LaserData - Laser SDK

This package provides the Python Laser SDK for Apache Iggy. LaserData, Inc. maintains it. PyO3 exposes the Rust SDK to Python, so both clients use the same data contract, codecs, and runtime.

Rust and Python share the data contract and Rust implementation. The bindings expose Python forms of the SDK operations, configuration, and errors. Shared examples and behavior scenarios cover language-neutral behavior.

The current release is 0.6.0. The wire contract and public API use semantic versioning. Before 1.0.0, minor releases can contain breaking changes.

spawn_agent(agent_id, ..., consumer_group=None) separates agent identity from its consumer group. The default group uses the agent ID spelling. Set consumer_group when the deployment needs a different group.

One Apache Iggy connection supports streaming and managed operations for queries, key-value state, graphs, and forks. The optional AGDX agent runtime sends messages and runs asynchronous handlers. It supports at-least-once delivery, per-conversation ordering, duplicate suppression, retries, and dead letters.

Apache Iggy is the underlying streaming core. Projections, the query layer, the key-value store, the knowledge graph, and forks are served by Laser Stack or LaserData Cloud over that same connection. Against Apache Iggy without a managed backend those calls raise UnsupportedError.

Install

pip install laser-sdk

Wheels ship for Linux (x86_64, aarch64) and macOS (Intel, Apple Silicon), Python 3.10 and later.

Every wheel and source build uses Apache Iggy's native VSR transport. Standard streaming commands, LaserData's non-replicated managed command band, and dedicated replicated authorization operations use the same connection.

Connect

import asyncio
from laser_sdk import Laser


async def main():
    laser = await Laser.connect("iggy:iggy@127.0.0.1:8090")
    readings = laser.stream("fleet").topic("readings")
    await readings.ensure(partitions=4)
    caps = await laser.capabilities()
    print(caps.query.available, caps.kv.cas, caps.graph)


asyncio.run(main())

Use a user:password@host:port connection string, or pass address= with credentials=(user, password) instead. Laser.connect_env() reads LASER_CONNECTION_STRING and an optional LASER_STREAM, Laser.local() connects to iggy:iggy@127.0.0.1:8090, and Laser.connect_with_stream(conn, stream) pins a default stream. The SDK supplies the Apache Iggy TCP scheme. Select a stream with laser.stream(name) and a topic with .topic(name). The optional stream= selects a default for the shorter laser.topic(name) form. It does not restrict access to other streams. Accessors select objects, and operations such as publish, replay, and ensure perform I/O.

Python uses the Rust client reconnect policy. TCP connections retry a failed dial and reconnect dropped sockets, by default without limit at one-second intervals. The 30-second connect budget stops the retries of the initial connection. Set reconnection_retries=<count|unlimited> and reconnection_interval=<duration> in the connection string. After reconnecting, the client reapplies those credentials.

Laser.connect calls Rust Laser::connect. Hosts under *.laserdata.cloud and *.laserdata.com use TLS with the bundled LaserData root CA. LASER_TLS_CERT=<path> selects an explicit certificate. LASER_NO_TLS=1 disables automatic TLS. Other hosts retain their connection-string configuration.

LaserData Cloud and Laser Stack enable managed surfaces only when their backend announcement reports ready. await laser.refresh_capabilities() re-probes a long-lived connection after startup or a backend restart. The returned Capabilities groups each managed surface: caps.query is a QueryCaps (available, consistency), caps.kv a KvCaps (available, cas, fenced_leases), caps.filters a FilterCaps, and caps.destinations a DestinationCaps. It also carries versions: OpVersions | None and the advertised backends. Apache Iggy keeps every managed surface off and reports no operation versions.

Connect timeout

Laser.connect gives up after 30 seconds. The budget covers the dial, the TLS handshake, the login, and the capability probe. Pass connect_timeout_ms to use another budget, or set LASER_CONNECT_TIMEOUT_MS. The argument overrides the variable. An expired budget raises TimeoutError that says whether the server never accepted the connection or never answered the login. laser.stream(name).delete() removes a stream you no longer need, and await laser.close() ends the shared connection. See connect timeout and cleanup.

Publish and consume

readings = laser.stream("fleet").topic("readings")
await readings.ensure(partitions=4)

committed = await (
    readings.publish()
    .index("host_id", "node-7")
    .index("cpu", "82")
    .inline_payload()
    .json({"host": "node-7", "cpu": 82})
    .send()
)
print(committed.confirmations)

Producers and publish builders return SendMessagesResponse. Each SendMessagesConfirmationResponse identifies a committed batch by stream, topic, partition, and first offset. The list can be empty when the server does not report offsets. Completion follows the topic durability policy.

Batch and any payload

publish_batch groups records for sending. A topic(..).replay() cursor reads retained records and saves the next offset for each partition. Each poll reads at most 10,000 messages per partition. Later polls resume from the saved offsets. Failed or canceled polls leave those offsets unchanged.

The payload contains bytes in the application-selected format. add_json, add_msgpack, and extend_json provide encoding helpers. add_payload sends raw bytes without inspecting their format. topic.send(payload, headers=, partition_key=) and topic.batch(messages, partition_key=) are the zero-overhead raw paths, and topic.batching(max_records=, max_bytes=, linger_ms=, partition_key=) returns a size-and-time BatchingProducer with send, flush, and close. Compressed data and application-defined formats use the same path. The following sections cover Avro and Protobuf.

batch = readings.publish_batch().inline_payload()
batch.extend_json([{"host": "node-7", "cpu": 82}, {"host": "node-8", "cpu": 40}])
batch.add_payload(b"\x00any-bytes-any-format")  # raw bytes, untouched by the SDK
committed = await batch.send()  # the whole batch, one round-trip

Live producer and consumer

Use Topic.producer, Topic.consumer, and Topic.consumer_group for continuous streaming through Apache Iggy. Producers support batches, delays, retries, resource creation, expiry, size, and routing. Consumers support first, last, next, offset, and timestamp reads. They also support groups, retries, automatic commits, explicit offset storage, and offset inspection.

topic = laser.stream("fleet").topic("events")
producer = topic.producer(
    batch_length=1000,
    linger_ms=5,
    retries=3,
    partitions=4,
)
await producer.init()
committed = await producer.send(b"one", headers={"type": ("uint16", 7)}, key=b"node-42")
batch_committed = await producer.send_batch(
    [(b"two", {"type": 8}), b"three"],
    key=b"node-42",
)

consumer = topic.consumer_group("workers").consumer(
    batch_length=1000,
    poll_interval_ms=5,
    auto_commit="disabled",
)
await consumer.init()
try:
    message = await consumer.next()
    if message is not None:
        await handle(message.payload, message.headers)
        await consumer.commit(message)
finally:
    await consumer.shutdown()

Header values accept ordinary Python scalar values. For an exact Apache Iggy numeric type, pass (kind, value). ConsumerMessage.header_kinds reports the received types. The default auto_commit="polling" with commit_interval_ms=0 matches the Rust default Polling policy. The other modes are "all", "each", "every" with commit_every=, "interval", and "disabled". A commit_interval_ms above zero adds a timer to the chosen mode, so auto_commit="each" with commit_interval_ms=1000 stores on each message or each second. "interval" requires commit_interval_ms above zero. next_within(wait_secs) waits a bounded time and raises TimeoutError when nothing arrives, like Rust next_within.

