AWS Bedrock LLM analytics installation

Install dependencies

Required

Install the OpenTelemetry SDK, OTLP exporter, and the AWS SDK instrumentation for your language.

pip install boto3 opentelemetry-instrumentation-botocore opentelemetry-sdk opentelemetry-exporter-otlp-proto-http
npm install @aws-sdk/client-bedrock-runtime @opentelemetry/instrumentation-aws-sdk @opentelemetry/sdk-node @opentelemetry/resources @posthog/ai

Set up the OpenTelemetry exporter

Required

Configure the OpenTelemetry SDK to export traces to PostHog's OTLP ingestion endpoint. PostHog converts gen_ai.* spans into $ai_generation events automatically.

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.sdk.resources import Resource, SERVICE_NAME
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.instrumentation.botocore import BotocoreInstrumentor

resource = Resource(attributes={
    SERVICE_NAME: "my-ai-app",
})

exporter = OTLPSpanExporter(
    endpoint="https://us.i.posthog.com/i/v0/ai/otel",
    headers={"Authorization": "Bearer <ph_project_token>"},
)

provider = TracerProvider(resource=resource)
provider.add_span_processor(BatchSpanProcessor(exporter))
trace.set_tracer_provider(provider)

BotocoreInstrumentor().instrument()
import { NodeSDK } from '@opentelemetry/sdk-node'
import { resourceFromAttributes } from '@opentelemetry/resources'
import { PostHogTraceExporter } from '@posthog/ai/otel'
import { AwsInstrumentation } from '@opentelemetry/instrumentation-aws-sdk'

const sdk = new NodeSDK({
  resource: resourceFromAttributes({
    'service.name': 'my-ai-app',
  }),
  traceExporter: new PostHogTraceExporter({
    apiKey: '<ph_project_token>',
    host: 'https://us.i.posthog.com',
  }),
  instrumentations: [new AwsInstrumentation()],
})
sdk.start()

Call Bedrock

Required

Make Bedrock API calls as normal. The instrumentation automatically captures gen_ai.* spans for Converse, ConverseStream, InvokeModel, and InvokeModelWithResponseStream operations.

import boto3

client = boto3.client("bedrock-runtime", region_name="us-east-1")

response = client.converse(
    modelId="us.anthropic.claude-3-5-haiku-20241022-v1:0",
    messages=[
        {
            "role": "user",
            "content": [{"text": "Tell me a fun fact about hedgehogs."}],
        }
    ],
)

print(response["output"]["message"]["content"][0]["text"])
// The AWS SDK must be imported after sdk.start() so the
// instrumentation can patch it.
const {
  BedrockRuntimeClient,
  ConverseCommand,
} = await import('@aws-sdk/client-bedrock-runtime')

const client = new BedrockRuntimeClient({ region: 'us-east-1' })

const response = await client.send(
  new ConverseCommand({
    modelId: 'us.anthropic.claude-3-5-haiku-20241022-v1:0',
    messages: [
      {
        role: 'user',
        content: [{ text: 'Tell me a fun fact about hedgehogs.' }],
      },
    ],
  })
)

console.log(response.output?.message?.content?.[0]?.text)

await sdk.shutdown()

Supported models

The instrumentation emits gen_ai.* spans for Amazon Titan, Amazon Nova, and Anthropic Claude models. Tool call instrumentation is available for Amazon Nova and Anthropic Claude 3+.

Note: If you want to capture LLM events anonymously, omit the posthog_distinct_id. See our docs on anonymous vs identified events to learn more.

You can expect captured $ai_generation events to have the following properties:

Property	Description
`$ai_model`	The specific model, like `gpt-5-mini` or `claude-4-sonnet`
`$ai_latency`	The latency of the LLM call in seconds
`$ai_time_to_first_token`	Time to first token in seconds (streaming only)
`$ai_tools`	Tools and functions available to the LLM
`$ai_input`	List of messages sent to the LLM
`$ai_input_tokens`	The number of tokens in the input (often found in response.usage)
`$ai_output_choices`	List of response choices from the LLM
`$ai_output_tokens`	The number of tokens in the output (often found in `response.usage`)
`$ai_total_cost_usd`	The total cost in USD (input + output)
[...]	See full list of properties

Verify traces and generations
Recommended
Confirm LLM events are being sent to PostHog
Let's make sure LLM events are being captured and sent to PostHog. Under LLM analytics, you should see rows of data appear in the Traces and Generations tabs.

Check for LLM events in PostHog

Next steps

Recommended

Now that you're capturing AI conversations, continue with the resources below to learn what else LLM Analytics enables within the PostHog platform.

Resource	Description
Basics	Learn the basics of how LLM calls become events in PostHog.
Generations	Read about the `$ai_generation` event and its properties.
Traces	Explore the trace hierarchy and how to use it to debug LLM calls.
Spans	Review spans and their role in representing individual operations.
Anaylze LLM performance	Learn how to create dashboards to analyze LLM performance.

AWS Bedrock LLM analytics installation

Contents

Install dependencies

Set up the OpenTelemetry exporter

Call Bedrock

Verify traces and generations

Next steps

Community questions

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