Quick Start
For semantic memory through MCP, install uv and pull the local embedder and optional cross-encoder reranker:
uvx citadeldb-mcp pull e5-large
uvx citadeldb-mcp pull ms-marco-minilm
Set CITADEL_KEY to your vault passphrase (export CITADEL_KEY="your-passphrase"
on macOS/Linux or $env:CITADEL_KEY = "your-passphrase" in PowerShell), then start:
uvx citadeldb-mcp --db memory.cdl --embedder e5-large --reranker ms-marco-minilm
The server communicates over stdio. See MCP for client configuration. Model downloads do not need a vault key; serving does.
Memory (Python)
Install the published package with pip install citadeldb. See the
Python source-build and semantic-memory guide.
Embedders implement embed_with_cancel(texts, cancel_token) and check cancellation
between bounded batches. Local Candle models require the candle-embed build feature.
Memory (Rust)
Uses citadeldb and citadeldb-mem with the candle-embed feature. This example
loads e5-large and a local cross-encoder reranker. Other presets or a custom
Embedder are supported.
use std::sync::Arc;
use citadel::DatabaseBuilder;
use citadel_mem::{AtomInput, CandleEmbedder, CrossEncoder, MemoryEngine, RecallQuery, RerankStrategy};
// Encrypted store (per-atom keys enable cryptographic forgetting)
let db = DatabaseBuilder::new("memory.db")
.passphrase(b"secret")
.enable_region_keys(true)
.create()?;
let mem = MemoryEngine::open(Arc::new(db))?;
let embedder = Arc::new(CandleEmbedder::e5_large("/path/to/e5-large")?);
mem.create_encrypted_region("chat", embedder)?;
mem.set_reranker(
Arc::new(CrossEncoder::ms_marco_minilm_l6("/path/to/ms-marco-minilm")?),
RerankStrategy::default(),
);
// Remember raw turns (no LLM)
mem.remember("chat", AtomInput::new("fact", "Alice's cat is named Mochi"))?;
let berlin = mem.remember("chat", AtomInput::new("fact", "Alice lives in Berlin"))?;
// Recall by relevance
for hit in mem.recall("chat", RecallQuery::by_text("where does Alice live?", 5))? {
println!("{:.3} {}", hit.relevance.expect("ranked recall"), hit.text);
}
// Cryptographic forgetting: destroy the atom's key
mem.forget_atom("chat", berlin)?;
SQL and key-value
Uses the citadeldb and citadeldb-sql crates - or try SQL with no install in the live playground.
use citadel::DatabaseBuilder;
use citadel_sql::Connection;
let db = DatabaseBuilder::new("my.db")
.passphrase(b"secret")
.create()?;
let conn = Connection::open(&db)?;
conn.execute("CREATE TABLE users (id INTEGER PRIMARY KEY, name TEXT NOT NULL);")?;
conn.execute("INSERT INTO users (id, name) VALUES (1, 'Alice');")?;
let result = conn.query("SELECT * FROM users;")?;
// Key-value API
let mut wtx = db.begin_write()?;
wtx.insert(b"key", b"value")?;
wtx.commit()?;
let mut rtx = db.begin_read();
assert_eq!(rtx.get(b"key")?.unwrap(), b"value");
// Named tables
let mut wtx = db.begin_write()?;
wtx.create_table(b"sessions")?;
wtx.table_insert(b"sessions", b"token-abc", b"user-42")?;
wtx.commit()?;
// In-memory (no file I/O - useful for testing and WASM)
let mem_db = DatabaseBuilder::new("")
.passphrase(b"secret")
.create_in_memory()?;
CLI
citadel --create my.db
citadel> CREATE TABLE users (id INTEGER PRIMARY KEY, name TEXT NOT NULL);
citadel> INSERT INTO users (id, name) VALUES (1, 'Alice'), (2, 'Bob');
citadel> SELECT * FROM users;
+----+-------+
| id | name |
+----+-------+
| 1 | Alice |
| 2 | Bob |
+----+-------+
citadel> .backup mydb.bak
citadel> .verify
citadel> .upgrade
citadel> .stats
citadel> .audit verify
citadel> .rekey
citadel> .compact clean.db
citadel> .dump users
# P2P sync
citadel> .keygen
citadel> .listen 4248 <KEY> # Terminal A
citadel> .sync 127.0.0.1:4248 <KEY> # Terminal B
Citadel Studio
A native desktop client for Windows, macOS, and Linux. Open encrypted vaults, browse tables and memory, run SQL with EXPLAIN and ANALYZE, and inspect vectors and integrity results.
