Google AI ADK Integration Docs (#3086)

docs/docs.json

@@ -254,6 +254,7 @@
                   "integrations/autogen",
                   "integrations/crewai",
                   "integrations/openai-agents-sdk",
+                  "integrations/google-ai-adk",
                   "integrations/mastra",
                   "integrations/vercel-ai-sdk",
                   "integrations/livekit",

docs/integrations/crewai.mdx

@@ -164,7 +164,7 @@ By combining CrewAI with Mem0, you can create sophisticated AI systems that main
 
 ## Help
 
-- For CrewAI documentation, visit [CrewAI Documentation](https://docs.crewai.com/)
+- [CrewAI Documentation](https://docs.crewai.com/)
-- For Mem0 documentation, refer to the [Mem0 Platform](https://app.mem0.ai/)
+- [Mem0 Platform](https://app.mem0.ai/)
 
 <Snippet file="get-help.mdx" />

docs/integrations/google-ai-adk.mdx

@@ -0,0 +1,284 @@
+---
+title: Google Agent Development Kit
+---
+
+<Snippet file="security-compliance.mdx" />
+
+Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [Google Agent Development Kit (ADK)](https://github.com/google/adk-python), an open-source framework for building multi-agent workflows. This integration enables agents to access persistent memory across conversations, enhancing context retention and personalization.
+
+## Overview
+
+1. Store and retrieve memories from Mem0 within Google ADK agents
+2. Multi-agent workflows with shared memory across hierarchies
+3. Retrieve relevant memories from past conversations
+4. Personalized responses
+
+## Prerequisites
+
+Before setting up Mem0 with Google ADK, ensure you have:
+
+1. Installed the required packages:
+```bash
+pip install google-adk mem0ai
+```
+
+2. Valid API keys:
+   - [Mem0 API Key](https://app.mem0.ai/dashboard/api-keys)
+   - Google AI Studio API Key
+
+## Basic Integration Example
+
+The following example demonstrates how to create a Google ADK agent with Mem0 memory integration:
+
+```python
+import os
+from google.adk.agents import Agent
+from google.adk.runners import Runner
+from google.adk.sessions import InMemorySessionService
+from google.genai import types
+from mem0 import MemoryClient
+
+# Set up environment variables
+os.environ["GOOGLE_API_KEY"] = "your-google-api-key"
+os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+
+# Initialize Mem0 client
+mem0 = MemoryClient()
+
+# Define memory function tools
+def search_memory(query: str, user_id: str) -> dict:
+    """Search through past conversations and memories"""
+    memories = mem0.search(query, user_id=user_id)
+    if memories:
+        memory_context = "\n".join([f"- {mem['memory']}" for mem in memories])
+        return {"status": "success", "memories": memory_context}
+    return {"status": "no_memories", "message": "No relevant memories found"}
+
+def save_memory(content: str, user_id: str) -> dict:
+    """Save important information to memory"""
+    try:
+        mem0.add([{"role": "user", "content": content}], user_id=user_id)
+        return {"status": "success", "message": "Information saved to memory"}
+    except Exception as e:
+        return {"status": "error", "message": f"Failed to save memory: {str(e)}"}
+
+# Create agent with memory capabilities
+personal_assistant = Agent(
+    name="personal_assistant",
+    model="gemini-2.0-flash",
+    instruction="""You are a helpful personal assistant with memory capabilities.
+    Use the search_memory function to recall past conversations and user preferences.
+    Use the save_memory function to store important information about the user.
+    Always personalize your responses based on available memory.""",
+    description="A personal assistant that remembers user preferences and past interactions",
+    tools=[search_memory, save_memory]
+)
+
+def chat_with_agent(user_input: str, user_id: str) -> str:
+    """
+    Handle user input with automatic memory integration.
+
+    Args:
+        user_input: The user's message
+        user_id: Unique identifier for the user
+
+    Returns:
+        The agent's response
+    """
+    # Set up session and runner
+    session_service = InMemorySessionService()
+    session = session_service.create_session(
+        app_name="memory_assistant",
+        user_id=user_id,
+        session_id=f"session_{user_id}"
+    )
+    runner = Runner(agent=personal_assistant, app_name="memory_assistant", session_service=session_service)
+
+    # Create content and run agent
+    content = types.Content(role='user', parts=[types.Part(text=user_input)])
+    events = runner.run(user_id=user_id, session_id=session.id, new_message=content)
+
+    # Extract final response
+    for event in events:
+        if event.is_final_response():
+            response = event.content.parts[0].text
+
+            return response
+
+    return "No response generated"
+
+# Example usage
+if __name__ == "__main__":
+    response = chat_with_agent(
+        "I love Italian food and I'm planning a trip to Rome next month",
+        user_id="alice"
+    )
+    print(response)
+```
+
+## Multi-Agent Hierarchy with Shared Memory
+
+Create specialized agents in a hierarchy that share memory:
+
+```python
+from google.adk.tools.agent_tool import AgentTool
+
+# Travel specialist agent
+travel_agent = Agent(
+    name="travel_specialist",
+    model="gemini-2.0-flash",
+    instruction="""You are a travel planning specialist. Use get_user_context to
+    understand the user's travel preferences and history before making recommendations.
