VantaDB × Mem0

Mem0 adapter for VantaDB.

Install

Not on PyPI yet. The vantadb-mem0 package builds and passes twine check locally; it will go live with the first adapters-v* tag release. Until then, install from source.

# Today, from a repo checkout
cd integrations/mem0 && pip install .

Install from PyPI (after first release)

pip install vantadb-mem0

Quickstart

from vantadb_mem0 import VantaDBVectorStore

store = VantaDBVectorStore(
    db_path="./my_data",
    namespace="memories",
)

store.add("VantaDB is an embedded vector database written in Rust.", user_id="alice")
results = store.search("vector database")
for r in results:
    print(r["payload"], r["score"])

API

• add(text, user_id=None, metadata=None) — store a memory
• search(query, k=4) — search memories
• delete(key) — delete a memory by key
• list(user_id=None, limit=100) — list memories

Why VantaDB?

• Embedded & local-first: the storage engine is a Rust library embedded in your process — no server to deploy, no network hop; data lives in your filesystem.
• Persistent hybrid search: vectors + BM25 text search out of the box as the vector backend for mem0's memory layer, where mem0's default storage targets its hosted platform.
• Zero-setup alternative to hosted stacks: unlike Zep (requires a server) or Cognee (spins up its own knowledge-graph runtime), VantaDB is a plain library you import.

Development

pip install -e .