citadeldb
Local-first encrypted memory for AI agents. Raw conversation turns are stored as
written, with no summarizer LLM in the ingest path. Citadel runs inside your process;
the database uses a companion .citadel-keys file for its encryption keys.
Install
pip install citadeldb
Requires Python 3.10 or later. The only runtime dependency is NumPy; embeddings are
bring-your-own. CandleEmbedder and CrossEncoder are not included in the default wheel.
To build from source, activate a virtual environment and run these commands from the repository root with Rust installed:
pip install maturin
maturin develop --release
Semantic embeddings
The default wheel can use a local Sentence Transformers model through Citadel's cancellation-aware embedder protocol:
pip install sentence-transformers
from sentence_transformers import SentenceTransformer
class LocalEmbedder:
metric = "cosine"
def __init__(self):
name = "sentence-transformers/all-MiniLM-L6-v2"
revision = "1110a243fdf4706b3f48f1d95db1a4f5529b4d41"
self.model = SentenceTransformer(name, revision=revision, device="cpu")
self.dim = self.model.get_sentence_embedding_dimension()
self.model_id = f"{name}@{revision}:sentence-transformers:normalized:v1"
def embed_with_cancel(self, texts, cancel_token):
vectors = []
for start in range(0, len(texts), 16):
if cancel_token is not None:
cancel_token.check()
vectors.extend(self.model.encode(
texts[start:start + 16], normalize_embeddings=True,
show_progress_bar=False,
).tolist())
if cancel_token is not None:
cancel_token.check()
return vectors
embedder = LocalEmbedder()
The model is downloaded on first construction and runs locally. Keep the model revision and encoding settings unchanged when reopening a region. This wrapper checks cancellation between batches, not during a batch's model inference.
Custom embedders require dim, metric, model_id, and
embed_with_cancel(texts, cancel_token). Return one dim-wide vector per input and
accept None as the token. Asymmetric models should also implement
embed_queries_with_cancel for query-specific encoding. MockEmbedder is only a
deterministic lexical test backend, not a semantic model.
Memory
Use the embedder from the example above:
import citadeldb
db = citadeldb.connect("memory.cdl", key="your-passphrase", region_keys=True)
mem = db.memory()
mem.create_encrypted_region("chat", embedder)
mochi = mem.remember("chat", {"kind": "fact", "text": "Alice's cat is named Mochi"})
berlin = mem.remember("chat", {"kind": "fact", "text": "Alice lives in Berlin"})
mem.link("chat", berlin, mochi, "refines")
for hit in mem.recall("chat", text="Where does Alice live?", k=2):
assert hit.relevance is not None
print(f"{hit.relevance:.3f} {hit.text}")
for edge in mem.fetch_edges("chat", src=berlin, limit=100):
print(edge["kind"], edge["dst"])
Recall fuses vector similarity, keyword match, recency, and importance. Stored text
is not summarized or rewritten. AtomHit.relevance is a higher-is-better ranking
score; distance is the metric distance when available. Either can be None on
hits returned by operations that do not calculate that value.
mem.recall_mmr("chat", text="Alice", k=2, fetch_k=10, lambda_mult=0.5) selects
diverse results using the stored candidate vectors, without re-embedding documents.
MMR requires a cosine region.
Edges are region-scoped: both endpoints must be live in the named region. Kind
summaries are bounded pages; pass next_after_kind back as after_kind until it
is None. mem.profile(...) returns recalled atoms together with their induced
region-local edges, and mem.unlink(...) removes one exact typed edge.
Local Candle models
To use the Rust-native embedder and cross-encoder in Python, activate a virtual environment and run these commands from the repository root. Building requires Rust:
pip install maturin
maturin develop --release --features candle-embed
pip install citadeldb-mcp
citadeldb-mcp pull e5-large
citadeldb-mcp pull ms-marco-minilm
Use the snapshot directories printed by pull below. For a new region, replace
LocalEmbedder() in the memory example with this CandleEmbedder, then set the
reranker on mem. An existing region requires re-embedding to change its model.
embedder = citadeldb.CandleEmbedder("/path/to/e5-large", preset="e5-large")
mem.set_reranker(citadeldb.CrossEncoder("/path/to/ms-marco-minilm"))
CandleEmbedder.model_id binds the model, tokenizer, configuration, preset, and
embedding pipeline revision. These local directories are user-trusted inputs.
Inspection and model changes
Inspect stored memory without loading an embedder:
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.
When a model changes, mem.reembed_region("chat", embedder) recomputes vectors from
stored text while preserving atom ids and authored edges. Managed similarity edges
are rebuilt. It refuses vector-bearing atoms whose original text was not stored.
For a provenance-only repair, use mem.reclassify_region("chat", embedder.model_id)
and then mem.attach_existing_region("chat", embedder). Do this only if the model,
artifacts, and encoding settings are exactly those that produced the stored vectors:
reclassification neither verifies nor recomputes them.
Forgetting
On encrypted regions, forgetting destroys the key that sealed each erased atom.
It does not erase plaintext already exported or copies of keys held in pre-erasure
backups or snapshots. Plaintext regions do not provide per-atom cryptographic erasure.
Targeted forgetting skips immutable atoms unless force=True; their ids appear in
receipt.immutable_skipped.
receipt = mem.forget("chat", [berlin])
print(receipt.cryptographic_erasure, receipt.algorithm)
# True AES-256-KW(RFC3394)
Pass cascade_dependents=True to erase the selected atoms together with their
transitive derived_from dependents. Without force=True, an immutable atom in
that closure rejects the entire cascade. The default remains targeted deletion.
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()
try:
report = db.integrity_check(quiet=True)
for finding in report["errors"]:
print(finding["kind"], finding["message"], finding["tampered"])
finally:
db.set_cancel(None)
Agents and LLM clients
LLMClient.complete() returns a response dictionary whose usage can be None.
Custom clients should return both input_tokens and output_tokens as nonnegative
integer counts inside usage; omitted or invalid counts mean usage is unavailable.
Explicit zero counts remain zero. usage["cost_usd"] is an optional estimate.
Returning only a string leaves usage unavailable. Callback request dictionaries
include seed, with None when unset; replay hashes include this field.
Agents do not automatically retry LLM requests. A run stops with
terminated_by == "token_usage_unavailable" when token accounting is unknown,
or "cost_usage_unavailable" when a configured cost cap
cannot be checked. A nonfinite or negative cost cap gives "invalid_cost_limit".
On trace-storage failure, AgentError.recovery contains usage, calls, and
confirmed_persisted; each call retains its request, identities, and response or
provider error. AgentError.storage_error holds the original storage exception.
Both attributes are None for other agent errors. The confirmed prefix counts
acknowledged writes; a failed write may still have persisted. Reconcile the retained
calls with stored traces before retrying persistence or making new calls.
MCP
pip install citadeldb-mcp installs the server executable for Claude Desktop,
Cursor, and other Model Context Protocol clients. The recommended setup is:
citadeldb-mcp pull e5-large
citadeldb-mcp pull ms-marco-minilm
citadeldb-mcp --db memory.cdl --embedder e5-large --reranker ms-marco-minilm
Set CITADEL_KEY to the vault passphrase before serving.
Full documentation is at citadeldb.dev; source and the Rust API are in the main repository.
License
Apache-2.0
1 maintainer
Release files for citadeldb 2.6.1
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