Python SDK

opensearch-genai-observability-sdk-py instruments Python AI agent applications using standard OpenTelemetry. It configures the OTEL pipeline in one call, provides a unified observe() primitive for tracing agents and tools, and enriches spans with GenAI semantic convention attributes automatically.

• PyPI: opensearch-genai-observability-sdk-py
• Python: 3.10+
• Source: github.com/opensearch-project/genai-observability-sdk-py

Installation

pip install opensearch-genai-observability-sdk-py

Auto-instrumentation for LLM providers is opt-in:

pip install "opensearch-genai-observability-sdk-py[openai]"
pip install "opensearch-genai-observability-sdk-py[anthropic]"
pip install "opensearch-genai-observability-sdk-py[bedrock]"
pip install "opensearch-genai-observability-sdk-py[langchain]"
pip install "opensearch-genai-observability-sdk-py[llamaindex]"
pip install "opensearch-genai-observability-sdk-py[otel-instrumentors]"  # all providers
pip install "opensearch-genai-observability-sdk-py[opensearch]"          # trace retrieval
pip install "opensearch-genai-observability-sdk-py[all]"                 # everything

API overview

The SDK exports these functions and classes. This page covers the instrumentation APIs. Evaluation APIs are documented in Evaluation & Scoring.

Export	Purpose	Docs
register()	Configure OTEL pipeline	This page
observe()	Trace agents, tools, LLM calls	This page
Op	Operation name constants	This page
enrich()	Set GenAI attributes on active span	This page
score()	Attach evaluation scores to traces	This page
AWSSigV4OTLPExporter	SigV4-signed OTLP exporter	This page
evaluate()	Run agent against dataset with scorers	Evaluation & Scoring
Experiment	Upload pre-computed eval results	Evaluation & Scoring
EvalScore	Scorer return type	Evaluation & Scoring
OpenSearchTraceRetriever	Query stored traces from OpenSearch	Evaluation & Scoring

Quick start

from opensearch_genai_observability_sdk_py import register, observe, Op, enrich

register(endpoint="http://localhost:4318/v1/traces", service_name="my-agent")

@observe(op=Op.EXECUTE_TOOL)
def get_weather(city: str) -> dict:
    return {"city": city, "temp": 22, "condition": "sunny"}

@observe(op=Op.INVOKE_AGENT)
def assistant(query: str) -> str:
    enrich(model="gpt-4o", provider="openai")
    data = get_weather("Paris")
    return f"{data['condition']}, {data['temp']}C"

result = assistant("What's the weather?")

---

register()

Configures the OTEL tracing pipeline. Call once at startup before any tracing occurs.

from opensearch_genai_observability_sdk_py import register

register(
    endpoint="http://localhost:4318/v1/traces",
    service_name="my-app",
)

Parameter	Type	Default	Description
endpoint	str	Data Prepper default	OTLP endpoint URL. Reads OTEL_EXPORTER_OTLP_TRACES_ENDPOINT or OTEL_EXPORTER_OTLP_ENDPOINT if not set.
protocol	"http" | "grpc"	inferred from URL	Force transport. grpc:// -> gRPC, grpcs:// -> gRPC+TLS, else HTTP.
service_name	str	"default"	Attached as service.name. Reads OTEL_SERVICE_NAME.
project_name	str		Alias for service_name. Reads OPENSEARCH_PROJECT.
service_version	str		Sets service.version. Reads OTEL_SERVICE_VERSION.
batch	bool	True	True = BatchSpanProcessor, False = SimpleSpanProcessor.
auto_instrument	bool	True	Discover and activate installed OTel instrumentor packages.
exporter	SpanExporter		Custom exporter. Overrides endpoint, protocol, headers.
set_global	bool	True	Register as the global TracerProvider.
headers	dict		Additional HTTP headers for the OTLP exporter.

Returns the configured TracerProvider.