+    After providing advice, use store_interaction to save travel-related information.""",
+    description="Specialist in travel planning and recommendations",
+    tools=[search_memory, save_memory]
+)
+
+# Health advisor agent
+health_agent = Agent(
+    name="health_advisor",
+    model="gemini-2.0-flash",
+    instruction="""You are a health and wellness advisor. Use get_user_context to
+    understand the user's health goals and dietary preferences.
+    After providing advice, use store_interaction to save health-related information.""",
+    description="Specialist in health and wellness advice",
+    tools=[search_memory, save_memory]
+)
+
+# Coordinator agent that delegates to specialists
+coordinator_agent = Agent(
+    name="coordinator",
+    model="gemini-2.0-flash",
+    instruction="""You are a coordinator that delegates requests to specialist agents.
+    For travel-related questions (trips, hotels, flights, destinations), delegate to the travel specialist.
+    For health-related questions (fitness, diet, wellness, exercise), delegate to the health advisor.
+    Use get_user_context to understand the user before delegation.""",
+    description="Coordinates requests between specialist agents",
+    tools=[
+        AgentTool(agent=travel_agent, skip_summarization=False),
+        AgentTool(agent=health_agent, skip_summarization=False)
+    ]
+)
+
+def chat_with_specialists(user_input: str, user_id: str) -> str:
+    """
+    Handle user input with specialist agent delegation and memory.
+
+    Args:
+        user_input: The user's message
+        user_id: Unique identifier for the user
+
+    Returns:
+        The specialist agent's response
+    """
+    session_service = InMemorySessionService()
+    session = session_service.create_session(
+        app_name="specialist_system",
+        user_id=user_id,
+        session_id=f"session_{user_id}"
+    )
+    runner = Runner(agent=coordinator_agent, app_name="specialist_system", session_service=session_service)
+
+    content = types.Content(role='user', parts=[types.Part(text=user_input)])
+    events = runner.run(user_id=user_id, session_id=session.id, new_message=content)
+
+    for event in events:
+        if event.is_final_response():
+            response = event.content.parts[0].text
+
+            # Store the conversation in shared memory
+            conversation = [
+                {"role": "user", "content": user_input},
+                {"role": "assistant", "content": response}
+            ]
+            mem0.add(conversation, user_id=user_id)
+
+            return response
+
+    return "No response generated"
+
+# Example usage
+response = chat_with_specialists("Plan a healthy meal for my Italy trip", user_id="alice")
+print(response)
+```
+
+
+
+## Quick Start Chat Interface
+
+Simple interactive chat with memory and Google ADK:
+
+```python
+def interactive_chat():
+    """Interactive chat interface with memory and ADK"""
+    user_id = input("Enter your user ID: ") or "demo_user"
+    print(f"Chat started for user: {user_id}")
+    print("Type 'quit' to exit")
+    print("=" * 50)
+
+    while True:
+        user_input = input("\nYou: ")
+
+        if user_input.lower() == 'quit':
+            print("Goodbye! Your conversation has been saved to memory.")
+            break
+        else:
+            response = chat_with_specialists(user_input, user_id)
+            print(f"Assistant: {response}")
+
+if __name__ == "__main__":
+    interactive_chat()
+```
+
+## Key Features
+
+### 1. Memory-Enhanced Function Tools
+- **Function Tools**: Standard Python functions that can search and save memories
+- **Tool Context**: Access to session state and memory through function parameters
+- **Structured Returns**: Dictionary-based returns with status indicators for better LLM understanding
+
+### 2. Multi-Agent Memory Sharing
+- **Agent-as-a-Tool**: Specialists can be called as tools while maintaining shared memory
+- **Hierarchical Delegation**: Coordinator agents route to specialists based on context
+- **Memory Categories**: Store interactions with metadata for better organization
+
+### 3. Flexible Memory Operations
+- **Search Capabilities**: Retrieve relevant memories through conversation history
+- **User Segmentation**: Organize memories by user ID
+- **Memory Management**: Built-in tools for saving and retrieving information
+
+## Configuration Options
+
+Customize memory behavior and agent setup:
+
+```python
+# Configure memory search with metadata
+memories = mem0.search(
+    query="travel preferences",
+    user_id="alice",
+    limit=5,
+    filters={"category": "travel"}  # Filter by category if supported
+)
+
+# Configure agent with custom model settings
+agent = Agent(
+    name="custom_agent",
+    model="gemini-2.0-flash",  # or use LiteLLM for other models
+    instruction="Custom agent behavior",
+    tools=[memory_tools],
+    # Additional ADK configurations
+)
+
+# Use Google Cloud Vertex AI instead of AI Studio
+os.environ["GOOGLE_GENAI_USE_VERTEXAI"] = "True"
+os.environ["GOOGLE_CLOUD_PROJECT"] = "your-project-id"
+os.environ["GOOGLE_CLOUD_LOCATION"] = "us-central1"
+```
+
+## Help
+
+- [Google ADK Documentation](https://google.github.io/adk-docs/)
+- [Mem0 Platform](https://app.mem0.ai/)
+- If you need further assistance, please feel free to reach out to us through the following methods:
+
+<Snippet file="get-help.mdx" />

