- Tool type
- Open-source agent framework
- Made for
- Developers and engineering teams
- Runs on
- Local, Docker, Google Cloud
- Run locally, in your own containers, or on Cloud Run, GKE or Agent Runtime.
What it does
Google's Agent Development Kit (ADK) is an open-source code library for building, testing and deploying AI agents. Developers write agents in Python, TypeScript, Go, Java or Kotlin, defining instructions, a model and tools such as Google Search. The output is a running agent that can be tested locally and deployed as a container or to Google Cloud.
Teams can start with a single prompt-and-tools agent and grow into multi-agent hierarchies or graph-based workflows that mix deterministic code with model reasoning, including routing, fan-out/fan-in, loops, retries and human-in-the-loop steps. ADK includes a dev UI, an evaluation command, and an Agents CLI for scaffolding and testing with AI coding assistants. It manages context by filtering events, summarizing older turns and lazy-loading artifacts. It is optimized for Gemini but works with other models, including locally run ones.
ADK suits software developers and engineering teams comfortable writing code. Installation uses a package manager, with Python 3.11+ required for the Python package and Go 1.25+ for ADK Go v2. The framework is Apache 2.0 licensed, while model usage and Google Cloud hosting may carry separate costs, so request a quote or check provider rates. Non-developers have only a limited no-code Agent Config option.
What it can do
- Build single and multi-agent systems in Python, TypeScript, Go, Java or Kotlin
- Graph-based workflow runtime with routing, fan-out/fan-in, loops, retry and human-in-the-loop
- Built-in dev UI and evaluation command for testing and debugging agents
- Context management that filters, summarizes and lazy-loads session content
- Deploy via Docker or one-command deployment to Cloud Run, GKE or Agent Runtime
Works with
- Google Search
- MCP tools
- OpenAPI specs
- Google Cloud Run
- GKE
- Gemini
Example workflows
- Research agent prototype (illustrative)
- Input: a short instruction and the Google Search tool. Steps: install google-adk, define an Agent with a model, run it with adk run or adk web. Output: a locally testable research assistant you can then evaluate and deploy.
- Two-step graph workflow (illustrative)
- Input: two agents, one generating a fruit name and one describing a health benefit. Steps: connect them as edges in a Workflow from START. Output: a deterministic pipeline you can run locally or deploy with adk deploy cloud_run.
Before you choose
- The framework code is free under Apache 2.0, but model API calls and Google Cloud hosting are separate costs; request a quote or check provider pricing for those.
- The Workflow runtime and some newer features are described as new in ADK 2.0, so check the documentation for your chosen language version.
- Aimed at coders; non-developers have only a limited no-code Agent Config option.
Listed under
Get started
For example pip install google-adk; guides at adk.dev.
- Create and activate a Python virtual environment, or set up a Go, Java, Kotlin or Node project.
- Install ADK (for example pip install google-adk or npm install @google/adk).
- Define an agent and run it with adk run or adk web, then deploy with adk deploy docker or cloud_run.
Plans and prices
The source code is available under Apache 2.0. Model-provider and deployment charges are separate.
- Apache 2.0 license
Source: github.com/google/adk-python, checked 28 Oct 2026. Prices can change; check before you buy.
Sources for this overview
Product pages, documentation, and pricing checked 28 Oct 2026. Each source supports part of the overview; availability and prices can change.
- Agent Development Kit (ADK) - Agent Development Kit (ADK) ↗adk.dev
- GitHub - google/adk-python: An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control. · GitHub ↗github.com
- Installation - Agent Development Kit (ADK) ↗adk.dev
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Last checked 28 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.