opentelemetry-instrumentation-haystack
OpenTelemetry Haystack instrumentation
Decision gist · record as of 2026-08-14
Yes, if you are building or operating Haystack LLM applications and need observability. The low install friction, active maintenance, and permissive license make it a straightforward addition. Be aware that content logging is on by default—set TRACELOOP_TRACE_CONTENT=false in production if your prompts or completions contain sensitive data.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (supports_current); set TRACELOOP_TRACE_CONTENT environment variable to control whether prompts and completions are logged to spans.
- Low install friction with four runtime dependencies, all from the OpenTelemetry ecosystem.
- Active maintenance with a release 4 days old and recent commits signal ongoing support.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows use in most commercial and open-source projects without significant 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) · 8,096,588 downloads/mo, #1,665 on PyPI
Alternatives
Verify before relying
pip install opentelemetry-instrumentation-haystack
from opentelemetry.instrumentation.haystack import HaystackInstrumentor
HaystackInstrumentor().instrument()- Whether tracing overhead is acceptable for production workloads at scale
- Compatibility with specific Haystack versions beyond the stated Python 3.10+ requirement
- Performance impact when TRACELOOP_TRACE_CONTENT is enabled vs. disabled
What it is and what it does
This package integrates OpenTelemetry tracing into Haystack-based LLM applications, automatically capturing the flow of data through your pipeline. It instruments Haystack components to emit spans that record prompts, completions, and embeddings, giving you visibility into how your LLM application behaves at runtime.
By default, the instrumentation logs the actual content of prompts and responses to span attributes, which helps with debugging and quality evaluation but may expose sensitive user data. You can disable content logging by setting the TRACELOOP_TRACE_CONTENT environment variable to false, reducing both privacy risk and trace size. The instrumentation is activated with a single call to HaystackInstrumentor().instrument() after importing.
Use it for
- Debug LLM application behavior by inspecting prompts, completions, and intermediate embeddings in distributed traces
- Monitor production Haystack pipelines by sending traces to a backend for centralized observability
- Evaluate LLM output quality by analyzing trace data to correlate inputs with outputs across your application
- Reduce trace storage costs by disabling content logging while preserving structural tracing information
- Correlate LLM operations with other services in a microservices architecture using OpenTelemetry's standard trace context
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building or operating Haystack LLM applications and need observability.
The low install friction, active maintenance, and permissive license make it a straightforward addition. Be aware that content logging is on by default—set TRACELOOP_TRACE_CONTENT=false in production if your prompts or completions contain sensitive data.
Install
opentelemetry-instrumentation-haystack on PyPI
Before you install
Low install friction with four runtime dependencies, all from the OpenTelemetry ecosystem. Active maintenance with a release 4 days old and recent commits signal ongoing support.
Requires Python 3.10 or later (supports_current); set TRACELOOP_TRACE_CONTENT environment variable to control whether prompts and completions are logged to spans.
License in practice
Apache-2.0 permissive license allows use in most commercial and open-source projects without significant restrictions.
Quickstart
pip install opentelemetry-instrumentation-haystack
from opentelemetry.instrumentation.haystack import HaystackInstrumentor
HaystackInstrumentor().instrument()Verify before relying
- Whether tracing overhead is acceptable for production workloads at scale
- Compatibility with specific Haystack versions beyond the stated Python 3.10+ requirement
- Performance impact when TRACELOOP_TRACE_CONTENT is enabled vs. disabled
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 | 8,096,588 / month, #1,665 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: opentelemetry_instrumentation_haystack-0.62.3-py3-none-any.whl
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See also opentelemetry-instrumentation-langchain · opentelemetry-instrumentation-mcp · haystack-experimental · opentelemetry-instrumentation-llamaindex · haystack-ai · opentelemetry-instrumentation-ollama · farm-haystack · opentelemetry-instrumentation-watsonx · opentelemetry-instrumentation-transformers · opentelemetry-instrumentation-groq