With automatic commits disabled, call commit(message) after successful handling. shutdown() does not advance the offset past that commit. Consumer waits for new records. A replay() cursor reads retained records in bounded polls and stops its iterator when caught up.

Publish recovery

By default, each publish attempt has a 60-second timeout and a failed attempt retries up to three times. Retry delays start at 250 milliseconds, double after each failure, and stop growing at 30 seconds. Pass publish_timeout_ms, publish_max_retries, and publish_retry_backoff_ms to Laser.connect, or set LASER_PUBLISH_TIMEOUT_MS, LASER_PUBLISH_MAX_RETRIES, and LASER_PUBLISH_RETRY_BACKOFF_MS. The arguments override the variables. Direct producers inherit this configuration while retries and retry_interval_ms stay None, and retries=0 disables resends. A publish that gives up raises PublishFailedError with committed, the confirmed ranges, and unconfirmed, the records without a confirmation. Its __cause__ is the original error. Inspect them before retrying, because unconfirmed records can already be on the server. A batching producer keeps a failed timer flush until the next send(), flush(), or close() reports it. See publish recovery and outage handling.

Typed topics

topic.json(Reading) binds a topic to a dataclass or pydantic model. publish(reading) encodes an instance as JSON, and publish_batch(readings) sends several in one round trip. records(reader_name) reads typed records with the same client-owned offsets as replay(). next() returns a decoded record or None when caught up. If decoding fails, it raises TypedDecodeError with the log position. The next read continues past that record.

from dataclasses import dataclass


@dataclass
class Reading:
    host: str
    cpu: int


readings = laser.stream("fleet").topic("readings").json(Reading)
await readings.publish(Reading(host="node-7", cpu=82)).send()

records = readings.records("metrics-export")
while (record := await records.next()) is not None:
    reading: Reading = record.value  # record.position names the log slot

topic.cbor(Reading) and await topic.schema(schema_id, Reading) bind CBOR and registered-schema bodies the same way. Use the plain laser.topic(name).publish() builder for explicit raw or codec-specific publication. Schema handles resolve and compile their writer schema once and share it between publication and typed reads. Typed Protobuf handles decode records, while publication requires encoded bytes through the plain builder.

Schema-first bodies (Avro / Protobuf)

Compile the registered writer schema before publishing records that use it. The client encodes each value and rejects values that do not match. The managed plane resolves the schema ID to extract indexed columns.

from laser_sdk import CompiledSchema

source = {"kind": "avro", "schema": reading_avro_schema}
schema_id = await laser.schemas().register(source, name="fleet_reading")
compiled = CompiledSchema.compile(source, id=schema_id)

batch = laser.stream("fleet").topic("readings_avro").publish_batch().inline_payload()
for reading in samples:
    batch = batch.add_avro(compiled, schema_id, reading)
await batch.send()

CompiledSchema provides validate, validate_value, decode, and encode_avro. laser.schemas() also provides get, list, and drop. The publish builder provides .avro(compiled, schema_id, value). For an encoded Protobuf body, use .raw_bytes(bytes, "protobuf") or batch .add_raw_bytes(..). Schema registration requires the laser-plane registry in Laser Stack or LaserData Cloud.

Query (managed)

result = await (
    laser.query("readings_v1")
    .where_eq("host_id", "node-7")
    .filter_gte("cpu", 90)
    .order_desc("cpu")
    .limit(10)
    .fetch()
)
for row in result.rows:
    print(result.value_text(row, "host_id"), result.value(row, "cpu"))

result.fields defines the ordered result schema. Each row contains tagged values in that order. Use value() to retain the value type or value_text() for stable display text. Values preserve integer widths, decimal precision, timestamps, UUIDs, bytes, structs, lists, maps, and nullability.

Filters and parameters accept bool, int, float, str, bytes, uuid.UUID, decimal.Decimal, date and time types, None, and lists. datetime.date and datetime.time retain their types. datetime.datetime with a timezone becomes a UTC instant, and a naive value retains no timezone. Non-finite numbers, out-of-range integers, and a time with tzinfo raise InvalidError.

fetch() returns one bounded page, and result.page is its Page. page.has_more is true exactly when page.next_cursor is present, and query.cursor(next_cursor) continues from it. fetch_all() follows the server-provided cursor. Request an exact match count only when needed. It requires a separate count over the full filter:

result = await laser.query("readings_v1").where_eq("host_id", "node-7").with_total().fetch()
print(result.page.total, result.page.has_more)

filter(Filter...) adds any predicate tree (Filter.pred, all, any, negate), having(Filter...) filters aggregate groups, and agg_as(func, alias, field=, fraction=) names an aggregate column. rows() and rows_typed() return asynchronous iterators that walk pages up to an explicit max_rows(n) ceiling and raise InvalidError without one, like Rust rows().

Each query keeps one execution identity and an absolute deadline. Use the same builder to inspect or cancel a running query:

request = laser.query("readings_v1").filter_gte("cpu", 90).deadline(30)
result = await request.fetch()
status = await request.status()
if status["state"] == "running":
    await request.cancel()

Lakehouse queries always name one destination generation and can select a retained snapshot explicitly:

result = await (
    laser.query_lakehouse(destination_id, destination_generation)
    .at_snapshot(snapshot_id)
    .filter_eq("host_id", "node-7")
    .limit(100)
    .fetch()
)
print(result.context)

The returned context proves the resolved engine and target. Lakehouse pages also carry destination, table, snapshot, schema, partition-spec, materialization-boundary, checkpoint, and global-state evidence.

