arize-phoenix-otel
LLM Observability
Decision gist · record as of 2026-08-14
Yes. Active maintenance, no vulnerabilities, low install friction, and permissive license make it a safe choice. Install it if you need to trace AI applications and are already using or planning to adopt Phoenix for observability; the auto-instrumentation and environment-based configuration save significant boilerplate compared to raw OpenTelemetry setup.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Auto-instrumentation of specific AI libraries (OpenAI, LangChain, LlamaIndex) requires installing corresponding openinference-instrumentation-* packages separately.
- Low friction: pure Python wheel with 8 runtime dependencies, all from the OpenTelemetry ecosystem.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows use in most commercial and open-source projects without significant restriction.
last release 2026-08-10 (4 days) · last repo commit 2026-08-14 · 11,052 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,990,947 downloads/mo, #3,379 on PyPI
Alternatives
Verify before relying
pip install arize-phoenix-otel
from phoenix.otel import register
tracer_provider = register(
auto_instrument=True,
project_name="my-app",
endpoint="http://localhost:6006/v1/traces"
)- Whether auto_instrument=True works without explicit instrumentation library imports or if they must be installed first
- Performance overhead of automatic instrumentation on typical AI application workloads
- Backward compatibility guarantees across Phoenix server versions
What it is and what it does
arize-phoenix-otel is a lightweight wrapper around OpenTelemetry that simplifies tracing setup for AI applications by providing Phoenix-aware defaults, automatic environment variable configuration, and drop-in replacements for standard OTel classes. It reads configuration from environment variables (PHOENIX_COLLECTOR_ENDPOINT, PHOENIX_API_KEY, PHOENIX_PROJECT_NAME, etc.) or a `.env.phoenix` file, with explicit arguments taking precedence.
The package's main value is reducing boilerplate: a single `register()` call with `auto_instrument=True` automatically instruments popular AI frameworks (OpenAI, LangChain, LlamaIndex) with zero code changes, batches spans for production use, and sends traces to a Phoenix instance over OTLP using OpenInference semantic conventions. It also provides tracing decorators for GenAI patterns and works with any recent Phoenix server version without version pairing.
Use it for
- Enable automatic tracing of OpenAI, LangChain, or LlamaIndex calls in an LLM application with a single function call and environment variables.
- Collect and export distributed traces from a multi-component AI system to a self-hosted or cloud Phoenix instance for debugging and monitoring.
- Manually instrument custom AI workflows using decorators like @tracer.chain and @tracer.tool for GenAI-specific patterns.
- Configure production-ready batching and authentication for trace export without writing custom OpenTelemetry span processor code.
- Organize traces by project and environment using Phoenix project names and custom headers for multi-tenant or multi-environment deployments.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Active maintenance, no vulnerabilities, low install friction, and permissive license make it a safe choice. Install it if you need to trace AI applications and are already using or planning to adopt Phoenix for observability; the auto-instrumentation and environment-based configuration save significant boilerplate compared to raw OpenTelemetry setup.
Install
arize-phoenix-otel on PyPI
Before you install
Low friction: pure Python wheel with 8 runtime dependencies, all from the OpenTelemetry ecosystem. Active maintenance (last commit 2026-08-14, release 4 days old) and no known vulnerabilities.
Requires Python 3.10 or later. Auto-instrumentation of specific AI libraries (OpenAI, LangChain, LlamaIndex) requires installing corresponding openinference-instrumentation-* packages separately.
License in practice
Apache-2.0 permissive license allows use in most commercial and open-source projects without significant restriction.
Quickstart
pip install arize-phoenix-otel
from phoenix.otel import register
tracer_provider = register(
auto_instrument=True,
project_name="my-app",
endpoint="http://localhost:6006/v1/traces"
)Verify before relying
- Whether auto_instrument=True works without explicit instrumentation library imports or if they must be installed first
- Performance overhead of automatic instrumentation on typical AI application workloads
- Backward compatibility guarantees across Phoenix server versions
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.15,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagesopeninference-instrumentationopeninference-semantic-conventionsopentelemetry-exporter-otlpopentelemetry-protoopentelemetry-sdkopentelemetry-semantic-conventionstyping-extensionswrapt |
| Maintenance | Actively maintained 4 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,990,947 / month, #3,379 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: arize_phoenix_otel-0.17.1-py3-none-any.whl
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See also arize-otel · arize-phoenix · arize-phoenix-client · openinference-instrumentation-google-genai · openinference-instrumentation-llama-index · openinference-instrumentation-litellm · openinference-instrumentation-haystack · openinference-instrumentation-agno · openinference-instrumentation-pydantic-ai · openinference-instrumentation-anthropic