Universal Memory Layer for AI Agents - Python SDK
Project description
MemoraDB Python SDK
Universal Memory Layer for AI Agents - The S3 + Redis for AI Memory
Installation
pip install memoradb
Quick Start
from memoradb import MemoraDB
import asyncio
async def main():
# Initialize client
client = MemoraDB(
api_url="https://api.memoradb.com",
api_key="your-api-key"
)
# Store a memory
result = await client.write(
namespace_id="your-namespace",
owner_id="user-123",
session_id="session-456",
text="User prefers dark mode and wants weekly reports",
kind="preference"
)
# Retrieve relevant memories
memories = await client.retrieve(
namespace_id="your-namespace",
owner_id="user-123",
query="user preferences",
k=5
)
print(f"Found {len(memories.results)} relevant memories")
asyncio.run(main())
Features
- 🧠 Persistent Memory: Store and retrieve memories across sessions
- 🔄 Portable Memory: Share context between different AI agents
- 🔍 Semantic Search: Find relevant memories using vector similarity
- 📄 Automatic Summarization: Compress memories into summaries
- 🛡️ GDPR Compliant: Right to forget and data portability
- 🏷️ Multi-kind Storage: Support for user messages, agent responses, tool outputs, and notes
API Reference
Write Memory
await client.write(
namespace_id="namespace-uuid",
owner_id="user-uuid",
session_id="session-uuid",
text="Memory content",
kind="user_msg", # user_msg, agent_msg, tool_io, note
metadata={"key": "value"},
pii_tags=["email", "phone"]
)
Retrieve Memories
memories = await client.retrieve(
namespace_id="namespace-uuid",
owner_id="user-uuid",
query="search query",
k=10,
use_summaries=True
)
Context Packing
context = await client.context_pack(
namespace_id="namespace-uuid",
owner_id="user-uuid",
query="context query",
budget_tokens=1000,
include_summaries=True
)
Forget Memories (GDPR)
await client.forget(
namespace_id="namespace-uuid",
owner_id="user-uuid",
reason="user_request"
)
License
MIT License - see LICENSE file for details.
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