See the Studio guide for screenshots and build instructions. Download Citadel Studio for Windows, macOS, or Linux.
Agent frameworks
The adapters implement framework-specific storage, session, and retrieval interfaces. Each requires an explicit embedder. See the package README for setup, search behavior, and supported filters.
pip install citadeldb-langgraph
One database serves every adapter on the thread that opened it, so a graph's long-term
store and its session transcripts can share one encrypted file. See packaging/ for each
package's own README.
MCP
Serve an encrypted memory region to Claude Desktop or any MCP client. citadeldb-mcp is
published to PyPI and listed in the official MCP registry
as dev.citadeldb/mcp. Run it without installing through uvx.
For the recommended semantic-recall setup, pull the embedder and cross-encoder reranker once:
uvx citadeldb-mcp pull e5-large
uvx citadeldb-mcp pull ms-marco-minilm
The pull commands do not need a vault key. Before starting the server, set CITADEL_KEY
to the vault passphrase: use export CITADEL_KEY="your-passphrase" on macOS/Linux or
$env:CITADEL_KEY = "your-passphrase" in PowerShell. Then run:
uvx citadeldb-mcp --db memory.cdl --embedder e5-large --reranker ms-marco-minilm
--db, --embedder, and CITADEL_KEY are required when serving. The reranker is optional,
but e5-large with ms-marco-minilm is the configuration used for the memory benchmarks.
To install the executable instead, run pip install citadeldb-mcp or
cargo install citadeldb-mcp. Pull the same models with citadeldb-mcp pull e5-large and
citadeldb-mcp pull ms-marco-minilm, then add it to claude_desktop_config.json:
{
"mcpServers": {
"citadel": {
"command": "citadeldb-mcp",
"args": [
"--db", "/absolute/path/to/memory.cdl",
"--embedder", "e5-large",
"--reranker", "ms-marco-minilm"
],
"env": { "CITADEL_KEY": "your-passphrase" }
}
}
}
Historical memory benchmarks
Recorded LoCoMo and LongMemEval results are summarized below; their configurations and limitations predate the current memory-engine changes. SQL comparisons with unencrypted SQLite across 59 cases are under Speed benchmarks.
LoCoMo - gpt-4o-mini reader and judge with the harness's prompts, mean of 3 runs measured August 18, 2026:
Retrieval is identical across the three runs; the spread is reader and judge nondeterminism. A manual audit estimates that ~6.4% of LoCoMo answer keys are erroneous, so raw accuracy should be interpreted with that annotation noise in mind.
Memory is built with no LLM - raw turns enriched with supplied photo captions and image-search text, indexed and recalled deterministically.
LongMemEval_S (arXiv 2410.10813) full-haystack split (~40-50 sessions/question), gpt-4o reader, official CoT prompt and gpt-4o-2024-08-06 judge:
Full-haystack stresses retrieval against distractors (not the oracle reader ceiling). Protocol and per-type results in citadel-membench.
Encrypted memory engine
The same encrypted pages that hold SQL tables also hold memory. Three crates make up the memory engine:
- citadeldb-vector - a
VECTOR(N)SQL type, distance operators (<->L2,<#>inner,<=>cosine), and a PRISM-backed filtered ANN index that reads through the encrypted page store. - citadeldb-mem - the memory engine (regions, atoms, edges) with hybrid recall and cryptographic forgetting: an atom or region is erased by destroying its key, at whole-store, per-region, and per-atom granularity.
- citadeldb-mcp - a Model Context Protocol server exposing a Citadel memory region (encrypted by default) to any MCP client (Claude Desktop, IDEs) as recall/remember/link/evolve/forget/verify tools.
Zero-LLM memory path
citadeldb-mem stores raw conversation content without a summarizer LLM. Recall uses embeddings, BM25 keyword matching, and an optional reranker. Local embedding and reranking backends keep this processing on-device; custom backends determine their own network use and costs. The benchmark readers and judges are separate LLMs - gpt-4o-mini for LoCoMo, gpt-4o for LongMemEval. The protocol and results are in citadel-membench.
