemma, Claude), enterprise services (Apigee, Cloud Run, GKE), and monitoring tools (observability with logging, metrics, traces). The framework is designed to move from prototype to production efficiently, supported by a CLI toolkit and comprehensive evaluation tools. ADK stands out as a Google-backed open-source solution that prioritizes enterprise readiness and developer experience.
Researching Google Agent Development Kit? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Key Features
- Multi-language SDKs (Python, TypeScript, Go, Java, Kotlin)
- Graph-based workflows with deterministic logic
- Multi-agent orchestration and collaboration
- Streaming agent support (Python, Java)
- Integration with Gemini, Gemma, Claude models
- Model routing via Ollama, vLLM, LiteLLM, LiteRT-LM
- Apigee AI Gateway for agent deployment
- Enterprise readiness with evaluation and observability
- Built-in CLI tools for local development and testing
- Deployment to Cloud Run, GKE, and other platforms
- Support for MCP (Model Context Protocol) and A2A protocol
- Google Search grounding integration
- Context caching and session management
- Open-source with MIT license
- Agent Skins and plugin architecture
Real-world workflow fit
Concrete scenarios for the personas Google Agent Development Kit actually fits — and what changes day-one when you adopt it.
ML engineer at a mid-size company
Build a multi-agent research assistant that searches the web, summarizes findings, and writes a report.
Outcome: Define a graph workflow with a planner agent, search tool (Google Search), and writer agent, deploy to Cloud Run in one day.
Enterprise architect at a financial firm
Create a fraud detection system with human-in-the-loop approval.
Outcome: Use the graph workflow to route suspicious transactions to a human agent while normal ones are processed automatically, with full observability.
Independent developer building a SaaS
Prototype a customer support chatbot that can escalate to a human.
Outcome: Leverage the streaming agent and human-in-the-loop features via the local dev UI; deploy to Cloud Run with minimal cost.
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
GeminiGemmaClaudeOllamavLLMLiteLLMLiteRT-LM
Limitations
Newer than LangGraph / AutoGen — smaller community, fewer third-party examples, and the API surface is still shifting between minor versions. Gemini-first means some patterns are idiomatic for Gemini tool calling but need adapters for OpenAI / Anthropic. Vertex deployment is easy only if you are already on GCP. The framework is Python-only (TypeScript, Go, Java are separate repos with less maturity).
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
Free
Over 12 months
Effective monthly
—
—
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
Free
Ideal for
Any developer or team building production AI agents on Google Cloud — free, no per-seat fees.
What this tier adds
Free entry point; entire framework and all features under MIT license.
Integrations
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 and open source under MIT license — no per-seat or per-agent fees. You pay only for underlying infrastructure (e.g., Gemini API tokens, Cloud Run compute). Fits any team size, but especially cost-effective for large-scale deployments on Google Cloud.
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.
ML engineer: ~2 hours to set up a multi-agent pipeline with graph workflow and deployment to Cloud Run. Enterprise architect: ~1 day to integrate human-in-the-loop and observability. Independent developer: ~30 minutes to get a streaming agent running locally via CLI. Requires basic familiarity with Python and Google Cloud for deployment.
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.
Migrating in
- →From LangGraph: manually translate graph definitions to ADK's graph model; port tool integrations via ADK's custom tool API.
- →From AutoGen: restructure agent teams into ADK's agent team or routing patterns; adapt model configs to ADK's model registry.
Migrating out
- ↗To LangGraph: export agent definitions as Python code; rewrite graph workflows to LangGraph's state machine.
- ↗To AutoGen: convert multi-agent patterns to AutoGen's agent chat paradigm; reimplement tools as AutoGen functions.
- ↗To Vertex AI Agent Builder: migrate agents to Vertex's managed environment with ADK as the development framework.
Recent material changes
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
- •ADK Python 2.0 GA released with graph workflows and collaborative agents (latest version).
- •Initial Kotlin support added (ADK Kotlin).
- •Added A2A protocol support for inter-agent communication.
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
Frequently Asked Questions
Featured Head-to-Head Comparisons
Google Adk vs Langchain
For teams needing deep observability and evaluation of complex multi-agent systems, LangChain's LangSmith platform provides unmatched debugging and monitoring, but at enterprise pricing. Google ADK is a free, open-source alternative with robust multi-language support, graph-based workflows, and seamless Google Cloud integration, making it ideal for teams already on GCP or those preferring a fully open-source stack. Choose LangChain for production-grade agent monitoring; choose Google ADK for cost-effective, open-source agent development.
Google Adk vs Langgraph
Choose Google ADK if you need multi-language support, deep Google Cloud integrations, and built-in evaluation/observability at enterprise scale. Choose LangGraph if you require fine-grained control, human-in-the-loop workflows, and persistent memory for complex multi-agent systems. LangGraph is more flexible for custom architectures, while ADK is better for teams already in the Google ecosystem.
Autogen vs Google Adk
If you're an enterprise team needing multi-language support, production-grade deployment, and extensive evaluation/observability, Google ADK is the robust choice—especially if you're already on Google Cloud. For researchers and developers focused on flexible multi-agent conversation patterns and human-in-the-loop workflows, AutoGen's open, customizable framework is ideal. Both are free and open-source, but ADK offers more enterprise features out of the box.
Crewai vs Google Adk
Choose Google ADK if you need a free, multi-language framework with deterministic graph workflows and tight Google Cloud integration. Choose CrewAI if you need enterprise governance, automation discovery, and human-in-the-loop controls, even if it means negotiating enterprise pricing. For most teams prioritizing cost and flexibility, ADK wins; for compliance-heavy deployments, CrewAI is the safer bet.
Google Adk vs N8n
Choose n8n if you need a visual, code-capable automation platform with 500+ integrations and flexible self-hosting for IT Ops or sales workflows. Choose Google ADK if you're an enterprise developer building sophisticated multi-agent systems on Google Cloud with multi-language SDKs and built-in evaluation/observability.
Popular in Developer Infrastructure
Used Google Agent Development Kit? Help shape our editorial sentiment research.