docs/integrations/langchain.mdx

@@ -152,7 +152,7 @@ By integrating LangChain with Mem0, you can build a personalized Travel Agent AI
 ## Help
 
 - For more details on LangChain, visit the [LangChain documentation](https://python.langchain.com/).
-- For Mem0 documentation, refer to the [Mem0 Platform](https://app.mem0.ai/).
+- [Mem0 Platform](https://app.mem0.ai/).
 - If you need further assistance, please feel free to reach out to us through the following methods:
 
 <Snippet file="get-help.mdx" />

docs/integrations/openai-agents-sdk.mdx

@@ -10,7 +10,7 @@ Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [OpenAI Agents SDK](ht
 
 1. Store and retrieve memories from Mem0 within OpenAI agents
 2. Multi-agent workflows with shared memory
-3. Semantic search for relevant past conversations
+3. Retrieve relevant memories for past conversations
 4. Personalized responses based on user history
 
 ## Prerequisites
@@ -44,7 +44,7 @@ mem0 = MemoryClient()
 
 # Define memory tools for the agent
 @function_tool
-def search_memory(query: str, user_id: str = "default") -> str:
+def search_memory(query: str, user_id: str) -> str:
     """Search through past conversations and memories"""
     memories = mem0.search(query, user_id=user_id, limit=3)
     if memories:
@@ -52,7 +52,7 @@ def search_memory(query: str, user_id: str = "default") -> str:
     return "No relevant memories found."
 
 @function_tool
-def save_memory(content: str, user_id: str = "default") -> str:
+def save_memory(content: str, user_id: str) -> str:
     """Save important information to memory"""
     mem0.add([{"role": "user", "content": content}], user_id=user_id)
     return "Information saved to memory."
@@ -68,7 +68,7 @@ agent = Agent(
     model="gpt-4o"
 )
 
-def chat_with_memory(user_input: str, user_id: str) -> str:
+def chat_with_agent(user_input: str, user_id: str) -> str:
     """
     Handle user input with automatic memory integration.
 
