VantaDB × Letta

Letta storage adapter for VantaDB.

Install

Not on PyPI yet. The vantadb-letta package declares Alpha status and will go live with the first adapters-v* tag release (no local dist/ built yet). Until then, install from source.

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

Install from PyPI (after first release)

pip install vantadb-letta

Quickstart

from vantadb_letta import VantaDBVectorStore

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

store.insert("VantaDB is a vector database written in Rust.", source="docs")
results = store.search("vector database")
for r in results:
    print(r["text"])

API

• insert(text, source=None, metadata=None) — store a memory record
• search(query, k=4) — search stored memories
• delete(key) — delete by key
• list(limit=100) — list all records

Status: experimental

Letta is a stateful platform with its own memory layer and no public vector-store contract; this adapter is a community convenience, not an officially supported integration. The API may change without notice and it is not exercised by Letta's own test suite. Prefer Letta's native memory unless you specifically need to back it with an embedded local store.

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, where Letta's own memory is designed for its server-side 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 .