Destinations and Arrow IPC

Destination reads and query routes are available from one managed accessor. Reads choose potentially stale or linearizable checkpoint state explicitly:

destinations = laser.destinations()
page = await destinations.list(limit=50, consistency="linearizable")
routes = await destinations.query_routes(
    consistency="potentially_stale", name_contains="readings", limit=50
)
current = await destinations.get(destination_id, consistency="linearizable")

Registration and desired-state changes are revision-guarded. register() takes one complete destination declaration and set_desired_state() compares both the global state revision and destination definition revision before applying a change.

For analytical batches, publish one complete self-contained Arrow IPC stream per message. The SDK validates the contract version, schema fingerprint, counts, bounds, and exact byte length before transport I/O:

metadata = {
    "contract_version": 1,
    "schema_fingerprint": schema_fingerprint,
    "encoded_bytes": len(arrow_stream),
    "field_count": 8,
    "record_batch_count": 1,
    "row_count": 10_000,
    "dictionary_count": 0,
}
await readings.publish().arrow_ipc(arrow_stream, metadata).send()

Arrow input must use stream format, be self-contained, use microsecond timestamps, avoid dictionary replacement and deltas, keep decimals within 128 bits, and contain no unions or extension types.

Query, the key-value store, the knowledge graph, and forks are managed features served by LaserData Cloud or Laser Stack. Against Apache Iggy without a managed backend they raise UnsupportedError.

Key-value

kv = laser.kv("sessions")
await kv.set("user:42").json({"state": "online"}).ttl(300).send()
state = await kv.get_typed("user:42")
entry = await kv.get_entry("user:42")
print(entry.source())  # origin stream/topic ids, partition, and offset when stamped
values = await kv.get_many(["user:42", "user:43"])  # one round trip (the mixed-operation batch)
await kv.copy_to("user:42", "user:42:2026", to_namespace="archive")  # one backend transaction
await kv.move_to("plan:draft", "plan:current")  # copy plus source delete
lease = await kv.lease("source-owner", "worker-1", 30)
state = await kv.get_entry_at_least("source-state", lease.position)
lease = await kv.renew_lease("source-owner", "worker-1", lease.token, 30)
await kv.release("source-owner", "worker-1", lease.token)
await kv.delete("user:42")

cas_fenced, copy_to, and move_to return request builders. Await one directly, or finish it with commit() for cas_fenced and send() for the copies.

Lease exposes token, granted_ttl_secs, and position, a MutationPosition. After takeover, pass that position to get_entry_at_least to exclude state older than the grant. Request a lifetime from 1 second to 5 minutes. The store can grant less time, but never more. Values outside the range fail before sending.

Renew a lease before its granted lifetime expires. Reacquisition does not extend a live lease. Acquisition uses a dedicated coordination connection. If the outcome is unknown, the SDK retires the connection and waits through the requested lifetime. It then raises an ambiguous-mutation error that requires operation-specific recovery.

Prepared coordination requests

FencedLeaseClient exposes the same prepared mutation flow as Rust. It accepts DedicatedKvTransport or an object with send(code, frame) and reset() methods. Reset must stop all in-flight requests before it returns. A custom close() performs terminal retirement when provided. Otherwise client close calls reset(). Each attempt times out after 10 seconds, and with_attempt_timeout(seconds) changes that.

from laser_sdk import FencedLeaseClient

async with FencedLeaseClient.connect_dedicated("iggy:laser@127.0.0.1:8090") as coordination:
    operation = coordination.prepare_release(
        {
            "v": 1,
            "namespace": "leases",
            "key": b"source-owner",
            "holder_id": "worker-1",
            "lease_token": lease.token,
        }
    )
    released = await coordination.release(operation)

Prepare a mutation once. The returned PreparedMutation holds its stable operation_id and exact request bytes. After an ambiguous result, read ambiguous_recovery. Renew and release repeat the same prepared request. Acquisition waits through the requested lifetime before a fresh acquisition. Fenced compare-and-swap requires a target-precondition read before deciding how to recover. These are caller actions on this low-level client.

Call close() or use async with to retire the client. Later execution fails before connection or send. A dedicated transport also exposes terminal close(). Its reset() retires a connection and permits later reuse.

Consumer filters

Filter before the network. Consumer filters select records on the server so each reader receives only its matching subset of a topic and its partitions. The shared CDC example delivers 4 of 240 records and saves 98.5% of payload transfer. It preserves original payloads and offsets, supports exact-width typed headers, and acknowledges only completed work. See the Consumer Filters guide and the three-language examples.

Configure a consumer group's policy once. Its ordinary consumers and page readers then use that saved policy. An unbound group receives all records without payload decoding.

import laser_sdk as ls

safe_mode = ls.ConsumerFilter.json(
    ls.FilterExpr.all(
        [
            ls.FilterExpr.pred("table", "eq", "satellites"),
            ls.FilterExpr.pred("changed", "contains", "mode"),
            ls.FilterExpr.pred("after.mode", "eq", "safe"),
        ]
    )
)
topic = laser.stream("orbit").topic("fleet_changes")
group = topic.consumer_group("anomaly-desk")
info = await group.create(filter=safe_mode)  # Run once during setup.

# Every consumer instance needs only the group name or info.id.
consumer = group.consumer(batch_length=100, auto_commit="disabled")
try:
    record = await consumer.next()
    print(record.position.partition_id, record.position.offset, record.payload)
    await consumer.commit(record)
finally:
    await consumer.shutdown()

batch_length=100 examines at most 100 source records per partition request. It may deliver fewer matches, including none. An empty scan advances over rejected records and does not mean end of stream. The consumer continues bounded scans and waits only when caught up. Manual commit(record) stores safe contiguous progress after processing. Automatic policies commit the delivered prefix at their configured cadence and at shutdown. The native Apache Iggy path retains its own documented commit timing.

For batch handling, build await group.reader(count=100, max_examined=1000), then use next_page() and ack_page(). start= takes "next", "first", "last", {"offset": n}, or {"timestamp": micros}. Here count limits returned records, while max_examined independently limits the scan. Each reader joins as a member, and Iggy distributes partitions across instances. Acknowledgments preserve pending earlier work and reject stale source or policy generations. A reader keeps at most 1024 unacknowledged record-bearing pages per partition by default, so a reader that never acknowledges stops at that bound instead of growing without limit. Set max_unacked_pages= to change it.

For an application checkpoint inside a filtered page, call await reader.ack_through(record) after persisting the checkpoint and processing all preceding records on that partition. Later records in the same page stay pending. Use ack_page when the whole page is complete.

