Initial commit

agent/agent.py

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+# Copyright 2025 Jeremy Longshore
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""ADK Orchestrator Agent - Production-ready A2A protocol manager for Vertex AI Engine"""
+
+from typing import Optional, List, Dict, Any
+from google.adk.agents import LlmAgent
+from google.adk.tools import FunctionTool
+from google.adk.runners import Runner
+from google.adk.sessions import VertexAiSessionService
+from google.adk.memory import VertexAiMemoryBankService
+from google.adk.artifacts import InMemoryArtifactService
+from google.adk.auth.credential_service import InMemoryCredentialService
+
+from .tools import (
+    discover_agents,
+    invoke_agent,
+    manage_agent_session,
+    validate_agent_card,
+    deploy_to_vertex_engine,
+    monitor_agent_health,
+    create_agent_team,
+    coordinate_workflow
+)
+
+
+def get_agent() -> LlmAgent:
+    """Returns the ADK Orchestrator agent configured for A2A protocol management.
+
+    This agent specializes in:
+    - Agent discovery via AgentCards
+    - A2A protocol implementation
+    - Multi-agent coordination
+    - Vertex AI Engine deployment
+    - Session and memory management
+    - Production monitoring
+    """
+
+    # Load system prompt from file
+    with open('system-prompt.md', 'r') as f:
+        system_instruction = f.read()
+
+    return LlmAgent(
+        name="adk-orchestrator",
+        model="models/gemini-2.0-flash-exp",  # Latest Gemini for orchestration
+        description="Production ADK orchestrator for A2A protocol and multi-agent coordination",
+        instruction=system_instruction,
+        tools=[
+            # Agent Discovery & Management
+            FunctionTool(discover_agents),
+            FunctionTool(invoke_agent),
+            FunctionTool(validate_agent_card),
+
+            # Session & Memory Management
+            FunctionTool(manage_agent_session),
+
+            # Deployment & Operations
+            FunctionTool(deploy_to_vertex_engine),
+            FunctionTool(monitor_agent_health),
+
+            # Multi-Agent Coordination
+            FunctionTool(create_agent_team),
+            FunctionTool(coordinate_workflow),
+        ],
+        # Enable features for production
+        enable_parallel_tool_calls=True,
+        enable_code_execution=True,
+        context_window_size=2_000_000,  # 2M token context for Gemini 2.0
+        output_key="orchestration_result",
+        metadata={
+            "version": "1.0.0",
+            "deployment_target": "vertex-ai-engine",
+            "capabilities": ["a2a", "multi-agent", "session-management", "monitoring"],
+            "compliance": "R5-ready"
+        }
+    )
+
+
+async def create_runner() -> Runner:
+    """Creates a production-ready runner with dual memory (Session + Memory Bank).
+
+    This configuration provides:
+    - VertexAiSessionService for conversation state
+    - VertexAiMemoryBankService for long-term memory (14-day TTL)
+    - Auto-save callback for R5 compliance
+    - Proper resource management
+    """
+
+    # Initialize services
+    session_service = VertexAiSessionService(
+        project_id="your-project-id",  # Will be configured via env
+        location="us-central1",
+        session_ttl_days=30
+    )
+
+    memory_service = VertexAiMemoryBankService(
+        project_id="your-project-id",
+        location="us-central1",
+        corpus_name="adk-orchestrator-memory",
+        ttl_days=14  # R5 compliance
+    )
+
+    # Create runner with production configuration
+    return Runner(
+        app_name="adk-orchestrator",
+        agent=get_agent(),
+        session_service=session_service,
+        memory_service=memory_service,
+        artifact_service=InMemoryArtifactService(),
+        credential_service=InMemoryCredentialService(),
+        # Auto-save session to memory for R5 compliance
+        callbacks={
+            "after_session": auto_save_session_to_memory
+        }
+    )
+
+
+async def auto_save_session_to_memory(session, memory_service):
+    """Callback to automatically save session to memory bank after each interaction.
+
+    This ensures R5 compliance by persisting all session data to long-term memory.
+    """
+    if session and memory_service:
+        await memory_service.save_session(
+            session_id=session.id,
+            session_data=session.to_dict(),
+            metadata={
+                "timestamp": session.updated_at,
+                "agent": "adk-orchestrator",
+                "compliance": "R5"
+            }
+        )
+
+
+# Export for ADK CLI
+root_agent = get_agent()