Amazon Bedrock

Moda captures Anthropic models on Amazon Bedrock automatically in Python when you call them through the AnthropicBedrock client from the anthropic package. Calls made through boto3 (invoke_model, converse) and all Bedrock calls from Node.js are not auto-instrumented — use manual capture, the HTTP API, or OpenTelemetry for those paths.

Support matrix

Path	Support
Python — `AnthropicBedrock` client (`anthropic` package)	Supported: auto-instrumented by `moda-ai`
Python — `boto3` (`invoke_model`, `converse`)	Not auto-instrumented: use the HTTP API or OpenTelemetry
Node.js — any Bedrock client	Not auto-instrumented: use `Moda.withLLMCall`, the HTTP API, or OpenTelemetry

Python: AnthropicBedrock client

Prerequisites

Python 3.10 or later
The moda-ai and anthropic packages
AWS credentials with Bedrock model access (environment variables, shared credentials file, or an IAM role)
A Moda API key, created at Settings → Ingestion keys (see Authentication)

Install the packages

pip install moda-ai anthropic

Initialize Moda before creating the client

moda.init() is synchronous. Call it before you construct the AnthropicBedrock client.

app.py

import os

import moda
from anthropic import AnthropicBedrock

moda.init(os.environ["MODA_API_KEY"])

client = AnthropicBedrock(aws_region="us-east-1")

moda.conversation_id = "session_8f2a"
moda.user_id = "user_1042"

response = client.messages.create(
    model="us.anthropic.claude-sonnet-4-20250514-v1:0",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.content[0].text)

moda.flush()

The examples on this page use a US cross-region inference profile ID; substitute the model or inference-profile ID that is enabled for your AWS account and region.Streaming through client.messages.stream(...) and async clients are captured the same way.

Verify in the dashboard

Open Conversations in the dashboard. Within minutes you should see a conversation with ID session_8f2a containing the user message and the assistant response, with the model name and token counts attached.

boto3 and Node.js

The Moda SDKs do not auto-instrument boto3 or any Node.js Bedrock client. Pick one of the following.

Node.js: manual capture with `Moda.withLLMCall`

If you already use the Moda SDK for Node.js, wrap the Bedrock call and report the request and response yourself:

app.ts

import { Moda } from 'moda-ai';
import { BedrockRuntimeClient, ConverseCommand } from '@aws-sdk/client-bedrock-runtime';

await Moda.init(process.env.MODA_API_KEY!);

const bedrock = new BedrockRuntimeClient({ region: 'us-east-1' });
const model = 'us.anthropic.claude-sonnet-4-20250514-v1:0';

const reply = await Moda.withLLMCall({ vendor: 'bedrock', type: 'chat' }, async ({ span }) => {
  span.reportRequest({
    model,
    messages: [{ role: 'user', content: 'Hello!' }],
    conversationId: 'session_8f2a',
    userId: 'user_1042',
  });

  const response = await bedrock.send(new ConverseCommand({
    modelId: model,
    messages: [{ role: 'user', content: [{ text: 'Hello!' }] }],
  }));

  const text = response.output?.message?.content?.[0]?.text ?? '';
  span.reportResponse({
    usage: {
      input_tokens: response.usage?.inputTokens,
      output_tokens: response.usage?.outputTokens,
    },
    completions: [{ role: 'assistant', content: text }],
  });
  return text;
});

console.log(reply);
await Moda.flush();

Errors thrown inside the callback propagate to your code after the span is recorded. See the Node.js SDK page for the full withLLMCall reference.

Any language: HTTP API

Make the Bedrock call as usual, then post the messages to POST /v1/ingest:

import os

import boto3
import requests

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

response = bedrock.converse(
    modelId="us.anthropic.claude-sonnet-4-20250514-v1:0",
    messages=[{"role": "user", "content": [{"text": user_message}]}],
)
assistant_message = response["output"]["message"]["content"][0]["text"]
usage = response.get("usage", {})

