CedroDB for Python

Keep agent checkpoints, searchable memory and local jobs in one database. CedroDB is an embedded document engine written in Rust, with a native Python API. Save agent state and its result/outbox writes atomically with expected-revision checks. Retrieve memory using text/BM25, supplied vectors/cosine or hybrid/RRF, with metadata prefilters and explicit scan budgets. No model service is required. A form submission and its outbox event can commit together, indexed queries find pending work, and a durable changefeed lets your application resume processing after restarting. Durable queues schedule local work with leases, retries and dead-job recovery. Conditional batches reserve resources and append events only when all expected documents still match. Integrity verification and portable backups preserve the recovery history.

Use it for offline upload queues, report generation, file-processing tools, desktop application state, or small services that need JSON documents without running a database server. The Python package calls the Rust engine directly; it does not spawn a process or maintain a second Python storage implementation.

Experimental software. Active documents and indexes live in RAM, a directory has one owning handle/process, and the log currently has no compaction. See the limits below before choosing it for important data. There are no performance claims or production-readiness guarantees.

Install on your operating system

Use CPython 3.9+ in a virtual environment. These commands use the environment's interpreter directly, so activation and shell execution-policy changes are unnecessary.

macOS / Linux

python3 -m venv .venv
.venv/bin/python -m pip install cedrodb==0.8.0
.venv/bin/python -m cedrodb doctor

Windows PowerShell (64-bit CPython)

py -3 -m venv .venv
.\.venv\Scripts\python.exe -m pip install cedrodb==0.8.0
.\.venv\Scripts\python.exe -m cedrodb doctor

With an environment already selected, the installation command is simply:

python -m pip install cedrodb==0.8.0

doctor prints JSON containing the actual interpreter, architecture and package version after loading the native extension. It opens no database. If your command is python3 or an environment-specific path, use that same command consistently.

The interpreter's architecture must match the wheel; an ARM64 machine running x86_64 Python uses an x86_64 wheel. Free-threaded Python, PyPy and mobile Python are outside the tested ABI matrix. A compatible wheel installs without Rust. To require a binary and fail clearly if none matches:

python -m pip install --only-binary=:all: cedrodb==0.8.0

A source build needs Python 3.9+, Rust 1.85+, Cargo and a platform C linker (Xcode command-line tools on macOS, a C toolchain on Linux, or compatible MSVC build tools on Windows). Source-build availability is not a support guarantee. The self-contained source archive bundles the Rust core:

python -m pip install ./cedrodb-0.8.0.tar.gz

Check GitHub Releases for actual assets and checksums. A missing package version, unavailable wheel, missing compiler and a database-lock error are different problems; changing database files does not fix installation. Keep the same Python interpreter for pip and execution.

Inspect performance and monitor a live application

from cedrodb import Database

with Database('./app.cedro') as db:
    memory = db.collection('memory')
    memory.put_many({
        'a': {'tenant': 'shop-1', 'text': 'refund policy'},
        'b': {'tenant': 'shop-2', 'text': 'shipping policy'},
    })
    filters = [{'field': 'tenant', 'op': 'eq', 'value': 'shop-1'}]
    before = memory.profile_query(filters)
    memory.create_index('tenant')
    after = memory.profile_query(filters)
    assert before['page']['records'] == after['page']['records']
    print(before['plan'], after['plan'])
    retrieval = memory.profile_search(text='refund', filters=filters)
    print(retrieval['elapsed_micros'], retrieval['plan'])
    print(db.diagnostics())

profile_query(filters=None, *, limit=100, after=None) and profile_search(...) execute once, returning {page, elapsed_micros, plan}. Search accepts exactly the same options as search(). The plan contains collection_records, candidate_records, scanned, returned, used_index. A keyset query may stop early, so candidate count and scanned count can differ. The returned page contains normal documents; redact it before exporting logs.

db.diagnostics() returns {stats, limits, collections, session, poisoned, writer_active} without document values. Collection entries contain collection, records, content_bytes, index_fields. session.query, .search, .commit contain attempts, successes, failures, total_elapsed_micros, max_elapsed_micros. Encoded content bytes are not process RSS. Snapshots may race concurrent metric completions; there is no global wall-clock snapshot.

