Google Adk

Google Agent Development Kit (ADK) Integration

The Google Agent Development Kit (ADK) is Google's framework for building AI agents with the Gemini API. OSSA provides seamless integration with Google ADK, enabling standardized agent definitions that work across Google Cloud Platform and other environments.

Overview

Google ADK enables developers to build sophisticated AI agents with:

Agent-to-Agent (A2A) Protocol: Standardized communication between agents
Gemini Integration: Direct access to Google's Gemini models
Agent Hierarchies: Organize agents in parent-child relationships
Tool Integration: Connect agents to external APIs and services
MCP Support: Model Context Protocol for tool exposure

OSSA brings portability and standardization to Google ADK agents, allowing you to define agent configurations once and deploy them anywhere.

Key Benefits

Standardized Manifests: Define Google ADK agents using OSSA format
Cross-Platform Portability: Run agents on GCP, AWS, Azure, or on-premises
A2A Protocol Support: Native support for agent-to-agent communication
Version Control: Track agent configurations in Git
Multi-Cloud Orchestration: Coordinate agents across cloud providers

Getting Started

Prerequisites

Google Cloud Platform account
Gemini API access
Google ADK SDK installed
OSSA-compatible runtime

Installation

# Install Google ADK
pip install google-adk

# Install OSSA runtime
pip install ossa-runtime

# Configure Google Cloud credentials
gcloud auth application-default login

Basic Setup

Create an OSSA manifest for your Google ADK agent:

# agents/gemini-assistant/manifest.ossa.yaml
ossa_version: "1.0"
agent:
  name: "Gemini Research Assistant"
  version: "1.0.0"
  description: "AI research assistant powered by Google Gemini"
  runtime: "google_adk"
  
capabilities:
  - name: "research"
    type: "analysis"
    provider: "google_gemini"
    model: "gemini-2.0-flash-exp"
    
  - name: "web_search"
    type: "tool"
    provider: "google_search"
    
configuration:
  google_adk:
    project_id: "{{env.GCP_PROJECT_ID}}"
    location: "us-central1"
    model: "gemini-2.0-flash-exp"
    
tasks:
  - id: "research_topic"
    name: "Research Topic"
    input:
      topic: "string"
    actions:
      - type: "gemini_generate"
        prompt: "Research the following topic and provide a comprehensive summary: {{input.topic}}"
        tools:
          - web_search
          - code_execution
      - type: "format_response"
        template: "markdown"

Run the agent using OSSA runtime:

ossa run agents/gemini-assistant/manifest.ossa.yaml \
  --input '{"topic": "quantum computing applications"}'

Use Cases

1. Multi-Agent Research Team

Create a hierarchy of agents using A2A protocol:

ossa_version: "1.0"
agent:
  name: "Research Team Coordinator"
  version: "1.0.0"
  runtime: "google_adk"
  
agents:
  - id: "researcher"
    name: "Primary Researcher"
    model: "gemini-2.0-flash-exp"
    role: "Research and gather information"
    
  - id: "analyst"
    name: "Data Analyst"
    model: "gemini-2.0-flash-exp"
    role: "Analyze research findings"
    
  - id: "writer"
    name: "Technical Writer"
    model: "gemini-2.0-flash-exp"
    role: "Synthesize findings into report"
    
workflow:
  - step: "gather_information"
    agent: "researcher"
    output: "research_data"
    
  - step: "analyze_data"
    agent: "analyst"
    input: "{{research_data}}"
    output: "analysis_results"
    
  - step: "write_report"
    agent: "writer"
    input:
      research: "{{research_data}}"
      analysis: "{{analysis_results}}"
    output: "final_report"
    
a2a_protocol:
  enabled: true
  communication_format: "json"
  message_queue: "google_pubsub"

2. Code Review Agent with Gemini

Leverage Gemini's code understanding capabilities:

ossa_version: "1.0"
agent:
  name: "Gemini Code Reviewer"
  version: "1.0.0"
  runtime: "google_adk"
  
capabilities:
  - name: "code_analysis"
    type: "analysis"
    provider: "google_gemini"
    model: "gemini-2.0-flash-exp"
    
tasks:
  - id: "review_pull_request"
    name: "Review Pull Request"
    input:
      repository: "string"
      pr_number: "integer"
    actions:
      - type: "fetch_pr_diff"
        repository: "{{input.repository}}"
        pr_number: "{{input.pr_number}}"
        
