opentelemetry-instrumentation-litellm
OpenTelemetry LiteLLM instrumentation
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, low install friction, and solves a real problem: getting consistent observability across LiteLLM's multi-provider abstraction. The privacy control via environment variable is a thoughtful addition. Install it if you're using LiteLLM and need tracing; skip it only if you have no observability requirements.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Low friction: pure Python wheel, four lightweight OpenTelemetry dependencies, active maintenance with a release 4 days ago and no known vulnerabilities.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 is permissive; you can use, modify, and distribute this package freely in commercial or proprietary projects with minimal restrictions.
last release 2026-08-10 (4 days) · last repo commit 2026-08-10 · 7,377 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 748,066 downloads/mo, #5,166 on PyPI
Alternatives
Verify before relying
pip install opentelemetry-instrumentation-litellm
from opentelemetry.instrumentation.litellm import LiteLLMInstrumentor
LiteLLMInstrumentor().instrument()- Whether the instrumentation works with all LiteLLM custom providers or only a subset
- Performance overhead of tracing on high-volume LLM applications
- Compatibility with specific OpenTelemetry exporters and collectors
What it is and what it does
This package wraps LiteLLM to emit OpenTelemetry spans and metrics for every LLM call. Because LiteLLM normalizes responses from all providers into a single OpenAI-style format, the instrumentation produces consistent gen_ai.* spans regardless of whether you're calling different providers or custom LLM implementations. By default it logs prompts, completions, and embeddings to span attributes for visibility and debugging; you can disable this via the TRACELOOP_TRACE_CONTENT environment variable if the data is sensitive.
The package depends on opentelemetry-api, opentelemetry-instrumentation, opentelemetry-semantic-conventions-ai, and opentelemetry-semantic-conventions, and requires Python 3.10 or later. It integrates directly into your LiteLLM calls with a single .instrument() call, making it straightforward to add observability to existing LLM applications without rewriting call sites.
Use it for
- Debug LLM application behavior by viewing exact prompts and completions in traces alongside latency and error data.
- Monitor token usage and cost across multiple LLM providers in a unified observability platform.
- Audit LLM interactions for compliance or quality review by exporting traces to a central collector.
- Correlate LLM calls with upstream application requests using OpenTelemetry's trace context propagation.
- Measure and optimize LLM performance across different providers or models using consistent metrics.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, low install friction, and solves a real problem: getting consistent observability across LiteLLM's multi-provider abstraction. The privacy control via environment variable is a thoughtful addition. Install it if you're using LiteLLM and need tracing; skip it only if you have no observability requirements.
Install
opentelemetry-instrumentation-litellm on PyPI
Before you install
Low friction: pure Python wheel, four lightweight OpenTelemetry dependencies, active maintenance with a release 4 days ago and no known vulnerabilities.
Requires Python 3.10 or later.
License in practice
Apache-2.0 is permissive; you can use, modify, and distribute this package freely in commercial or proprietary projects with minimal restrictions.
Quickstart
pip install opentelemetry-instrumentation-litellm
from opentelemetry.instrumentation.litellm import LiteLLMInstrumentor
LiteLLMInstrumentor().instrument()Verify before relying
- Whether the instrumentation works with all LiteLLM custom providers or only a subset
- Performance overhead of tracing on high-volume LLM applications
- Compatibility with specific OpenTelemetry exporters and collectors
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <4,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesopentelemetry-apiopentelemetry-instrumentationopentelemetry-semantic-conventions-aiopentelemetry-semantic-conventions |
| Maintenance | Actively maintained 4 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 748,066 / month, #5,166 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: opentelemetry_instrumentation_litellm-0.62.3-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “trace llm prompts completions”
- opentelemetry-instrumentation-litellmAdds OpenTelemetry tracing to LiteLLM, capturing LLM prompts,…
- opentelemetry-instrumentation-groqAutomatically captures traces of Groq SDK API calls, logging prompts,…
- opentelemetry-instrumentation-watsonxAutomatically captures and traces IBM Watsonx prompts, completions,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Monitoring packages
Wraps any iterable to display a real-time progress bar in the terminal or Jupyter notebook, showing iteration count, elapsed time, and estimated time remaining.
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 the reference implementation of the OpenTelemetry API for collecting and exporting traces, metrics, and logs from Python applications.
Provides the abstract API and interfaces for OpenTelemetry instrumentation in Python, defining how to emit traces, metrics, and logs without tying code to a specific SDK implementation.
Exports OpenTelemetry observability data to an OpenTelemetry Collector using Protobuf-encoded messages over HTTP.
Install it if you are using OpenTelemetry in Python and need to send data to a Collector over HTTP.
Provides automatic instrumentation commands and programmatic APIs to inject distributed tracing into Python applications without code changes, detecting and instrumenting packages used by your program.
Install it if you need distributed tracing without code changes and have compatible instrumented packages in your environment.
See also opentelemetry-instrumentation-langchain · opentelemetry-instrumentation-llamaindex · opentelemetry-instrumentation-openai · opentelemetry-instrumentation-groq · promptlayer · openlit · opentelemetry-instrumentation-ollama · opentelemetry-instrumentation-replicate · opentelemetry-semantic-conventions-ai · opentelemetry-instrumentation-transformers