Packages
Implements the B3 trace context propagation format for OpenTelemetry, enabling distributed tracing across services by injecting and extracting trace headers.
Propagates trace context in Google Cloud's X-Cloud-Trace-Context format for OpenTelemetry, enabling distributed tracing integration with Google Cloud services.
Provides trace context propagation for the Jaeger distributed tracing format, enabling trace correlation across service boundaries in OpenTelemetry instrumented applications.
Provides OpenTelemetry trace context propagation using the OT Trace format, enabling distributed tracing across services by serializing and deserializing trace IDs, span IDs, and baggage in HTTP headers.
However, if you are building new distributed systems, consider whether alternative trace context standards would be more appropriate.
Provides generated protobuf message classes for OpenTelemetry's data model, enabling serialization and deserialization of telemetry data in the OTLP format.
Adds OpenTelemetry tracing to the Valkey Python client, capturing operations and performance metrics for observability.
However, note that it is a community-maintained external package pending official Valkey support in opentelemetry-python-contrib; verify compatibility with your…
Provides OpenTelemetry resource detectors that automatically identify and tag telemetry data with Azure environment metadata from App Service, Functions, and Virtual Machines.
Detects and registers container runtime metadata (container ID, image name, pod details) as OpenTelemetry resource attributes for traces and metrics.
Extracts Docker container metadata and populates OpenTelemetry Resource attributes automatically, enabling telemetry systems to identify and tag traces and metrics with container-specific information.
Automatically detects and reports GCP resource metadata (GCE, GKE, etc.) to OpenTelemetry, enabling observability tools to understand the cloud environment where your application runs.
Detects and populates OpenTelemetry Resource attributes specific to Kubernetes pods, including container ID, pod UID, and namespace/pod name via the Kubernetes Downward API.
Extracts process-level metadata and populates OpenTelemetry Resource objects with process attributes for use in tracing and observability pipelines.
Provides the reference implementation of the OpenTelemetry API for collecting and exporting traces, metrics, and logs from Python applications.
Provides OpenTelemetry SDK components to make distributed traces compatible with AWS X-Ray, including a custom ID generator and resource detectors for AWS services.
Install it if you need X-Ray compatibility; skip it if you are not using X-Ray or are not instrumenting AWS workloads.
Provides generated Python code for OpenTelemetry semantic conventions, enabling standardized attribute naming and constant definitions for instrumentation and telemetry collection.
Install it if you are using OpenTelemetry and want to follow semantic conventions correctly.
Provides OpenTelemetry semantic conventions and attributes for instrumenting generative AI applications, enabling structured logging of prompts, completions, token usage, and other LLM-specific telemetry.
Provides test utilities and helpers for writing unit tests for OpenTelemetry instrumentation and SDK code.
Provides standardized instrumentation utilities for generative AI libraries, managing telemetry (spans, metrics, events) around LLM and embedding invocations via OpenTelemetry.
Provides ASGI and WSGI middleware and HTTP utilities for OpenTelemetry instrumentation of web frameworks like Django, Starlette, and FastAPI to track request timing.
OpenTimelineIO provides an interchange format and Python API for representing and manipulating editorial timeline information—the order, length, and timing of cuts—without storing media itself.
A Python library for creating and verifying OpenTimestamps cryptographic proofs, which anchor data to the Bitcoin blockchain for tamper-evident timestamping.
However, verify that the consensus-critical modules remain compatible with your target Bitcoin protocol version, and test thoroughly on your Python version before…
Command-line tool to create and verify cryptographic timestamp proofs anchored to the Bitcoin blockchain, with support for timestamping and verifying PGP-signed Git commits.
However, the aging maintenance status (591 days since last release) means you should verify that known issues listed in the documentation do not affect your use case,…
Server-side SDK for generating OpenTok sessions, tokens, and managing session archives via the Tokbox/OpenTok platform API.
However, if you are starting a new project, verify whether you should use the newer Vonage Server SDK for Python instead, which supports the unified Vonage Video API…
Provides the OpenTracing API for Python, enabling distributed tracing instrumentation across services through span creation, context propagation, and scope management.
Separates music into individual stems (vocals, drums, bass, other instruments) using pre-trained deep learning models, with optional speech enhancement.
However, dormancy (850 days since last release) means no active maintenance or bug fixes; consider it if your use case matches the pre-trained models and you do not…
Provides utility functions for Odoo database migrations, including record merging, field renaming, model updates, and XML transformations to support version upgrades.
Install only if you are performing Odoo version upgrades; it has no use outside that context.
OpenViking is a context database for AI agents that organizes memories, resources, and skills as a virtual filesystem under the `viking://` protocol, with tiered content loading (abstract, overview, details) and observable retrieval trajectories.
However, the AGPL-3.0 license requires copyleft compliance in derivative works, and the 58 runtime dependencies create medium install friction.
OpenVINO converts and optimizes deep learning models from various frameworks for inference on CPUs, GPUs, and AI accelerators without requiring the original training frameworks.
Provides command-line tools and Python APIs to convert deep learning models from multiple frameworks (TensorFlow, PyTorch, ONNX, Caffe, etc.) to OpenVINO IR format, download pre-trained models, and optimize them for inference deployment.
openvino-genai simplifies running inference on generative AI models by wrapping OpenVINO's inference engine with a high-level pipeline API that handles tokenization, model loading, and text generation.
The main gotcha is strict version pinning of openvino_tokenizers; verify compatibility before updating dependencies.
Sends telemetry data from OpenVINO toolkit components to Google Analytics with user consent, using category, action, and label parameters to structure event data.
Install only if you intend to integrate it into an OpenVINO-based tool; it is not a general-purpose analytics library.
Converts HuggingFace tokenizers into OpenVINO models for text processing, enabling tokenization and detokenization within OpenVINO inference pipelines without external tokenizer dependencies.
openWakeWord detects wake words and phrases in audio streams using pre-trained neural network models, enabling voice-activated applications to listen for specific spoken triggers in real-time.
However, the aging maintenance status and lack of recent updates warrant caution if you require ongoing support or compatibility with the latest Python ecosystem.
A command-line interface and Python library for interacting with OpenXLab services, handling authentication, configuration management, and API token generation.
However, the aging maintenance status (297 days since last release) suggests the project is not under active development—install with the understanding that you may…
Ophyd provides a Python abstraction layer for hardware control and data acquisition, enabling experiment orchestration code to work with devices and control systems like EPICS without device-specific details.
Install it if you are building experiment control code or integrating with EPICS or similar control systems.
ophyd-async provides asynchronous hardware abstraction for experiment orchestration and data acquisition, supporting EPICS PVA, EPICS CA, and Tango control systems alongside the Bluesky Run Engine.
However, it is still in Alpha status, so evaluate stability requirements for production use.
Opik is an open-source LLM observability and evaluation platform that logs traces of LLM calls, agents, and pipelines, then evaluates them with datasets, experiments, and LLM-as-a-judge metrics.
Fetches historical and forecasted energy usage and cost data from utility companies that use the Opower platform, supporting dozens of US utilities through their web APIs.
The main gotcha is that it requires Python 3.11+ and async code; if your utility isn't listed, you'd need to contribute support for it.
A Python framework for developing charms—declarative, event-driven applications that manage Kubernetes workloads and machines in Juju environments.
ops-scenario is a state-transition testing framework for Ops charms that lets you write unit tests by arranging a Juju state, emulating an event, and asserting on the resulting state without needing a full Juju installation.
Install it if you are writing or maintaining Ops charms and want to test them without a full Juju setup.