      - type: "gemini_code_review"
        code: "{{pr_diff}}"
        prompt: |
          Review this code for:
          1. Security vulnerabilities
          2. Performance issues
          3. Code quality and best practices
          4. Test coverage
          
          Provide specific line-by-line feedback.
        tools:
          - code_execution
          
      - type: "post_review_comment"
        repository: "{{input.repository}}"
        pr_number: "{{input.pr_number}}"
        comment: "{{review_results}}"

3. Customer Support Agent

Build an intelligent customer support agent:

ossa_version: "1.0"
agent:
  name: "Customer Support Agent"
  version: "1.0.0"
  runtime: "google_adk"
  
capabilities:
  - name: "conversation"
    type: "chat"
    provider: "google_gemini"
    model: "gemini-2.0-flash-exp"
    
  - name: "knowledge_base"
    type: "retrieval"
    provider: "vertex_ai_search"
    
configuration:
  google_adk:
    model: "gemini-2.0-flash-exp"
    temperature: 0.7
    max_tokens: 2048
    
  vertex_ai_search:
    datastore_id: "{{env.DATASTORE_ID}}"
    
tasks:
  - id: "handle_inquiry"
    name: "Handle Customer Inquiry"
    input:
      customer_message: "string"
      customer_id: "string"
    actions:
      - type: "search_knowledge_base"
        query: "{{input.customer_message}}"
        top_k: 5
        
      - type: "gemini_generate"
        prompt: |
          You are a helpful customer support agent. 
          
          Customer inquiry: {{input.customer_message}}
          
          Relevant knowledge base articles:
          {{knowledge_base_results}}
          
          Provide a helpful, accurate response.
        context:
          - type: "customer_history"
            customer_id: "{{input.customer_id}}"
            
      - type: "log_interaction"
        customer_id: "{{input.customer_id}}"
        message: "{{input.customer_message}}"
        response: "{{agent_response}}"

4. Data Analysis Pipeline

Create an agent for automated data analysis:

ossa_version: "1.0"
agent:
  name: "Data Analysis Agent"
  version: "1.0.0"
  runtime: "google_adk"
  
capabilities:
  - name: "data_analysis"
    type: "analysis"
    provider: "google_gemini"
    model: "gemini-2.0-flash-exp"
    
  - name: "bigquery"
    type: "tool"
    provider: "google_bigquery"
    
tasks:
  - id: "analyze_dataset"
    name: "Analyze Dataset"
    input:
      dataset_id: "string"
      analysis_type: "string"
    actions:
      - type: "bigquery_query"
        query: |
          SELECT * FROM `{{input.dataset_id}}`
          LIMIT 1000
        output: "sample_data"
        
      - type: "gemini_analyze"
        prompt: |
          Analyze this dataset and provide:
          1. Statistical summary
          2. Data quality assessment
          3. Insights and patterns
          4. Recommendations for {{input.analysis_type}}
        data: "{{sample_data}}"
        tools:
          - code_execution
          
      - type: "generate_visualization"
        data: "{{sample_data}}"
        insights: "{{analysis_results}}"
        
      - type: "create_report"
        template: "data_analysis_report"
        output_format: "pdf"

Agent-to-Agent (A2A) Protocol

OSSA provides native support for Google's A2A protocol:

Basic A2A Communication

ossa_version: "1.0"
agent:
  name: "Coordinator Agent"
  version: "1.0.0"
  
a2a_protocol:
  enabled: true
  version: "1.0"
  transport: "google_pubsub"
  
  endpoints:
    - id: "receive_tasks"
      type: "subscriber"
      topic: "projects/{{project_id}}/topics/agent-tasks"
      
    - id: "send_results"
      type: "publisher"
      topic: "projects/{{project_id}}/topics/agent-results"
      
tasks:
  - id: "process_task"
    name: "Process Incoming Task"
    trigger:
      type: "a2a_message"
      endpoint: "receive_tasks"
    actions:
      - type: "execute_task"
        task_data: "{{a2a_message.payload}}"
        
