> ## Documentation Index
> Fetch the complete documentation index at: https://arizeai-433a7140.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Amazon Bedrock Agent Runtime JavaScript

> Instrument and observe AWS Bedrock Agent Runtime calls in JavaScript/Node.js

This module provides automatic instrumentation for the [AWS SDK for JavaScript Bedrock Agent Runtime Client](https://docs.aws.amazon.com/AWSJavaScriptSDK/v3/latest/client/bedrock-agent-runtime/), which may be used in conjunction with [@arizeai/phoenix-otel](https://www.npmjs.com/package/@arizeai/phoenix-otel).

## Install

```bash theme={null}
npm install @arizeai/openinference-instrumentation-bedrock-agent-runtime @aws-sdk/client-bedrock-agent-runtime @arizeai/phoenix-otel
```

## Setup

To instrument your application, use the `register` function from `@arizeai/phoenix-otel` and manually instrument the Bedrock Agent Runtime SDK.

Create the `instrumentation.ts` file:

```typescript expandable theme={null}
import { register } from "@arizeai/phoenix-otel";
import { BedrockAgentRuntimeClient } from "@aws-sdk/client-bedrock-agent-runtime";
import { BedrockAgentRuntimeInstrumentation } from "@arizeai/openinference-instrumentation-bedrock-agent-runtime";

// Initialize Phoenix tracing
const tracerProvider = register({
  projectName: "bedrock-agent-app",
  // If Phoenix is running elsewhere:
  // url: "https://your-phoenix.example.com",
  // apiKey: process.env.PHOENIX_API_KEY,
  // If using self-hosted Phoenix:
  // url: "http://localhost:6006",
});

// Set up Bedrock Agent Runtime instrumentation
const instrumentation = new BedrockAgentRuntimeInstrumentation();
instrumentation.manuallyInstrument(BedrockAgentRuntimeClient);

console.log("Bedrock Agent Runtime instrumentation registered");
```

## Run Bedrock Agents

Import the `instrumentation.ts` file first, then use Bedrock Agent Runtime as usual.

```typescript expandable theme={null}
import "./instrumentation.js";
import { BedrockAgentRuntimeClient, InvokeAgentCommand } from "@aws-sdk/client-bedrock-agent-runtime";

const client = new BedrockAgentRuntimeClient({ region: "us-east-1" });

async function main() {
  const command = new InvokeAgentCommand({
    agentId: "YOUR_AGENT_ID",
    agentAliasId: "YOUR_AGENT_ALIAS_ID",
    sessionId: `session-${Date.now()}`,
    inputText: "What's the weather like today?",
  });

  const response = await client.send(command);
  
  // Process streaming response
  if (response.completion) {
    for await (const event of response.completion) {
      if (event.chunk?.bytes) {
        const text = new TextDecoder().decode(event.chunk.bytes);
        console.log(text);
      }
    }
  }
}

main();
```

## Observe

After setting up instrumentation and running your Bedrock Agent application, traces will appear in the Phoenix UI for visualization and analysis. This includes:

* Agent invocations
* Action group calls (as tools)
* Knowledge base lookups
* LLM calls within the agent

## Resources

* [NPM Package](https://www.npmjs.com/package/@arizeai/openinference-instrumentation-bedrock-agent-runtime)

* [OpenInference package for AWS Bedrock Agent Runtime](https://github.com/Arize-ai/openinference/tree/main/js/packages/openinference-instrumentation-bedrock-agent-runtime)