Agent runtime
- citadeldb-llm - the provider-neutral LLM client layer (Claude, OpenAI, Ollama, Gemini) behind one factory, with canonical request hashing and a non-secret client request identity.
- citadeldb-ai - an autonomous agent runtime (ReAct + Reflexion, tool registry, budget caps, pluggable LLM backends) that uses citadeldb-mem for persistence.
Features
- Encrypted at rest - AES-256-CTR + HMAC-SHA256 per page, verified before decryption
- SQL - JOINs, subqueries, CTEs (recursive + WITH-DML), UNION/INTERSECT/EXCEPT, window functions, views, materialized views, triggers, TEMP tables, generated columns (STORED + VIRTUAL), constraints, full FK actions, UPSERT, RETURNING, JSON/JSONB (14 Postgres operators + SQL/JSON path language), full-text search, prepared statements with plan caching, and a queryable system catalog. Full list under SQL
- ACID - Copy-on-Write B+ tree, shadow paging, no WAL. Snapshot isolation with concurrent readers
- Authenticated commit slots - the commit metadata (table roots, catalog) carries its own HMAC; older files migrate one-way via
.upgrade - P2P sync - Merkle-based table diffing over Noise-encrypted channels with PSK auth
- CLI - SQL shell with tab completion, syntax highlighting, 27 dot-commands (.backup, .verify, .upgrade, .rekey, .sync, .dump, ...)
- Citadel Studio - Native desktop client for SQL, stored memory, vector inspection, and vault diagnostics
- 3-tier key hierarchy - Passphrase -> Argon2id -> Master Key -> AES-KW -> REK -> HKDF -> DEK + MAC
- Cryptographic forgetting - Whole-store and per-region / per-atom key erasure via citadeldb-mem. Pre-erasure backups, copied keys, and exported plaintext are outside that erasure
- FIPS-oriented at-rest profile - PBKDF2-HMAC-SHA256 + AES-256-CTR for database storage; not a claim of whole-product validation
- Audit log - HMAC-SHA256 chained within files and across retained v2 generations; retained-history verification detects record edits and broken retained links, but there is no external anti-rollback anchor
- Hot backup - Consistent snapshots via MVCC, no write blocking
- Overflow pages - Large values handled transparently, up to 1 GiB per value
- Cross-platform - Windows, Linux, macOS. Python, C FFI, and WebAssembly bindings
- Thousands of tests - Unit, integration, and torture tests across the workspace
Speed benchmarks
Measured on September 13, 20 and 27, 2026 (UTC) on an Intel Core i9-12900HX, Windows 11 Pro, Rust 1.98.0, and SQLite 3.51.3. Runs use one fixed logical processor, with durability disabled and both caches configured for 4,096 pages (about 32 MiB). Most cases use 100K rows; schemas and operations vary as listed below.
Each time is the arithmetic mean of two or four per-run sample medians, with 30 samples per run. Ratios use unrounded SQLite time / Citadel time: above 1 means Citadel is faster, below 1 means Citadel is slower. For example, 0.5x means Citadel takes twice as long as SQLite.
Sixteen comparisons were refreshed on September 27 at 0dbe126c: nine execution cases and seven cached repeat reads. Each refreshed row uses four new runs per engine in Citadel/SQLite/SQLite/Citadel, then SQLite/Citadel/Citadel/SQLite order. Other rows retain their September 13 or 20 measurements and source revisions. This is a combined snapshot, not a full-suite run at the latest revision. Source revisions, run settings, medians, 95% intervals, and drift identify every row.
Execution speed
37 comparisons of writes and reads that execute each iteration, including rotating-parameter queries. Fixture resets are excluded unless the case description says otherwise.
