chore: import upstream snapshot with attribution

examples/integrations/a2a-middleware/agents/orchestrator.py

@@ -0,0 +1,86 @@
+"""
+Orchestrator Agent - Coordinates between Research and Analysis agents.
+Speaks AG-UI Protocol to the UI, delegates tasks to A2A agents via middleware.
+"""
+
+from __future__ import annotations
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import os
+import uvicorn
+from fastapi import FastAPI
+from ag_ui_adk import ADKAgent, add_adk_fastapi_endpoint
+from google.adk.agents import LlmAgent
+
+orchestrator_agent = LlmAgent(
+    name="OrchestratorAgent",
+    model="gemini-2.5-pro",
+    instruction="""
+    You are an orchestrator agent that coordinates research and analysis tasks.
+
+    AVAILABLE SPECIALIZED AGENTS:
+
+    1. **Research Agent** (LangGraph) - Gathers and summarizes information about a topic
+    2. **Analysis Agent** (ADK) - Analyzes research findings and provides insights
+
+    CRITICAL CONSTRAINTS:
+    - You MUST call agents ONE AT A TIME, never make multiple tool calls simultaneously
+    - After making a tool call, WAIT for the result before making another tool call
+    - Do NOT make parallel/concurrent tool calls - this is not supported
+
+    WORKFLOW FOR RESEARCH TASKS:
+
+    When the user asks to research a topic:
+
+    1. **Research Agent** - First, gather information about the topic
+       - Pass: The user's research query or topic
+       - Wait for structured JSON response with research findings
+
+    2. **Analysis Agent** - Then, analyze the research results
+       - Pass: The research results from step 1
+       - Wait for structured JSON with analysis and insights
+
+    3. Present the complete research and analysis to the user
+
+    IMPORTANT WORKFLOW DETAILS:
+    - Always call the Research Agent first to gather information
+    - Then call the Analysis Agent to analyze the findings
+    - Wait for each agent to complete before calling the next one
+    - Build your final response using information from both agents
+
+    RESPONSE STRATEGY:
+    - After each agent response, briefly acknowledge what you received
+    - Build up the complete answer incrementally
+    - At the end, present a well-organized summary
+    - Don't just list agent responses - synthesize them into a cohesive answer
+
+    IMPORTANT: Once you have received a response from an agent, do NOT call that same
+    agent again for the same information. Use the information you already have.
+    """,
+)
+
+# Wrap with AG-UI middleware to expose via AG-UI Protocol
+adk_orchestrator_agent = ADKAgent(
+    adk_agent=orchestrator_agent,
+    app_name="orchestrator_app",
+    user_id="demo_user",
+    session_timeout_seconds=3600,
+    use_in_memory_services=True,
+)
+
+app = FastAPI(title="A2A Orchestrator (ADK + AG-UI Protocol)")
+add_adk_fastapi_endpoint(app, adk_orchestrator_agent, path="/")
+
+if __name__ == "__main__":
+    if not os.getenv("GOOGLE_API_KEY"):
+        print("⚠️  Warning: GOOGLE_API_KEY not set!")
+        print("   Set it with: export GOOGLE_API_KEY='your-key-here'")
+        print("   Get a key from: https://aistudio.google.com/app/apikey")
+        print()
+
+    port = int(os.getenv("ORCHESTRATOR_PORT", 9000))
+    print(f"🚀 Starting Orchestrator Agent (ADK + AG-UI) on http://localhost:{port}")
+    uvicorn.run(app, host="0.0.0.0", port=port)