Agent Memory Hub: Region-Governed Memory for AI Agents

Agent Memory Hub is the enterprise-standard solution for managing long-term memory for AI agents with strict region governance. Designed for developers building scalable agentic workflows, it provides a unified interface to store, recall, and manage agent state across diverse storage backends while ensuring compliance with data residency laws (GDPR, CCPA).

Whether you are building a simple chatbot or a complex multi-agent system, agent-memory-hub abstracts the complexity of state management, letting you focus on agent logic.

🚀 What is Agent Memory Hub?

Agent Memory Hub is a Python SDK that acts as a middleware between your AI agents (built with LangChain, AutoGen, OpenAI, etc.) and your storage infrastructure. It creates a structured "brain" for your agents where every interaction, fact, or retrieved context is indexed by Agent ID and Session ID.

Crucially, it introduces Region Governance as a first-class citizen. You can strictly enforce that an agent's memory never leaves a specific geographic region (e.g., europe-west1), which is critical for enterprise applications handling sensitive user data.

💡 Why Use It?

• Data Sovereignty & Compliance: Native support for region governance. If an agent is configured for europe-west1, the SDK physically prevents writes to us-central1 storage buckets.
• Backend Agnostic: Switch from Google Cloud Storage to AlloyDB, Redis, or Firestore without changing your agent code.
• Session Isolation: Automatically segregates memories by session, making it perfect for conversational agents and RAG pipelines.
• Production Ready: Typed, tested, and security-scanned. No hardcoded secrets.

⚙️ How It Works

The library uses an Adapter Pattern to connect to various storage backends. When you initialize a MemoryClient, you specify the Agent, Session, and Region.

graph LR
    A[AI Agent] -->|Write/Recall| B(MemoryClient)
    B -->|Region Check| C{Region Allowed?}
    C -->|Yes| D[Storage Adapter]
    C -->|No| E[Error]
    D -->|Persist| F[(GCS / AlloyDB / Redis)]

1. Initialize: Create a client with specific region constraints.
2. Interact: Use .write() to save state and .recall() to fetch context.
3. Govern: The SDK handles the routing and compliance checks transparently.

🛠️ Installation

pip install agent-memory-hub

# For specific backends
pip install "agent-memory-hub[alloydb]"
pip install "agent-memory-hub[redis]"

⚡ Quick Start & Examples

We provide ready-to-use examples for common scenarios:

1. OpenAI Agent Integration

Inject long-term memory into your OpenAI API calls to personalize responses.

• View Example

from agent_memory_hub import MemoryClient
# ... initialization ...
memory.write("User prefers concise Python code.")
context = memory.recall()
# Inject 'context' into your system prompt

2. Multi-Region Architecture

Manage distinct compliance requirements for global user bases.

• View Example

# This client will ONLY write to EU-based storage
eu_memory = MemoryClient(agent_id="eu_bot", region="europe-west1", region_restricted=True)

3. RAG Agent with Memory

Enhance Retrieval-Augmented Generation (RAG) by caching retrieved context and user interactions.

• View Example

📚 Documentation

Full documentation is available at https://sgmoorthy.github.io/agent-memory-hub/.

• Benchmarking Guide
• Security & Access Flow
• Semantic Memory Models
• API Reference

🤝 Contributing

We welcome contributions! Please check CONTRIBUTING.md and our ROADMAP.md.

🛡️ Security

This project adheres to strict security practices. See SECURITY.md for details.