Benchmark Citadel SQLite Ratio
----------------------------------------------------------------------
join_param 2.6 us 51.2 us 19.7x
fts_rank_first_execution 6.86 ms 64.5 ms 9.4x
insert_returning 80.8 us 351 us 4.34x
update_returning 65.7 us 232 us 3.53x
window_agg 33.6 ms 108 ms 3.2x
upsert_returning 124 us 363 us 2.94x
sort_paginate_pk 9.13 us 26.1 us 2.86x
delete_returning 97.2 us 269 us 2.76x
window_rank 69.2 ms 182 ms 2.63x
fts_phrase 5.66 ms 14.6 ms 2.58x
fts_match 4.94 ms 12.1 ms 2.44x
json_extract 22.6 ms 49 ms 2.17x
scan 6.31 ms 13.2 ms 2.09x
insert 25.5 us 51.8 us 2.03x
insert_gen_virtual 36.4 us 66.4 us 1.82x
wide_proj_full 6.83 ms 12.1 ms 1.77x
insert_gen_stored 37.3 us 65.7 us 1.76x
upsert_all_new 36.2 us 63.5 us 1.76x
wide_proj_pk 416 us 728 us 1.75x
truncate 57.6 us 101 us 1.75x
upsert_dedup 31.9 us 53.7 us 1.68x
savepoint_rollback 2.08 ms 3.22 ms 1.55x
delete 75.1 us 116 us 1.54x
wide_proj_2col 651 us 998 us 1.53x
covered_count 377 us 561 us 1.49x
wide_proj_3col 1.28 ms 1.89 ms 1.47x
savepoint_nested 232 us 322 us 1.39x
update 36.1 us 45.8 us 1.27x
insert_select 171 us 214 us 1.25x
with_dml 122 us 147 us 1.21x
fk_cascade_delete_only 52.4 us 63.2 us 1.2x
fk_cascade 122 us 144 us 1.18x
savepoint_create 916 ns 1.07 us 1.16x
upsert_mixed 56.1 us 64.7 us 1.15x
covered_range 105 us 119 us 1.14x
update_gen_propagate 65.7 us 70.8 us 1.08x
upsert_counter 79.1 us 82.7 us 1.05x
Cached repeat reads
22 comparisons of identical reads against unchanged data. Citadel reuses cached results; union reuses projected branch rows and reconstructs UNION ALL output. SQLite executes the query again. These timings do not represent the first query after a write.
Benchmark Citadel SQLite Ratio
----------------------------------------------------------------------
correlated_in 242 ns 2.78 s 11500000x
fts_rank 534 ns 63.8 ms 120000x
correlated_exists 239 ns 9.61 ms 40200x
jsonb_contains 1.81 us 40.5 ms 22400x
sort_nocase 446 ns 4.71 ms 10600x
cte 1.46 us 9.29 ms 6350x
sort 674 ns 4.03 ms 5990x
group_by 2.47 us 14.5 ms 5860x
sum 529 ns 2.74 ms 5180x
distinct 1.82 us 5.94 ms 3260x
full_outer_join 25.4 us 31.7 ms 1250x
correlated_scalar 23.6 us 28.1 ms 1190x
recursive_cte 267 ns 175 us 654x
partial_index_point 269 ns 22.6 us 84.1x
view_point 300 ns 22.8 us 75.9x
point 302 ns 22.6 us 74.9x
filter 38.6 us 2.74 ms 70.9x
view_filter 38.6 us 2.65 ms 68.8x
count 855 ns 37.4 us 43.7x
select_gen_virtual 2.21 us 34.5 us 15.6x
join 24.7 us 147 us 5.94x
union 50.6 us 230 us 4.54x
Citadel-only
No SQLite comparison is reported for these seven cases. json_table executes each iteration; the other six measure cached repeat reads.
Benchmark Citadel SQLite Ratio
----------------------------------------------------------------------
json_table 7.42 ms - -
lateral 2.63 us - -
date_sort 1.81 us - -
date_extract 848 ns - -
date_groupby 576 ns - -
date_arith 260 ns - -
date_range_scan 257 ns - -
Index comparisons
The same query within Citadel, with and without its index. Ratios are unindexed / indexed time. json_gin rotates unique JSON-id probes; fts_index repeats a fixed query on a TEXT column. Both execute each iteration.
Benchmark Without index With index Ratio
----------------------------------------------------------------------
json_gin 8.26 ms 5.77 us 1430x
fts_index 1.95 s 4.76 ms 409x
Exact queries, schemas, input sizes, and timed boundaries are in the H2H implementations. Shared database settings and result collection are in common.rs.
