OpenInference LlamaIndex Instrumentation

Python auto-instrumentation library for LlamaIndex.

These traces are fully OpenTelemetry compatible and can be sent to an OpenTelemetry collector for viewing, such as Arize Phoenix or Arize AX.

pypi

Installation

pip install openinference-instrumentation-llama-index

Compatibility

llama-index version openinference-instrumentation-llama-index version
>=0.12.3 >=4.0
>=0.11.0 >=3.0
>=0.10.43 >=2.0, <3.0
>=0.10.0, <0.10.43 >=1.0, <0.2
>=0.9.14, <0.10.0 0.1.3

Quickstart

Install packages needed for this demonstration.

python -m pip install --upgrade \
    openinference-instrumentation-llama-index \
    opentelemetry-sdk \
    opentelemetry-exporter-otlp \
    "opentelemetry-proto>=1.12.0" \
    arize-phoenix

Start the Phoenix app in the background as a collector. By default, it listens on http://localhost:6006. You can visit the app via a browser at the same address.

The Phoenix app does not send data over the internet. It only operates locally on your machine.

python -m phoenix.server.main serve

The following Python code sets up the LlamaIndexInstrumentor to trace llama-index and send the traces to Phoenix at the endpoint shown below.

from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.sdk.trace.export import SimpleSpanProcessor

endpoint = "http://127.0.0.1:6006/v1/traces"
tracer_provider = trace_sdk.TracerProvider()
tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(endpoint)))

LlamaIndexInstrumentor().instrument(tracer_provider=tracer_provider)

To demonstrate tracing, we'll use LlamaIndex below to query a document.

First, download a text file.

import tempfile
from urllib.request import urlretrieve
from llama_index.core import SimpleDirectoryReader

url = "https://raw.githubusercontent.com/Arize-ai/phoenix-assets/main/data/paul_graham/paul_graham_essay.txt"
with tempfile.NamedTemporaryFile() as tf:
    urlretrieve(url, tf.name)
    documents = SimpleDirectoryReader(input_files=[tf.name]).load_data()

Next, we'll query using OpenAI. To do that you need to set up your OpenAI API key in an environment variable.

import os

os.environ["OPENAI_API_KEY"] = "<your openai key>"

Now we can query the indexed documents.

from llama_index.core import VectorStoreIndex

query_engine = VectorStoreIndex.from_documents(documents).as_query_engine()
print(query_engine.query("What did the author do growing up?"))

Visit the Phoenix app at http://localhost:6006 to see the traces.

More Info

Project links

Data verified by PyPI on Sep 4, 2026
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  • Released:
Newer release available (4.5.2)

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Avatar for arizeai from gravatar.com arizeai

Credits

Author: OpenInference Authors

GitHub Statistics

Data verified by PyPI on Sep 4, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
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Requires

Python <3.15, >=3.10

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