resp = requests.post(
    "https://moda-ingest.modas.workers.dev/v1/ingest",
    headers={
        "Authorization": f"Bearer {os.environ['MODA_API_KEY']}",
        "Content-Type": "application/json",
    },
    json={
        "events": [
            {
                "conversation_id": "session_8f2a",
                "user_id": "user_1042",
                "role": "user",
                "message": user_message,
            },
            {
                "conversation_id": "session_8f2a",
                "user_id": "user_1042",
                "role": "assistant",
                "message": assistant_message,
                "model": "us.anthropic.claude-sonnet-4-20250514-v1:0",
                "provider": "bedrock",
                "input_tokens": usage.get("inputTokens"),
                "output_tokens": usage.get("outputTokens"),
            },
        ]
    },
    timeout=10,
)
print(resp.json())

import { BedrockRuntimeClient, ConverseCommand } from '@aws-sdk/client-bedrock-runtime';

const bedrock = new BedrockRuntimeClient({ region: 'us-east-1' });
const userMessage = 'Hello!';

const response = await bedrock.send(new ConverseCommand({
  modelId: 'us.anthropic.claude-sonnet-4-20250514-v1:0',
  messages: [{ role: 'user', content: [{ text: userMessage }] }],
}));
const assistantMessage = response.output?.message?.content?.[0]?.text ?? '';

const res = await fetch('https://moda-ingest.modas.workers.dev/v1/ingest', {
  method: 'POST',
  headers: {
    Authorization: `Bearer ${process.env.MODA_API_KEY}`,
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    events: [
      {
        conversation_id: 'session_8f2a',
        user_id: 'user_1042',
        role: 'user',
        message: userMessage,
      },
      {
        conversation_id: 'session_8f2a',
        user_id: 'user_1042',
        role: 'assistant',
        message: assistantMessage,
        model: 'us.anthropic.claude-sonnet-4-20250514-v1:0',
        provider: 'bedrock',
        input_tokens: response.usage?.inputTokens,
        output_tokens: response.usage?.outputTokens,
      },
    ],
  }),
});
console.log(await res.json());

A successful request returns:

{ "success": true, "count": 2, "requestId": "5b2f8a3e-1c9d-4e7a-9f0b-2d6c8e4a1b3f" }

The full event schema and limits are on the HTTP API page.

OpenTelemetry

If your service already exports OpenTelemetry spans, point them at Moda’s OTLP endpoint and add GenAI attributes to your Bedrock spans — see OpenTelemetry.

In Python, moda.init() also activates the opentelemetry-instrumentation-bedrock package for boto3 when both are installed. That package is third-party and is not bundled with moda-ai; install it separately if you want to try this path.

Troubleshooting

Symptom	Fix
`boto3` calls do not appear	Expected — `boto3` is not auto-instrumented. Use manual capture, the HTTP API, or OpenTelemetry as shown above.
Node.js Bedrock calls do not appear	Expected — the Node.js SDK has no Bedrock instrumentation. Use `Moda.withLLMCall` or the HTTP API.
`AnthropicBedrock` calls do not appear (Python)	Call `moda.init()` before constructing the client, and `moda.flush()` before the process exits.
Token counts missing on HTTP API events	`/v1/ingest` only stores tokens you send. Pass `input_tokens` and `output_tokens` from the Bedrock response `usage` object.

Next steps

Moda SDK for Python — init options, context APIs, and flush semantics.
HTTP API — full event schema, limits, and error envelope.
OpenTelemetry — send OTLP spans from an existing tracing setup.
Anthropic — auto-instrumentation for the Anthropic API outside Bedrock.

Send data

SDKs

Providers

Frameworks and agents

OpenTelemetry

API reference

Amazon Bedrock

Support matrix

Python: AnthropicBedrock client

Prerequisites

boto3 and Node.js

Node.js: manual capture with `Moda.withLLMCall`

Any language: HTTP API

OpenTelemetry

Troubleshooting

Next steps

​Support matrix

​Python: AnthropicBedrock client

​Prerequisites

​boto3 and Node.js

​Node.js: manual capture with Moda.withLLMCall

​Any language: HTTP API

​OpenTelemetry

​Troubleshooting

​Next steps

Support matrix

Python: AnthropicBedrock client

Prerequisites

boto3 and Node.js

Node.js: manual capture with `Moda.withLLMCall`

Any language: HTTP API

OpenTelemetry

Troubleshooting

Next steps