logbrew-sdk 0.1.4

Public LogBrew Python SDK for building, validating, and flushing event batches.

logbrew-sdk

Public Python SDK for creating LogBrew event batches, validating them locally, and flushing them through a transport.

Install

python3 -m pip install logbrew-sdk

The package includes py.typed, public type aliases such as ReleaseAttributes, SpanAttributes, MetricAttributes, and TraceparentContext, and copyable examples for wiring LogBrew into your Python service. Keep the real key in your app configuration and use preview_json() when you want to inspect queued JSON before sending.

Example

import json
import sys

from logbrew_sdk import LogBrewClient, RecordingTransport

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="logbrew-python",
    sdk_version="0.1.0",
)

client.release(
    "evt_release_001",
    "2026-06-02T10:00:00Z",
    {
        "version": "1.2.3",
        "commit": "abc123def456",
        "notes": "Public release marker",
    },
)
client.environment(
    "evt_environment_001",
    "2026-06-02T10:00:01Z",
    {"name": "production", "region": "global"},
)
client.issue(
    "evt_issue_001",
    "2026-06-02T10:00:02Z",
    {
        "title": "Checkout timeout",
        "level": "error",
        "message": "Request timed out after retry budget",
    },
)
client.log(
    "evt_log_001",
    "2026-06-02T10:00:03Z",
    {"message": "worker started", "level": "info", "logger": "job-runner"},
)
client.span(
    "evt_span_001",
    "2026-06-02T10:00:04Z",
    {
        "name": "GET /health",
        "traceId": "trace_001",
        "spanId": "span_001",
        "status": "ok",
        "durationMs": 12.5,
    },
)
client.action(
    "evt_action_001",
    "2026-06-02T10:00:05Z",
    {"name": "deploy", "status": "success"},
)

print(client.preview_json())

transport = RecordingTransport.always_accept()
response = client.shutdown(transport)
print(
    json.dumps(
        {"ok": True, "status": response.status_code, "attempts": response.attempts, "events": 6}
    ),
    file=sys.stderr,
)

Use a clearly fake placeholder like LOGBREW_API_KEY in examples. Call flush() or shutdown() to send queued events through a transport, and use preview_json() when you want a stable local JSON preview before sending anything.

Queue Pressure

LogBrewClient keeps a bounded in-memory queue so a transport outage or burst of logs cannot grow without limit. The default capacity is 10_000 events. Pass max_queue_size when a service needs a smaller or larger cap:

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.4.0",
    max_queue_size=1000,
)

When the queue is full, new events are dropped and existing queued context is preserved. Use pending_events() and dropped_events() for local diagnostics, then call flush() or shutdown() with your transport:

if client.dropped_events() > 0:
    print({"pending": client.pending_events(), "dropped": client.dropped_events()})

This counter is local process state only. Usage, quota, and billing remain backend-owned and must not be inferred from queue size or drop counts.

Automatic Delivery

Pass an app-owned transport when you want the client to deliver retained events automatically. Automatic delivery starts lazily after the first accepted event, sends at 50 queued events or after 5 seconds by default, and uses the same bounded queue and exact-prefix acknowledgement as explicit flushes:

from logbrew_sdk import HttpTransport, LogBrewClient

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.4.0",
    transport=HttpTransport(),
)

client.log(
    "evt_worker_ready",
    "2026-07-20T08:00:00Z",
    {"message": "worker ready", "level": "info", "logger": "checkout-api"},
)

health = client.delivery_health()
print({"state": health.lifecycle, "pending": health.pending_events})
client.shutdown()

Set delivery_interval_seconds and delivery_queue_threshold to tune the bounded wake policy. Set automatic_delivery=False to own a transport while keeping fully explicit delivery. A client created without a transport keeps the existing manual contract and still accepts flush(transport) and shutdown(transport).

Only one daemon scheduler and one flush can be active for a client. Retryable network, 408, and server failures retain an immutable request prefix and use bounded jitter or a bounded retry-after-ms hint. Authentication, quota, and other terminal responses pause automatic sends; a successful explicit flush() through the owned transport clears that pause. No process-exit, signal, fork, or framework hook is installed.

DeliveryHealthSnapshot is immutable and contains only fixed lifecycle values, bounded counters, pending count/bytes, drop count, in-flight/coalesced state, pause class, and retry delay. It never contains event content or IDs, API keys, transport configuration, request/response data, status codes, exception text, or arbitrary metadata.

First Useful Telemetry

For a new Python service, capture a small set of signals that explain what changed, where the service ran, what the user or job attempted, which outbound dependency mattered, how long it took, and how the request links to a distributed trace:

import logging

from logbrew_sdk import (
    LogBrewClient,
    LogBrewLoggingHandler,
    RecordingTransport,
    create_network_milestone_attributes,
    create_product_action_attributes,
    span_attributes_from_traceparent,
)

traceparent = "00-4bf92f3577b34da6a3ce929d0e0e4736-00f067aa0ba902b7-01"
trace_id = "4bf92f3577b34da6a3ce929d0e0e4736"
route_template = "/checkout/:cart_id"

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.4.0",
)
client.release(
    "evt_release_checkout_api",
    "2026-06-15T08:00:00Z",
    {"version": "1.4.0", "commit": "abc123def456"},
)
client.environment(
    "evt_environment_checkout_api",
    "2026-06-15T08:00:01Z",
    {"name": "production", "region": "us-east-1"},
)

logger = logging.getLogger("checkout-api")
logger.addHandler(LogBrewLoggingHandler(client, metadata={"service": "checkout-api"}))
logger.setLevel(logging.INFO)
logger.info("checkout request accepted", extra={"routeTemplate": route_template, "traceId": trace_id})

