Hue Python SDK

For frozen datasets, local experiments, custom scorers, durable retries and historical rescoring, see Local evaluations.

Python helpers around official OpenTelemetry 1.44.0 trace and log SDKs and OTLP HTTP/protobuf exporters. Provider requests run in your application. This package does not proxy model calls or configure global OTel providers.

The distribution is named hue-run (import hue_sdk). Python 3.10+ is supported by the package contract; recorded validation below identifies the tested runtime.

Install

pip install hue-run
# Or, in a uv project:
uv add hue-run

Send a trace

In the consuming application, use only public imports:

import os
from hue_sdk import Hue

with Hue(
    api_key=os.environ["HUE_API_KEY"],
    capture_content=False,  # Required: choose explicitly.
) as hue:
    project = hue.validate_project()
    with hue.context(session_id="conversation-42", user_id="observed-user-7"):
        with hue.span("agent.run") as run:
            run.set_input({"question": "What is 2 + 2?"})
            with hue.tool("add") as tool:
                tool.set_input({"a": 2, "b": 2})
                tool.set_output(4)
            run.set_output({"answer": 4})
    if not hue.force_flush():
        raise RuntimeError("Telemetry export failed; inspect Hue export_status.")

The SDK uses https://app.hue.run by default. Set base_url only for a different Hue deployment or a local receiver, using an origin without an API suffix. Existing Hue(base_url, api_key, ...) calls remain supported. The project service key determines the project. The SDK validates the project at GET /api/v1/projects/current when explicitly requested; construction itself does not perform a request. HTTP is permitted only for localhost and loopback IPs. Userinfo, query strings, fragments, paths and redirects are rejected. The key is sent only as Authorization: Bearer …; repr(hue), SDK errors and status counters omit it.

Content and semantic fields

capture_content has no default. False makes set_input, set_output and inference-log bodies omit content before it reaches an OTel queue. Explicit JSON null, empty strings and absent content stay distinct when capture is enabled. Exception recording includes the exception type and ERROR status; exception messages and stacks are always excluded by these helpers.

This setting is not a blanket PII filter. Custom attributes, span names, session/user identifiers, resource attributes, third-party instrumentors and other exporters remain under your control. The server stores received content; there is no automatic telemetry expiry. Delete scoped data explicitly when required by your retention policy.

Use redactor=lambda field, value: ... to transform content in supported helpers. It runs synchronously before serialization and export. Return a redacted JSON value; failures raise a generic ValueError and the field is not recorded. It does not inspect arbitrary OTel attributes or logs:

def redact(field, value):
    if isinstance(value, dict):
        return {key: "[redacted]" if key == "email" else item for key, item in value.items()}
    return value

The callback should cover your actual nested input format; this small example is only a top-level dictionary transformation.

For model helpers, pass the message representation produced by your integration. Prefer current GenAI message conventions when authoring your own messages. Use either span content or correlated logs for a given input/output, avoiding duplicate copies. Structured log fields contain JSON strings so null remains distinguishable from protobuf's absent value.

Existing instrumentation

Pass an existing opentelemetry.sdk.trace.TracerProvider through tracer_provider=provider to add Hue's exporter. Hue does not call set_tracer_provider. It exposes hue.tracer_provider, hue.tracer and hue.logger_provider for explicit integration. shutdown() closes Hue's processors; a borrowed tracer provider and its other processors stay usable. Do not repeatedly attach Hue clients to one long-lived provider: OTel has no public processor-removal API. Create one client per provider lifecycle.

An instrumentor that accepts tracer_provider can receive hue.tracer_provider; follow that instrumentor's own capture/redaction configuration. OpenInference and other OTel instrumentors are optional dependencies, not implicitly enabled. They can emit content even when Hue helper capture is disabled. The optional compatibility group pins OpenAI 3.14.0, OpenInference OpenAI 0.1.60 and its resolved OpenInference instrumentation 0.1.63. A synthetic HTTP streaming response verifies parentage, canonical model/usage attributes and enabled/disabled message capture with TraceConfig(enable_genai_semconv=True, hide_inputs=..., hide_outputs=..., hide_input_messages=..., hide_output_messages=...). This is a tested adapter combination, not a claim about all OpenAI APIs or live-provider compatibility. See the instrumentor's official source. See the Python integration guide for application setup.

