Google’s development kit for constructing, evaluating, and deploying modular AI agents.
Google’s development kit for constructing, evaluating, and deploying modular AI agents.
Google Agent Development Kit is google’s development kit for constructing, evaluating, and deploying modular AI agents. The product is most relevant to builders and business teams that want a focused system rather than assembling every component themselves. Its reported capabilities include agent composition, tool integrations, evaluation support, deployment workflows. Those capabilities suggest a practical workflow in which a team can start with a bounded problem, connect the data or services it already uses, review early results, and expand automation only after quality and governance expectations are clear.
Practical use cases include build gemini agents, create multi-agent systems, test agent behavior. Buyers should evaluate the product with representative work, including difficult examples and failure cases, instead of relying only on a polished demonstration. Important evaluation criteria include output accuracy, setup effort, permissions, data retention, export options, auditability, latency, and the ability for a human to correct or override the system. For a production rollout, teams should also test access controls, vendor support, integration limits, and how usage grows as more users or workloads are added.
Google Agent Development Kit is presented here as MCP-compatible in the client role, which can make it useful in environments where agents need a standard way to discover or invoke external capabilities. Because the vendor pages could not be reached during this automated run, that MCP classification should be checked against current vendor documentation before procurement or production architecture decisions.
Pricing could not be verified from the vendor website in this run because outbound page fetches returned no usable HTML. Accordingly, this profile does not invent plan names or dollar amounts: the pricingTiers array is intentionally empty and the record is flagged for manual verification. Before purchase, confirm current plan boundaries, included usage, overage charges, contract minimums, trial availability, and enterprise security terms directly with the vendor. The same caution applies to the feature list, which is a concise discovery summary and should be reconciled with live product documentation. This profile is therefore useful for catalog discovery and initial comparison, but it is not a substitute for a current quote, security review, or hands-on proof of concept.
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**Best multi-agent framework for Google Cloud ecosystems.** Google ADK's built-in evaluation tools, local debugging UI, and native MCP support provide a more structured development experience than LangChain or CrewAI. With four official SDKs (Python, TypeScript, Go, Java), it is one of the most polyglot agent frameworks available. The primary trade-off is a smaller third-party ecosystem due to the framework's youth (launched April 2025) and the strongest feature integration being tied to Gemini and Google Cloud services. Teams already invested in Google Cloud will find ADK's Vertex AI Agent Engine deployment path and native Workspace integrations compelling, while teams on other clouds should weigh the Gemini-optimized features they may not fully leverage.
Build hierarchical agent teams using sequential, parallel, and loop workflow agents alongside LLM agents and custom agents. Agents can delegate to sub-agents through agent routing, and the new Python 2.0 Beta introduces agent teams for coordinated multi-agent execution with shared state and task delegation patterns.
Test agent performance with criteria-based scoring, user simulation, environment simulation, custom metrics, and an optimization module for iterative improvement. Score trajectory accuracy (correct steps taken), tool usage quality, and final response relevance — capabilities that most competing frameworks require third-party integrations to achieve.
ADK natively supports the Model Context Protocol, allowing agents to consume any MCP-compatible tool server without custom integration code. Combined with OpenAPI tool generation and traditional function tools with action confirmations, ADK provides one of the most flexible tooling ecosystems among agent frameworks.
Ships with three runtime modes: a web-based UI for visual debugging of agent interactions and tool calls, a CLI for terminal-driven workflows, and an API server for production integrations. The web UI provides real-time inspection of agent decision-making, making it significantly easier to debug multi-agent coordination than log-based approaches.
Available in four official SDKs — Python 2.0 Beta, TypeScript 1.0 (new in 2026), Go, and Java — all maintained under github.com/google. Supports Gemini, Gemma, Claude, Ollama, vLLM, LiteLLM, LiteRT-LM, and Apigee AI Gateway for flexible model selection across providers.
Free
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ADK TypeScript 1.0 is now available as of 2026, opening the framework to JavaScript/Node.js teams alongside the existing Python, Go, and Java SDKs. ADK Python 2.0 Beta also launched with new workflow primitives, agent teams, ambient agents, resumable runs, and cancelable execution — significantly expanding the framework's orchestration capabilities.
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