Python SDK for Kizuna-Mem temporal graph memory engine
Project description
kizuna-mem
Python SDK for Kizuna-Mem -- a temporal graph-based memory engine for AI agents.
Kizuna-Mem replaces stateless per-request context with an evolving knowledge graph that remembers, consolidates, and retrieves relevant context using spreading activation rather than naive vector similarity.
Installation
pip install kizuna-mem
Quick Start
import asyncio
from kizuna_mem import KizunaMem
async def main():
async with KizunaMem(
endpoint="http://localhost:8080",
api_key="your-api-key",
tenant_id=1,
) as mem:
# Observe a conversation turn
episode_id = await mem.observe(
speaker="user",
text="I just moved to Tokyo for the new job at Anthropic.",
)
print(f"Stored episode: {episode_id}")
# Retrieve relevant context for a query
result = await mem.retrieve(
query="Where does the user live?",
top_k=5,
)
if result.context_found:
print(f"Context: {result.assembled_context}")
for node in result.nodes:
print(f" [{node.kind}] {node.text} (score: {node.score:.3f})")
asyncio.run(main())
Features
- Observe conversations and events into a temporal knowledge graph
- Retrieve context using spreading activation (multi-hop graph traversal) or static fusion
- Profiles -- access consolidated user traits and preferences
- Multi-tenant isolation with
with_tenant() - GDPR --
forget_entity()andforget_tenant()for right-to-erasure compliance - Export/Import -- full data portability in JSON-LD format
- Async-first -- built on
httpxwith nativeasync/await
Retrieval Modes
# Default: static fusion (BM25 + vector + temporal)
result = await mem.retrieve(query="billing issues")
# Spreading activation: multi-hop graph traversal
result = await mem.retrieve(
query="billing issues",
retrieval_mode="spreading_activation",
)
Requirements
- Python 3.10+
- A running Kizuna-Mem server
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