client.action(
    "evt_action_checkout_started",
    "2026-06-15T08:00:03Z",
    create_product_action_attributes(
        {
            "name": "checkout started",
            "status": "running",
            "sessionId": "sess_checkout_123",
            "traceId": trace_id,
            "routeTemplate": route_template,
            "funnel": "checkout",
            "step": "payment",
        }
    ),
)
client.action(
    "evt_network_payment_authorized",
    "2026-06-15T08:00:04Z",
    create_network_milestone_attributes(
        {
            "routeTemplate": "/payments/:payment_id",
            "method": "POST",
            "statusCode": 202,
            "durationMs": 43,
            "sessionId": "sess_checkout_123",
            "traceId": trace_id,
        }
    ),
)
client.metric(
    "evt_metric_checkout_duration",
    "2026-06-15T08:00:05Z",
    {
        "name": "checkout.duration",
        "kind": "histogram",
        "value": 128,
        "unit": "ms",
        "temporality": "delta",
        "metadata": {"routeTemplate": route_template, "traceId": trace_id},
    },
)
client.span(
    "evt_span_checkout_request",
    "2026-06-15T08:00:06Z",
    span_attributes_from_traceparent(
        traceparent,
        name="POST /checkout/:cart_id",
        span_id="b7ad6b7169203331",
        status="ok",
        duration_ms=17,
        metadata={"routeTemplate": route_template, "service": "checkout-api"},
    ),
)
client.shutdown(RecordingTransport.always_accept())

The packaged example prints a local JSON preview of this flow:

python -m logbrew_sdk.examples first-useful-telemetry

This path is intentionally app-owned. It uses Python's standard logging module, explicit W3C traceparent continuation, and explicit product, network, and metric helpers. It does not patch global HTTP clients, does not collect request or response bodies, does not capture arbitrary headers, and timeline helpers strip query strings and hashes from route templates.

Support Ticket Drafts

Use create_support_ticket_draft() when a user or agent explicitly asks to prepare a support ticket payload. The helper is local-only: it validates the planned public support-ticket fields, redacts diagnostics, and returns a dictionary. It does not send data, open a ticket, use account/session API credentials, or call backend support routes.

from logbrew_sdk import create_support_ticket_draft

draft = create_support_ticket_draft(
    source="sdk",
    category="sdk_install_failure",
    title="Python import fails after install",
    description="Wheel installs, but the app cannot import logbrew_sdk.",
    environment="production",
    runtime="python 3.13",
    framework="fastapi",
    sdk_package="logbrew-sdk",
    sdk_version="0.1.2",
    release="checkout-api@1.4.0",
    trace_id="4bf92f3577b34da6a3ce929d0e0e4736",
    diagnostics={
        "install_command": "python3 -m pip install logbrew-sdk",
        "endpoint": "https://api.example.com/v1/events?debug=true",
        "authorization": "Bearer hidden",
        "local_path": "/Users/example/service/app.py",
        "error": RuntimeError("private failure message"),
    },
)

The returned draft keeps only structured JSON-like diagnostics. Auth-like keys, cookies, tokens, URL origins, local paths, unsupported objects, and exception messages/stacks are redacted or omitted before the dictionary is returned.

Metrics

Use metric() for explicit, application-owned measurements. LogBrew validates the metric name, kind, value, unit, temporality, and optional metadata before queueing the event:

from logbrew_sdk import LogBrewClient

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="logbrew-python",
    sdk_version="0.1.0",
)

client.metric(
    "evt_metric_queue_depth",
    "2026-06-02T10:00:06Z",
    {
        "name": "queue.depth",
        "kind": "gauge",
        "value": 42,
        "unit": "{items}",
        "temporality": "instant",
        "metadata": {"service": "worker"},
    },
)

Supported metric kinds are counter, gauge, and histogram. Counters and histograms require delta or cumulative temporality and non-negative values; gauges require instant temporality and may be negative. Keep metadata low-cardinality and primitive. This SDK does not automatically collect runtime or framework metrics yet.

Trace Context

Use the W3C helpers when a Python service needs to interoperate with distributed tracing headers:

from logbrew_sdk import (
    create_logbrew_trace_context,
    create_traceparent_headers,
    parse_traceparent,
    span_attributes_from_trace_context,
    trace_metadata,
    use_logbrew_trace,
)

traceparent = "00-4bf92f3577b34da6a3ce929d0e0e4736-00f067aa0ba902b7-01"
context = parse_traceparent(traceparent)
trace = create_logbrew_trace_context(traceparent, span_id="b7ad6b7169203331")
attributes = span_attributes_from_trace_context(
    trace,
    name="GET /health",
    status="ok",
    duration_ms=12.5,
    metadata={"service": "checkout"},
)
headers = create_traceparent_headers(
    trace_id=attributes["traceId"],
    span_id=attributes["spanId"],
    trace_flags="01",
)
with use_logbrew_trace(trace):
    metadata = trace_metadata()

parse_traceparent() validates W3C shape, rejects all-zero trace/span IDs, normalizes IDs to lowercase, and exposes the sampled flag. create_logbrew_trace_context() creates the request-local LogBrewTraceContext used to correlate request spans, app-owned logs, issues, actions, metrics, and outgoing milestones with one safe set of IDs. use_logbrew_trace() makes that context available through trace_metadata() and get_active_logbrew_trace() during framework handler work, including async work that keeps Python contextvars. create_traceparent_headers() returns an explicit outbound carrier with only traceparent for app-owned HTTP clients. FastAPI and Django integrations use these helpers automatically for valid inbound traceparent headers and start a fresh W3C-shaped local trace when the header is missing or malformed. The helpers do not patch HTTP clients or capture request payloads, headers, cookies, query strings, or the raw traceparent value.

If your app already installs OpenTelemetry, copy the active OTel parent into a LogBrew child context without adding an SDK dependency on OTel:

from logbrew_sdk import (
    logbrew_trace_context_from_current_open_telemetry_span,
    span_attributes_from_trace_context,
    use_logbrew_trace,
)

trace = logbrew_trace_context_from_current_open_telemetry_span()
if trace is not None:
    with use_logbrew_trace(trace):
        attributes = span_attributes_from_trace_context(
            trace,
            name="checkout.otel_child",
            status="ok",
            metadata={"service": "checkout"},
        )

logbrew_trace_context_from_current_open_telemetry_span() returns None when OpenTelemetry is not installed or no valid current OTel span exists. It copies only the validated trace ID, parent span ID, and sampled flag into a fresh LogBrew child context. It does not install OTel, own exporters or processors, read attributes/events/links, ingest baggage or tracestate, serialize raw propagation metadata, patch clients, or capture payloads, headers, cookies, full URLs, query strings, or fragments.

