autogen-vertexai-memory 0.2.0

VertexAI Memory integration for Autogen agents

autogen-vertexai-memory

VertexAI Memory integration for Autogen agents. Store and retrieve agent memories using Google Cloud's VertexAI Memory service with semantic search capabilities and intelligent caching.

Features

• Persistent Memory Storage - Store agent memories in Google Cloud VertexAI
• Semantic Search - Find relevant memories using natural language queries
• Automatic Memory Generation - Extract and store facts from conversation events
• Intelligent Caching - Reduce API calls with configurable cache TTL (default 5 minutes)
• Automatic Cache Invalidation - Cache updates automatically on write operations
• Automatic Context Updates - Seamlessly inject memories into chat contexts
• Async/Await Support - Full async API compatible with Autogen's runtime
• User-Scoped Isolation - Multi-tenant memory management
• Tool Integration - Ready-to-use tools for agent workflows

Installation

pip install autogen-vertexai-memory

Prerequisites

1. Google Cloud Project with VertexAI API enabled
2. Authentication configured (Application Default Credentials)
3. VertexAI Memory Resource created in your project

# Set up authentication
gcloud auth application-default login

# Enable VertexAI API
gcloud services enable aiplatform.googleapis.com

Quick Start

Basic Memory Usage

from autogen_vertexai_memory import VertexaiMemory, VertexaiMemoryConfig
from autogen_core.memory import MemoryContent, MemoryMimeType

# Configure memory with caching enabled (default)
config = VertexaiMemoryConfig(
    api_resource_name="projects/my-project-id/locations/us-central1/reasoningEngines/1234567890123456789",
    app_name="1234567890123456789",
    project_id="my-project-id",
    location="us-central1",
    user_id="user123",
    cache_enabled=True,        # Enable caching (default)
    cache_ttl_seconds=300      # Cache for 5 minutes (default)
)

memory = VertexaiMemory(config=config)

# Store a memory directly (invalidates cache)
await memory.add(
    content=MemoryContent(
        content="User prefers concise responses and uses Python",
        mime_type=MemoryMimeType.TEXT
    )
)

# Generate memories from conversation events (non-blocking)
events = [
    {"role": "user", "content": "I love hiking in the mountains"},
    {"role": "model", "content": "That's wonderful! Do you have a favorite trail?"},
    {"role": "user", "content": "Yes, the Pacific Crest Trail is amazing"}
]
result = await memory.generate_memories_from_events(
    user_id="user123",
    events=events
)
# VertexAI will automatically extract facts like "User loves hiking in mountains"

# Semantic search for relevant memories
results = await memory.query(query="programming preferences")
for mem in results.results:
    print(mem.content)
# Output: User prefers concise responses and uses Python

# Retrieve all memories
all_memories = await memory.query(query="")

Using with Autogen Agents

from autogen_core.model_context import ChatCompletionContext
from autogen_core.models import UserMessage

# Create chat context
context = ChatCompletionContext()

# Add user message
await context.add_message(
    UserMessage(content="What programming language should I use?")
)

# Inject relevant memories into context (uses caching)
# First call: Fetches from VertexAI and caches
# Subsequent calls: Returns cached results if still valid
result = await memory.update_context(context)
print(f"Added {len(result.memories.results)} memories to context")

# Now the agent has access to stored preferences

Environment Variables

You can also configure using environment variables:

export VERTEX_PROJECT_ID="my-project-id"
export VERTEX_LOCATION="us-central1"
export VERTEX_USER_ID="user123"
export VERTEX_APP_NAME="1234567890123456789"
export VERTEX_API_RESOURCE_NAME="projects/my-project-id/locations/us-central1/reasoningEngines/1234567890123456789"

# Auto-loads from environment
config = VertexaiMemoryConfig()
memory = VertexaiMemory(config=config)

Memory Tools for Agents

Integrate memory capabilities directly into your Autogen agents:

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_vertexai_memory.tools import (
    SearchVertexaiMemoryTool,
    UpdateVertexaiMemoryTool,
    VertexaiMemoryToolConfig
)

# Configure memory tools
memory_config = VertexaiMemoryToolConfig(
    project_id="my-project-id",
    location="us-central1",
    user_id="user123",
    app_name="1234567890123456789",
    api_resource_name="projects/my-project-id/locations/us-central1/reasoningEngines/1234567890123456789"
)

# Create memory tools
search_tool = SearchVertexaiMemoryTool(config=memory_config)
update_tool = UpdateVertexaiMemoryTool(config=memory_config)

# Create agent with memory tools
agent = AssistantAgent(
    name="memory_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4"),
    tools=[search_tool, update_tool],
    system_message="""You are a helpful assistant with memory capabilities.
    