Query/search timers include native validation and waiting for the engine mutex; commit timers start in the nonempty shared commit path and exclude waiting for that mutex. Python/JSON transport and external model calls are excluded. Calls rejected by Python validation never enter native counters. Commit metrics exclude conflicts, empty commits, and write errors rejected before the shared commit path. Metrics are handle-local, fixed-size and reset on reopening; no histogram or percentile is inferred from their totals.

Run a fresh temporary workload with correctness checks and measured timings:

python -m cedrodb benchmark --records 1000 --iterations 30
python -m cedrodb benchmark --records 10000 --iterations 100 > benchmark.json

The JSON report includes environment/workload settings, native and Python-call latency samples summarized with empirical nearest-rank p50/p95/p99, scan/index plans, batch writes, index build and reopen time. Small samples cannot establish tail-latency guarantees. Vectors are synthetic and no retrieval-quality claim is made. OS caches remain uncontrolled; results describe this machine/run. The tool opens no application database and removes its temporary database on exit.

For periodic JSON snapshots, see examples/performance_monitor.py. Sample the application's existing owner handle; a second process cannot open the same database concurrently. Choose your sampling interval and logging policy.

Quickstart: save and query documents

from cedrodb import Database

with Database("./app.cedro") as db:
    orders = db.collection("orders")
    orders.put_many({
        "order:1": {"status": "paid", "total": 120},
        "order:2": {"status": "pending", "total": 35},
    })
    orders.create_index("status")
    page = orders.find_page(
        [{"field": "status", "op": "eq", "value": "paid"}], limit=20
    )
    print(page["records"])       # [{"key": "order:1", "value": {...}}]
    print(page["used_index"])    # status
    print(orders.get("order:1"))
    print(db.stats())

Collections store any supported JSON value, including arrays and null. put replaces the complete value for a key; delete removes it. put_many accepts a mapping or an iterable of (key, value) pairs and commits them atomically.

find() returns one list of {key, value} records. find_page() also returns next_after, scanned and used_index. To continue, pass next_after as after; None means there is no continuation. iter_records(page_size=100) iterates bounded result pages. Results are ordered by key; pages do not share a snapshot. Writes between requests can change subsequent results. Limits are 1..1000 records.

Filters are combined with AND. Operations are eq, ne, gt, gte, lt, lte, and exists (with a boolean value). A field is a top-level name or an RFC 6901 JSON Pointer, such as /customer/city. Missing fields differ from JSON null; use exists to test absence. Equality indexes stay consistent with writes and must be recreated after reopening or restoring a database.

Offline writes and the atomic outbox

Commit the document and the event in the same transaction. Your uploader can then find the event after a restart without losing the corresponding form.

from cedrodb import Database

with Database("./field-app.cedro") as db:
    with db.transaction() as tx:
        tx.put("forms", "inspection:1", {
            "site": "Santo Domingo", "status": "pending_upload"
        })
        tx.put("outbox", "inspection:1:submitted", {
            "event_id": "inspection:1:submitted",
            "type": "inspection.submitted", "form_id": "inspection:1"
        })

    page = db.changes(since=0, limit=100)
    for transaction in page["transactions"]:
        for operation in transaction["operations"]:
            if operation["collection"] == "outbox" and operation["kind"] == "put":
                print(operation["value"])
    # Save page["next_cursor"] only after processing succeeds.

The feed survives close/reopen and contains complete put/delete operations in transaction order. since is an exclusive transaction cursor, starting at zero, and belongs to this database history. limit counts transactions (1..1000), not operations. has_more tells you whether another page is available. Pages also cap encoded frame bytes at max_frame_bytes + 12 without splitting a transaction; decoded Python objects require additional memory.

