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Advanced Patterns

Purpose: Explore cutting-edge ADK capabilities for real-time interaction, standardized protocols, and distributed agent systems.

Source of Truth: google/adk-python/src/google/adk/agents/live_request_queue.py (ADK 1.15) + MCP/A2A implementations

Table of Contents​

  1. Streaming & Real-Time Interaction
    • Live conversations with users
  2. MCP Protocol
    • Universal tool standards
  3. Agent-to-Agent Communication
    • Distributed agent systems

Streaming & Real-Time Interaction​

SSE (Server-Sent Events)​

# Text streaming to users
async def stream_response(query):
    runner = Runner()
    async for event in runner.run_async(streaming=SSE):
        if event.type == 'content':
            yield f"data: {event.content}\n\n"
        elif event.type == 'done':
            yield "data: [DONE]\n\n"

BIDI (Bidirectional Streaming)​

# Voice/video conversations
queue = LiveRequestQueue()
runner = Runner()

async def live_conversation():
    async for event in runner.run_live(queue):
        if event.type == 'audio_response':
            play_audio(event.audio_data)

        # Send user input
        queue.send_realtime(audio_blob)

Models: gemini-2.0-flash-live-*, gemini-live-2.5-*

🔌 MCP (Model Context Protocol)​

Universal Tool Standard​

# Standardized tool interface
mcp_tools = MCPToolset(
    connection_params=StdioConnectionParams(
        command='npx',
        args=['-y', '@modelcontextprotocol/server-filesystem', '/data']
    )
)

# Works with any MCP-compatible server
# - Filesystem operations
# - Database queries
# - Git operations
# - Slack/Teams integration

MCP Benefits​

  • Interoperability: One protocol, many tools
  • Security: Built-in authentication
  • Discovery: Auto-detect capabilities
  • Community: 100+ MCP servers available

🤝 A2A (Agent-to-Agent Communication)​

Microservices Architecture​

# Remote agent integration
youtube_agent = RemoteA2aAgent(
    name='youtube_expert',
    base_url='https://youtube-agent.company.com'
)

# Local agent uses remote expertise
orchestrator = Agent(
    name="content_strategist",
    tools=[AgentTool(youtube_agent)],
    instruction="Create strategy using YouTube analytics"
)

A2A vs Local Multi-Agent​

  • Distribution: Agents on different services
  • Scaling: Independent deployment/scaling
  • Teams: Cross-team collaboration
  • Specialization: Domain-specific experts

🚀 Next-Level Capabilities​

Multimodal Integration​

  • Images: Vision analysis and generation
  • Audio: Speech recognition and synthesis
  • Video: Real-time video processing
  • Documents: PDF/text extraction and analysis

Code Execution​

# Built-in Python interpreter
code_agent = Agent(
    name="programmer",
    model="gemini-2.0-flash",  # Code execution enabled
    instruction="Write and test Python code"
)

Custom Planners​

# Advanced reasoning strategies
reasoning_planner = CustomPlanner(
    strategy="tree_of_thought",
    max_depth=5
)

agent = Agent(
    name="deep_reasoner",
    planner=reasoning_planner
)

🎯 Key Takeaways​

  1. Streaming: Real-time text (SSE) and voice/video (BIDI)
  2. MCP: Universal tool protocol for interoperability
  3. A2A: Distributed agent communication
  4. Multimodal: Images, audio, video, documents
  5. Code Execution: Built-in Python interpreter
  6. Custom Planners: Advanced reasoning strategies

🔗 Next: Master Decision Frameworks for choosing the right patterns.