If your app already owns an OpenTelemetry TracerProvider, register a LogBrew processor to convert ended ReadableSpan objects into queued LogBrew spans:

from logbrew_sdk import LogBrewClient, create_logbrew_open_telemetry_span_processor
from opentelemetry.sdk.trace import TracerProvider

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.0.0",
)
provider = TracerProvider()
provider.add_span_processor(
    create_logbrew_open_telemetry_span_processor(
        client=client,
        include_trace_summary=True,
        link_attribute_keys=["messaging.operation.name"],
        metadata={"service": "checkout"},
    )
)

If a framework accepts only an OpenTelemetry SpanExporter, use the exporter-compatible helper with your app-owned OTel processor:

from logbrew_sdk import LogBrewClient, create_logbrew_open_telemetry_span_exporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.0.0",
)
exporter = create_logbrew_open_telemetry_span_exporter(
    client=client,
    include_trace_summary=True,
    link_attribute_keys=["messaging.operation.name"],
    metadata={"service": "checkout"},
)
provider = TracerProvider()
provider.add_span_processor(BatchSpanProcessor(exporter))

The processor and exporter follow normal sampled-span behavior by default and can emit one synthetic opentelemetry.trace:<root-name> summary span on force_flush() or shutdown(). Detail spans copy a small safe allowlist such as service, environment, route, method, status code, span kind, instrumentation scope, dropped-count metadata, type-only exception events, and up to eight span links. Trace summaries include bounded exception event counts and type names so failed OTel traces stay searchable without sending exception messages or stacks. Link summaries retain only normalized trace ID, span ID, sampled state, and explicitly allowlisted primitive link metadata such as messaging.operation.name; sensitive keys such as message IDs, full URLs, headers, query strings, payloads, cookies, private auth values, DB statements, exception messages, and stacks stay blocked. Additional OTel attributes require explicit allowlists. These helpers do not add an OTel dependency to default LogBrew installs, own your provider/exporter pipeline, serialize baggage or tracestate, patch clients, or capture request/response bodies.

Span payloads may include up to eight privacy-bounded event summaries through events or helper-level span_events, and up to eight privacy-bounded links for batch, fan-in, retry, or queue relationships. Each event summary has a required name, optional timezone-aware timestamp, and primitive-only metadata; each link summary has W3C-shaped traceId and spanId, optional sampled, and primitive-only metadata. LogBrew drops nested objects and helper deny-listed key names so milestones and related-span links can improve trace timelines without sending payloads, headers, query parameters, cache keys, queue messages, exception messages, stack traces, or raw propagation data. Dependency helpers add one automatic type-only exception event when the wrapped operation fails.

Outbound HTTP Client Spans

Use urlopen_with_logbrew_span() when you want one dependency-free outbound HTTP client span around an app-owned urllib.request call:

from urllib.request import Request

from logbrew_sdk import LogBrewClient, urlopen_with_logbrew_span

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.0.0",
)

response = urlopen_with_logbrew_span(
    Request("https://api.example.com/payments/123?coupon=summer", method="GET"),
    client=client,
    event_id="evt_payment_lookup",
    timestamp="2026-06-19T08:00:00Z",
    route_template="/payments/:payment_id",
    metadata={"service": "checkout-api"},
)

The helper clones the caller request, writes exactly one normalized W3C traceparent, runs the opener under a child LogBrewTraceContext, queues one span with method, query-free route, status, duration, primitive metadata, and type-only failure metadata, then returns the original response or re-raises the original HTTP/network error. Telemetry capture failures are reportable through on_capture_error and do not replace the app-owned HTTP result. It does not patch urllib, does not capture request or response payloads, and does not store headers, cookies, query strings, fragments, exception messages, baggage, tracestate, or raw propagation values.

For apps that use requests, use requests_request_with_logbrew_span() with your own requests.Session or request callable. LogBrew does not add requests as a dependency and does not monkeypatch the library:

import requests

from logbrew_sdk import LogBrewClient, requests_request_with_logbrew_span

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.0.0",
)
session = requests.Session()

response = requests_request_with_logbrew_span(
    "POST",
    "https://api.example.com/payments/123?coupon=summer",
    client=client,
    event_id="evt_payment_submit",
    timestamp="2026-06-19T08:00:03Z",
    session=session,
    timeout=3.5,
    headers={"x-caller": "checkout-api"},
    json={"amount": 42},
    route_template="/payments/:payment_id",
    metadata={"service": "checkout-api"},
)

The requests helper clones caller headers, replaces any caller-supplied traceparent with one normalized child header, runs the request under that child trace context, queues one sanitized dependency span, and returns the original requests.Response or re-raises the original exception. It records method, route template, status code, duration, sampled flag, primitive metadata, and type-only failure metadata. It does not capture payloads, response bodies, headers, cookies, full URLs, query strings, fragments, exception messages, baggage, tracestate, or raw propagation values.

If most calls go through one app-owned requests.Session, install reversible per-session instrumentation once instead of wrapping every call:

import requests

from logbrew_sdk import LogBrewClient, instrument_requests_session_with_logbrew_spans

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.0.0",
)
session = requests.Session()
instrumentation = instrument_requests_session_with_logbrew_spans(
    session,
    client=client,
    route_template_resolver=lambda method, url: "/payments/:payment_id",
    metadata={"service": "checkout-api"},
)

response = session.post(
    "https://api.example.com/payments/123?coupon=summer",
    timeout=3.5,
    json={"amount": 42},
)
instrumentation.uninstall()

The session helper returns a LogBrewRequestsSessionInstrumentation handle. It wraps only the session instance you pass, returns the existing handle on duplicate install, restores the original request method with uninstall(), generates safe event IDs by default, and preserves the same traceparent injection, failure, and privacy behavior as requests_request_with_logbrew_span(). It does not patch the global requests module, create sessions, install dependencies, capture payloads, capture headers, capture full URLs or queries, or open support tickets.