Your application owns the uploader, checkpoint storage and event deduplication. A crash after an external effect but before its checkpoint can repeat delivery; CedroDB does not promise exactly-once delivery or provide a background worker.

db.transaction() buffers writes and copies staged values. A successful context exit commits one batch; an exception rolls it back. Reads continue to observe committed data, without read-your-writes, snapshot isolation or automatic conflict detection. Alternatively, pass operations directly to db.batch([...]):

with Database("./app.cedro") as db:
    db.batch([
        {"kind": "put", "collection": "orders", "key": "order:3", "value": {"total": 10}},
        {"kind": "delete", "collection": "drafts", "key": "order:3"},
    ])

Run durable local jobs

Create an application-managed worker loop without adding a separate queue server. Job state survives reopening and is included in database backups.

from cedrodb import Database

with Database("./worker.cedro") as db:
    queue = db.queue("reports")
    enqueued = queue.enqueue(
        "report:1", {"order_id": "order:1"}, max_attempts=3, priority=10
    )
    job = queue.claim("worker-1", lease_ms=30_000)
    if job is not None:
        # Generate the local result while the lease is active.
        completed = queue.ack(job["id"], job["lease_token"], operations=[
            {"kind": "put", "collection": "reports", "key": job["id"],
             "value": {"status": "ready"}},
            {"kind": "put", "collection": "outbox", "key": job["id"] + ":ready",
             "value": {"event_id": job["id"] + ":ready", "type": "report.ready"}},
        ])
        print("Completed:", completed)
    print(queue.stats())

enqueue() returns {inserted, job}. Job IDs are retained after completion or failure: enqueueing an existing ID returns its existing job, without replacing its payload or resetting its state. This supports application-level deduplication while that job is retained. Use a new ID for new work, or retry_dead() to reset the attempts of a dead job explicitly.

claim() returns a Job or None, prioritizing eligible jobs by descending priority. delay_ms controls when a newly queued job becomes available. Each claim consumes one attempt and returns an opaque lease_token. Acknowledgment, failure and lease extension require the matching token and a lease that has not expired. A stale or nonmatching token returns False without changing the job.

ack(..., operations=[...]) writes business documents, outbox events and the job's done state in one durable transaction. The operation list uses the same put/delete dictionaries as batch(). Invalid operations leave the job leased and apply no business writes. Operations cannot write to any _cedro.jobs.* collection; that namespace is reserved for the queue implementation.

with Database("./worker.cedro") as db:
    queue = db.queue("reports")
    job = queue.claim("worker-1", lease_ms=30_000)
    if job is not None:
        queue.fail(
            job["id"], job["lease_token"], "temporary upload failure",
            retry_delay_ms=5_000,
        )
    recovery = queue.recover_expired(limit=10)
    print(recovery)  # requeued, dead, has_more

fail() schedules a retry or marks the job dead when its attempt budget is exhausted. claim() can reclaim an expired lease if another attempt remains. Run recover_expired() periodically to requeue expired work and mark exhausted leases dead; repeat while has_more is true. Recovery processes 1..1000 jobs per call, additionally bounded by the transaction frame limit. extend() renews an active lease and never shortens its existing deadline. retry_dead() resets attempts to zero and makes a dead job available again.

All queue methods accept an optional now_ms where applicable. The default is the current Unix epoch time in milliseconds; explicit timestamps make testing deterministic. Times and durations must be integers in 0..MAX_SAFE_INTEGER (2**53 - 1); leases must be positive, and timestamp-plus-duration must also stay within that bound. Booleans are rejected as numeric options. Use a consistent clock across your workers: backward or forward clock jumps affect lease eligibility.

Queue payload numbers are finite and restricted recursively to [-MAX_SAFE_INTEGER, MAX_SAFE_INTEGER] so Rust, Node and Python can exchange jobs safely. Ordinary collection documents and Python ack() business writes retain the full [-2**63, 2**64 - 1] integer range. Encode larger identifiers or exact decimal quantities as strings in job payloads. The job envelope also counts toward document and transaction limits, so the available payload budget is smaller than max_document_bytes. Allow headroom for lease tokens, worker names, errors and acknowledgment writes: a queued job fitting the configured limits does not mean every later transition will fit. Size-limit errors apply no partial transition or business writes. If a transition reaches capacity, close and reopen the store with higher document, frame or state limits as needed before retrying it.

Queue names contain 1..128 ASCII letters, digits, _, . or -. Job IDs contain 1..4096 UTF-8 bytes, workers 1..128 bytes and errors 1..4096 bytes. IDs, workers, errors and tokens must be nonempty without NUL. max_attempts is 1..1000 and priority is 0..1000. Job dictionaries include schema_version, id, payload, status, attempts, max_attempts, priority, created_at, available_at, lease_until, lease_token, worker and last_error. Statuses are queued, leased, done and dead. Lease fields are null when the job is not leased. stats() reports queued/leased/done/dead counts plus ready and expired counts; ready can include reclaimable expired leases.