Export behavior and limits

• Traces go to /api/v1/otlp/v1/traces; correlated logs go to /api/v1/otlp/v1/logs. Both use the official OTLP HTTP/protobuf exporter, uncompressed. The official retry policy remains active for retryable network/service failures. There is no proprietary provider transport.
• Batches initially contain at most 64 records and are split by encoded protobuf size to fit 1 MiB, both on wire and after decoding. A single oversized record fails visibly through export status. Helper content exceeding 256 KiB UTF-8 JSON raises before enqueue; it is never silently truncated by Hue. Third-party record validation remains the receiver's responsibility. OTel's own attribute/count/environment limits can still affect externally configured providers.
• Hue accepts at most 2,000 distinct spans per trace. This is enforced by the receiver across distributed producers; the client cannot guarantee a global count.
• HTTP errors, malformed/non-200 success responses and OTLP partial_success rejected counts cause a failed export status. Partial rejection is not retried wholesale. Receiver error text is not echoed. A warning-only partial-success response with zero rejected records remains successful.
• force_flush(timeout_millis=30000) drains both processors and returns False for a timeout or any recorded failed export batch since this client was created. Because OTel 1.44 ignores its processor timeout, Hue serializes flushes in one background worker and bounds the caller's wait. Pending exports continue after timeout. export_status exposes cumulative failure counters. shutdown() stops new helpers, drains and closes owned exporters within the caller's wait budget; repeated calls wait for the same shutdown. After a timeout, keep the process alive and call shutdown again to confirm completion. Context-manager exit calls shutdown; check flush explicitly when an exit code must reflect delivery failure.
• Standard OTel batch queues hold 2,048 records per signal and are in-memory. Queue overflow, process termination and sampling can lose telemetry. Flush success reports observed exporter outcomes, not durable local delivery or proof that every application operation was instrumented. The exporter timeout controls individual export/retry operations. A caller timeout does not cancel an HTTP request already in flight; background workers continue until the operation completes.

Confirm a trace reached Hue

Hue.verify_trace() checks a server receipt for a known trace from a real application request. It does not send a synthetic trace, invoke a model, or flush an exporter. Finish the request, then flush the provider that produced it. If you borrow a provider, call its force_flush() first, then check Hue's force_flush() result before verifying:

# Retain these IDs while your application's instrumented request runs.
# After the request finishes and the relevant providers have flushed:
confirmation = hue.verify_trace(
    request_trace_id,
    expected_span_ids=[request_span_id, model_span_id],
    required_fields=["input", "output", "model", "usage", "session"],
    timeout_millis=10_000,
)
if confirmation.verified:
    print(confirmation.receipt.trace_url)
else:
    # The latest partial receipt, or None if this trace has not appeared yet.
    print(confirmation.receipt)

Only require fields your instrumentation emits and your capture policy permits. For metadata-only capture, omit input and output. The receipt contains presence booleans and counts, not captured content. verified=True confirms the requested trace, every supplied expected span, and every required field; it does not prove that unlisted application operations were instrumented. Missing spans or fields continue polling until the deadline, then return verified=False with the latest receipt. Trace IDs must be nonzero and contain 32 lowercase hexadecimal characters; expected span IDs must be nonzero, contain 16, be unique, and number at most 100. The timeout must be positive and at most 60,000 ms.

Only trace-not-found responses, HTTP 429 and HTTP 503 are retried. Authentication, unsupported receipt endpoints, malformed responses and connection failures raise TraceVerificationError with a safe code and optional status_code; response bodies and keys are omitted. Redirects are rejected. The deadline covers connection, polling and response-body reads. An in-flight network read may finish in the background after the caller times out. This helper requires a Hue deployment that implements /api/v1/traces/{traceId}/receipt.

Supported runtimes and verification

Python 3.10+ is supported. CI tests Python 3.10 and 3.14, source imports and an independently installed wheel. Tests use synthetic loopback HTTP receivers and decode official OTLP protobuf messages to verify trace/log correlation, metadata-only capture, redaction, propagation, existing-provider ownership, authentication failures, redirects, partial rejection, retries and encoded request limits. Compatibility tests also exercise local evaluations and the optional OpenInference adapter. No live model provider is required for these checks.

See the documentation for integration guidance and troubleshooting for export failures.

Managed targets

Start a frozen dataset run in Hue while your existing agent stays in your application:

import os
from hue_sdk.managed import ManagedTargetHandler, ManagedTargetResult

def target(invocation):
    # Your function consumes unchanged inputs and verified attachment bytes.
    result = run_agent_for_evaluation(invocation)
    return ManagedTargetResult(output=result)

handler = ManagedTargetHandler(
    machine_credential=os.environ["HUE_MANAGED_TARGET_SECRET"],
    target=target,
    flush_telemetry=hue.force_flush,  # Existing client; False keeps telemetry pending.
)

Your POST route calls handler.handle(raw_body_bytes, request_headers) and returns its JSON body, status code and headers. Limit request bodies to 1 MiB; use asyncio.to_thread from an async route. The target must honor cancelled and deadline_monotonic. Use a 120-second host limit for the default 90-second callback and 30-second finalization budget.

For generated files, return ManagedOutputFile entries containing actual bytes, filename, content type and an optional primary flag. The helper claims the invocation, verifies files and saves the outcome without automatically rerunning the agent. See the managed-run guide and full adapter contract for registration, existing-provider flush callbacks and recovery. Local/CI runners remain available.