Google ADK (Agent Development Kit) in Practice: A2A Protocol, Vertex AI Integration, and Java Dual-Language

Google ADK is Google's official agent SDK released in 2025, designed specifically for the Gemini ecosystem. Its two big differentiators let it establish itself in the enterprise market by 2026: dual-language Java support + the A2A cross-agent protocol. This article systematically covers its design, usage, deployment, and selection.

1. Why ADK Exists

In 2024-2025, the enterprise market had several pain points with agent frameworks:

• Traditional Java teams had no framework available: LangGraph and CrewAI are Python-only
• No standard for cross-agent communication: every framework defined its own protocol
• High integration cost for production: IAM, audit, compliance all needed custom implementation
• GCP ecosystem lacked native agent abstractions: using LangChain to call Gemini always felt like an indirection

ADK is Google's official response to these pain points.

2. Four Core Abstractions

Abstraction	Purpose	Analogy
Agent	LLM + toolset + system instruction	Similar to the OpenAI Agents SDK
Tool	Function call with auto-generated schema in Python/Java	Like function calling
Session	State management (cross-turn conversation context)	Like LangGraph state
Runner	Execution orchestration (sync/async/streaming)	Like the OpenAI Runner

The overall design closely resembles the OpenAI Agents SDK but with Google-flavored Session and Runner abstractions.

3. Python Quickstart

from google.adk import Agent, Runner
from google.adk.sessions import InMemorySessionService

agent = Agent(
    name="research_agent",
    model="gemini-2.5-pro",
    instruction="You help users research topics using web search.",
    tools=[web_search_tool, fetch_url_tool],
)

session_service = InMemorySessionService()
runner = Runner(agent=agent, session_service=session_service)

response = await runner.run_async("Summarize the latest on agent frameworks.")

4. Java Quickstart (The Key Enterprise Selling Point)

Agent agent = Agent.builder()
    .name("researchAgent")
    .model("gemini-2.5-pro")
    .instruction("You help users research topics.")
    .tools(List.of(webSearchTool, fetchUrlTool))
    .build();

Runner runner = Runner.builder()
    .agent(agent)
    .sessionService(new InMemorySessionService())
    .build();

String response = runner.run("Summarize the latest on agent frameworks.");

Java support matters hugely for traditional enterprise teams: finance, insurance, and manufacturing are heavy Java backends, and adding agent capabilities previously meant building new Python services. With ADK, you can embed agents directly into Spring Boot applications.

5. The A2A (Agent-to-Agent) Protocol

A2A is Google's cross-agent communication protocol introduced in 2025. It's positioned similarly to MCP but solves a different problem:

Protocol	Solves	Analogy
MCP	Agent-to-tool connections	Like LSP (Language Server Protocol)
A2A	Agent-to-agent communication	Like HTTP for web services

A2A lets agents built on different frameworks (ADK, LangGraph, CrewAI) communicate with each other, removing framework lock-in. This is a major ecosystem strategy for Google — an open protocol attracts non-Google frameworks.

Example: calling an external agent via A2A

from google.adk.a2a import RemoteAgent

# Connect to an agent deployed elsewhere
crewai_agent = RemoteAgent(url="https://my-crewai-app.com/a2a")

result = await crewai_agent.invoke("Process this task")

6. Deep Vertex AI Integration

ADK's deployment on Vertex AI is its strongest enterprise selling point:

• IAM integration: caller permissions managed via Google Cloud IAM
• VPC-SC support: runs safely inside a Service Perimeter
• CMEK: customer-managed encryption keys for finance/healthcare compliance
• Audit logs: every agent call automatically goes to Cloud Audit Logs
• Model Garden: in addition to Gemini, can call 200+ open-source / third-party models
• Reasoning Engine: managed agent runtime with auto-scaling

These features make the cost of adding agents to existing GCP workloads extremely low.

7. Local Development Flexibility

Important clarification: ADK does not force Vertex AI. Local development can:

from google.adk import Agent

# Use OpenAI models via LiteLLM
agent = Agent(
    name="my_agent",
    model="litellm/gpt-5",
    instruction="..."
)

This design makes ADK ideal for the "want GCP production deployment but multi-model local development" scenario.

8. Comparison With Similar Frameworks

Dimension	Google ADK	OpenAI Agents SDK	LangGraph	Claude Agent SDK
Primary language	Python + Java	Python	Python + TS	Python + TS
Model support	Gemini-first, multi-model	OpenAI only	Multi-vendor	Claude only
Cross-agent protocol	A2A + MCP	Handoff	None native	Sub-agent
Enterprise features	IAM/VPC-SC/CMEK/audit	Weak	Self-built	Weak
Deployment	Vertex AI / local	OpenAI platform	Self-hosted	Anthropic API
Best fit	Enterprise	Small/mid teams	Any	Small/mid teams

9. When to Use ADK

Strongly recommended:

• You have GCP investment and want agents to leverage existing IAM/VPC/audit
• Java-stack teams wanting agent capabilities in existing backends
• Multi-agent systems that may need cross-framework communication in the future (A2A)
• High compliance (finance, healthcare, government) needing CMEK/audit logs

Not great:

• Small startup teams without GCP investment — OpenAI Agents SDK / LangGraph is lighter
• Heavy OpenAI model dependency — just use the OpenAI Agents SDK
• Complex state machines needing fine-grained flow control — LangGraph

10. Migration Path

If migrating from LangGraph to ADK:

1. Keep LangGraph as the tool layer: wrap existing LangGraph workflows as ADK Tools
2. Migrate agent-by-agent: first change the entry agent to ADK, keep internal agents on LangGraph
3. Bridge with A2A: during migration, use A2A for new/old agent communication

This progressive path avoids a big-bang rewrite.

11. Common Pitfalls

• Vertex AI pricing: Reasoning Engine bills by runtime; shut down long-idle instances
• Gemini quotas: the free tier is small; request quota increases before production
• Java version lag: the Java version lags Python by about 1-2 versions; new features land in Python first
• A2A compatibility: A2A is still evolving; test interop with community frameworks (LangGraph/CrewAI)

Conclusion

ADK isn't trying to replace LangGraph — it's an official answer for the enterprise market, especially Java teams and GCP customers. If your team fits this profile, ADK is the most worthwhile agent framework investment in 2026.

---

Prepared by the AgentList team. Browse the AgentList project directory for more agent frameworks, tools, and applications.

Related Projects Mentioned

• Google ADK Python — the subject of this article, Google's official Agent SDK
• Gemini CLI — Google's official terminal Agent, sibling to ADK, with generous free quotas for budget-sensitive developers
• OpenAI Agents Python — OpenAI's equivalent official SDK, for OpenAI-ecosystem teams
• Microsoft Agent Framework — Microsoft's unified Agent framework, the .NET + Java enterprise counterpoint
• LangGraph — a state-graph option for production-grade complex workflows
• CrewAI — a role-based multi-agent collaboration comparison