Use separate groups for A/B revisions so their offsets remain independent, and pause or resume a revision with set_revision_enabled. A fresh named consumer with the default polling="next" starts at the first retained record, and a stable name resumes durable progress. On the native Apache Iggy path the default auto_commit="polling" commits each polled batch before delivery, so a later consumer can resume after records the application did not process. Disable auto-commit and commit after successful processing when that matters.

group.filter() provides configure, get, revisions, revise, set_revision_enabled, release, delete, preview and test. delete() removes the group's own filter with every revision and releases the group first. Pass operation_id to configuration when setup must resume with the same operation ID after a lost reply. topic.consumer_group_id(id) addresses the saved group by numeric ID. Catalog failures, denied reads and paused revisions never broaden into unfiltered delivery. Original Apache Iggy uses native group consumption, while an older managed server without group-aware reads returns an explicit upgrade error.

Filters support JSON, CBOR, Avro, Protobuf and typed headers. Avro and Protobuf filters use registered writer schemas, immutable schema IDs in the filter, and the agdx.sid header on each record. Headers-only filters work with any payload format and never decode the payload. CompiledFilter.compile(filter).evaluate(payload) runs the evaluator locally, and explain(payload) returns its verdict tree. FilterExpr.text(field, kind, pattern) matches case-sensitively, and chaining .case_insensitive() ignores case. with_fault_policy, with_foreign_policy and with_mismatch_policy control invalid records. Passed invalid records and unfiltered records have evaluated false. Failures raise FilterError with a reason, and every LaserError carries filter_reason. A fault or oversized stop also sets partition_id and offset. Unknown header value kinds remain raw bytes. Malformed entries set headers_malformed while valid headers and the payload remain readable. A structurally truncated block has empty headers. A next_record call cancelled after it read a record yields that record again on the next call.

group.reader(local_guard=True) checks delivered records against the filter with the same evaluator the server runs. It verifies records marked unevaluated under the reported decoder bounds and requires the applicable pass policy.

Knowledge graph

import laser_sdk as ls

auth = ls.graph_node_entity("Service", "auth")
pool = ls.graph_node_entity("Component", "db-pool")

graph = laser.graph("ops")
await graph.upsert([auth, pool], [ls.graph_edge_relate(auth, "depends_on", pool)])
await graph.link("service:auth", "mitigated_by", "component:read-replica")

around = await graph.neighbors(auth["id"], "out", None, 2)
deps = await (
    graph.start_match(ls.Filter.pred("label", "eq", "Service")).out("depends_on").limit(50).fetch()
)
print(around.get("nodes", []), deps.get("nodes", []))

graph_node_entity derives an ID from label and value, so repeating the same entity preserves its ID. Nodes, edges, and results are dicts, with attributes as [name, value] pairs. link(from, relation, to) connects two kind:value entities. relink closes active edges of the same single-valued relation before recording the new value. unlink closes an edge valid-time window and retains its nodes.

A traversal starts from start_ids, from every node matching start_match(Filter), or from start_nearest(embedding, k). out, incoming, and both add hops. return_edges, return_triplets, and return_paths select the result shape, nodes by default. as_of uses epoch microseconds to select edges valid at that instant, and conversation scopes the read. fetch() runs it and neighbors(node, dir, edge_type, depth) is the one-hop shorthand. node_id_content(label, value) and edge_id_content(from, edge_type, to) compute the content IDs every SDK agrees on.

Agents

from laser_sdk import Laser


async def handle(ctx, message):
    text = bytes(message.payload).decode()
    await ctx.respond(f"echo: {text}".encode())


laser = await Laser.connect("iggy:iggy@127.0.0.1:8090", stream="agents")
await laser.bootstrap(partitions=4)

handle_agent = laser.spawn_agent("echo", "agent.commands", handle, respond_on="agent.responses")
await handle_agent.ready()

from laser_sdk import Provenance

reply = await laser.request(
    "agent.commands",
    "agent.responses",
    b"hello",
    Provenance(agent="caller"),
    timeout_secs=10,
)
print(bytes(reply.payload).decode())

await handle_agent.shutdown()

Keep the handle until shutdown() or join(). Dropping it signals a graceful stop and cancels periodic consolidation, and abort() stops the agent immediately. async with laser.spawn_agent(...) as handle: waits for readiness and stops the agent on exit.

Agent memory and configuration

MemoryHandler(inner, memory).auto_remember("message") remembers each successful turn under its conversation and source agent. A failed handler writes no memory. A failed memory write does not repeat an effect that the handler completed.

spawn_agent accepts capability descriptor dicts as well as skill names. fixed_inbox sets the handler's default fan-out route. governor_retention=(capacity, idle_ttl_secs) bounds the process-local evidence heads for an agent governor. dedup_window sets the default in-memory capacity in keys. A custom dedup callback replaces that window, and passing both raises InvalidError.

Periodic consolidation requires both consolidate_every_ms and a callable or an object with consolidate(scope). When the agent has an ID, each pass receives that agent in the scope dict. Otherwise the scope is empty. Pass failures are logged and later passes continue. Shutdown and context exit stop new passes and request cancellation of an active asynchronous callback. join() keeps consolidation active until the agent stops.

AGDX send refinements

command, respond, emit, status, and fail accept cause, cause_at, deadline_micros, idempotency_key, metadata, tool, usage, and claim_check. A causal position is ls.LogPosition(stream_id, topic_id, partition_id, offset) and requires a cause record ID. The returned envelope retains the Rust packed 20-byte position capsule. claim_check=(store, threshold_bytes) moves a large body to the supplied blob store.

An AGDX producer's signing_key signs those five send verbs. Chunk streams and request_input use the unsigned Rust helpers. request_input accepts the reply topic, prompt, and timeout.

Signed, principal-bound contracts

Laser.agent(id).contract(...) uses the same contract refinements as Laser.contract(...). Its source identity comes from the scope. Both accept reply-topic, conversation, fence, pickup-expiry, principal, fixed-inbox, and registration settings.