Endpoint schemes

URL scheme	Transport
http:// or https://	OTLP HTTP (default)
grpc://	OTLP gRPC, insecure
grpcs://	OTLP gRPC with TLS

Examples

# Self-hosted with Data Prepper
register(service_name="my-agent")

# OTel Collector on localhost
register(endpoint="http://localhost:4318/v1/traces", service_name="my-agent")

# gRPC
register(endpoint="grpc://localhost:4317", service_name="my-agent")

# AWS OpenSearch Ingestion with SigV4
from opensearch_genai_observability_sdk_py import AWSSigV4OTLPExporter
exporter = AWSSigV4OTLPExporter(
    endpoint="https://pipeline.us-east-1.osis.amazonaws.com/v1/traces",
    service="osis", region="us-east-1",
)
register(service_name="my-agent", exporter=exporter)

---

observe()

The unified tracing primitive. Works as a decorator (sync, async, generator, async generator) and as a context manager.

Usage forms

# Bare decorator - span name = function qualname
@observe
def my_function():
    ...

# Parameterized - set operation type, name, span kind
@observe(name="weather_agent", op=Op.INVOKE_AGENT)
def run_agent(query: str) -> str:
    ...

# Context manager - for inline tracing blocks
with observe("llm_call", op=Op.CHAT) as span:
    response = llm.chat(messages)

Parameters

Parameter	Type	Default	Description
name	str	function __qualname__	Span entity name.
op	str		Sets gen_ai.operation.name. Span name becomes "{op} {name}".
kind	SpanKind	INTERNAL	OTel SpanKind.
name_from	str		Function parameter whose runtime value becomes the span name.

Op constants

Constant	Value	Use for
Op.INVOKE_AGENT	"invoke_agent"	Agent invocations and orchestration
Op.EXECUTE_TOOL	"execute_tool"	Tool/function calls
Op.CHAT	"chat"	LLM chat completions
Op.CREATE_AGENT	"create_agent"	Agent initialization
Op.RETRIEVAL	"retrieval"	RAG retrieval
Op.EMBEDDINGS	"embeddings"	Embedding generation
Op.GENERATE_CONTENT	"generate_content"	Content generation
Op.TEXT_COMPLETION	"text_completion"	Text completions

Any custom string also works for op.

Automatic behavior

In decorator mode, observe() automatically:

• Captures input as gen_ai.input.messages (or gen_ai.tool.call.arguments for tools). Skips self/cls.
• Captures output as gen_ai.output.messages (or gen_ai.tool.call.result for tools). Won’t overwrite if already set.
• Records errors as span status ERROR with an exception event.
• Sets entity attributes - gen_ai.agent.name for agents, gen_ai.tool.name + gen_ai.tool.type="function" for tools.

All values truncated at 10,000 characters.

Span hierarchy

flowchart TD
    A["@observe op=INVOKE_AGENT"] --> B["@observe op=CHAT - LLM call"]
    A --> C["@observe op=EXECUTE_TOOL - tool call"]
    C --> D["LLM call (auto-instrumented)"]

Dispatcher pattern

When the tool name is only known at call time:

@observe(op=Op.EXECUTE_TOOL, name_from="tool_name")
def execute_tool(self, tool_name: str, arguments: dict) -> dict:
    return self._tools[tool_name](**arguments)
# Produces: "execute_tool web_search", "execute_tool calculator", etc.

Async support

@observe(op=Op.EXECUTE_TOOL)
async def async_search(query: str) -> list:
    return await search_api.query(query)

---

enrich()

Adds GenAI semantic convention attributes to the currently active span. Call inside @observe-decorated functions or with observe(...) blocks.

