Rius Python SDK

OpenTelemetry-native tracing for AI agents and LLM applications. glassflow-rius emits OpenTelemetry GenAI traces over OTLP to the managed GlassFlow observability platform (or any OTLP-compatible backend).

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Install

pip install glassflow-rius

Quickstart

import rius
from rius import observe, start_as_current_generation, start_as_current_span
from rius.semconv import SpanKind

rius.init(
    api_key="glassflow_...",          # or set RIUS_API_KEY
    service_name="my-agent",          # or set RIUS_SERVICE_NAME
)

# 1. Decorator — trace a whole function
@observe
def handle(query: str) -> str: ...

# 2. Context manager — trace a block
with start_as_current_span("retrieve", kind=SpanKind.RETRIEVER) as obs:
    obs.set_output(docs)

# 3. LLM generations — gen_ai-native
with start_as_current_generation("chat", model="gpt-4o", input=messages) as gen:
    gen.set_output(reply)
    gen.set_usage(input_tokens=42, output_tokens=17)

Span names are optional. Left out, a span is named the way the OpenTelemetry GenAI conventions say it should be: the operation followed by what it acted on, such as chat gpt-4o, embeddings text-embedding-3-small, execute_tool get_weather, invoke_agent planner or retrieval product-kb. When that identifier is unknown, the name falls back to the bare operation (chat). @observe keeps the function's qualified name for a plain CHAIN step, which has no operation; an unnamed start_span() of that kind is called chain. An explicit name always wins.

Each surface has a manual variant for lifetimes a with block can't express (streaming, callbacks): start_span(...) / start_generation(...) return a handle you .update() and must .end() yourself. A manual handle does not auto-record exceptions, so report a failure with .record_exception(exc), which records the exception event, sets the ERROR status and sets error.type together:

gen = start_generation("chat", model="gpt-4o", input=messages)
try:
    reply = call_the_model(messages)
except Exception as exc:
    gen.record_exception(exc)
    raise
finally:
    gen.end()

Configuration is resolved from explicit arguments first, then environment variables:

Argument	Environment variable	Default	Description
endpoint	RIUS_ENDPOINT	https://ingest.eu.console.rius-glassflow.com	Base OTLP endpoint. Traces are sent to <endpoint>/v1/traces.
api_key	RIUS_API_KEY	none	Injected as an Authorization: Bearer <key> header on every export.
service_name	RIUS_SERVICE_NAME	unknown_service	Sets the OpenTelemetry service.name resource attribute.
disabled	RIUS_DISABLED	false	Kill switch. When true, spans are created but never exported.
sample_rate	RIUS_SAMPLE_RATE	1.0	Head sampling ratio 0.0–1.0 (whole-trace; children follow root).
capture_content	RIUS_CAPTURE_CONTENT	true	When false, prompt/response content is stripped at export (metadata still sent).
bridge_foreign_provider	RIUS_BRIDGE_FOREIGN_PROVIDER	false	Also export the spans of another OpenTelemetry SDK that owns the global provider (see below).
agent_name	RIUS_AGENT_NAME	service_name	The agent this process is. Also the gen_ai.main_agent.id when main_agent_id is unset, so renaming it changes that id.
main_agent_id	RIUS_MAIN_AGENT_ID	a gen_ai.main_agent.id in OTEL_RESOURCE_ATTRIBUTES, else the agent name	A stable id for the agent (a registry id or an ARN), stamped as gen_ai.main_agent.id. Never a per-process value.
main_agent_description	RIUS_MAIN_AGENT_DESCRIPTION	none	What the agent does, stamped as gen_ai.main_agent.description.

The agent this process is is described on the resource under the GenAI conventions' gen_ai.main_agent.*. gen_ai.main_agent.id is the entity's identity: it is main_agent_id when you set it (the argument, then RIUS_MAIN_AGENT_ID), then a gen_ai.main_agent.id you already put in OTEL_RESOURCE_ATTRIBUTES, and otherwise it falls back to the agent name, so renaming the agent changes the id unless main_agent_id is set (a one-time DEBUG log on the rius.client logger says so). gen_ai.main_agent.name is the resolved agent_name, and gen_ai.main_agent.description is main_agent_description when set; a name or description you set in OTEL_RESOURCE_ATTRIBUTES is kept instead. When there is neither an id nor a name (the unknown_service placeholder), the SDK stamps none of the gen_ai.main_agent.* keys (any you set yourself pass through), because the conventions do not allow the entity without an identifier. The deprecated rius.main_agent.name, .id and .description keep their old behaviour through their deprecation window (rius.main_agent.id only ever carries a configured main_agent_id), and rius.main_agent.version (main_agent_version, RIUS_MAIN_AGENT_VERSION), which has no upstream twin, carries the optional agent version. gen_ai.agent.name is still stamped alongside them, unchanged: the keys are additive so that no deployment loses its agent identity between an SDK upgrade and a backend one.

mask is a code-only option (no env var): pass a callable to init(mask=...) and it is applied to every content attribute value at export, across our spans and any bundled third-party instrumentation. A mask that accepts a key keyword also receives the attribute key, for per-attribute decisions.

Both controls cover the whole span, not just its attributes: event and link attributes, the exception.message / exception.stacktrace of a recorded exception, and the ERROR status description, since provider errors routinely echo the rejected request back. With capture_content=False those are stripped and the status code and exception.type are kept, so failures stay visible; with a mask, the mask runs over them (the status description is offered under key="status.description").

rius.init(mask=lambda value: "[REDACTED]")   # redact all captured content
rius.init(mask=lambda value, *, key: hash_pii(value) if "input" in key else value)
rius.init(capture_content=False)             # drop content entirely, keep metadata

Running next to another OpenTelemetry SDK

OpenTelemetry has one global tracer provider per process. If another SDK (Langfuse v3, for one) claims it before rius.init(), your own job and tool spans go to that SDK only, while the LLM spans inside them still reach Rius, so Rius receives traces whose parents it never gets. rius detects this: those LLM spans carry rius.parent.foreign=true, the resource records rius.sdk.global_provider=foreign:<class>, and the first one logs a warning.

