citadeldb

Local-first encrypted memory for AI agents. Raw conversation turns are stored as written, with no summarizer LLM in the ingest path, in a single encrypted file that lives inside your process.

Install

pip install citadeldb

The only runtime dependency is NumPy; embeddings are bring-your-own.

Memory

import citadeldb

db = citadeldb.connect("memory.cdl", key="your-passphrase", region_keys=True)
mem = db.memory()
mem.create_encrypted_region("chat", citadeldb.MockEmbedder(dim=64))

mem.remember("chat", {"kind": "fact", "text": "Alice's cat is named Mochi"})
berlin = mem.remember("chat", {"kind": "fact", "text": "Alice lives in Berlin"})

for hit in mem.recall("chat", text="where does Alice live?", k=2):
    print(f"{hit.score:.3f}  {hit.text}")
# 0.850  Alice lives in Berlin
# 0.200  Alice's cat is named Mochi

Recall fuses vector similarity, keyword match, recency, and importance. Nothing is summarized or rewritten on the way in, so a date or a number recalls exactly as it was said.

MockEmbedder needs no download and is enough to try the API. For real recall quality use CandleEmbedder with a local e5-large.

Forgetting

Deleting a memory destroys the key its ciphertext was sealed with, so the bytes on disk stay unreadable rather than being marked deleted.

receipt = mem.forget("chat", [berlin])
print(receipt.cryptographic_erasure, receipt.algorithm)
# True AES-256-KW(RFC3394)

SQL and vector search

The same file is a full SQL database with JSON, full-text search, and filtered vector search.

db.execute("CREATE TABLE notes(id INTEGER PRIMARY KEY, body TEXT)")
db.execute("INSERT INTO notes VALUES (1, $1)", ["hello"])
print(db.query("SELECT body FROM notes").to_dicts())  # [{'body': 'hello'}]

MCP

pip install citadeldb-mcp exposes the memory engine to Claude Desktop, Cursor, and any Model Context Protocol client.

Full documentation is at citadeldb.dev; source and the Rust API are in the main repository.

License

Apache-2.0

Project links

Data verified by PyPI on Aug 10, 2026
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Key dates

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  • Released:
Newer release available (2.5.0)

1 maintainer

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Avatar for yp3y5akh0v from gravatar.com yp3y5akh0v

Credits

Author: Yuriy Peysakhov

GitHub Statistics

Data verified by PyPI on Aug 10, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python >=3.10

Tags

database encryption sql vector-search ann embedded-database memory agent llm cryptography

Classifiers

Report project as malware

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2.5.0

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2.4.0

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2.3.0

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2.2.0

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2.1.0

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2.0.0

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This release

1.15.0 This release

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1.14.0

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1.13.0

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1.12.0

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1.11.0

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1.10.0

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1.9.0

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1.8.0

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1.7.0

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