The application owns the worker loop, error handling, lease renewal and expired recovery. Operations are synchronous and scheduling scans jobs in O(n) time. One process owns the database directory; share its handle across threads. There is no background worker, retention policy or distributed scheduler. Completed/dead jobs remain stored and the append-only log continues growing. Generic CRUD, batch, CAS and conditional write operations targeting _cedro.jobs.* are rejected. Read-only collection inspection and conditions on that namespace remain available. Use the queue API to change job state.

Lease tokens fence changes within the same database history. Restoring or copying a backup creates a history branch, where an old token may still exist; tokens do not fence external systems or independently restored stores. A worker can perform an external effect and crash before acknowledgment. Consumers must deduplicate effects by stable job/event ID; delivery is not exactly-once.

Run python examples/local_worker.py for a deterministic retry, reopen and atomic acknowledgment with a business result and outbox event.

Reserve inventory and publish its event atomically

Use a conditional batch when a decision depends on more than one document. The engine checks every condition against the same committed state while holding the write lock, then commits all operations together. This closes the gap between checking inventory and updating inventory plus its outbox event.

from cedrodb import Database

with Database("./shop.cedro") as db:
    db.collection("inventory").put("desk", {"available": 10})
    result = db.conditional_batch([
        {"kind": "equals", "collection": "inventory", "key": "desk", "value": {"available": 10}},
        {"kind": "absent", "collection": "outbox", "key": "order:1"},
    ], [
        {"kind": "put", "collection": "inventory", "key": "desk", "value": {"available": 9}},
        {"kind": "put", "collection": "outbox", "key": "order:1",
         "value": {"event_id": "order:1", "type": "inventory.reserved"}},
    ])
    print(result)  # {"applied": True, "failed_condition": None, "cursor": 2}

Conditions are absent or equals for a complete JSON value, with at most 64 conditions per call. absent forbids a value field. equals requires value; value=None matches stored null and does not match a missing key. Operations use the same put/delete dictionaries as batch().

On a mismatch, the result has applied=False, the zero-based index of the first failing condition, and the unchanged current cursor; no writes or log frame are produced. On success, failed_condition is None and cursor identifies the committed transaction. An empty operation list checks conditions without advancing the cursor. Empty conditions provide an unconditional atomic batch.

The entire request is validated before mutation, even if a condition would fail. Unsupported JSON, extra fields, invalid locations or limits raise an exception. A conditional batch provides atomic optimistic checks within one database handle; it does not provide a distributed lock or prevent repeated external effects. Retry stale inventory decisions after reading current state, and deduplicate event deliveries in the consumer.

Run python examples/inventory_outbox.py for a successful reservation, a rejected stale request, and its durable history after reopening.

Verify, back up and restore

A backup contains the committed log inside a versioned container with a length and CRC checksum. It preserves transaction history and changefeed cursors.

from pathlib import Path
from cedrodb import Database

backup = Path("./field-app.backup")      # Must not already exist.
restored = Path("./recovered.cedro")      # Must not already exist, even empty.

with Database("./field-app.cedro") as db:
    print(db.verify())
    print(db.backup(backup))

report = Database.restore_backup(backup, restored)
print(report)  # log_bytes, transactions, records, content_bytes

with Database(restored) as db:
    db.collection("forms").create_index("status")
    print(db.changes(since=0))

verify() reads and validates the committed log; it does not repair data. backup(path) creates a new file and never overwrites an existing file or folder. restore_backup(backup_path, destination_path) validates the complete container and log before creating a new destination directory. Parent directories must already exist. Static restore returns an IntegrityReport dictionary, without retaining an open handle or file lock. Index definitions are handle-local and must be recreated; the committed documents and history are retained.

