mnestic 0.9.0

Embedded graph + vector + full-text database with Datalog — a maintained fork of CozoDB, tuned as a substrate for agentic memory.

mnestic (Python)

Embedded graph + vector + full-text database with Datalog queries — a maintained fork of CozoDB, tuned as a substrate for agentic memory. This package is the in-process Python binding (no server required).

mnestic is not the official CozoDB and is not affiliated with or endorsed by its original authors. All credit for the original design belongs to Ziyang Hu and the Cozo Project Authors. See the fork repository for provenance and licensing.

pip install mnestic

from mnestic import CozoDbPy

db = CozoDbPy("mem", "", "{}")  # engines: "mem", "sqlite" (file path), ...
db.run_script("?[x] <- [[1],[2],[3]]", {}, False)

# One-call hybrid retrieval (HNSW + full-text fused with Reciprocal Rank Fusion),
# over a relation that has an HNSW index and an FTS index:
hits = db.hybrid_search({
    "relation": "docs",
    "vector_index": "vec", "query_vector": [0.1, 0.9], "vector_k": 5,
    "fts_index": "fts", "query_text": "vector search", "fts_k": 5,
})
# -> {"headers": ["id", "score"], "rows": [["d3", 0.033], ...], "next": None}

# Pass "detailed": True for per-leg contributions — one row per (item, leg)
# with the within-leg rank the fusion used and the leg's raw score:
# headers ["id","score","list_id","leg_rank","leg_score"]

For idiomatic LangChain / LlamaIndex usage, install the integration packages (langchain-mnestic, llama-index-vector-stores-mnestic).

The query language (CozoScript / Datalog) and engine semantics follow CozoDB; see the upstream documentation and the fork changelog.

License

Mozilla Public License 2.0. Original work © 2022 The Cozo Project Authors; fork modifications © 2026 Shan Rizvi.