    Use search_vertexai_memory_tool to retrieve relevant information about the user.
    Use update_vertexai_memory_tool to store important facts you learn during conversations.
    """
)

# Now the agent can search and store memories automatically!

API Reference

VertexaiMemoryConfig

Configuration model for VertexAI Memory with caching support.

VertexaiMemoryConfig(
    api_resource_name: str,      # Full resource name: "projects/{project}/locations/{location}/reasoningEngines/{engine_id}"
    app_name: str,               # Application name (typically the reasoning engine ID)
    project_id: str,             # Google Cloud project ID
    location: str,               # GCP region (e.g., "us-central1", "europe-west1")
    user_id: str,                # Unique user identifier for memory isolation
    cache_ttl_seconds: int = 300,  # Cache time-to-live in seconds (0 to disable)
    cache_enabled: bool = True   # Whether to enable caching
)

Caching Behavior:

• Cache is used by update_context() to reduce repeated API calls
• Cache is automatically invalidated on add() and clear() operations
• Set cache_ttl_seconds=0 or cache_enabled=False to disable caching
• query() method does NOT use caching as queries may vary

Environment Variables:

• VERTEX_API_RESOURCE_NAME
• VERTEX_APP_NAME
• VERTEX_PROJECT_ID
• VERTEX_LOCATION
• VERTEX_USER_ID

VertexaiMemory

Main memory interface implementing Autogen's Memory protocol with intelligent caching.

VertexaiMemory(
    config: Optional[VertexaiMemoryConfig] = None,
    client: Optional[Client] = None
)

Methods:

add(content, cancellation_token=None)

Store a new memory and invalidate the cache.

await memory.add(
    content=MemoryContent(
        content="Important fact to remember",
        mime_type=MemoryMimeType.TEXT
    )
)

query(query="", cancellation_token=None, **kwargs)

Search memories or retrieve all. Does NOT use caching.

# Semantic search (top 3 results by default)
results = await memory.query(query="user preferences")

# Get all memories
all_results = await memory.query(query="")

Returns: MemoryQueryResult with list of MemoryContent objects

update_context(model_context)

Inject memories into chat context as system message. Uses caching to reduce API calls.

context = ChatCompletionContext()
result = await memory.update_context(context)
# Context now includes relevant memories in chronological order

Caching Details:

• First call: Fetches from VertexAI and caches results
• Subsequent calls: Returns cached results if still valid
• After cache expiry: Fetches fresh data and updates cache

Returns: UpdateContextResult with retrieved memories

generate_memories_from_events(user_id, events)

Generate memories from conversation events. Non-blocking operation that triggers VertexAI to automatically extract and store relevant facts.

events = [
    {"role": "user", "content": "I'm working on a machine learning project"},
    {"role": "model", "content": "That's great! What framework are you using?"},
    {"role": "user", "content": "I prefer TensorFlow for deep learning"}
]

result = await memory.generate_memories_from_events(
    user_id="user123",
    events=events
)
print(result)
# Output: {'status': 'ok', 'message': 'Generating memories for user user123'}

Parameters:

• user_id (str): User ID for memory generation
• events (List[Dict[str, str]]): Events with "role" (user/model) and "content" keys

Returns: Dict with status and message

Note: Non-blocking operation (wait_for_completion=False). VertexAI processes asynchronously.

clear()

Permanently delete all memories and invalidate cache (irreversible).

await memory.clear()  # Use with caution!

close()

Release resources and clear cache.

await memory.close()

Memory Tools

VertexaiMemoryToolConfig

Configuration for memory tools.

VertexaiMemoryToolConfig(
    project_id: str,
    location: str,
    user_id: str,
    api_resource_name: str,
    app_name: str
)

Environment Variables:

• VERTEX_PROJECT_ID
• VERTEX_LOCATION
• VERTEX_USER_ID
• VERTEX_API_RESOURCE_NAME
• VERTEX_APP_NAME

SearchVertexaiMemoryTool

Tool for semantic memory search.

SearchVertexaiMemoryTool(config: Optional[VertexaiMemoryToolConfig] = None, **kwargs)

Tool Name: search_vertexai_memory_tool
Description: Perform a search with given parameters using vertexai memory bank
Parameters:

• query (str): Semantic search query
• top_k (int, default=5): Maximum number of memories to retrieve

Returns: SearchQueryReturn with list of matching memory strings

UpdateVertexaiMemoryTool

Tool for storing new memories.