- SQLite uses
page_size=8192, journal_mode=MEMORY, synchronous=OFF, cache_size=4096. Citadel usesSyncMode::Offandcache_size=4096; its 8,208-byte stored pages contain an 8,160-byte decrypted body. Cache entry counts match, not exact byte use. These runs do not measure durable commit latency. - Result rows, including RETURNING output, are fully collected. Most read cases reuse a prepared statement. Dataset creation is outside the timer.
insert_selectincludes creating the destination table and copying 1K rows into it, each as a separate autocommit statement. Dropping it is excluded.fts_rank_first_executionuses a fresh prepared statement each iteration; preparation and disposal are excluded. It is not a disk-cold I/O measurement.fts_rankreuses the prepared result. Citadel TS_RANK and SQLite BM25 are different ranking algorithms.fk_cascadeincludes inserting one parent and 100 children, committing, then deleting the parent.fk_cascade_delete_onlytimes only the cascading delete.savepoint_createincludes BEGIN, SAVEPOINT, RELEASE, and COMMIT.savepoint_nestedcreates ten nested savepoints with 100 inserts at each level, rolls back to the sixth, releases the remaining savepoints, and commits.savepoint_rollbackinserts 1K rows before a savepoint and 10K after it, rolls back the latter, and commits.- Criterion uses 30 samples per arm. September 13 cohorts use a 1-second warmup and a 2-second measurement target; September 20 and 27 cohorts use 3 and 8 seconds. Slow cases run longer to complete all samples. Runs are serial on logical processor 0, with no concurrent builds.
- September 27 cohorts use one source build and eight engine-filtered jobs: Citadel/SQLite/SQLite/Citadel, then SQLite/Citadel/Citadel/SQLite. Every one of the four medians per engine contributes. Earlier source-comparison cohorts retain their recorded candidate runs and matching SQLite controls.
- Per-run 95% median intervals and chronological drift are retained in the data. Range or drift above 5% is flagged for refreshed rows; flagged runs remain included. Intervals are not pooled, and ratios do not establish a universal speedup or measure change from a previous release.
For example, rerun the refreshed UPDATE cases at their recorded revision
with this filter; replace citadel with sqlite for the other engine:
cargo bench --locked -p citadeldb-sql --bench h2h_bench -- \
'^(update|update_gen_propagate|update_returning)/citadel/$' \
--sample-size 30 --warm-up-time 3 --measurement-time 8 --noplot
Preserve the built executable, use a fresh CRITERION_HOME per job, and run the
recorded eight-job engine order with fixed CPU affinity. The command above
runs the current checkout; reproducing a row requires its recorded source
revision and cohort. Exact Criterion IDs, executable hashes, all per-run
medians, intervals and evidence hashes are in
sql-benchmarks.json.
SQL
Statements - CREATE/DROP TABLE (incl. TEMP), ALTER TABLE (ADD/DROP/RENAME COLUMN, RENAME TABLE, DISABLE/ENABLE TRIGGER), CREATE/DROP INDEX (incl. partial WHERE, expression keys, CONCURRENTLY), REINDEX [DATABASE] / REINDEX [TABLE | INDEX] name, CREATE/DROP VIEW, CREATE/DROP MATERIALIZED VIEW (with REFRESH [CONCURRENTLY]), CREATE/DROP TRIGGER (BEFORE/AFTER/INSTEAD OF, FOR EACH ROW/STATEMENT, REFERENCING NEW/OLD TABLE, WHEN, UPDATE OF cols), INSERT (VALUES, SELECT, ON CONFLICT DO NOTHING/DO UPDATE, ON CONSTRAINT), SELECT, UPDATE (including correlated subqueries in SET), DELETE, TRUNCATE TABLE, RETURNING (with OLD/NEW), BEGIN [READ ONLY | READ WRITE]/COMMIT/ROLLBACK, SAVEPOINT/RELEASE/ROLLBACK TO, SET [LOCAL] TIME ZONE, EXPLAIN, REFRESH MATERIALIZED VIEW
Constraints - PRIMARY KEY, NOT NULL, UNIQUE, DEFAULT, CHECK (column + table level), FOREIGN KEY with full referential actions (ON DELETE / ON UPDATE CASCADE / SET NULL / SET DEFAULT / RESTRICT / NO ACTION), GENERATED ALWAYS AS (...) STORED|VIRTUAL
Collations - BINARY, NOCASE (ASCII case-insensitive), and RTRIM (ignores trailing spaces). Text primary keys use their declared collation; foreign keys use the referenced columns' collation. Column index keys inherit their column's collation unless overridden with COLLATE. INTERVAL keys (primary, unique, foreign and index) compare by length as = does, so '1 month' and '30 days' are one key; REINDEX converts interval columns stored by earlier versions.