For custom content limits, pass the same configuration to restore and open:

limits = {"max_document_bytes": 2_097_152, "max_state_bytes": 134_217_728}
Database.restore_backup("./large.backup", "./large-restored.cedro", **limits)
with Database("./large-restored.cedro", **limits) as db:
    print(db.verify())

All three methods are synchronous and perform disk I/O; the native operation releases Python's GIL. Verification replays state in memory and can require another copy of the live state plus a frame buffer. CRC detects accidental corruption; it does not authenticate or encrypt a backup. Interrupted I/O can require inspecting and removing an incomplete output before retrying. See backup format and recovery details.

Run the complete offline-form example with python examples/offline_forms.py.

Conditional updates and null

from cedrodb import Database, DELETE, MISSING

with Database("./app.cedro") as db:
    claims = db.collection("claims")
    created = claims.compare_exchange(
        "job:1", expected=MISSING, replacement={"owner": "worker-1"}
    )
    removed = claims.compare_exchange(
        "job:1", expected={"owner": "worker-1"}, replacement=DELETE
    )
    missing = claims.get("unknown", MISSING) is MISSING

replacement is required. None stores JSON null; DELETE removes the record. MISSING as expected means the key must not exist, while expected=None requires a stored null. A mismatch returns False without appending a transaction. The comparison covers the complete JSON value. Exact integers and integral floats compare equally only within the safe range ±2**53; booleans remain distinct.

Persist agent state and its outbox together

db.agent(namespace) stores framework-neutral JSON checkpoints. A revision check prevents a stale worker from replacing newer state. Checkpoint and business/outbox operations are committed in the same durable transaction.

from cedrodb import Database

with Database("./assistant.cedro") as db:
    agent = db.agent("support")
    current = agent.get("conversation:1")
    result = agent.checkpoint(
        "conversation:1", {"messages": [{"role": "assistant", "text": "Saved."}]},
        expected_revision=None if current is None else current["revision"],
        status="completed", metadata={"owner": "ana", "state_format": "support.v1"},
        operations=[{
            "kind": "put", "collection": "outbox", "key": "conversation:1:answered",
            "value": {"event_id": "conversation:1:answered", "type": "answer.ready"},
        }],
    )
    if not result["applied"]:
        print("Reload before retrying:", result["checkpoint"])

expected_revision=None means create only if absent. It is never an unconditional overwrite. Pass the revision returned by get() when updating. On conflict, applied is false, cursor stays unchanged and checkpoint is the current checkpoint (or None for an absent thread). No checkpoint, outbox or other business write is applied. Invalid requests raise before mutation, including when a revision would conflict.

Success returns applied=True, a cursor and the new checkpoint. Its revision is the durable transaction cursor: it increases with database history and need not be consecutive for a particular thread. The envelope contains schema_version=1, thread_id, revision, state, metadata, status and updated_at. Status is running (default), waiting or completed; metadata defaults to {} and must be a JSON object. State may be any supported portable JSON value, including null. now_ms defaults to Unix epoch milliseconds. Checkpoints survive reopening and are included in backups and the changefeed.

Namespace names contain 1..128 ASCII letters, digits, _, . or -; thread IDs contain 1..4096 UTF-8 bytes without NUL. State and metadata numbers must be finite and within ±MAX_SAFE_INTEGER, recursively. Revisions and timestamps use integers in the safe range; booleans are rejected as numeric options. Optional business operations keep ordinary collection integer limits. The checkpoint envelope and all operations count toward document/frame/state limits; an oversized request applies no partial writes.

Generic writes to _cedro.agents.* and _cedro.jobs.* are rejected. Use their dedicated APIs. Namespaces organize data; they are not an authorization boundary. The application owns state schemas, orchestration, tool permissions and external effect deduplication. A checkpoint records a completed persistence step; it does not resume a model/tool call or promise exactly-once external delivery. Restoring a backup creates a history branch; revisions do not fence independently restored copies or external systems. This API is not a LangGraph BaseCheckpointSaver.