UpdateVertexaiMemoryTool(config: Optional[VertexaiMemoryToolConfig] = None, **kwargs)

Tool Name: update_vertexai_memory_tool
Description: Store a new memory fact in the VertexAI memory bank for the user
Parameters:

• content (str): Memory content to store

Returns: UpdateMemoryReturn with success status and message

Real-World Example: Personal Shopping Assistant

Here's a complete example of building a shopping assistant that remembers user preferences:

import os
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_vertexai_memory.memory import VertexaiMemory, VertexaiMemoryConfig
from autogen_vertexai_memory.tools import (
    SearchVertexaiMemoryTool,
    UpdateVertexaiMemoryTool,
    VertexaiMemoryToolConfig,
)

# Configure memory for a specific user
memory_config = VertexaiMemoryConfig(
    api_resource_name="projects/my-project-id/locations/us-central1/reasoningEngines/1234567890123456789",
    app_name="1234567890123456789",
    project_id="my-project-id",
    location="us-central1",
    user_id="customer123",
    cache_enabled=True,
    cache_ttl_seconds=300
)

# Initialize memory
memory = VertexaiMemory(config=memory_config)

# Configure tools with same settings
tool_config = VertexaiMemoryToolConfig(
    api_resource_name="projects/my-project-id/locations/us-central1/reasoningEngines/1234567890123456789",
    app_name="1234567890123456789",
    project_id="my-project-id",
    location="us-central1",
    user_id="customer123"
)

# Create memory tools
search_tool = SearchVertexaiMemoryTool(config=tool_config)
update_tool = UpdateVertexaiMemoryTool(config=tool_config)

# Create shopping assistant agent
shopping_agent = AssistantAgent(
    name="shopping_assistant",
    model_client=OpenAIChatCompletionClient(
        model="gpt-4o",
        api_key=os.environ["OPENAI_API_KEY"]
    ),
    tools=[search_tool, update_tool],
    memory=[memory],
    system_message="""You are a helpful shopping assistant that remembers customer preferences.
    
    Use search_vertexai_memory_tool to recall what you know about the customer.
    Use update_vertexai_memory_tool to store new preferences you learn.
    
    Always personalize recommendations based on stored memories."""
)

# Example conversation
async def main():
    # First interaction - agent learns preferences
    print("User: I'm looking for running shoes, I prefer Nike brand")
    async for chunk in shopping_agent.run_stream(
        task="I'm looking for running shoes, I prefer Nike brand"
    ):
        if hasattr(chunk, "content") and chunk.content:
            print(chunk.content, end="", flush=True)
    
    print("\n\n---\n")
    
    # Later conversation - agent recalls preferences
    print("User: Show me some athletic shoes")
    async for chunk in shopping_agent.run_stream(
        task="Show me some athletic shoes"
    ):
        if hasattr(chunk, "content") and chunk.content:
            print(chunk.content, end="", flush=True)
    # Agent automatically searches memory and finds "prefers Nike brand"

# Batch import customer history
async def import_customer_history():
    """Import past purchase history as memories."""
    past_conversations = [
        {"role": "user", "content": "I bought those blue Nike Air Max last month"},
        {"role": "model", "content": "Great choice! How are they working out?"},
        {"role": "user", "content": "Love them! I wear size 10"},
        {"role": "model", "content": "Perfect, I'll remember your size."},
        {"role": "user", "content": "I usually shop during sales"}
    ]
    
    # VertexAI extracts facts like:
    # - "Customer bought Nike Air Max in blue"
    # - "Customer wears size 10"
    # - "Customer prefers shopping during sales"
    result = await memory.generate_memories_from_events(
        user_id="customer123",
        events=past_conversations
    )
    print(result)

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())

What Happens Behind the Scenes:

1. First Message: Agent uses search_tool to check existing memories (cache miss, fetches from VertexAI)
2. Learning: Agent uses update_tool to store "Customer prefers Nike brand"
3. Subsequent Requests: Agent uses search_tool again (cache hit, returns instantly)
4. Personalization: Recommendations automatically include Nike products

Benefits:

• Users don't repeat preferences
• Context persists across sessions
• Reduced API calls with intelligent caching
• Multi-user isolation via user_id

# Disable caching completely
config = VertexaiMemoryConfig(
    api_resource_name="projects/my-project-id/locations/us-central1/reasoningEngines/1234567890123456789",
    app_name="1234567890123456789",
    project_id="my-project-id",
    location="us-central1",
    user_id="user123",
    cache_enabled=False
)

