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Instrumenting your application

Introduced 3.6

The opensearch-genai-observability-sdk-py package instruments Python AI agents using OpenTelemetry. The SDK provides two instrumentation approaches:

• Auto-instrumentation: Automatically captures LLM calls from supported providers (OpenAI, Anthropic, Amazon Bedrock, LangChain, LlamaIndex) without code changes.
• Manual instrumentation: Use the @observe decorator to trace custom agent logic, tool calls, and orchestration code.

For most applications, combine both approaches: enable auto-instrumentation for LLM calls and use @observe for application-specific operations.

Prerequisites

Before you start, ensure that you have the following:

• Python 3.10 or later.
• An OpenSearch cluster with Data Prepper configured for trace ingestion.
• An OpenTelemetry Collector to normalize spans using generative AI semantic conventions.

Installation

Install the base package:

pip install opensearch-genai-observability-sdk-py

To enable auto-instrumentation for specific providers, install the corresponding optional dependencies. For example, to instrument OpenAI and LangChain:

pip install opensearch-genai-observability-sdk-py[openai,langchain]

The following providers support auto-instrumentation:

Core API

register()

The register() function configures the OpenTelemetry tracer and exporter. Call it once at application startup:

from opentelemetry_genai_sdk import register

register(
    endpoint="http://localhost:4318",
    service_name="my-agent-app",
    protocol="http",
    auto_instrument=True
)

The following table describes the register() parameters.

@observe decorator

The @observe decorator wraps functions to create spans automatically. Use it to trace agent invocations, tool calls, and other operations:

from opentelemetry_genai_sdk import observe, Op

@observe(op=Op.INVOKE_AGENT)
def run_agent(prompt: str):
    # Agent logic here
    return response

@observe(op=Op.EXECUTE_TOOL)
def search_database(query: str):
    # Tool logic here
    return results

The decorator supports synchronous functions, asynchronous functions, generators, and async generators. Span names are generated automatically from function names, or you can provide a custom name using the name_from parameter.

enrich()

The enrich() function adds GenAI semantic attributes to the active span:

from opentelemetry_genai_sdk import enrich

enrich(
    model="gpt-4",
    provider="openai",
    input_tokens=150,
    output_tokens=50,
    finish_reason="stop"
)

score()

The score() function attaches evaluation metrics to traces or individual spans:

from opentelemetry_genai_sdk import score

score(
    trace_id="abc123",
    name="relevance",
    value=0.95,
    explanation="Response directly addresses the query"
)

Operation types

The Op class provides standardized operation names that map to GenAI semantic conventions. These operation types determine how spans are categorized and displayed in the Agent Traces page.

Framework integrations

The SDK integrates with popular agent frameworks. The general pattern is to combine auto-instrumentation (for LLM calls) with manual @observe decorators (for agent-specific logic).

Strands Agents

Strands supports both approaches:

• Native OpenTelemetry: Use StrandsTelemetry to automatically emit spans for agent invocations, tool executions, and LLM interactions.
• Manual decoration: Use @observe on custom functions for additional instrumentation.

LangGraph

Wrap LangGraph nodes and the orchestration layer with @observe. Enable auto-instrumentation to capture model calls within nodes automatically.

CrewAI

Use @observe to wrap crew execution functions. Install the appropriate provider package (for example, [openai]) to automatically capture LLM calls made by crew members.

OpenAI Agents SDK

Enable auto-instrumentation for comprehensive LLM coverage. Supplement with @observe for top-level coordination logic and custom operations.

Amazon Bedrock

Install [bedrock] to automatically capture converse and invoke_model calls. Use @observe for agent orchestration and custom tool implementations.

Next steps

After instrumenting your application, configure OpenSearch Dashboards to view your traces. See Viewing agent traces for configuration and visualization options.