promptflow-tracing
Prompt flow tracing
Decision gist · record as of 2026-08-14
Yes. If you are building LLM applications with Prompt Flow, langchain, or similar frameworks and need observability, this package provides a low-friction, actively maintained way to add OpenTelemetry-compatible tracing. No known vulnerabilities, permissive license, and straightforward dependencies make it a safe choice.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later; OpenTelemetry SDK must be configured for tracing to be exported.
- Low friction install with three straightforward runtime dependencies (openai, opentelemetry-sdk, tiktoken).
- Active maintenance with recent commits and no known vulnerabilities.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions—suitable for most projects.
last release 2026-05-01 (105 days) · last repo commit 2026-08-05 · 11,217 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 209,678 downloads/mo, #9,511 on PyPI
Alternatives
Verify before relying
pip install promptflow-tracing
from promptflow_tracing import trace
from opentelemetry.sdk.trace import TracerProvider
@trace
def my_llm_function():
pass- Whether the package works with all listed frameworks (langchain, semantic kernel, OpenAI, agents) equally well or if some require additional setup.
- How to configure and export traces to a backend observability system beyond the OpenTelemetry SDK.
- Performance overhead of tracing on production LLM applications.
What it is and what it does
promptflow-tracing is a tracing library designed to instrument LLM applications built with Prompt Flow, Flex Flow, or other frameworks like langchain and semantic kernel. It integrates with OpenTelemetry to capture detailed execution traces—showing how data flows through your LLM application, where time is spent, and what inputs and outputs occur at each step. This is useful for debugging, monitoring, and understanding the behavior of complex LLM pipelines.
The package sits on top of three core dependencies: openai for LLM calls, opentelemetry-sdk for the tracing infrastructure, and tiktoken for token counting. It is actively maintained by Microsoft, supports Python 3.9 through 3.13, and carries no known security vulnerabilities. Installation is straightforward and the maintenance signal is strong.
Use it for
- Debug LLM application behavior by tracing execution flow and identifying where failures or unexpected outputs occur.
- Monitor production LLM applications to measure latency, token usage, and API call patterns across Flex Flow or DAG Flow pipelines.
- Integrate tracing into langchain or semantic kernel applications to gain visibility into agent decision-making and tool calls.
- Export traces to an observability backend (e.g., Jaeger, Datadog) for centralized monitoring and analysis of LLM workflows.
- Profile and optimize LLM application performance by identifying bottlenecks in multi-step flows.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
If you are building LLM applications with Prompt Flow, langchain, or similar frameworks and need observability, this package provides a low-friction, actively maintained way to add OpenTelemetry-compatible tracing. No known vulnerabilities, permissive license, and straightforward dependencies make it a safe choice.
Install
promptflow-tracing on PyPI
Before you install
Low friction install with three straightforward runtime dependencies (openai, opentelemetry-sdk, tiktoken). Active maintenance with recent commits and no known vulnerabilities.
Requires Python 3.9 or later; OpenTelemetry SDK must be configured for tracing to be exported.
License in practice
MIT license permits commercial and private use with minimal restrictions—suitable for most projects.
Quickstart
pip install promptflow-tracing
from promptflow_tracing import trace
from opentelemetry.sdk.trace import TracerProvider
@trace
def my_llm_function():
passVerify before relying
- Whether the package works with all listed frameworks (langchain, semantic kernel, OpenAI, agents) equally well or if some require additional setup.
- How to configure and export traces to a backend observability system beyond the OpenTelemetry SDK.
- Performance overhead of tracing on production LLM applications.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesopenaiopentelemetry-sdktiktoken |
| Maintenance | Actively maintained 105 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 209,678 / month, #9,511 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9 |
Evidence: promptflow_tracing-1.18.5-py3-none-any.whl
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See also promptflow-devkit · opentelemetry-instrumentation-langchain · promptflow · aliyun-trace · opentelemetry-instrumentation-alephalpha · opentelemetry-instrumentation-groq · opentelemetry-instrumentation-llamaindex · promptflow-core · langfuse · prompty