      - type: "send_a2a_message"
        endpoint: "send_results"
        message:
          task_id: "{{a2a_message.task_id}}"
          status: "completed"
          result: "{{execution_result}}"

Agent Hierarchy

Define parent-child agent relationships:

ossa_version: "1.0"
agent:
  name: "Parent Orchestrator"
  version: "1.0.0"
  
agents:
  - id: "child_agent_1"
    manifest: "./agents/specialist-1/manifest.ossa.yaml"
    relationship: "child"
    
  - id: "child_agent_2"
    manifest: "./agents/specialist-2/manifest.ossa.yaml"
    relationship: "child"
    
workflow:
  - step: "delegate_to_specialist_1"
    agent: "child_agent_1"
    a2a_message:
      type: "task_assignment"
      priority: "high"
      
  - step: "delegate_to_specialist_2"
    agent: "child_agent_2"
    a2a_message:
      type: "task_assignment"
      priority: "normal"
    parallel: true
    
  - step: "aggregate_results"
    wait_for:
      - "child_agent_1"
      - "child_agent_2"
    action: "combine_results"

MCP Integration

Integrate with Model Context Protocol for tool exposure:

ossa_version: "1.0"
agent:
  name: "MCP-Enabled Agent"
  version: "1.0.0"
  runtime: "google_adk"
  
mcp:
  enabled: true
  servers:
    - name: "filesystem"
      type: "mcp_server"
      command: "npx"
      args: ["-y", "@modelcontextprotocol/server-filesystem", "/workspace"]
      
    - name: "github"
      type: "mcp_server"
      command: "npx"
      args: ["-y", "@modelcontextprotocol/server-github"]
      env:
        GITHUB_TOKEN: "{{env.GITHUB_TOKEN}}"
        
capabilities:
  - name: "file_operations"
    type: "tool"
    provider: "mcp"
    server: "filesystem"
    
  - name: "github_operations"
    type: "tool"
    provider: "mcp"
    server: "github"
    
tasks:
  - id: "code_analysis"
    name: "Analyze Code Repository"
    actions:
      - type: "mcp_tool_call"
        server: "github"
        tool: "list_repositories"
        
      - type: "gemini_generate"
        prompt: "Analyze these repositories and suggest improvements"
        tools:
          - filesystem
          - github

Advanced Configuration

Custom Gemini Settings

configuration:
  google_adk:
    model: "gemini-2.0-flash-exp"
    temperature: 0.9
    top_p: 0.95
    top_k: 40
    max_output_tokens: 8192
    
    safety_settings:
      - category: "HARM_CATEGORY_HARASSMENT"
        threshold: "BLOCK_MEDIUM_AND_ABOVE"
      - category: "HARM_CATEGORY_HATE_SPEECH"
        threshold: "BLOCK_MEDIUM_AND_ABOVE"
        
    generation_config:
      candidate_count: 1
      stop_sequences: ["END"]

Vertex AI Integration

configuration:
  vertex_ai:
    project_id: "{{env.GCP_PROJECT_ID}}"
    location: "us-central1"
    endpoint_id: "{{env.ENDPOINT_ID}}"
    
  google_adk:
    use_vertex_ai: true
    model: "gemini-2.0-flash-exp"

Best Practices

Use A2A Protocol: Leverage agent-to-agent communication for complex workflows
Implement Error Handling: Add retry logic and fallback mechanisms
Monitor Costs: Track Gemini API usage and set budgets
Version Control: Store manifests in Git with semantic versioning
Security: Use Secret Manager for API keys and credentials
Testing: Validate agents locally before deploying to production
Logging: Enable comprehensive logging for debugging

Troubleshooting

Common Issues

Authentication errors:

Verify Google Cloud credentials are configured
Check service account permissions
Ensure API is enabled in GCP project

A2A communication failures:

Verify Pub/Sub topics exist
Check IAM permissions for topics
Validate message format

Model errors:

Check Gemini API quotas
Verify model name is correct
Review safety settings configuration