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Amazon Bedrock AgentCore → OpenObserve

Capture latency and invocation metadata for every Amazon Bedrock AgentCore runtime call using manual OpenTelemetry spans.

Prerequisites

Python 3.8+
An OpenObserve account (cloud or self-hosted)
Your OpenObserve organisation ID and Base64-encoded auth token
AWS credentials with BedrockAgentCoreFullAccess and AmazonBedrockFullAccess permissions
A deployed AgentCore runtime ARN. See Get started with AgentCore runtime.

Installation

pip install openobserve-telemetry-sdk boto3 python-dotenv

Configuration

Create a .env file in your project root:

OPENOBSERVE_URL=https://api.openobserve.ai/
OPENOBSERVE_ORG=your_org_id
OPENOBSERVE_AUTH_TOKEN=Basic <your_base64_token>

AWS_ACCESS_KEY_ID=your-access-key-id
AWS_SECRET_ACCESS_KEY=your-secret-access-key
AWS_DEFAULT_REGION=your-aws-region
BEDROCK_AGENTCORE_RUNTIME_ARN=arn:aws:bedrock-agentcore:us-east-1:123456789012:runtime/your-runtime-id

Instrumentation

AgentCore does not have an auto-instrumentation package. Wrap each invocation in a manual span.

from dotenv import load_dotenv
load_dotenv()

from openobserve import openobserve_init
openobserve_init(resource_attributes={"service.name": "amazon-bedrock-agentcore"})

from opentelemetry import trace
import os, json, uuid, boto3

RUNTIME_ARN = os.environ["BEDROCK_AGENTCORE_RUNTIME_ARN"]

client = boto3.client(
    "bedrock-agentcore",
    region_name=os.environ.get("AWS_DEFAULT_REGION", "us-east-1"),
)

tracer = trace.get_tracer(__name__)

with tracer.start_as_current_span("bedrock_agentcore.invoke") as span:
    span.set_attribute("bedrock_agentcore.runtime_arn", RUNTIME_ARN)
    span.set_attribute("bedrock_agentcore.input", "Explain observability in one sentence.")

    response = client.invoke_agent_runtime(
        agentRuntimeArn=RUNTIME_ARN,
        runtimeSessionId=str(uuid.uuid4()),
        payload=json.dumps({"input": "Explain observability in one sentence."}).encode(),
        contentType="application/json",
        accept="application/json",
    )
    output = json.loads(response["response"].read()).get("output", "")
    span.set_attribute("bedrock_agentcore.output_length", len(output))
    print(output)

What Gets Captured

Attribute	Description
`operation_name`	Always `bedrock_agentcore.invoke`
`bedrock_agentcore_runtime_arn`	ARN of the invoked runtime
`bedrock_agentcore_input`	Input text sent to the agent
`bedrock_agentcore_output_length`	Character length of the response
`span_status`	`OK` on success, `ERROR` on failure
`duration`	End-to-end invocation latency

Viewing Traces

Log in to OpenObserve and navigate to Traces in the left sidebar
Click any bedrock_agentcore.invoke span to inspect latency and input/output attributes

Amazon Bedrock AgentCore trace in OpenObserve

Next Steps

Track agent latency over time, set alerts on slow or failing invocations, and correlate agent spans with downstream Bedrock model calls.

Read More