For apps that use httpx, use httpx_request_with_logbrew_span() for sync calls or async_httpx_request_with_logbrew_span() for async calls. LogBrew does not add httpx as a dependency and does not patch httpx.Client, httpx.AsyncClient, or transports:

import httpx

from logbrew_sdk import (
    LogBrewClient,
    async_httpx_request_with_logbrew_span,
    httpx_request_with_logbrew_span,
)

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.0.0",
)

with httpx.Client() as session:
    response = httpx_request_with_logbrew_span(
        "POST",
        "https://api.example.com/payments/123?coupon=summer",
        client=client,
        event_id="evt_payment_submit",
        timestamp="2026-06-19T09:00:00Z",
        session=session,
        timeout=3.5,
        headers={"x-caller": "checkout-api"},
        json={"amount": 42},
        route_template="/payments/:payment_id",
        metadata={"service": "checkout-api"},
    )

async def submit_payment(async_session: httpx.AsyncClient) -> httpx.Response:
    return await async_httpx_request_with_logbrew_span(
        "POST",
        "https://api.example.com/payments/123?coupon=summer",
        client=client,
        event_id="evt_payment_submit_async",
        timestamp="2026-06-19T09:00:01Z",
        session=async_session,
        timeout=3.5,
        route_template="/payments/:payment_id",
        metadata={"service": "checkout-api"},
    )

The httpx helpers follow the same privacy and failure behavior as the requests helper: cloned caller headers, exactly one normalized child traceparent, active child trace context during the call or awaited call, sanitized dependency span capture, original response/error preservation, type-only failure metadata, and optional on_capture_error reporting for telemetry failures. They do not capture payloads, response bodies, headers, cookies, full URLs, query strings, fragments, exception messages, baggage, tracestate, or raw propagation values.

For shared app-owned httpx.Client or httpx.AsyncClient instances, use the reversible client instrumentation:

import httpx

from logbrew_sdk import LogBrewClient, instrument_httpx_client_with_logbrew_spans

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.0.0",
)

with httpx.Client() as session:
    instrumentation = instrument_httpx_client_with_logbrew_spans(
        session,
        client=client,
        route_template_resolver=lambda method, url: "/payments/:payment_id",
        metadata={"service": "checkout-api"},
    )
    response = session.post(
        "https://api.example.com/payments/123?coupon=summer",
        timeout=3.5,
        json={"amount": 42},
    )
    instrumentation.uninstall()

The httpx helper returns a LogBrewHttpxClientInstrumentation handle. It wraps only the provided sync or async client instance, puts the original request method back with uninstall(), and keeps the same type-only failure metadata and sanitized route/status/duration span behavior as the explicit helpers. It does not patch httpx.Client, httpx.AsyncClient, transports, request hooks, response hooks, payloads, headers, full URLs, query strings, baggage, tracestate, or raw propagation metadata.

For apps that use aiohttp, use aiohttp_request_with_logbrew_span() for one explicit async request, or install reversible instrumentation on one app-owned aiohttp.ClientSession:

import aiohttp

from logbrew_sdk import LogBrewClient, instrument_aiohttp_client_session_with_logbrew_spans

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.0.0",
)

async def submit_payment() -> int:
    async with aiohttp.ClientSession() as session:
        instrumentation = instrument_aiohttp_client_session_with_logbrew_spans(
            session,
            client=client,
            route_template_resolver=lambda method, url: "/payments/:payment_id",
            metadata={"service": "checkout-api"},
        )
        try:
            async with session.post(
                "https://api.example.com/payments/123?coupon=summer",
                timeout=3.5,
                json={"amount": 42},
            ) as response:
                return response.status
        finally:
            instrumentation.uninstall()

The aiohttp helpers add no aiohttp dependency to LogBrew. They write one normalized child traceparent, keep the child trace active during the awaited request, queue one sanitized dependency span, and return the original response or re-raise the original exception. instrument_aiohttp_client_session_with_logbrew_spans() returns a LogBrewAiohttpClientSessionInstrumentation handle. The handle wraps only the provided session instance, returns the existing handle on duplicate install, and puts the original _request coroutine back with uninstall(). It does not patch aiohttp.ClientSession globally, add TraceConfig, own sessions/connectors, capture payloads, response bodies, headers, cookies, full URLs, query strings, fragments, exception messages, baggage, tracestate, or raw propagation values.

Database Operation Spans

Use database_operation_with_logbrew_span() for sync database calls and async_database_operation_with_logbrew_span() for async calls when you want one app-owned DB span without installing or patching a database driver:

from logbrew_sdk import LogBrewClient, database_operation_with_logbrew_span

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.0.0",
)

result = database_operation_with_logbrew_span(
    "SELECT checkout_order",
    client=client,
    event_id="evt_checkout_db_query",
    timestamp="2026-06-19T10:30:00Z",
    operation=lambda: session.execute("SELECT * FROM checkout_order WHERE id = ?", [cart_id]),
    system="postgresql",
    db_name="checkout",
    statement_template="SELECT * FROM checkout_order WHERE id = ?",
    row_count_from_result=lambda rows: rows.rowcount,
    metadata={"service": "checkout-api"},
    span_events=[
        {"name": "db.cursor.ready", "metadata": {"poolSlot": 2}},
    ],
)

The helper activates a child LogBrewTraceContext while your callable runs, queues one span named from the DB system and operation, preserves the original result or exception, and reports telemetry capture failures through on_capture_error without replacing the database result. Metadata is intentionally bounded to primitive caller metadata, dbSystem, dbOperation, optional dbName, optional statementTemplate, optional non-negative rowCount, sampled state, optional bounded span events, and exception type. It does not monkeypatch SQLAlchemy or DB-API drivers, does not open support tickets, and does not capture SQL parameters, result rows, connection strings, network addresses, sensitive configuration values, payloads, baggage, tracestate, stack traces, or exception messages.