To send the other SDK's spans to Rius as well, opt in with init(bridge_foreign_provider=True) or RIUS_BRIDGE_FOREIGN_PROVIDER=true. rius then exports that SDK's spans too, subject to rius's sample_rate, capture_content and mask, and it never modifies what the other SDK exports: the other vendor receives exactly what it would without rius.

Auto-instrumentation

The SDK bundles existing OTel instrumentors (OpenInference) as optional extras, so a single install captures your LLM provider and framework calls. Install the extras you need and init() enables whatever it finds; the instrumentation spans nest under your @observe / start_as_current_span traces automatically.

pip install "glassflow-rius[openai]"        # one provider
pip install "glassflow-rius[instruments]"   # everything supported

An extra installs the instrumentation for a library, never the library itself, so the SDK never pins or upgrades the versions your code runs against: glassflow-rius[anthropic] expects anthropic to be your own dependency, which it already is in any project that calls Anthropic. When an instrumentation's library is missing or too old, init() logs a warning naming the version it needs and leaves that one integration off. For example, openinference-instrumentation-anthropic 3.x needs anthropic>=1.8.0; with an older anthropic, upgrade it or keep the instrumentation on 2.x.

rius.init()                          # auto-enables installed instrumentors
rius.init(instruments=["openai"])    # restrict to specific ones
rius.init(instruments=[])            # disable auto-instrumentation

Supported instruments: openai, anthropic, langchain, llama-index, litellm, and mcp. Content captured by instrumentors is covered by the same mask / capture_content controls as our own spans.

The mcp instrument is built in and has no extra: there is no third-party instrumentation to install, and an extra that installed mcp for you would break the rule above. If the mcp package is present, every ClientSession.call_tool() your agent makes becomes a first-class TOOL span (execute_tool <name>) with gen_ai.tool.name, the arguments and result as input.value/output.value, latency, and error status (including tools that return isError results).

Instrumentors patch libraries process-wide, so a scoped client (init(set_global=False)) only enables them when instruments=[...] is passed explicitly. Calling init() again while a client is active logs a warning and returns the existing client unchanged; call client.shutdown() first to reconfigure.

Span attribute limit

OpenTelemetry caps a span at 128 attributes by default and, once a span is full, silently evicts its OLDEST keys. The OpenInference instrumentors write one attribute per message field and per tool field, so an agent loop with ten tools passes 128 within about six (Anthropic) to nine (OpenAI) turns, and what goes first is what they wrote first: the model and request parameters, the tool definitions, and the system prompt.

init() therefore gives its tracer provider a limit of 4096 attributes per span. The value-length limit is left alone (the SDK caps its own JSON attributes). If you set OTEL_SPAN_ATTRIBUTE_COUNT_LIMIT or OTEL_ATTRIBUTE_COUNT_LIMIT to a non-negative integer, your value wins (a blank value is ignored, and an invalid one is ignored with a warning) and OpenTelemetry resolves the limits as usual. A span that still hits the limit reports how many attributes it dropped (dropped_attributes_count on the wire), so a loss is visible rather than silent.

The limit applies to the provider init() builds. init() does not accept a provider of your own; if you run OpenInference instrumentors without this SDK, on your own TracerProvider, raise the limit yourself, either with OTEL_SPAN_ATTRIBUTE_COUNT_LIMIT=4096 in the environment or in code:

from opentelemetry.sdk.trace import SpanLimits, TracerProvider

provider = TracerProvider(span_limits=SpanLimits(max_span_attributes=4096))

Reliability

Export is designed to never block or crash your application:

• Async batched export. Spans are queued in-process and exported in batches from a background thread (BatchSpanProcessor). Span creation stays fast even when the backend is slow or unreachable.
• Retries. Transient failures (connection errors, 429/5xx) are retried with exponential backoff and jitter, bounded by the export timeout.
• Graceful degradation. If the backend stays down, spans are dropped and an error is logged. Exceptions never propagate into application code. A failing mask callable drops only the affected attribute value (fail closed), never the batch.
• Flush on shutdown. Pending spans are flushed automatically at interpreter exit. Call client.flush() to force an export, or client.shutdown() to drain and stop.

Batching and backpressure are tunable via the standard OpenTelemetry env vars: OTEL_BSP_MAX_QUEUE_SIZE (default 2048; spans beyond this are dropped), OTEL_BSP_SCHEDULE_DELAY (default 5000 ms), OTEL_BSP_MAX_EXPORT_BATCH_SIZE (default 512), and OTEL_BSP_EXPORT_TIMEOUT (default 30000 ms).

Development

uv sync --group dev
uv run pytest
uv run ruff check . && uv run ruff format --check .
uv run mypy

See CONTRIBUTING.md for the full workflow, and SECURITY.md to report a vulnerability.

Releasing

Releases are automated with release-please and published to PyPI via Trusted Publishing.

1. Merge changes to main using Conventional Commits (feat: → minor, fix: → patch). Mark a breaking change with ! in the PR title (feat!: → major); PRs are squash-merged with a blank body, so a BREAKING CHANGE: footer is lost.
2. release-please keeps a Release PR open that bumps __version__ and updates CHANGELOG.md. Merge it when you want to cut a release.
3. Merging tags vX.Y.Z, creates a GitHub Release, and publishes to PyPI automatically.

Non-conventional commits are ignored for versioning.

License

MIT