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Installation

npm install @raindrop-ai/bedrock @aws-sdk/client-bedrock-runtime

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Quick Start

import { createRaindropBedrock } from "@raindrop-ai/bedrock";
import { BedrockRuntimeClient, ConverseCommand } from "@aws-sdk/client-bedrock-runtime";

const raindrop = createRaindropBedrock({
  writeKey: "your-write-key",
  userId: "user-123",
});

const client = new BedrockRuntimeClient({ region: "us-east-1" });
const wrapped = raindrop.wrap(client);

const response = await wrapped.send(
  new ConverseCommand({
    modelId: "anthropic.claude-3-5-sonnet-20241022-v2:0",
    messages: [
      { role: "user", content: [{ text: "What is the capital of France?" }] },
    ],
  }),
);

console.log(response.output?.message?.content?.[0]?.text);
await raindrop.flush();

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What Gets Traced

The Bedrock integration automatically captures:
  • Converse API — input messages, output text, model ID, token usage (inputTokens/outputTokens), stop reason, cached token counts
  • InvokeModel API — raw request/response, model ID, token usage (supports Claude, Titan, and Llama response formats), stop reason, cached tokens (Claude)
  • Errors — captured with error status on the span, re-thrown to caller

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Captured Properties

PropertySourceDescription
ai.usage.prompt_tokensBoth APIsInput/prompt token count
ai.usage.completion_tokensBoth APIsOutput/completion token count
ai.usage.cached_tokensConverse: cacheReadInputTokenCount; Claude InvokeModel: cache_read_input_tokensTokens read from cache
ai.usage.cache_write_tokensConverse: cacheWriteInputTokenCountTokens written to cache
bedrock.finish_reasonConverse: stopReason; InvokeModel: varies by modelWhy the model stopped generating (e.g. end_turn, tool_use, max_tokens)

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Configuration

const raindrop = createRaindropBedrock({
  writeKey: "your-write-key",       // Optional: your Raindrop write key (omit to disable telemetry)
  endpoint: "...",          // Optional: custom API endpoint
  userId: "user-123",      // Optional: associate events with a user
  convoId: "convo-456",    // Optional: conversation/thread ID
  debug: false,             // Optional: enable verbose logging
});

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Using InvokeModel

The wrapper also supports the legacy InvokeModel API. Token usage extraction works with Claude, Titan, and Llama response formats: 
import { InvokeModelCommand } from "@aws-sdk/client-bedrock-runtime";

const response = await wrapped.send(
  new InvokeModelCommand({
    modelId: "anthropic.claude-3-5-sonnet-20241022-v2:0",
    contentType: "application/json",
    body: JSON.stringify({
      anthropic_version: "bedrock-2023-05-31",
      messages: [{ role: "user", content: "Hello!" }],
      max_tokens: 256,
    }),
  }),
);

const result = JSON.parse(new TextDecoder().decode(response.body));
console.log(result.content[0].text);
await raindrop.flush();

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Identifying Users

Associate a user with optional traits: 
raindrop.users.identify({ userId: "user-123", traits: { plan: "pro", company: "Acme" } });

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Tracking Signals

Track feedback, edits, or custom signals: 
raindrop.signals.track({
  eventId: "evt_abc123",
  name: "thumbs_up",
  type: "feedback",
  sentiment: "POSITIVE",
  comment: "Great answer!",
});

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Flushing and Shutdown

Always call flush() before your process exits to ensure all telemetry is shipped: 
await raindrop.flush();     // flush pending data
await raindrop.shutdown();  // flush + release resources