Instrument: Python

As part of the OpenInference library, Phoenix provides helpful abstractions to make manual instrumentation easier.

OpenInference OTEL Tracing

This documentation provides a guide on using OpenInference OTEL tracing decorators and methods for instrumenting functions, chains, agents, and tools using OpenTelemetry.

These tools can be combined with, or used in place of, OpenTelemetry instrumentation code. They are designed to simplify the instrumentation process.

If you'd prefer to use pure OTEL instead, see Instrument: Python using Base OTEL

Installation

Ensure you have OpenInference and OpenTelemetry installed:

pip install openinference opentelemetry-api opentelemetry-sdk

Setting Up Tracing

You can configure the tracer using either TracerProvider from openinference.instrumentation or using phoenix.otel.register.

Using TracerProvider

from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor

from openinference.instrumentation import TracerProvider
from openinference.semconv.resource import ResourceAttributes

endpoint = "http://127.0.0.1:6006/v1/traces"
resource = Resource(attributes={ResourceAttributes.PROJECT_NAME: "openinference-tracer"})
tracer_provider = TracerProvider(resource=resource)
tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(endpoint)))
tracer_provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))
tracer = tracer_provider.get_tracer(__name__)

Using phoenix.otel.register

from phoenix.otel import register

tracer_provider = register(protocol="http/protobuf")
tracer = tracer_provider.get_tracer(__name__)

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Using your Tracer

Your tracer object can now be used in two primary ways:

1. As a decorator to trace entire functions

@tracer.chain
def my_func(input: str) -> str:
    return "output"

This entire function will appear as a Span in Phoenix. Input and output attributes in Phoenix will be set automatically based on my_func's parameters and return. The status attribute will also be set automatically.

2. As a with clause to trace specific code blocks

with tracer.start_as_current_span(
    "my-span-name",
    openinference_span_kind="chain",
) as span:
    span.set_input("input")
    span.set_output("output")
    span.set_status(Status(StatusCode.OK))

The code within this clause will be captured as a Span in Phoenix. Here the input, output, and status must be set manually.

This approach is useful when you need only a portion of a method to be captured as a Span.

OpenInference Span Kinds

OpenInference Span Kinds denote the possible types of spans you might capture, and will be rendered different in the Phoenix UI.

The possible values are:

Span Kind	Use
CHAIN	General logic operations, functions, or code blocks
LLM	Making LLM calls
TOOL	Completing tool calls
RETRIEVER	Retrieving documents
EMBEDDING	Generating embeddings
AGENT	Agent invokations - typically a top level or near top level span
RERANKER	Reranking retrieved context
UNKNOWN	Unknown
GUARDRAIL	Guardrail checks
EVALUATOR	Evaluators - typically only use by Phoenix when automatically tracing evaluation and experiment calls

Chains

Using Context Managers

with tracer.start_as_current_span(
    "chain-span-with-plain-text-io",
    openinference_span_kind="chain",
) as span:
    span.set_input("input")
    span.set_output("output")
    span.set_status(Status(StatusCode.OK))

Using Decorators

@tracer.chain
def decorated_chain_with_plain_text_output(input: str) -> str:
    return "output"

decorated_chain_with_plain_text_output("input")

Using JSON Output

@tracer.chain
def decorated_chain_with_json_output(input: str) -> Dict[str, Any]:
    return {"output": "output"}

decorated_chain_with_json_output("input")

Overriding Span Name

@tracer.chain(name="decorated-chain-with-overriden-name")
def this_name_should_be_overriden(input: str) -> Dict[str, Any]:
    return {"output": "output"}

this_name_should_be_overriden("input")

---

Agents

Using Context Managers

with tracer.start_as_current_span(
    "agent-span-with-plain-text-io",
    openinference_span_kind="agent",
) as span:
    span.set_input("input")
    span.set_output("output")
    span.set_status(Status(StatusCode.OK))

Using Decorators

@tracer.agent
def decorated_agent(input: str) -> str:
    return "output"

decorated_agent("input")

---

Tools

Using Context Managers

with tracer.start_as_current_span(
    "tool-span",
    openinference_span_kind="tool",
) as span:
    span.set_input("input")
    span.set_output("output")
    span.set_tool(
        name="tool-name",
        description="tool-description",
        parameters={"input": "input"},
    )
    span.set_status(Status(StatusCode.OK))

Using Decorators

@tracer.tool
def decorated_tool(input1: str, input2: int) -> None:
    """
    tool-description
    """

decorated_tool("input1", 1)

Overriding Tool Name

@tracer.tool(
    name="decorated-tool-with-overriden-name",
    description="overriden-tool-description",
)
def this_tool_name_should_be_overriden(input1: str, input2: int) -> None:
    """
    this tool description should be overriden
    """

this_tool_name_should_be_overriden("input1", 1)

---

Additional Features

Suppress Tracing

with suppress_tracing():
    with tracer.start_as_current_span(
        "THIS-SPAN-SHOULD-NOT-BE-TRACED",
        openinference_span_kind="chain",
    ) as span:
        span.set_input("input")
        span.set_output("output")
        span.set_status(Status(StatusCode.OK))

Using Context Attributes

with using_attributes(session_id="123"):
    with tracer.start_as_current_span(
        "chain-span-with-context-attributes",
        openinference_span_kind="chain",
    ) as span:
        span.set_input("input")
        span.set_output("output")
        span.set_status(Status(StatusCode.OK))

---

This documentation provides an overview of how to use OpenInference OTEL tracing decorators with chains, agents, and tools. For more details, refer to the OpenInference official documentation.