@@ -82,22 +82,24 @@ def chat_with_memory(user_input: str, user_id: str) -> str:
     # Run the agent (it will automatically use memory tools when needed)
     result = Runner.run_sync(agent, user_input)
 
-    # Store the conversation in memory
-    conversation = [
-        {"role": "user", "content": user_input},
-        {"role": "assistant", "content": result.final_output}
-    ]
-    mem0.add(conversation, user_id=user_id)
-
     return result.final_output
 
 # Example usage
 if __name__ == "__main__":
-    response = chat_with_memory(
+
+    # preferences will be saved in memory (using save_memory tool)
+    response_1 = chat_with_agent(
         "I love Italian food and I'm planning a trip to Rome next month",
         user_id="alice"
     )
-    print(response)
+    print(response_1)
+
+    # memory will be retrieved using search_memory tool to answer the user query
+    response_2 = chat_with_agent(
+        "Give me some recommendations for food",
+        user_id="alice"
+    )
+    print(response_2)
 ```
 
 ## Multi-Agent Workflow with Handoffs
@@ -133,12 +135,11 @@ triage_agent = Agent(
     For travel-related questions (trips, hotels, flights, destinations), hand off to Travel Planner.
     For health-related questions (fitness, diet, wellness, exercise), hand off to Health Advisor.
     For general questions, you can handle them directly using available tools.""",
-    tools=[get_user_context],
     handoffs=[travel_agent, health_agent],
     model="gpt-4o"
 )
 
-def chat_with_handoffs(user_input: str, user_id: str = "default") -> str:
+def chat_with_handoffs(user_input: str, user_id: str) -> str:
     """
     Handle user input with automatic agent handoffs and memory integration.
 
@@ -230,5 +231,6 @@ mem0.add(
 
 - [OpenAI Agents SDK Documentation](https://openai.github.io/openai-agents-python/)
 - [Mem0 Platform](https://app.mem0.ai/)
+- If you need further assistance, please feel free to reach out to us through the following methods:
 
 <Snippet file="get-help.mdx" />

examples/misc/test.py

@@ -0,0 +1,88 @@
+from agents import Agent, Runner, function_tool, handoffs, enable_verbose_stdout_logging
+from dotenv import load_dotenv
+
+from mem0 import MemoryClient
+
+enable_verbose_stdout_logging()
+
+load_dotenv()
+
+# Initialize Mem0 client
+mem0 = MemoryClient()
+
+
+# Define memory tools for the agent
+@function_tool
+def search_memory(query: str, user_id: str) -> str:
+    """Search through past conversations and memories"""
+    memories = mem0.search(query, user_id=user_id, limit=3)
+    if memories:
+        return "\n".join([f"- {mem['memory']}" for mem in memories])
+    return "No relevant memories found."
+
+
+@function_tool
+def save_memory(content: str, user_id: str) -> str:
+    """Save important information to memory"""
+    mem0.add([{"role": "user", "content": content}], user_id=user_id)
+    return "Information saved to memory."
+
+
+# Specialized agents
+travel_agent = Agent(
+    name="Travel Planner",
+    instructions="""You are a travel planning specialist. Use get_user_context to
+    understand the user's travel preferences and history before making recommendations.
+    After providing your response, use store_conversation to save important details.""",
+    tools=[search_memory, save_memory],
+    model="gpt-4o"
+)
+
+health_agent = Agent(
+    name="Health Advisor",
+    instructions="""You are a health and wellness advisor. Use get_user_context to
+    understand the user's health goals and dietary preferences.
+    After providing advice, use store_conversation to save relevant information.""",
+    tools=[search_memory, save_memory],
+    model="gpt-4o"
+)
+
+# Triage agent with handoffs
+triage_agent = Agent(
+    name="Personal Assistant",
+    instructions="""You are a helpful personal assistant that routes requests to specialists.
+    For travel-related questions (trips, hotels, flights, destinations), hand off to Travel Planner.
+    For health-related questions (fitness, diet, wellness, exercise), hand off to Health Advisor.
+    For general questions, you can handle them directly using available tools.""",
+    handoffs=[travel_agent, health_agent],
+    model="gpt-4o"
+)
+
+
+def chat_with_handoffs(user_input: str, user_id: str) -> str:
+    """
+    Handle user input with automatic agent handoffs and memory integration.
+
+    Args:
+        user_input: The user's message
+        user_id: Unique identifier for the user
+
+    Returns:
+        The agent's response
+    """
+    # Run the triage agent (it will automatically handoffs when needed)
+    result = Runner.run_sync(triage_agent, user_input)
+
+    # Store the original conversation in memory
+    conversation = [
+        {"role": "user", "content": user_input},
+        {"role": "assistant", "content": result.final_output}
+    ]
+    mem0.add(conversation, user_id=user_id)
+
+    return result.final_output
+
+
+# Example usage
+# response = chat_with_handoffs("Which places should I vist?", user_id="alex")
+# print(response)