Retrieve local knowledge by text, vector or both

Collection.search() ranks committed documents with Unicode alphanumeric lowercase tokenization and BM25, exact cosine similarity, or reciprocal rank fusion (RRF) when both queries are provided. Embeddings come from your application; the SDK does not call a model or a network service.

with Database("./assistant.cedro") as db:
    knowledge = db.collection("knowledge")
    # Synthetic vectors illustrate the API; these are not model embeddings.
    knowledge.put_many({
        "backup": {"owner": "ana", "text": "local backup recovery", "embedding": [1, 0]},
        "jobs": {"owner": "ana", "text": "durable worker retries", "embedding": [0.6, 0.8]},
    })
    knowledge.create_index("owner")
    page = knowledge.search(
        text="backup", vector=[1, 0],
        filters=[{"field": "owner", "op": "eq", "value": "ana"}],
        limit=5, max_scanned=1000,
    )
    for hit in page["hits"]:
        print(hit["key"], hit["score"], hit["text_score"], hit["vector_score"])

Use text alone, vector alone or both. The public signature is:

collection.search(
    *, text=None, vector=None, vector_field="embedding", text_fields=None,
    filters=None, limit=10, max_scanned=10_000, min_vector_score=None,
)

text_fields=None means ["text"]; fields can be top-level names or JSON Pointers such as /content/body. Supply 1..8 unique text fields. Text must contain 1..4096 UTF-8 bytes and 1..64 distinct alphanumeric terms. Vectors contain 1..4096 finite numeric coordinates and must have nonzero magnitude. Present eligible document vectors must be valid and match the query dimensions; missing vectors are skipped. An invalid eligible vector raises instead of silently removing a possible match. min_vector_score, when supplied, requires a vector query and a finite value in [-1, 1].

Metadata filters use the same ANDed predicates as find_page() and run before document vector validation/scoring. Apply the appropriate owner/tenant filter on every request; the database does not authorize access. An equality index can reduce the candidate set and remains local to the open handle. Recreate it on reopen. max_scanned (1..100000) counts candidates visited before filters; exceeding it raises, without returning a partial top-k. limit is 1..1000.

The result contains hits, scanned, matched, used_index and mode (text, vector or hybrid). Hits include the original key/value, final score and nullable text_score/vector_score. matched counts matches before the result limit. Text mode uses BM25 (k1=1.2, b=0.75), vector mode uses cosine, and hybrid mode fuses complete channel rankings with RRF (k=60). Ties are ordered by key. Scores from different modes are not directly comparable.

This is exact bounded scanning over a small local corpus, without a full-text inverted index or approximate nearest-neighbor index. All live documents and ranking buffers use memory; retain the existing database content limits and use max_scanned to bound work. It does not extract facts, generate embeddings, split documents, manage model versions or provide a RAG pipeline.

Run python examples/agent_memory.py for a network-free retrieval, checkpoint/outbox, stale-write rejection and reopen example.

Optional PydanticAI conversation recipe

The package has no agent-framework dependency. An optional recipe uses the published pydantic-ai-slim==2.56.0 API with Python >=3.11 (tested with Python 3.12.15). The base CedroDB SDK continues to support Python >=3.9:

python -m pip install 'pydantic-ai-slim==2.56.0'
python examples/pydantic_ai_history.py

It serializes result.conversation using ConversationTypeAdapter, closes and reopens CedroDB, and passes the restored bundle as conversation= to a fresh agent. Assertions check preserved conversation identity, accumulated request usage, and growing message history. The updated conversation and an outbox event are then committed together. Its TestModel makes no network calls and needs no model credentials. This is conversation storage, not a durable-execution backend: it does not resume interrupted model/tool calls or authorize tool approvals. Configure usage limits in your application and load conversations only from trusted application storage. Pin the framework version and retest serialization when upgrading. See the upstream conversation documentation.

API reference

Database(path, *, max_document_bytes=1_048_576, max_frame_bytes=16_777_216, max_state_bytes=67_108_864) opens or creates a directory. Paths accept strings and os.PathLike objects returning strings. Use a context manager or close(); closing is idempotent and releases the native lock immediately. db.closed reports its state. Existing collection handles fail with DatabaseClosedError after close.

Engine failures raise CedroError. Unsupported JSON raises TypeError or ValueError; integers outside [-2**63, 2**64 - 1] raise OverflowError. NaN, infinity, cycles, non-string object keys and unsupported objects are rejected before mutation. Supported integers round-trip exactly. JSON depth is limited to 64 levels, and the transport always uses allow_nan=False. Type annotations and a py.typed marker are included.