DB-API Connection Spans

Use connect_dbapi_connection_with_logbrew_spans() when your app controls a Python DB-API connect callable and you want one sanitized connect span plus cursor execution spans. If your app already has an open connection, use instrument_dbapi_connection_with_logbrew_spans() to wrap only that connection:

import sqlite3

from logbrew_sdk import LogBrewClient, connect_dbapi_connection_with_logbrew_spans

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.0.0",
)

db = connect_dbapi_connection_with_logbrew_spans(
    sqlite3.connect,
    connect_args=(":memory:",),
    client=client,
    system="sqlite",
    db_name="checkout",
    trace_fetch_methods=True,
    metadata={"service": "checkout-api"},
)

cursor = db.cursor()
cursor.execute("SELECT id FROM checkout_order WHERE id = ?", (order_id,))
rows = cursor.fetchall()

raw_connection = db.uninstall()

The helper traces the caller-supplied connect callable, then keeps connection ownership with your app, wraps cursors returned by cursor(), and supports execute(...), executemany(...), callproc(...), transaction commit() and rollback(), plus common connection shortcut execute(...) and executemany(...) calls. Fetch spans for fetchone(), fetchmany(...), and fetchall() are opt-in through trace_fetch_methods=True because high-volume row-reading loops can be noisy. The wrapper derives only the connect, SQL verb, fetch, transaction, or procedure label, records framework=dbapi, dbMethod, optional caller dbName, optional non-negative row count, active child trace IDs, sampled state, and type-only errors. It does not patch DB-API modules, driver classes, or connect functions, and does not capture connect arguments, SQL text, bind values, result rows, connection URLs, network addresses, user names, baggage, tracestate, stack traces, or exception messages. Call uninstall() to stop future spans and get the original connection back.

SQLAlchemy Engine Spans

Use instrument_sqlalchemy_engine_with_logbrew_spans() when your app already uses SQLAlchemy and you want one safe span per cursor execution from a caller-owned engine:

from sqlalchemy import create_engine, text

from logbrew_sdk import LogBrewClient, instrument_sqlalchemy_engine_with_logbrew_spans

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.0.0",
)
engine = create_engine("sqlite:///:memory:")

instrumentation = instrument_sqlalchemy_engine_with_logbrew_spans(
    engine,
    client=client,
    db_name="checkout",
    metadata={"service": "checkout-api"},
)

with engine.begin() as connection:
    connection.execute(text("SELECT 1"))

instrumentation.uninstall()

The helper imports SQLAlchemy only when you opt in, attaches listeners only to the engine you pass, returns the existing instrumentation on duplicate calls, activates a child trace while SQLAlchemy executes the statement, and removes listeners with uninstall(). For async SQLAlchemy engines, pass the owned async_engine.sync_engine. Captured metadata is bounded to primitive caller metadata, framework=sqlalchemy, dbSystem, dbOperation, optional dbName, optional non-negative rowCount, sampled state, and exception type. It does not patch global SQLAlchemy factories, wrap sessions, capture raw SQL, SQL parameters, connection URLs, hosts, usernames, result rows, baggage, tracestate, stack traces, or exception messages.

Cache Operation Spans

Use cache_operation_with_logbrew_span() for sync cache calls and async_cache_operation_with_logbrew_span() for async calls when you want one app-owned cache span without installing or patching Redis, memcached, Django cache, or Flask cache clients:

from logbrew_sdk import LogBrewClient, cache_operation_with_logbrew_span

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.0.0",
)

profile = cache_operation_with_logbrew_span(
    "GET profile",
    client=client,
    event_id="evt_checkout_profile_cache_get",
    timestamp="2026-06-19T11:15:00Z",
    operation=lambda: redis_client.get(profile_cache_key),
    system="redis",
    cache_name="profiles",
    cache_hit=True,
    item_count=1,
    metadata={"service": "checkout-api"},
    span_events=[
        {"name": "cache.lookup", "metadata": {"cacheTier": "primary"}},
    ],
)

The helper activates a child LogBrewTraceContext while your callable runs, queues one span named from the cache system and operation, preserves the original result or exception, and reports telemetry capture failures through on_capture_error without replacing the cache result. Metadata is intentionally bounded to primitive caller metadata, cacheSystem, cacheOperation, optional cacheName, optional hit state, optional non-negative item size/count, sampled state, optional bounded span events, and exception type. It drops key-like metadata fields and does not monkeypatch cache clients, open support tickets, capture cache keys, values, commands, payloads, headers, cookies, network addresses, baggage, tracestate, stack traces, or exception messages.

Django Cache Spans

Use instrument_django_cache_with_logbrew_spans() when your app already owns a Django cache object and you want one span per supported cache method on that object:

from django.core.cache import cache

from logbrew_sdk import LogBrewClient, instrument_django_cache_with_logbrew_spans

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.0.0",
)

instrumentation = instrument_django_cache_with_logbrew_spans(
    cache,
    client=client,
    cache_name="profiles",
    metadata={"service": "checkout-api"},
)

cache.set(profile_cache_key, profile, timeout=60)
profile = cache.get(profile_cache_key)
instrumentation.uninstall()

The helper returns a LogBrewDjangoCacheInstrumentation handle, does not add Django as a LogBrew dependency, and does not patch Django globally. It wraps only the cache object you pass, returns the existing instrumentation on duplicate calls, activates a child trace around supported get, get_many, set, set_many, add, delete, delete_many, and clear calls, derives hit state and item count/size when safely knowable, and puts the original methods back with uninstall(). It does not read Django settings, capture cache keys, values, timeout/version arguments, backend locations, hosts, ports, arbitrary command text, response payloads, baggage, tracestate, stack traces, or exception messages.

Flask-Caching Spans

Use instrument_flask_cache_with_logbrew_spans() when your app already owns a Flask-Caching Cache object and you want one span per supported cache method on that object:

from flask import Flask
from flask_caching import Cache

from logbrew_sdk import LogBrewClient, instrument_flask_cache_with_logbrew_spans

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.0.0",
)

app = Flask(__name__)
app.config["CACHE_TYPE"] = "SimpleCache"
cache = Cache(app)
instrumentation = instrument_flask_cache_with_logbrew_spans(
    cache,
    client=client,
    cache_name="profiles",
    metadata={"service": "checkout-api"},
)

cache.set(profile_cache_key, profile, timeout=60)
profile = cache.get(profile_cache_key)
instrumentation.uninstall()

The helper returns a LogBrewFlaskCacheInstrumentation handle, does not add Flask or Flask-Caching as LogBrew dependencies, and does not patch Flask-Caching globally. It wraps only the cache object you pass, returns the existing instrumentation on duplicate calls, activates a child trace around supported get, get_many, set, set_many, add, delete, delete_many, and clear calls, derives hit state and item count/size when safely knowable, and puts the original methods back with uninstall(). It does not capture cache keys, values, timeout arguments, key prefixes, backend locations, hosts, ports, arbitrary command text, response payloads, baggage, tracestate, stack traces, or exception messages.