Rust and Python use the same Ed25519 verifier and routing rules. Enroll trusted keys before connecting. Give each signing agent its key. For sensitive routes, require an authenticated principal. One connection can advertise one agent. A second identity raises a conflict without replacing the first.

from laser_sdk import Contract, KeyRegistry, Laser, SigningKey

risk_key = SigningKey(bytes(range(32)))
keys = KeyRegistry()
keys.enroll("42", risk_key.verifying_key)

laser = await Laser.connect(connection, stream="agents", verifier=keys)
risk = laser.spawn_agent(
    "risk",
    "risk.commands",
    handle,
    capabilities=["screen-change"],
    signing_key=risk_key,
    verifier=keys,
)
await risk.ready()

result = await laser.contract(
    "screen-change",
    b'{"change":"rotate the storage credentials"}',
    source="planner",
    principal=42,
)
match result:
    case Contract.Completed(reply):
        assert reply.verified_principal == "42"
    case Contract.Failed(reply):
        print("refused:", bytes(reply.body()).decode())
    case Contract.NotConsumed() | Contract.TimedOut():
        print("no reply")

contract defaults to a 30-second deadline, like Rust and TypeScript, and returns a Contract: Contract.Completed(reply), Contract.Failed(reply), Contract.NotConsumed(), or Contract.TimedOut(). Route to one named agent with agent= and skill=None. expire_if_not_consumed_ms lets an unpicked task end as not_consumed, and reply_on, conversation, fence, and registered mirror the Rust contract builder. laser.agent(id) returns an AgentScope with send, ask, contract, publish_card, and advertise. laser.agent_registry() reads folded cards, quarantine facts, and live presence. scatter returns the reply bodies of the agents that completed. scatter_report returns a ScatterReport whose outcomes hold each agent's Contract or failure, with completed() and failures() views. Read reply.verified_principal when policy or UI code must inspect the signer. With a verifier configured, unsigned, invalid, and wrong-principal replies are ignored rather than returned with an empty identity.

For a human-in-the-loop pause, the typed AGDX producer's request_input publishes a prompt and blocks on the human's correlated reply, which a handler resolves with AgentCtx.respond_input:

decision = await laser.agdx("agent.human_input", "orchestrator", conversation_id).request_input(
    "agent.responses", b"approve draining node-7?", timeout_secs=15
)

The same identity rules as contracts apply. A caller connected with verifier= accepts only signed responses, so an approver spawned with signing_key= resumes it and nothing else can. A producer signs its own sends the same way: laser.agdx(..., signing_key=key) signs every envelope it publishes (command/respond/emit/status/fail), the Python spelling of the per-send .signed_by builder in Rust and TypeScript.

Fan-out and human approval from a handler

AgentCtx (the ctx a handler receives) carries two more coordination verbs beyond respond/send/request, mirroring the Rust AgentCtx:

async def orchestrate(ctx, message):
    # Fan a task out to every agent advertising "diagnose", gathering
    # replies on this handler's own respond_on topic.
    gather = await ctx.fan_out("diagnose", b"scan", deadline_ms=10_000)
    for agent, reply in gather.ok:
        print(agent, bytes(reply.body()).decode())
    for agent, error in gather.failures:
        print(agent, error)


async def gatekeeper(ctx, message):
    # Pause on a human decision before continuing, the ctx-scoped sibling
    # of the top-level request_input above.
    decision = await ctx.approval_gate(
        "agent.responses", b"approve draining node-7?", timeout_secs=15
    )
    await ctx.respond(decision)

fan_out's policy is "require_all" (default, wait for every branch), "quorum" (pass quorum=<n> to stop once that many succeed), or "best_effort" (take whatever landed by deadline_ms). Unavailable and quarantined agents are excluded, and a target that resolves no inbox is a failures entry, never silently rerouted. fixed_inbox routes every branch to a fixed topic instead of each agent's advertised inbox, the same knob contract/scatter take. Presence is connection-scoped (one connection may advertise one agent), so capability-advertising workers under test each need their own connection.

Unit-testing a handler

agent_message and agent_ctx build a message and a ctx directly, with no live consumer or server involved, so a handler function is testable like any other callable:

from laser_sdk import agent_ctx, agent_message, Provenance

message = agent_message(b"hello", Provenance(agent="tester"))
ctx = agent_ctx(laser, message, agent="tester", respond_on="agent.responses")
await handle(ctx, message)  # call your handler function directly

laser only needs to be live for whatever ctx helpers the handler actually calls (respond/fan_out/...). A handler that only reads its message needs no server at all.

A policy decides before an SDK effect. It can allow, observe, block, require approval, modify, or defer the action. Enforce mode applies the decision, while observe mode records it. Decisions that are not allow produce linked evidence on the audit topic. Typed refusals include PolicyBlockedError, StepUpRequiredError, and PolicyDeferredError:

from laser_sdk import ActionDecision, PolicyBlockedError, PolicyRef


class NoDrains:
    async def decide(self, action):
        if action.payload.startswith(b"drain-node"):
            return ActionDecision.block("drains need approval").with_policy(
                PolicyRef("ops", "3", ["no-drains"])
            )
        return ActionDecision.allow()


governed = laser.with_governor(NoDrains(), mode="enforce")
try:
    await governed.send_agent("agent.commands", b"drain-node node-7", provenance)
except PolicyBlockedError as refused:
    print(refused)  # policy blocked: drains need approval

# Per-agent: everything the handler publishes is governed too.
handle_agent = laser.spawn_agent("operator", "agent.commands", handle, governor=NoDrains())

The governor receives a GovernedAction whose counters (ActionCounters) report the sends, requests, and bytes already spent in that scope. decision.verdict is a Verdict. Compare it with Verdict.block() or read verdict.as_str(), and read a step-up scope or modified body from verdict.scope and verdict.body. decision.policy is a PolicyRef. laser.with_governor_retention(governor, mode, GovernorRetention(capacity=, idle_ttl_secs=)) bounds the process-local evidence heads.