@observe(op=Op.CHAT, name="llm_call")
def call_llm(messages: list) -> str:
    response = openai.chat.completions.create(model="gpt-4o", messages=messages)
    enrich(
        model="gpt-4o",
        provider="openai",
        input_tokens=response.usage.prompt_tokens,
        output_tokens=response.usage.completion_tokens,
        finish_reason=response.choices[0].finish_reason,
    )
    return response.choices[0].message.content

Parameter-to-attribute mapping

Parameter	OTel Attribute
model	gen_ai.request.model
provider	gen_ai.provider.name
input_tokens	gen_ai.usage.input_tokens
output_tokens	gen_ai.usage.output_tokens
total_tokens	gen_ai.usage.total_tokens
response_id	gen_ai.response.id
finish_reason	gen_ai.response.finish_reasons
temperature	gen_ai.request.temperature
max_tokens	gen_ai.request.max_tokens
session_id	gen_ai.conversation.id
agent_id	gen_ai.agent.id
agent_description	gen_ai.agent.description
tool_definitions	gen_ai.tool.definitions
system_instructions	gen_ai.system_instructions
input_messages	gen_ai.input.messages
output_messages	gen_ai.output.messages
**extra	key used as-is

All parameters are optional. Only provided values are set.

---

Auto-instrumentation

register() discovers and activates installed instrumentor packages. Install the extra for your provider - no code changes needed.

Provider / framework	Extra
OpenAI, OpenAI Agents	[openai]
Anthropic	[anthropic]
Amazon Bedrock	[bedrock]
LangChain	[langchain]
LlamaIndex	[llamaindex]
Cohere	[cohere]
Mistral	[mistral]
Groq	[groq]
Ollama	[ollama]
Google Generative AI + Vertex AI	[google]
All of the above + 20 more	[otel-instrumentors]

register(auto_instrument=False)  # to disable

---

score()

Attaches an evaluation score to a trace or span. Scores are emitted as OTEL spans through the same OTLP pipeline - no separate client or index needed.

from opensearch_genai_observability_sdk_py import score

# Score an entire trace
score(name="relevance", value=0.92, trace_id="abc123...",
      explanation="Response addresses the user's query")

# Score a specific span
score(name="accuracy", value=0.95, trace_id="abc123...", span_id="def456...",
      label="pass")

Parameter	Type	Description
name	str	Metric name, e.g. "relevance", "factuality".
value	float	Numeric score.
trace_id	str	Hex trace ID to score. Omit for standalone scores.
span_id	str	Hex span ID for span-level scoring.
label	str	Human-readable label, e.g. "pass".
explanation	str	Evaluator rationale (truncated to 500 chars).
response_id	str	LLM completion ID for correlation.
attributes	dict	Additional span attributes.

For running evaluations at scale (evaluate(), Experiment, OpenSearchTraceRetriever), see Evaluation & Scoring.

---

AWS authentication

For AWS-hosted endpoints, use AWSSigV4OTLPExporter to sign requests with SigV4:

from opensearch_genai_observability_sdk_py import AWSSigV4OTLPExporter, register

exporter = AWSSigV4OTLPExporter(
    endpoint="https://pipeline.us-east-1.osis.amazonaws.com/v1/traces",
    service="osis",           # "osis" for OSIS, "es" for OpenSearch Service
    region="us-east-1",       # auto-detected if omitted
)
register(service_name="my-agent", exporter=exporter)

Credentials: AWS_ACCESS_KEY_ID/AWS_SECRET_ACCESS_KEY -> ~/.aws/credentials -> IAM role/IMDS.

Caution

SigV4 + gRPC is not supported. Use https:// for AWS endpoints.

---

Environment variables

Variable	Description	Default
OTEL_EXPORTER_OTLP_TRACES_ENDPOINT	OTLP traces endpoint
OTEL_EXPORTER_OTLP_ENDPOINT	OTLP endpoint (appends /v1/traces)	Data Prepper default
OTEL_SERVICE_NAME	Service name	"default"
OPENSEARCH_PROJECT	Project name (fallback)	"default"
OTEL_SERVICE_VERSION	Service version

---

Related links

• AI Observability - Getting Started - end-to-end walkthrough
• Evaluation & Scoring - score traces, run experiments
• Trace Retrieval - query stored traces from OpenSearch
• Agent Traces - viewing traces in OpenSearch Dashboards
• GenAI semantic conventions - OTel spec reference