Pymemcache Client Spans

Use instrument_pymemcache_client_with_logbrew_spans() when your app already owns a pymemcache style client and you want safe spans for calls made through that one object:

from pymemcache.client.base import Client

from logbrew_sdk import LogBrewClient, instrument_pymemcache_client_with_logbrew_spans

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.0.0",
)
cache_client = Client(("localhost", 11211))

instrumentation = instrument_pymemcache_client_with_logbrew_spans(
    cache_client,
    client=client,
    cache_name="profiles",
    metadata={"service": "checkout-api"},
)

profile = cache_client.get(profile_cache_key, default=None)
cache_client.set(profile_cache_key, profile, expire=60)
instrumentation.uninstall()

The helper returns a LogBrewPymemcacheInstrumentation handle, does not add pymemcache as a LogBrew dependency, and does not patch pymemcache classes globally. It wraps only the client object you pass, returns the existing instrumentation on duplicate calls, activates a child trace around supported get, get_many, get_multi, gets, gets_many, set, set_many, set_multi, add, replace, append, prepend, cas, delete, delete_many, incr, decr, touch, stats, version, flush_all, and quit calls, derives hit state and item count/size when safely knowable, and puts the original methods back with uninstall(). It does not capture cache keys, values, expiration or noreply arguments, backend locations, hosts, ports, arbitrary command text, response payloads, baggage, tracestate, stack traces, or exception messages.

Redis Client Spans

Use instrument_redis_client_with_logbrew_spans() when your app already owns a redis-py style client and you want safe spans for calls that go through that one client's execute_command method:

import redis

from logbrew_sdk import LogBrewClient, instrument_redis_client_with_logbrew_spans

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.0.0",
)
redis_client = redis.Redis.from_url("redis://localhost:6379/0")

instrumentation = instrument_redis_client_with_logbrew_spans(
    redis_client,
    client=client,
    cache_name="profiles",
    trace_pipelines=True,  # opt in when Redis pipeline execute timing matters
    metadata={"service": "checkout-api"},
)

profile = redis_client.get(profile_cache_key)
pipeline_results = redis_client.pipeline().get(profile_cache_key).set(profile_cache_key, "fresh").execute()
instrumentation.uninstall()

The helper does not add redis as a LogBrew dependency and does not patch Redis classes globally. It wraps only the client instance you pass, returns the existing instrumentation on duplicate calls, activates a child trace during sync or async execute_command work, derives command name, read/write/delete kind, cache hit, result count, and byte size when safely knowable from the result, and reinstates the original method with uninstall(). With trace_pipelines=True, it also wraps pipelines returned by that client instance and records one sanitized redis PIPELINE span around execute(), including only pipeline length and capped operation names such as GET,SET. It does not capture Redis keys, values, command arguments, pipeline arguments, connection URLs, network endpoints, ports, usernames, arbitrary command text, response payloads, baggage, tracestate, stack traces, or exception messages.

Queue Operation Spans

Use queue_operation_with_logbrew_span() for sync queue calls and async_queue_operation_with_logbrew_span() for async calls when you want one app-owned publish/process span without installing or patching Celery, RQ, Dramatiq, or broker clients:

from logbrew_sdk import LogBrewClient, queue_operation_with_logbrew_span

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-worker",
    sdk_version="1.0.0",
)

queued = queue_operation_with_logbrew_span(
    "publish checkout.email",
    client=client,
    event_id="evt_checkout_email_publish",
    timestamp="2026-06-19T13:00:00Z",
    operation=lambda: celery_task.apply_async(args=[order_id]),
    system="celery",
    operation_kind="publish",
    queue_name="email",
    task_name="checkout.email",
    message_count=1,
    metadata={"service": "checkout-worker"},
    span_events=[
        {"name": "queue.publish.confirmed", "metadata": {"brokerPartition": 4}},
    ],
)

The helper activates a child LogBrewTraceContext while your callable runs, queues one span named from the queue system and operation, preserves the original result or exception, and reports telemetry capture failures through on_capture_error without replacing the queue result. Metadata is intentionally bounded to primitive caller metadata, queueSystem, queueOperation, optional operation kind, optional queue/task names, optional non-negative message count/attempt, sampled state, optional bounded span events, and exception type. It drops message-like metadata fields and does not monkeypatch queue frameworks, write broker metadata, open support tickets, capture job arguments, message bodies, headers, cookies, broker URLs, baggage, tracestate, stack traces, or exception messages.

For RQ jobs, use rq_operation_with_logbrew_span() when you want LogBrew to derive safe func_name and origin metadata from an app-owned job object without installing RQ as a LogBrew dependency or patching Queue/Worker globally:

from logbrew_sdk import LogBrewClient, rq_operation_with_logbrew_span

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-worker",
    sdk_version="1.0.0",
)

job = queue.create_job(checkout_email_task, args=[order_id])
queued = rq_operation_with_logbrew_span(
    client=client,
    event_id="evt_checkout_email_rq_publish",
    timestamp="2026-06-19T14:00:00Z",
    job=job,
    operation=lambda: queue.enqueue_job(job),
    operation_kind="publish",
    metadata={"service": "checkout-worker"},
)

The RQ helper records one rq queue span using explicit caller control. It reads only string-like job.func_name and job.origin by default, lets you override queue/task names, accepts the same bounded span_events option as the generic queue helper, and still avoids job args, kwargs, descriptions, broker metadata writes, global worker patching, baggage, and tracestate.