Types - INTEGER, REAL, TEXT, BLOB, BOOLEAN, DATE, TIME, TIMESTAMP (WITH TIME ZONE), INTERVAL, JSON, JSONB, TSVECTOR, TSQUERY, ARRAY
JSON / JSONB - Postgres operators plus SQL/JSON path functions and the SQL:2023 item methods .bigint(), .decimal(), .integer(), .number(), .string(), .boolean(), .date(), .time(), .time_tz(), .timestamp(), and .timestamp_tz(). Time-zone-dependent evaluation uses the connection's transactional SET [LOCAL] TIME ZONE context.
Clauses - JOINs (INNER, LEFT, RIGHT, CROSS, FULL OUTER; LATERAL with CROSS/INNER/LEFT), subqueries (scalar, IN, EXISTS, correlated), CTEs (WITH / WITH RECURSIVE / WITH-DML: WITH x AS (INSERT/UPDATE/DELETE ... [RETURNING *]) SELECT ...), UNION/INTERSECT/EXCEPT [ALL], CASE, BETWEEN, LIKE, DISTINCT, ANY / ALL (subquery + array forms), GROUP BY/HAVING, ORDER BY, LIMIT/OFFSET
Window functions - ROW_NUMBER, RANK, DENSE_RANK, NTILE, LAG, LEAD, FIRST_VALUE, LAST_VALUE, SUM/COUNT/AVG/MIN/MAX OVER with PARTITION BY, ORDER BY, ROWS/RANGE frames. In grouped queries, window functions operate on the groups remaining after GROUP BY and HAVING.
Views - CREATE/DROP VIEW, OR REPLACE, IF NOT EXISTS/IF EXISTS, column aliases, nested views
Materialized views - CREATE MATERIALIZED VIEW [IF NOT EXISTS] name AS SELECT ..., REFRESH MATERIALIZED VIEW [CONCURRENTLY] name (CONCURRENTLY does a diff-merge - DELETE removed rows, UPDATE changed rows, INSERT new rows - instead of TRUNCATE+repopulate), DROP MATERIALIZED VIEW [CASCADE], full backing-table semantics (indexes, joins, planner sees a real table), pg_matviews introspection
Triggers - CREATE TRIGGER name {BEFORE|AFTER|INSTEAD OF} {INSERT|UPDATE [OF cols]|DELETE} ON table FOR EACH {ROW|STATEMENT} [REFERENCING NEW TABLE AS new_t OLD TABLE AS old_t] [WHEN (expr)] BEGIN ... END. INSTEAD OF triggers make views writable. Transition tables work as virtual tables in trigger bodies. ALTER TABLE ... DISABLE/ENABLE TRIGGER [name|ALL]. PG-faithful name-order firing. Introspection via information_schema.triggers and SHOW TRIGGERS [ON table].
TEMP tables - CREATE TEMP TABLE ... lives in a per-connection in-memory database, dropped on disconnect. Full DDL/DML/index/constraint/trigger parity with persistent tables.
Functions - COUNT, SUM, AVG, MIN, MAX, LENGTH, UPPER, LOWER, SUBSTR/SUBSTRING, TRIM/LTRIM/RTRIM, REPLACE, INSTR, CONCAT, HEX, ABS, ROUND, CEIL/CEILING, FLOOR, SIGN, SQRT, RANDOM, COALESCE, NULLIF, GREATEST, LEAST, CAST, TYPEOF, IIF. Non-window aggregates support FILTER (WHERE ...).