Current limits

• One open owning handle/process per directory; its native calls are serialized.
• Documents, equality indexes and transaction offsets are held in RAM. Encoded content limits exclude allocator overhead, indexes and temporary buffers.
• Append-only log with no compaction, retention or bounded total disk use.
• Handle-local indexes and key-based query pagination, without snapshot isolation.
• aggregate(field) uses floating-point arithmetic on a top-level numeric field and can lose integer precision above 2**53; stored documents remain exact.
• No SQL, MVCC, replication, encryption or automatic synchronization.
• Queue scheduling scans jobs synchronously, without background execution, retention or distributed fencing; job numbers use the cross-SDK safe range.
• Agent state uses revision checks within one database history, without an agent runtime or framework checkpointer protocol implementation.
• Retrieval uses exact scans, without ANN or full-text inverted indexes; embedding generation and corpus lifecycle are application responsibilities.

Upgrade from 0.2 to 0.3

Existing document, batch, query, CAS and changefeed APIs keep their signatures. The database log format is unchanged; 0.2 stores can be opened directly. Version 0.3 adds db.verify(), db.backup(path) and Database.restore_backup(...). Backup containers are distinct from raw cedro.log files: use backup() to create them. Restore requires a new destination and preserves the original history/cursor sequence. Recreate equality indexes after opening the restored store, and configure matching limits when your store exceeds the defaults.

Upgrade from 0.3 to 0.4

Existing APIs and the database/backup formats stay compatible. Version 0.4 adds db.conditional_batch(conditions, operations) and typed Condition and ConditionalResult definitions. Replace application-level get/check/batch sequences with conditional batches when a stale read could overwrite another update. Conditions test complete pre-state documents; they do not observe writes earlier in the operation list. A failed condition is a normal result, while an invalid request raises an exception.

Upgrade from 0.4 to 0.5

Existing APIs and the database/backup formats remain compatible. Version 0.5 adds db.queue(name) and the typed Queue, Job, EnqueueResult, QueueStats and RecoveryResult APIs. Jobs use schema version 1 in reserved _cedro.jobs.<name> collections and participate in the existing durable log, changefeed and backup format. Generic writes to that namespace are now rejected. If a 0.4 application used _cedro.jobs.* for its own data, export or move those records to another collection with the old version before upgrading. Existing logs still replay and can be inspected; legacy application records are not automatically converted into valid jobs. Keep that namespace out of application writes. Queue payloads have a smaller, portable numeric range; existing collection documents keep their previous range. Run expired recovery in your application and deduplicate external effects; existing outbox consumers may need to filter out internal queue operations from the changefeed.

Upgrade from 0.5 to 0.6

Existing APIs and database/backup formats remain compatible. The new APIs are db.agent(namespace) for revision-fenced checkpoints and collection.search() for bounded text/vector/hybrid retrieval. _cedro.agents.* is now reserved, in addition to _cedro.jobs.*. If an older application wrote its own records there, export or move them with the old SDK before upgrading; they are not automatically converted into checkpoint envelopes. Internal checkpoint writes appear in the changefeed, so outbox consumers should select their intended collections. State/metadata use portable numbers; ordinary documents retain their previous numeric range.

Build and test

python -m venv .venv
# Activate the environment with your platform's activation command.
python -m pip install 'maturin>=1.15,<2'
python scripts/prepare_core.py
maturin build --release --out dist
maturin sdist --out dist
python -m pip install ./dist/<your-platform-wheel>.whl
python -m unittest discover -s tests -v
python scripts/verify_sdist.py dist/cedrodb-0.8.0.tar.gz
python examples/offline_forms.py
python examples/inventory_outbox.py
python examples/local_worker.py
python examples/agent_memory.py

prepare_core.py copies only the core manifest, license and Rust source into ignored core/. Regenerate that copy after engine changes. The sdist includes those files and has no dependency on ../.. at installation. verify_sdist.py rebuilds an extracted archive, installs the resulting wheel in an isolated target and runs its tests. Keep Cargo.lock for reproducible dependency versions.

Source · Demo · Issues

Command-line interface (0.8.0)

The current package registers cedrodb and keeps python -m cedrodb as an equivalent entry point. Run cedrodb --help for document, query, retrieval, maintenance and backup commands. See the CLI guide. Earlier published 0.7.0 wheels only contain doctor and benchmark; build/install the current wheel to use the expanded CLI.