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.

Repairing memory-model provenance

CandleEmbedder.model_id binds the friendly model name to the exact model, tokenizer, and configuration bytes, the selected preset, and Citadel's embedding pipeline revision. MockEmbedder records a versioned algorithm identity instead of a generic mock label. A legacy region carrying an earlier identity will not attach to the current embedder automatically.

If—and only if—the embedder is exactly the one that produced the stored vectors, update the recorded provenance and reattach. For a region created with the old mock identity, use its original dimension and metric (replace these example values):

embedder = citadeldb.MockEmbedder(dim=64, metric="cosine")
mem.reclassify_region("chat", embedder.model_id)
mem.attach_existing_region("chat", embedder)

For a Candle region, builds with the candle-embed feature can perform the same repair when the model files and preset are unchanged:

embedder = citadeldb.CandleEmbedder("/path/to/model", preset="e5-large")
mem.reclassify_region("chat", embedder.model_id)
mem.attach_existing_region("chat", embedder)

reclassify_region changes provenance only; it does not verify or recompute vectors. If the embedder changed, or you cannot prove it is unchanged, recompute the vectors instead:

report = mem.reembed_region("chat", embedder)

Re-embedding refuses a vector-bearing atom whose original text was not stored, because such a vector cannot be recomputed safely.

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.

Operational memory always has an explicit embedder. Inspection and erasure are a separate capability for tools that reopen an unfamiliar vault and do not have its model:

maintenance = db.memory_maintenance()
for item in maintenance.inventory():
    print(item.region.name, item.region.model_id, item.live_atoms, item.unavailable)

MemoryMaintenance can inventory, fetch, verify, and forget existing atoms. It cannot remember or recall, so opening it never invents a replacement embedding model.

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'}]

Long SQL, integrity, and memory operations can be stopped cooperatively from another Python thread. Tokens are one-shot; install a fresh one for each unit of work and clear it afterwards.

token = citadeldb.CancelToken()
db.set_cancel(token)
# another thread may call token.cancel()

report = db.integrity_check(quiet=True)
for finding in report["errors"]:
    print(finding["kind"], finding["message"], finding["tampered"])

MCP

pip install citadeldb-mcp installs the server executable for Claude Desktop, Cursor, and other Model Context Protocol clients. Server invocations require an explicit embedder; use --embedder mock only for intentional keyword-only recall.

The main citadeldb wheel exposes citadeldb.mcp.serve("memory.cdl", embedder="mock") for programmatic keyword-only serving; set CITADEL_KEY before calling it.

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 30, 2026
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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 30, 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 This release

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2.0.0

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1.15.0

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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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