Integrations

Rius integrates with the client libraries, agent frameworks, and protocols agents are built on: model calls arrive as generations with tokens and cost, and run structure arrives from the framework’s own spans or a thin layer of observe wrappers. Each integration’s page covers setup, what lands in the trace, and the blind spots.

Support matrix

Integration	Type	Model calls captured by	Python	TypeScript
OpenAI	Client library	the `openai` instrumentation	✓	✓
Anthropic	Client library	the `anthropic` instrumentation	✓	✓
LangChain	Framework	its callbacks, hooked by the `langchain` instrumentation	✓	✓
LlamaIndex	Framework	the `llama-index` instrumentation	✓	N/A
LiteLLM	Router	the `litellm` instrumentation	✓	N/A
MCP	Protocol	instrumentation bundled in the SDK	✓	✓
Vercel AI SDK	Framework	its OpenTelemetry telemetry	N/A	✓
OpenAI Agents SDK	Agent framework	the `openai` instrumentation underneath	✓	N/A
Pydantic AI	Agent framework	its native OpenTelemetry spans	✓	N/A
CrewAI	Agent framework	the `openai` instrumentation (`litellm` when LiteLLM is installed)	✓	N/A
AutoGen	Agent framework	its native OpenTelemetry spans plus the `openai` instrumentation	✓	N/A
Claude Agent SDK	Agent framework	manual tracing: an `observe` root plus a generation filled from the run result	✓	N/A

“N/A” means no integration in that language today; trace those calls with manual generations. Install commands and package names live on each integration’s page.

Installing

Python

Each integration is an optional extra, and extras can be combined:


pip install "glassflow-rius[openai]"
pip install "glassflow-rius[anthropic,langchain]"
pip install "glassflow-rius[instruments]"    # all of them

Each extra installs the instrumentation for a library, never the library itself. glassflow-rius[anthropic] expects anthropic to be in your environment already, which it is in any project that calls Anthropic: Rius deliberately takes no dependency on provider SDKs, so it never pins or upgrades the versions your code runs against. An extra installed without its library makes init() log a DependencyConflict from OpenTelemetry and leaves that integration off, which is the one failure worth recognizing here; see Troubleshooting.

mcp needs no extra at all. Its instrumentation ships inside the SDK rather than as a separate package, so there is nothing for an extra to add: it turns itself on whenever the mcp package is importable, which it is in any agent that calls MCP tools.

The client-library integrations are powered by OpenInference , the open source (Apache-2.0) instrumentation project from Arize AI; the SDK bundles its instrumentors and wires them into the Rius pipeline. The MCP integration is built into the SDK itself.

Enabling

With a default (global) init(), every installed integration is enabled automatically:

Python


rius.init()                       # everything installed
rius.init(instruments=["openai"]) # only OpenAI
rius.init(instruments=[])         # none

Details worth knowing:

Requesting an integration whose package is not installed logs a warning and continues; a broken instrumentor never blocks init().
If a library is already instrumented by other OpenTelemetry code (not by this SDK), it is left alone.
Scoped clients (set_global=False) do not auto-instrument, because instrumentors patch libraries process-wide; pass instruments=[...] explicitly to opt in.

In TypeScript the loud and quiet cases are deliberately split: a package you did not install stays silent, while a package that is installed but fails to load logs a warning naming the integration and the underlying error. Either way init() continues, and the remaining integrations still attach.