Automatic Celery spans

Install the same Python package with its Celery extra when you want producer and worker spans without wrapping each task call:

pip install "logbrew-sdk[celery]"

from celery import Celery

from logbrew_sdk import (
    HttpTransport,
    LogBrewClient,
    instrument_celery_app_with_logbrew_spans,
)

app = Celery("checkout")
client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-worker",
    sdk_version="1.0.0",
)
transport = HttpTransport()

celery_instrumentation = instrument_celery_app_with_logbrew_spans(
    app,
    client=client,
    metadata={"service": "checkout-worker"},
)

# Existing delay(), apply_async(), and app.send_task() calls keep their behavior.
send_receipt.delay(order_id)

# Flush on your normal batch/graceful-shutdown path, after worker tasks drain.
celery_instrumentation.uninstall()
client.shutdown(transport)

The integration wraps only the supplied app instance's send_task method and filters worker signal callbacks to tasks owned by that exact app. Duplicate installation returns the existing handle, and uninstall() puts the original app method back and removes only LogBrew's receivers. Uninstall fails clearly while owned tasks are still running so an active task trace is never silently detached.

For a prefork worker, register child-process ownership in the module Celery imports before the pool starts:

from celery import Celery

from logbrew_sdk import (
    HttpTransport,
    LogBrewClient,
    instrument_celery_worker_processes_with_logbrew,
)

worker_app = Celery("checkout-worker")

worker_lifecycle = instrument_celery_worker_processes_with_logbrew(
    worker_app,
    client_factory=lambda: LogBrewClient.create(
        api_key="LOGBREW_API_KEY",
        sdk_name="checkout-worker",
        sdk_version="1.0.0",
        max_retries=2,
    ),
    transport_factory=HttpTransport,
    metadata={"service": "checkout-worker"},
)

For crash-safe worker delivery, install the optional crypto provider with pip install "logbrew-sdk[celery,persistence]", then opt in to one encrypted local queue per Celery pool slot. Create a dedicated owner-only root before the worker starts, load a 32-byte key from your application's key-management service, and derive the slot inside client_factory, after Celery has forked the child:

import os
from pathlib import Path

from logbrew_sdk import celery_worker_persistent_queue_directory

queue_root = Path(os.environ["LOGBREW_QUEUE_ROOT"]).resolve()
queue_root.mkdir(mode=0o700, parents=True, exist_ok=True)


def create_worker_client() -> LogBrewClient:
    persistence_key = bytes.fromhex(os.environ["LOGBREW_PERSISTENCE_KEY_HEX"])
    return LogBrewClient.create(
        api_key=os.environ["LOGBREW_API_KEY"],
        sdk_name="checkout-worker",
        sdk_version="1.0.0",
        max_retries=2,
        persistent_queue_directory=celery_worker_persistent_queue_directory(queue_root),
        persistent_queue_encryption_key=persistence_key,
    )

Persistence is disabled by default, so memory-only clients do not load the optional crypto package. Encrypted persistence requires a supported POSIX filesystem with owner and link metadata. It uses AES-256-GCM with a unique nonce per write and authenticates the queue schema, SDK identity, sequence, compact event bytes, stable local record ID, accepted prefix, and admitted high-water mark. Missing sole, interior, or trailing records, a wrong key, tamper, unsafe permissions, hard links, replacement, or concurrent ownership fail closed with content-free errors.

The caller owns the encryption key lifecycle. LogBrew never writes or logs the key and has no silent plaintext fallback or built-in key rotation. Keep the same key until a queue is drained or explicitly purged; rotating it while encrypted records remain makes that queue intentionally unreadable. Existing unencrypted queue directories are rejected. Drain and remove those directories with the build that created them before enabling encrypted persistence.

Completed admission stores encrypted compact event JSON before returning, and replacement children for the same Celery slot replay the oldest records first. The database never retains plaintext event content, API keys, endpoints, headers, process IDs, machine identity, worker names, or local paths. Filesystem observers can still see the queue directory, encrypted file size, row count, and write timing, so keep the root on app-owned protected storage and include it in your data-retention policy.

The default bounds are 10,000 events, 4 MiB of compact event JSON, 100 events per request, and 256 KiB per request. Override them with max_queue_size, max_queue_bytes, max_batch_events, and max_batch_bytes. New records are dropped when either queue bound is full; a record that cannot fit one request is also dropped. dropped_events(), pending_events(), and pending_event_bytes() expose local pressure without reading event content. flush() and shutdown() acknowledge only accepted prefixes, reuse an identical body for retries, and leave failed or later records for the next owner. TransportResponse.batches and accepted_events report aggregate progress when one flush requires multiple requests.

Use recover_pending_events() to revalidate authenticated state explicitly and purge_pending_events() only for an explicit local data-deletion action. Both fail while delivery or shutdown is active. The events property remains available for compatibility but returns a detached decrypted snapshot; mutating it never changes the queue. A hard exit cannot run Python finalizers, so durability comes from each completed admission transaction, not from atexit or a terminal signal callback. Delivery remains at least once: the service should use stable public event IDs to make a replay harmless.

The factories run once in each worker child after fork, and only when Celery's current worker app is the supplied app, so the parent never shares a client, queue, transport, or connection state with its children. On graceful worker_process_shutdown, LogBrew first removes that child's task receivers and then sends its retained queue through the child-owned transport. Duplicate init/shutdown signals are idempotent, active tasks defer delivery instead of losing their spans, and configured client retries reuse the exact serialized batch. Signal-path failures are reduced to generic on_capture_error notifications and never replace Celery task results.

Use a separate producer-owned Celery app with instrument_celery_app_with_logbrew_spans() when the producer and prefork worker run in different processes. Direct app instrumentation and worker-process lifecycle ownership cannot be mixed on the same app instance. Without persistent_queue_directory, delivery is limited to retries that complete during graceful Celery process shutdown. Billiard exits prefork children without running Python atexit; enable the persistent queue when committed events must survive an exhausted retry, hard kill, or native process crash.