QuorumGovernor combines named voters through all, any, or at_least(n). A voter can implement deterministic rules or call a model. Every mandatory voter must return allow, observe, or modify. A mandatory denial or error blocks the operation under every policy:

from laser_sdk import QuorumGovernor, QuorumPolicy

quorum = QuorumGovernor(QuorumPolicy.at_least(2))
quorum.voter("safety", NoDrains(), mandatory=True)
quorum.voter("llm_reviewer", llm_voter, mandatory=False)

governed = laser.with_governor(quorum, mode="enforce")

SwappableGovernor replaces the active policy without reconnecting or dropping existing handles. An operator, configuration reload, or recorded update can trigger the replacement. It affects the next decision and leaves recorded decisions unchanged:

from laser_sdk import SwappableGovernor

swappable = SwappableGovernor(NoDrains())
governed = laser.with_governor(swappable, mode="enforce")
...
previous = swappable.swap(a_stricter_policy)  # returns the replaced policy

Durable approvals are native typed records. They publish and replay directly, while the SDK keeps log ownership explicit:

import time
from laser_sdk import Decision, Intent, IntentPolicy, Vote, decide

intent = Intent(
    conversation=conversation_id,
    proposer="planner",
    body=b"rotate the storage credentials",
    eligible_voters=["safety"],
    policy=IntentPolicy.all(),
    policy_version=7,
    deadline_micros=time.time_ns() // 1_000 + 30_000_000,
)
await laser.stream("agents").topic("intents").json(Intent).publish(intent).send()
vote = Vote.cast(intent, "safety", "allow")
decision = decide(intent, [vote], time.time_ns() // 1_000)
if decision and decision.authorizes(intent):
    await laser.stream("agents").topic("decisions").json(Decision).publish(decision).send()

Construction, casting, and folding raise IntentError on malformed state. It subclasses InvalidError, and its kind names the broken rule, such as IntentError.INVALID_DEADLINE. Mandatory voters must affirm, and ballots outside the intent's time window never count. A voter name remains a record claim unless signing or topic ACLs bind it to an authenticated principal.

SwarmActivity builds a read model from governance evidence. Read PolicyEvidence records from the audit topic, then apply them to inspect each agent activity:

from laser_sdk import AgentTopic, PolicyEvidence, SwarmActivity

swarm = SwarmActivity()
for message in await laser.assemble_context(conversation_id, topics=[AgentTopic.Audit]):
    envelope = message.envelope
    if envelope and envelope.get("operation") == "policy_decision":
        swarm.observe(PolicyEvidence.decode(bytes(envelope["body"])))

activity = swarm.agent("planner")
if activity:
    print(activity.decisions, activity.count("block"))

CrashContext combines a journal tail, an optional dead-letter record, and the latest available decision for the conversation. Its summary is deterministic. It does not call a model:

from laser_sdk import CrashContext

journal = await laser.assemble_context(conversation_id, topics=[AgentTopic.Commands])
context = CrashContext(journal=journal, dead_letter=None, last_decision=activity.last_decision)
print(context.summarize())

Runs (managed)

The managed run registry answers "what happened to that task" without folding topics yourself. Gated on the agent_workflow capability, UnsupportedError elsewhere.

runs = laser.runs()
await runs.register_source("agents", "agent.status")
run = await runs.submit("diagnoser", b'{"incident": "INC-7"}')
info = await runs.status(run.run_id)
page = await runs.list(state="running", limit=25)
await runs.cancel(run.run_id)  # records the intent, the engine observes it
await runs.remove_source("agents", "agent.status")

wf = laser.workflow("incident-response")
wf.registered()  # the run's lifecycle lands in the registry

# A fenced external effect must use the same namespace in the workflow lease
# and in the handler's kv("storage").cas_fenced(...) commit.
wf.step(
    "rotate",
    to="rotator",
    build=lambda outputs: b'{"credential":"storage"}',
    fence_namespace="storage",
    on_timeout="reassign",
)

Change feed (managed)

Await a view's advance instead of polling it blind. A projection binding built with notify makes the plane publish one change record per committed batch, and laser.watch() reads that feed. Gated on the watch capability: laser.watch() raises UnsupportedError elsewhere. Resume with watch(from_offsets=) or reader.from_offsets(saved).

feed = laser.watch(index="readings_v1")
for change in await feed.poll():
    print(change.index, change.from_offset, change.to_offset, change.rows)
    rows = await laser.query(
        "readings_v1"
    ).fetch_all()  # the record is a wakeup, the rows come from query
saved = feed.offsets  # persist to resume after a restart

Consume and replay

import laser_sdk as ls

# A resumable reader over a topic. Each poll drains what is new. Persist the
# offsets to resume after a restart.
cursor = laser.stream("fleet").topic("readings").replay()
for message in await cursor.poll():
    print(message.json())
saved = cursor.offsets

# Replay a conversation's history off the log (agent runtime). `token_budget`
# trims the selection to an estimated token count, applied after `last_n`.
history = await laser.assemble_context(conversation_id, last_n=50, token_budget=4_000)

# An agent session: typed turns over the conversation-level topics, a
# model-ready context, scoped memory, and checkpointed replay.
session = laser.sessions().create("agent-42")
await session.append("instruction", b"summarize the ticket")
await session.append("model.response", b"it is a login bug")
for turn in await session.context():
    print(turn.kind, turn.text())
checkpoint = await session.checkpoint()
saved = checkpoint.to_json()
later = await session.turns_since(ls.Checkpoint.from_json(saved))

laser.context(conversation) returns a ContextScope. Its fetch(n=50, token_budget=) and block read agent.commands and agent.responses unless topics= names others, and return ContextMessage records (id, provenance, payload, envelope, topic). fetch_with(topics, Chain([LastN(20), TokenBudget(4_000)])) takes the policy objects LastN, TokenBudget(max_tokens, estimator=None), RoleFilter(agents), and Chain. state(topics, init, fold, last_n=|from_offsets=|from_checkpoint=|at=|full=True), state_with(store, topics, init, fold), and checkpoint(topics) fold the conversation log like Rust. context_checkpoint(laser, topics) captures a checkpoint without a conversation, and ConversationState.load(laser, conversation, topics, init, fold) folds one directly with the same bounds. Each read covers at most the newest CONTEXT_READ_WINDOW (10,000) messages per partition.

laser.sessions(stream=..., topics={"instruction": "support.turns"}, memory_namespace=..., context_turns=..., context_tokens=...) configures the stream, topics, memory namespace, and context limits for sessions. Every turn kind needs a distinct topic.

Memory and state

Agent memory is a MemoryHandle with remember, recall, forget(id), and improve(target, weight) over a log-based backend, a local vector backend, or a custom one. laser.memory(namespace) returns the log-based handle, and the LogMemory(laser), VectorMemory(embedder), and RerankedMemory(inner, reranker) subclasses build each backend directly. The log-based handle also supports named state through set(key, value), fetch(key), update(key, patch), and remove(key), which LogMemory also spells set_named, fetch_named, update_named, and forget_named. These named operations raise UnsupportedError on the vector and custom backends.