Date/Time Functions - NOW, CURRENT_TIMESTAMP, CURRENT_DATE, CURRENT_TIME, LOCALTIMESTAMP, LOCALTIME, CLOCK_TIMESTAMP, EXTRACT, DATE_PART, DATE_TRUNC, DATE_BIN, AGE, MAKE_DATE, MAKE_TIME, MAKE_TIMESTAMP, MAKE_INTERVAL, JUSTIFY_DAYS, JUSTIFY_HOURS, JUSTIFY_INTERVAL, ISFINITE, DATE, TIME, DATETIME, STRFTIME, JULIANDAY, UNIXEPOCH, TIMEDIFF, AT TIME ZONE. Supports INTERVAL '1 year 2 months', DATE '2024-01-15', TIMESTAMP '2024-01-15 12:30:00Z', infinity/-infinity sentinels, BC dates, full IANA zone parsing (jiff), PG-normalized INTERVAL comparison.
Full-text search - tsvector / tsquery types, to_tsvector / to_tsquery / plainto_tsquery / phraseto_tsquery / websearch_to_tsquery builders, @@ match operator, ts_rank / ts_rank_cd ranking with weighted positions (A/B/C/D), prefix matching (term:*), phrase distance (<N>), inverted indexes via CREATE INDEX ... USING fts
System catalog - information_schema.tables, information_schema.columns, information_schema.key_column_usage, information_schema.table_constraints, information_schema.triggers, pg_timezone_names, pg_timezone_abbrevs, pg_matviews (virtual tables, queryable). SHOW TRIGGERS [ON table] and SHOW MATERIALIZED VIEWS shorthands for the corresponding catalog queries.
Prepared statements - $1, $2, ... positional parameters with LRU statement cache plus snapshot-tagged plan caching for joins and compound queries (cache invalidates only on commit, never per-call)
Multi-statement scripts - Connection::execute_script(sql) runs ;-separated statements in one call, returning per-statement outcomes with partial-success preserved. WASM: db.run(sql) returns [{type, ...}, ...].
UPSERT - INSERT ... ON CONFLICT (cols) DO NOTHING / DO UPDATE SET col = excluded.col ... WHERE ... and ON CONFLICT ON CONSTRAINT idx_name. excluded.* refers to the proposed row; bare col refers to the existing row.
Security
No plaintext on disk. Every page is encrypted before writing and authenticated before reading.
Separate key file. Encryption keys live in {dbname}.citadel-keys, not inside the database. The passphrase derives a master key in memory via Argon2id (or PBKDF2 in the FIPS-oriented at-rest profile) and never touches disk.
Key backup. Export an encrypted key backup with a separate recovery passphrase. Restore access without re-encrypting the entire database.
Instant rekey. Changing the passphrase re-wraps the root encryption key. No page re-encryption - instant regardless of database size.
Encrypted sync. Noise protocol (NNpsk0_25519_ChaChaPoly_BLAKE2s) with a 256-bit pre-shared key. Ephemeral Curve25519 keys per session for forward secrecy.
Architecture
Clients and bindings:
+---------------------------------------------+
| citadel-studio | Memory, SQL, and vault client
+----------------------+----------------------+
| citadel-cli | citadel-python | CLI, Python wheel
+----------------------+----------------------+
| citadel-ffi | citadel-wasm | C FFI, WebAssembly
+----------------------+----------------------+
Agent layer:
+---------------------------------------------+
| citadel-ai | Agent runtime (ReAct + Reflexion)
+---------------------------------------------+
| citadel-llm | LLM clients: Claude, OpenAI, Ollama, Gemini
+---------------------------------------------+
Memory layer:
+---------------------------------------------+
| citadel-mcp | MCP server for memory tools
+---------------------------------------------+
| citadel-mem | Regions, atoms, recall, erasure
+---------------------------------------------+
| citadel-vector | VECTOR(N) type + PRISM filtered ANN
+---------------------------------------------+
Encrypted database engine:
+----------------------+----------------------+
| citadel-sql | sql-json-path | SQL frontend, SQL/JSON paths
+----------------------+----------------------+
| citadel | Database API, builder, vault lifecycle
+-------------+--------------+----------------+
| citadel-txn | citadel-sync | citadel-crypto | Transactions, replication, keys
+-------------+--------------+----------------+
| citadel-buffer | citadel-page | Buffer pool (SIEVE), page codec
+----------------------------+----------------+
| citadel-io | File I/O, fsync, io_uring
+---------------------------------------------+
| citadel-core | Types, errors, cancellation
+---------------------------------------------+
Evaluation harnesses:
+----------------------+----------------------+
| citadel-membench | citadel-swe | Memory and agent benchmarks
+----------------------+----------------------+
Studio calls the database and SQL APIs directly and uses MemoryMaintenance for
stored-memory inspection and erasure. It needs no MCP server or embedding model.