Each brokered task gets a producer span and a worker span connected by one W3C traceparent. The worker span keeps its trace active during task code and records bounded task name, routing key, zero-based retry attempt, task state, queue wait, duration, sampled state, and exception type. The integration copies caller headers before adding traceparent and logbrew-enqueued-at-ms, so the caller's mapping is unchanged. It never captures or serializes task IDs, arguments, keyword arguments, results, message bodies, existing headers, broker URLs, exchanges, worker machine names, exception messages, stack traces, baggage, or tracestate. Instrumentation and telemetry-capture failures are reported through on_capture_error and do not replace Celery results or exceptions. Queue pressure still follows LogBrewClient.max_queue_size; inspect dropped_events() and flush in bounded batches instead of deriving hosted usage locally.

Use the explicit helper below instead when you need per-call opt-in control, eager-mode producer spans, or custom instrumentation around a queue operation that does not pass through this app's send_task method.

For Celery tasks, use celery_operation_with_logbrew_span() when you want safe task and queue metadata without registering Celery signals or patching apply_async. To connect producer and worker spans, create an explicit W3C carrier with create_celery_trace_headers() and pass it to your own apply_async(...) call:

from logbrew_sdk import (
    LogBrewClient,
    celery_operation_with_logbrew_span,
    create_celery_trace_headers,
)

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-worker",
    sdk_version="1.0.0",
)

queued = celery_operation_with_logbrew_span(
    client=client,
    event_id="evt_checkout_receipt_celery_publish",
    timestamp="2026-06-19T15:00:00Z",
    task=send_receipt_task,
    operation=lambda: send_receipt_task.apply_async(
        args=[order_id],
        headers=create_celery_trace_headers(),
    ),
    operation_kind="publish",
    queue_name="receipts",
    metadata={"service": "checkout-worker"},
)

On the worker side, pass the task object as usual. If task.request.headers contains a valid traceparent, the helper uses it as the upstream parent for the processing span. You can also extract the parent yourself with logbrew_trace_context_from_celery_headers(task.request.headers) and pass it as trace=... when you need explicit control.

The Celery helper reads only string-like task.name, an optional routing key from task.request.delivery_info, and a valid W3C traceparent from app-owned task headers. create_celery_trace_headers() writes only one traceparent key; it does not write baggage, tracestate, arbitrary headers, task args, kwargs, broker URLs, or payload data. The helper still avoids signal registration, global patching, hidden header mutation, task IDs, request-header capture, exception messages, and stack traces.

Agent-Readable Timelines

Use create_product_action_attributes() and create_network_milestone_attributes() when your service already knows important product steps or API milestones. The helpers create normal action event attributes with primitive metadata that AI assistants can analyze across sessions without visual replay, global HTTP patching, payload capture, or header capture.

from logbrew_sdk import (
    LogBrewClient,
    create_network_milestone_attributes,
    create_product_action_attributes,
)

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="checkout-api",
    sdk_version="1.0.0",
)

client.action(
    "evt_checkout_submit",
    "2026-06-02T10:00:05Z",
    create_product_action_attributes(
        {
            "name": "checkout.submit",
            "status": "running",
            "sessionId": "sess_123",
            "traceId": "4bf92f3577b34da6a3ce929d0e0e4736",
            "routeTemplate": "/checkout/:step",
            "funnel": "checkout",
            "step": "submit",
            "metadata": {"service": "checkout"},
        }
    ),
)
client.action(
    "evt_payment_api",
    "2026-06-02T10:00:06Z",
    create_network_milestone_attributes(
        {
            "routeTemplate": "/payments/:id",
            "method": "POST",
            "statusCode": 202,
            "durationMs": 94,
            "sessionId": "sess_123",
            "traceId": "4bf92f3577b34da6a3ce929d0e0e4736",
            "metadata": {"service": "checkout"},
        }
    ),
)

Timeline helpers keep only primitive metadata, strip query strings and hashes from route templates, normalize HTTP methods, infer failed network milestones from status codes 400 and above, and serialize through the existing action event type. Keep metadata low-cardinality, such as sessionId, traceId, routeTemplate, method, statusCode, durationMs, screen, funnel, and step.

The packaged agent-timeline example shows a two-event checkout timeline with explicit traceparent propagation and sanitized product/network metadata:

python -m logbrew_sdk.examples agent-timeline

HTTP Delivery

Use HttpTransport for real outbound delivery from server-side Python apps:

from logbrew_sdk import HttpTransport, LogBrewClient

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="logbrew-python",
    sdk_version="0.1.0",
)
transport = HttpTransport(
    endpoint="https://api.logbrew.co/v1/events",
    headers={"x-logbrew-source": "python-worker"},
)

client.log(
    "evt_worker_started",
    "2026-06-02T10:00:06Z",
    {"message": "worker started", "level": "info", "logger": "worker"},
)
client.flush(transport)

HttpTransport uses Python's standard-library HTTP stack, posts JSON, passes the SDK key through the authorization header, supports custom endpoint/header/timeout settings, and maps connection failures into retryable TransportError.network(...) failures so LogBrewClient.flush() can preserve queued events and retry.

Standard Logging

Use LogBrewLoggingHandler when an application already uses Python's standard logging module:

import logging

from logbrew_sdk import LogBrewClient, LogBrewLoggingHandler, RecordingTransport

client = LogBrewClient.create(
    api_key="LOGBREW_API_KEY",
    sdk_name="logbrew-python",
    sdk_version="0.1.0",
)
transport = RecordingTransport.always_accept()
handler = LogBrewLoggingHandler(
    client,
    transport,
    flush_on_emit=True,
    metadata={"service": "checkout"},
)

logger = logging.getLogger("checkout.worker")
logger.addHandler(handler)
logger.setLevel(logging.INFO)

logger.info("worker started", extra={"order_id": "ord_123"})

The handler does not change global logging configuration. It maps standard logging levels into canonical LogBrew severities (info, warning, error, critical), keeps the logger name, captures primitive extra={...} values as metadata, and records source file name, function, line, thread, and process names without sending the full source path by default. Python DEBUG records are captured as info and CRITICAL records as critical; the original Python level name and number remain available in metadata. Exception type and message are captured when exc_info is present; full exception text is opt-in with include_exception_text=True.