Log-based writes publish to the memory topic and work on Apache Iggy. Default recall and fetch reads use a managed key-value view, so they need Laser Stack or LaserData Cloud. Use recall(folded=True) or fetch_folded to build the view locally from the topic. remember(payload, kind=, durable=, dedup=, user=, application=, stream=) mirrors the Rust remember builder, recall(..., block=True) and context(conversation, token_budget=) render a prompt block, and consolidate(max_items, ...) prunes a scope. laser.memory_with(namespace, "vector", embedder=...) selects the backend explicitly. ContextScope.memory(..) and Session.memory(..) return a ScopedMemory with recall(folded=), search(folded=), remember, block, consolidate, forget, and improve, and Session.memory_in(namespace) picks another namespace. The local vector backend ranks records by similarity. Its embedder can return list[float] or an awaitable for an external model call.

from laser_sdk import VectorMemory


async def embed(text: str) -> list[float]: ...  # your model, or a deterministic stand-in


memory = VectorMemory.governed(laser, embed)  # or VectorMemory(embed) without a governor
await memory.remember("auth latency traces to the database pool", conversation=cid)
hits = await memory.recall(conversation=cid, semantic="why is auth slow", limit=3)
print([item.text() for item in hits])

# A durable key/value seam for agent state, the same vocabulary as the managed store.
from laser_sdk import InMemoryStore  # or FileStore("/var/lib/agent")

store = InMemoryStore()
await store.set("cursor", saved_bytes)
value = await store.get("cursor")

VectorMemory.governed(laser, embedder) inherits the governor enrolled on that Laser. A blocked write never mutates the local index, and a modified decision replaces the proposed memory body before embedding. Rust and Python therefore apply the same effect-boundary policy to local semantic memory.

Edge interop (A2A / MCP / AG-UI)

Reach an agent as an A2A task source or an MCP tool server, and render a conversation as AG-UI events, all over the durable log:

from laser_sdk import A2aBridge, McpBridge

# A2A: submit a task, poll for the result.
a2a = A2aBridge(laser, "a2a-gateway", "agent.commands", "agent.responses")
task = await a2a.submit({"message": {"role": "user", "parts": [{"kind": "text", "text": "hi"}]}})
status = await a2a.task(task["id"])

# MCP: advertise tools, route tools/call to the agent.
mcp = McpBridge(
    laser,
    "mcp-gateway",
    "agent.tool_calls",
    "agent.tool_results",
    "laser-mcp",
    tools=[{"name": "ask", "input_schema": {"type": "object"}}],
)
tools = mcp.list_tools()
result = await mcp.call_tool("ask", {"q": "what is AGDX?"})


# An agent answers a bridge request from its handler:
async def handle(ctx, message):
    await ctx.respond_input("agent.responses", b"the answer")


# AG-UI: snapshot + deltas reconstruct shared state off the log.
await laser.publish_state_snapshot("agent.llm_io", "ui", conversation_id, {"count": 1})
state = await laser.reconstruct_state(conversation_id, "agent.llm_io")
events = await laser.agui_events(conversation_id, "agent.llm_io")

Host the actual HTTP endpoint with your Python web framework over these adapter methods.

Errors

Every failure raises a subclass of LaserError. The classes are ConfigError (with NoStreamError, NoRespondTopicError, and HandlerConfigError below it), HandlerError, RejectedError, TimeoutError, AmbiguousMutationError, StateStoreError, QueryError, KvError, ForkError, GraphError, AgentError, AuthzError, CheckpointError, FilterError (with FilterFaultError, FilterOversizedRecordError, and ConsumerGroupSetupError), SignatureError, UnsupportedError, InvalidError (with IntentError, ValidateError, and IdError), CodecError (with TypedDecodeError), ProtocolError, TransportError, PublishFailedError, IntegrityError, PolicyBlockedError, StepUpRequiredError, PolicyDeferredError, RoutingError (with NoCapableAgentError, NoInboxError, and RoutePrincipalMismatchError), PresenceConflictError, FenceViolationError, BudgetExceededError, CancelledError, QuarantinedError, and ProvenanceError. Each instance carries code, retryable, iggy_error_code, filter_reason, unavailable, unsupported, not_found, version_skew, version_conflict, ambiguous_mutation, stale, permission_denied, stream_or_topic_not_found, no_capable_agent, lease_lost, fence_violation, budget_exceeded, quarantined, and not_leader attributes so you can branch without matching on the type. IntentError, ValidateError, IdError, and ProvenanceError add a kind with class constants such as IdError.INVALID_ULID. A publish that gives up raises PublishFailedError, whose committed and unconfirmed report what it left. A handler rejection raises RejectedError. TimeoutError also subclasses the builtin TimeoutError, InvalidError also subclasses ValueError, and CancelledError also subclasses asyncio.CancelledError, so stdlib-style except TimeoutError and except asyncio.CancelledError catch them too.

Reading

Use async for message in laser.stream("fleet").topic("events").replay() to read raw records. WatchReader and topic.records(reader_name) also support asynchronous iteration. They stop when caught up. A later iteration resumes from the same offsets. Use poll() for a batch of records.

Lifecycle

Use async with await Laser.connect(conn) as laser: to manage a connection. The shared connection closes when its last handle is dropped. with_default_stream and with_ops_stream return handles that share that connection. await laser.close() ends the shared connection for every handle.

0.6.0 additions

0.6.0 also changes existing Python behavior. Middleware after_handle and the dead_letter callback receive new arguments, consumers default to commit_interval_ms=0, contract waits 30 seconds by default, and Capabilities drops sessions and durable_dedup. The client behavior guide lists every change with its replacement. These additions match the Rust and TypeScript surfaces:

• Topic.producer(background=True) buffers sends through Iggy's background mode with its defaults, and background=BackgroundConfig(...) sets shards, flush limits, the failure mode, and an error_callback. await producer.shutdown() waits for sends in flight, flushes, and closes.
• Topic.cbor(cls) and await Topic.schema(schema_id, cls) add typed topics. PublishRequest.claim_check(store, threshold_bytes) stores large bodies by reference.
• Laser.memory_custom(backend) plugs in a custom memory backend. MemoryHandle.reranker(callable) reranks semantic recall.
• Session.context_with(policy) and Session.graph(name).
• ForkHandle.create(continuous=True), Workflow.run_id(id), Intent.validate().
• Capabilities.is_open_only() and serves_consistency(level).
• AgdxStream options (with_deadline_micros, with_target, content_type, buffered, and flush), SigningKey.sign, and the KeyRegistry verify family.
• BatchPublishRequest.add_json, add_msgpack, add_payload, and add_raw_bytes take projection_ref= for one record.
• Laser.contract, scatter, scatter_report, AgentScope.contract, and Workflow.step take policy= with a route policy word: any, cheapest, fastest, least_loaded, or sticky:<agent>. AgentCtx.fan_out takes the same word as route_policy=. AgentScope.contract(None, payload, skill=...) routes by capability.
• The Json, Cbor, Msgpack, and Bson codec classes carry content_type, encode, and decode, and plug into encode_with, Kv.get_as, and fetch_typed_with.
• Message.id and ConsumerMessage.position are a MessageId (partition_id, offset, and str() as p:o).
• laser.consumed(ConsumerRef.Group(name), LogPosition(...)) returns ConsumptionStatus.Consumed(committed, head) or ConsumptionStatus.NotYetConsumed(behind_by).
• ConsumerGroup.create() and info() return a ConsumerGroupInfo (id, name, identity, filter), and Kv.exists returns a KvMetadata or None.
• Checkpoint.topic_offsets(topic) returns a dict of partition to offset, or None.
• PolicyEvidence.encode() returns the evidence body that decode reads.
• laser.projections().list(topics=[...], search=...) narrows by several source topics and by a substring of the id or name. laser.bindings() and laser.schemas() group the binding and schema registry calls the same way.
• MemoryHandle.consolidate and ScopedMemory.consolidate take summarizer= (a sync or async callable from list[bytes] to bytes) and prune_summarized=True.
• memory_id_content(kind, body, stream=, agent=, user=, application=) returns the content-derived memory id. memory_kind_class(kind) returns episodic, semantic, or procedural.
• Sessions.turn_topic(kind) and Sessions.turn_kind(topic) map a session turn kind to its default topic and back.
• AgentRegistry.agents, lookup, and resolve return RegisteredCard objects (agent, card, observed_at_micros) with is_fresh(now_micros), serves(skill), and available_for(skill).

Laser.assemble_context accepts across_subconversations, from_offsets, from_checkpoint, to_checkpoint, and an explicit context policy. A start-offset map applies to every topic. A start checkpoint replaces the raw offset map. Topics absent from it start at offset zero. An end checkpoint bounds the read to saved offsets. An explicit policy replaces roles, last_n, and token_budget and cannot be combined with them.

MemoryHandle.recall and ScopedMemory.recall accept stream, durable, and token_budget. The full scope and query reach the selected backend. durable sets the scope lifetime for custom callbacks. Built-ins do not persist or filter that value. Omit conversation to read across conversations. A token budget in the query is advisory and does not trim the returned item list. Use context or block to render a budgeted text block. MemoryHandle.improve, forget, and consolidate accept the full scope. Feedback can include a note.

Provenance(fence_token=...) stamps the fence used by the reliable consumer. Received messages expose it through message.provenance.fence_token.

Context policies also accept a synchronous callable or an object with select(messages). It returns the selected ContextMessage objects. A callback failure raises after the read. An asynchronous policy is rejected because the Rust policy seam is synchronous.

spawn_agent(None, ..., consumer_group="name") creates an unscoped reliable consumer. It has no agent routing identity and cannot advertise capabilities. A named agent keeps its own default consumer group.

Use to_context_block(items, token_budget=None) to render the exact result of a scoped, ranked, or folded recall. It keeps the supplied order. The Rust formatter always retains the first item and adds an omission marker when the budget excludes later items.

BatchPublishRequest.add_record(payload, content_type=, index=, headers=, projection_ref=, schema_id=, inline_payload=, logical_schema_fingerprint=) sets every option of one record and does not inherit the batch defaults. A fingerprint must contain 32 bytes and requires content_type="arrow". This path stamps metadata without parsing the payload, so supply a complete Arrow IPC stream. Use add_arrow_ipc(payload, metadata) when the SDK must validate the declared message metadata and payload length.

Agent abort and an expired shutdown grace request cancellation of the active Python handler. Cancellation of a memory call also requests cancellation of its active asynchronous callback. Callbacks must propagate asyncio.CancelledError. A synchronous callback completes before cancellation takes effect.

A custom memory backend can add append(scope, id, kind, payload) to preserve an explicit memory kind and content ID. It returns the stored ID. The SDK falls back to the original remember(scope, payload) callback when append is absent. Consolidation passes a durable scope and the summary kind through this optional hook.

MemoryHandle.append(id, payload, kind=, ...) writes with a supplied ID and memory kind under the full scope. Built-in backends preserve both.

Workflow build, verify, and compensate callbacks can return directly or through an awaitable. Objects with build or verify methods also work. SDK exceptions keep their error class. A build must produce bytes, and a verifier must produce a boolean. Cancelling a running workflow retires its active Python callback task.

SnapshotStore(backend) wraps custom latest(conversation) and save(snapshot) methods. Both can return directly or through an awaitable. A snapshot is a dict with conversation, inclusive per-partition as_of offsets, and opaque state bytes. ContextScope.state_with also accepts the backend object directly. It JSON-decodes the saved state and resumes after those offsets.

Python middleware and dead-letter observers receive full outcomes. after_handle(message, result, attempt) receives {"ok": bool, "error": typed_exception_or_none}. dead_letter(message, capsule, publish_error) receives the full wire capsule and a typed publish error or None. Both can return directly or through an awaitable.

Custom route policies accept None, a built-in word, or a synchronous callable or select(skill_id, candidates) object. Each candidate is a RouteCandidate with agent, card, and capability. Return a candidate index or None. Async scorers are rejected.

Portable native helpers include encode_snapshot, decode_snapshot, resume_offsets, and fuse_reciprocal_rank. Snapshot bytes match the Rust fixture. Pure request conversions preserve the original bytes through command_from_message_send and tool_call_from_request. Their outbound counterparts are task_from_envelope and tool_result_from_envelope. Card and delegation helpers are sign_card_value, verify_card, and verify_delegation. check_in and resolve_body share claim-check thresholds and digest checks with publication and consumption.

SystemClock and TestClock subclass Clock. SystemClock().now_micros() returns epoch microseconds. TestClock(start_micros=0) exposes now_micros, set, and advance. Inputs fit unsigned 64-bit values. Advancement wraps at the native limit.

Both bridges expose handle_rpc(request), an awaitable that returns a JSON-RPC response dict. It supports malformed-request errors without requiring an HTTP framework. KvKeyRegistry.enroll(principal, verifying) enrolls an agent key, and enroll_record(record) returns the lifecycle-aware record version.

License

Apache-2.0. Copyright LaserData, Inc.

Apache and Apache Iggy are trademarks of the Apache Software Foundation. Use of these marks does not imply endorsement by the Apache Software Foundation.