arize-phoenix-client
LLM Observability
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, uses a permissive license, and offers low install friction. Install it if you are already running a Phoenix instance and need programmatic access to its API from Python; it is purpose-built for that workflow. If you do not have a Phoenix server running or do not need API-level control over datasets and experiments, it will not be useful.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a running Phoenix server instance (local or remote) at the configured endpoint; PHOENIX_ENDPOINT environment variable or explicit base_url parameter must be set.
- Low install friction with a pure-Python wheel distribution.
- Active maintenance with a recent release (3 days old) and steady commit activity.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must retain license notices and provide a copy of the license.
last release 2026-08-11 (3 days) · last repo commit 2026-08-14 · 11,052 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,120,895 downloads/mo, #4,338 on PyPI
Alternatives
Verify before relying
pip install arize-phoenix-client
from phoenix.client import Client
client = Client(base_url="http://localhost:6006")
traces = client.traces.get_traces(project_identifier="my-llm-app")- Whether the pytest integration (Eval CI) requires additional setup beyond the optional [pytest] extra.
- Performance characteristics when working with large trace datasets or high-volume queries.
- Exact retry and timeout behavior for network failures against the Phoenix API.
What it is and what it does
Phoenix Client is a Python SDK for interacting with the Arize Phoenix observability platform via REST API. It abstracts away HTTP details and provides resource-oriented methods for managing LLM application data: creating and versioning prompts with template variables, building evaluation datasets from DataFrames or dictionaries, querying and filtering traces, recording experiment results, and collecting human feedback and automated evaluations.
The package is designed for teams running LLM applications who need to track behavior, debug issues, and run structured evaluations programmatically. It supports both synchronous and asynchronous clients, discovers configuration from environment variables or a `.env.phoenix` dotenv file, and integrates with pytest for running LLM evaluations as ordinary test cases. Its runtime dependencies (httpx, OpenTelemetry SDK and exporters, openinference conventions, tqdm, typing-extensions) are all lightweight and widely used.
Use it for
- Create and version prompt templates with variable substitution, then retrieve and format them for use with LLM APIs.
- Build evaluation datasets from pandas DataFrames or CSV files and manage them centrally for reproducible experiment runs.
- Query and filter application traces by time range, status, or custom criteria to debug LLM behavior in production.
- Run LLM evaluations as pytest tests and automatically record results as Phoenix experiments for tracking.
- Collect human feedback and automated evaluation scores on LLM outputs and associate them with specific traces.
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, uses a permissive license, and offers low install friction. Install it if you are already running a Phoenix instance and need programmatic access to its API from Python; it is purpose-built for that workflow. If you do not have a Phoenix server running or do not need API-level control over datasets and experiments, it will not be useful.
Install
arize-phoenix-client on PyPI
Before you install
Low install friction with a pure-Python wheel distribution. Active maintenance with a recent release (3 days old) and steady commit activity. Supports modern Python versions (3.10–3.14).
Requires a running Phoenix server instance (local or remote) at the configured endpoint; PHOENIX_ENDPOINT environment variable or explicit base_url parameter must be set.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must retain license notices and provide a copy of the license.
Quickstart
pip install arize-phoenix-client
from phoenix.client import Client
client = Client(base_url="http://localhost:6006")
traces = client.traces.get_traces(project_identifier="my-llm-app")Verify before relying
- Whether the pytest integration (Eval CI) requires additional setup beyond the optional [pytest] extra.
- Performance characteristics when working with large trace datasets or high-volume queries.
- Exact retry and timeout behavior for network failures against the Phoenix API.
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 | 7 packageshttpxopeninference-instrumentationopeninference-semantic-conventionsopentelemetry-exporter-otlpopentelemetry-sdktqdmtyping-extensions |
| Maintenance | Actively maintained 3 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,120,895 / month, #4,338 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_client-3.1.0-py3-none-any.whl
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See also arize-phoenix · arize-phoenix-otel · arize · openinference-instrumentation-anthropic · openinference-instrumentation-haystack · openinference-instrumentation-llama-index · axiom-py · openinference-instrumentation-pydantic-ai · dbs3-client · aleph-alpha-client