Agent Development Kit (ADK)
Provide a code-first, model-agnostic Python and Java framework for composing LLM agents with deterministic workflow agents into multi-agent applications that can be evaluated and deployed at enterprise scale.
Description
Agent Development Kit (ADK) is Google's open-source, Apache-2.0 framework for building, evaluating, and deploying AI agents from a single LlmAgent up to multi-agent workflows. The 2.0 release adds a graph-based Workflow Runtime that interleaves deterministic execution flows with LLM reasoning, plus a Task API for structured agent-to-agent delegation. Core abstractions include LlmAgent (instructions + model + tools), the workflow agents Sequential / Parallel / Loop, sub_agents trees, MCP tool integration via McpToolset, an evaluation harness (AgentEvaluator, eval sets, tool-trajectory and response-match criteria), and first-class deployment to Cloud Run, GKE, and Vertex AI Agent Runtime.
Solution
Hierarchical agent tree with a graph-based Workflow Runtime. An LlmAgent runs the standard reason-act-observe loop over its tools; workflow agents (Sequential / Parallel / Loop) compose sub_agents with fixed execution logic; in collaborative workflows an LlmAgent coordinator dynamically routes work across sub_agents. ADK 2.0 adds a graph-based execution engine that interleaves deterministic nodes with LLM agents inside one run.
Primary use cases
- production multi-agent workflows on Google Cloud (Agent Runtime, Cloud Run, GKE)
- deterministic orchestration via SequentialAgent / ParallelAgent / LoopAgent
- LLM-coordinated dynamic routing across sub-agents
- evaluation-driven agent development with eval sets and trajectory matching
- MCP-tool integration into Gemini and other LLMs
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