# Short cache TTL (30 seconds)
config = VertexaiMemoryConfig(
    api_resource_name="projects/my-project-id/locations/us-central1/reasoningEngines/1234567890123456789",
    app_name="1234567890123456789",
    project_id="my-project-id",
    location="us-central1",
    user_id="user123",
    cache_ttl_seconds=30
)

# Long cache TTL (1 hour)
config = VertexaiMemoryConfig(
    api_resource_name="projects/my-project-id/locations/us-central1/reasoningEngines/1234567890123456789",
    app_name="1234567890123456789",
    project_id="my-project-id",
    location="us-central1",
    user_id="user123",
    cache_ttl_seconds=3600
)

Generating Memories from Conversations

# Automatically extract and store memories from conversation history
conversation_events = [
    {"role": "user", "content": "I'm a software engineer at Google"},
    {"role": "model", "content": "That's impressive! What technologies do you work with?"},
    {"role": "user", "content": "Mainly Kubernetes and Go for cloud infrastructure"},
    {"role": "model", "content": "Great combination for scalable systems!"},
    {"role": "user", "content": "I also enjoy contributing to open source on weekends"}
]

# Let VertexAI automatically analyze and extract relevant facts
result = await memory.generate_memories_from_events(
    user_id="user123",
    events=conversation_events
)

print(result)
# Output: {'status': 'ok', 'message': 'Generating memories for user user123'}

# VertexAI processes the conversation and extracts facts like:
# - "User is a software engineer at Google"
# - "User works with Kubernetes and Go"
# - "User works on cloud infrastructure"
# - "User contributes to open source projects on weekends"

# Wait for processing, then query the extracted memories
import asyncio
await asyncio.sleep(2)

results = await memory.query(query="user's work")
for mem in results.results:
    print(mem.content)

Custom Client Configuration

from vertexai import Client

# Create custom client with specific settings
client = Client(
    project="my-project-id",
    location="us-central1"
)

memory = VertexaiMemory(config=config, client=client)

Multi-User Isolation

# User 1's memories
user1_config = VertexaiMemoryConfig(
    api_resource_name="projects/my-project-id/locations/us-central1/reasoningEngines/1234567890123456789",
    app_name="1234567890123456789",
    project_id="my-project-id",
    location="us-central1",
    user_id="user1"
)
user1_memory = VertexaiMemory(config=user1_config)

# User 2's memories (isolated from User 1)
user2_config = VertexaiMemoryConfig(
    api_resource_name="projects/my-project-id/locations/us-central1/reasoningEngines/1234567890123456789",
    app_name="1234567890123456789",
    project_id="my-project-id",
    location="us-central1",
    user_id="user2"
)
user2_memory = VertexaiMemory(config=user2_config)

Sharing Config Across Tools

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_vertexai_memory.tools import (
    SearchVertexaiMemoryTool,
    UpdateVertexaiMemoryTool,
    VertexaiMemoryToolConfig
)

# Create config once
config = VertexaiMemoryToolConfig(
    project_id="my-project-id",
    location="us-central1",
    user_id="user123",
    app_name="1234567890123456789",
    api_resource_name="projects/my-project-id/locations/us-central1/reasoningEngines/1234567890123456789"
)

# Share across multiple tools
search_tool = SearchVertexaiMemoryTool(config=config)
update_tool = UpdateVertexaiMemoryTool(config=config)

# Use in multiple agents
agent1 = AssistantAgent(
    name="agent1",
    model_client=OpenAIChatCompletionClient(model="gpt-4"),
    tools=[search_tool, update_tool]
)

agent2 = AssistantAgent(
    name="agent2",
    model_client=OpenAIChatCompletionClient(model="gpt-4"),
    tools=[search_tool]  # This agent can only search, not update
)

Troubleshooting

Authentication Issues

# Verify authentication
gcloud auth application-default print-access-token

# Set explicit credentials
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account-key.json"

Empty Query Results

# Check if memories exist
all_memories = await memory.query(query="")
print(f"Total memories: {len(all_memories.results)}")

# Verify user_id and app_name match
print(f"Using user_id: {memory.user_id}")
print(f"Using app_name: {memory.app_name}")

Cache Not Working

# Check cache configuration
print(f"Cache enabled: {memory._cache_enabled}")
print(f"Cache TTL: {memory._cache_ttl_seconds}")

# Manually invalidate cache if needed
memory._invalidate_cache()