devto 2026-08-15 원문 보기 ↗
markdown
AI agents are everywhere now. But they still struggle with one thing: memory.
Most agent frameworks store memory in a vector database, but that's not enough. A vector DB just stores embeddings; it doesn't understand memory types, consolidation, forgetting, or portability.
So I built Mneme — a portable memory layer for AI agents.
.mneme file.
bash
pip install mneme-memory
Usage
python
import mneme
memory = mneme.Store(agent_id="my-agent", backend="memory.db")
memory.remember("User prefers email over Slack", memory_type="semantic")
context = memory.recall("How does the user like to be contacted?")
print(context)
That's it. Three verbs: remember, recall, forget.
Why not just use a vector DB?
A vector DB gives you similarity search, but not:
Memory types (episodic vs semantic vs procedural)
Consolidation (episodic → semantic summarization)
Controlled forgetting + audit trail
True portability across frameworks
Mneme is not a database; it's a memory layer that can sit on top of SQLite or Postgres.
Performance
Retrieval precision@1: 1.00 on synthetic test
Recall latency: 7.34 ms average
Write latency: 0.088 ms
Check it out
GitHub: https://github.com/GamingBoyOfficial/Mneme
PyPI: https://pypi.org/project/mneme-memory/
What's next?
I'm planning to add HNSW vector index, TypeScript SDK, and more adapters. Let me know what you think!