Page Layout (8,208 bytes)
+----------+--------------------+----------+
| IV 16B | Ciphertext 8160B | MAC 32B |
+----------+--------------------+----------+
Fresh random IV per page. HMAC verified before decryption.
Commit Protocol
Shadow paging with a god byte - one byte selects the active commit slot. Atomic commits without WAL:
- Write dirty pages to new locations (CoW)
- Compute Merkle hashes bottom-up
- Update the inactive commit slot
- Flip the god byte
SyncMode::Full flushes the pages and commit slot before the flip, then flushes
the selector before returning success. If that final flush fails, the commit's
durability is uncertain and further writes return Error::ReopenRequired.
Close and reopen the database before writing again.
Integrity Boundary
What the at-rest integrity machinery does and does not guarantee against an attacker with file access:
- Per-page HMAC binds
(epoch, page_id, IV, ciphertext). Any modification of a page's bytes is detected before decryption. It does not bind the commit generation: a page image validly written in the past for the same(page_id, epoch)verifies forever. - Commit slots have two accepted formats. V1 slots carry a truncated HMAC-SHA256 over every field except the MAC itself; legacy slots carry only a keyless checksum over a prefix. Checksum-valid legacy slots remain readable only while no V1 requirement is recorded. Once both physical slots are valid V1 and the vault records that one-way requirement, any checksum-valid legacy slot is rejected as downgrade evidence, and writers refuse to create one.
- Rollback to an older genuine state is outside this boundary. An earlier authenticated slot plus its matching pages can pass the data-file checks; an older internally consistent snapshot of all local vault state, including the data, key, and retained audit files, also passes local authentication. Detecting freshness requires an external anchor - for example, store the latest commit's
txn_idand Merkle root outside the attacker's reach and compare them after opening.
Language Bindings
C / C++
Static or dynamic library with auto-generated citadel.h (cbindgen). Exported entry points are panic-safe.
#include "citadel.h"
int main(void) {
struct CitadelDb *db = NULL;
struct CitadelSqlConn *conn = NULL;
struct CitadelSqlResult *result = NULL;
citadel_error_t status = citadel_create(
"my.db", (const uint8_t *)"secret", 6, NULL, &db);
if (status != CITADEL_ERROR_T_OK) goto cleanup;
status = citadel_sql_open(db, &conn);
if (status != CITADEL_ERROR_T_OK) goto cleanup;
status = citadel_sql_execute(conn, "SELECT 1 + 1 AS value;", &result);
cleanup:
citadel_sql_result_free(result);
citadel_sql_close(conn);
citadel_close(db);
return status == CITADEL_ERROR_T_OK ? 0 : 1;
}
WebAssembly
Install with npm install @citadeldb/wasm.
import init, { CitadelDb } from "@citadeldb/wasm";
await init();
const db = new CitadelDb("secret");
db.execute("CREATE TABLE t (id INTEGER PRIMARY KEY, name TEXT);");
db.execute("INSERT INTO t (id, name) VALUES (1, 'Alice');");
const result = db.query("SELECT * FROM t;");
// { columns: ["id", "name"], rows: [[1, "Alice"]] }
db.put(new Uint8Array([1, 2, 3]), new Uint8Array([4, 5, 6]));
db.free();
Build the npm package: bash scripts/publish-wasm.sh
Python
One importable wheel with the full engine (SQL, vectors, memory, agent runtime) and bundled type stubs.
pip install citadeldb
import citadeldb
db = citadeldb.connect("my.db", key="secret", create=True)
db.execute("CREATE TABLE t (id INTEGER PRIMARY KEY, name TEXT)")
db.execute("INSERT INTO t VALUES (1, 'Alice')")
db.query("SELECT * FROM t").to_dicts()
# [{'id': 1, 'name': 'Alice'}]
Building
Rust 1.95+.
git clone https://github.com/yp3y5akh0v/citadel.git
cd citadel
cargo build --release