Bedrock AgentCore

pip install bedrock-agentcore bedrock-agentcore-starter-toolkit \
  openinference-instrumentation-strands-agents \
  strands-agents

bedrock-agentcore
openinference-instrumentation-strands-agents
strands-agents
opentelemetry-api
opentelemetry-sdk
opentelemetry-exporter-otlp-proto-grpc

export ARIZE_SPACE_ID="<your-space-id>"
export ARIZE_API_KEY="<your-api-key>"
export ARIZE_PROJECT_NAME="bedrock-agentcore-tracing-example"

# AWS credentials — long-lived or SSO/STS temporary.
export AWS_ACCESS_KEY_ID="<your-aws-access-key-id>"
export AWS_SECRET_ACCESS_KEY="<your-aws-secret-access-key>"
export AWS_REGION="us-west-2"   # AgentCore region — check service availability
export AWS_SESSION_TOKEN=""     # optional — only for SSO / STS / federated

# strands_claude.py — the file AgentCore deploys as your agent
import os

from opentelemetry import trace
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import (
    OTLPSpanExporter,
)

from bedrock_agentcore.runtime import BedrockAgentCoreApp
from strands import Agent
from strands.models.bedrock import BedrockModel
from openinference.instrumentation.strands_agents import (
    StrandsAgentsToOpenInferenceProcessor,
)

# Build a TracerProvider with the Arize project name baked into the
# Resource and register it globally so Strands' internal
# `trace.get_tracer(...)` finds it.
resource = Resource.create({
    "openinference.project.name": os.environ["ARIZE_PROJECT_NAME"],
    "service.name": "bedrock-agentcore-strands-agent",
})
provider = TracerProvider(resource=resource)
provider.add_span_processor(StrandsAgentsToOpenInferenceProcessor())

# The OTLP gRPC exporter reads endpoint + headers from
# OTEL_EXPORTER_OTLP_ENDPOINT and OTEL_EXPORTER_OTLP_HEADERS — both
# set at launch() time below.
provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter()))
trace.set_tracer_provider(provider)


# The deployed app — AgentCore calls strands_agent_bedrock for every
# /invocations POST.
app = BedrockAgentCoreApp()


@app.entrypoint
def strands_agent_bedrock(payload, context=None):
    agent = Agent(
        name="OceanAssistant",
        model=BedrockModel(
            model_id="us.anthropic.claude-sonnet-4-6",
            region_name=os.environ.get("AWS_DEFAULT_REGION", "us-west-2"),
        ),
        system_prompt="Answer concisely in two sentences.",
    )
    response = agent(payload.get("prompt", ""))
    return response.message["content"][0]["text"]


if __name__ == "__main__":
    app.run()

# deploy.py — runs locally; ships strands_claude.py to AgentCore
import os

from bedrock_agentcore_starter_toolkit import Runtime
from boto3.session import Session

region = Session().region_name

agentcore_runtime = Runtime()

agentcore_runtime.configure(
    entrypoint="strands_claude.py",
    auto_create_execution_role=True,
    auto_create_ecr=True,
    requirements_file="requirements.txt",
    region=region,
    agent_name="strands_agentcore_arize_observability",
    memory_mode="NO_MEMORY",
    # Disable AgentCore's built-in OTel — we're wiring our own
    # TracerProvider in strands_claude.py.
    disable_otel=True,
)

# Inject the Arize OTLP endpoint + auth headers as runtime env vars.
# The deployed container reads these and routes spans to Arize.
otlp_headers = (
    f"arize-space-id={os.environ['ARIZE_SPACE_ID']},"
    f"authorization={os.environ['ARIZE_API_KEY']},"
    f"arize-interface=python"
)

agentcore_runtime.launch(
    env_vars={
        "ARIZE_PROJECT_NAME":            os.environ["ARIZE_PROJECT_NAME"],
        "OTEL_EXPORTER_OTLP_ENDPOINT":   "https://otlp.arize.com:443",
        "OTEL_EXPORTER_OTLP_HEADERS":    otlp_headers,
        "OTEL_EXPORTER_OTLP_PROTOCOL":   "grpc",
        # AWS recommends this when using a non-AWS observability
        # backend so AgentCore doesn't double-export to CloudWatch.
        "DISABLE_ADOT_OBSERVABILITY":    "true",
    },
)

# Invoke the deployed runtime. agentcore_runtime.invoke() POSTs to
# the AgentCore Runtime endpoint and returns the response.
result = agentcore_runtime.invoke(
    {"prompt": "Why is the ocean salty? Answer in two sentences."}
)
print(result)