ChatBedrockConverse integration

Amazon Bedrock Converse is a fully managed service that makes Foundation Models (FMs) from leading AI startups and Amazon available via an API. You can choose from a wide range of FMs to find the model that is best suited for your use case. It provides a unified conversational interface for Bedrock models.

This will help you getting started with ChatBedrockConverse chat models. For detailed documentation of all ChatBedrockConverse features and configurations head to the API reference.

Overview

Integration details

Class	Package	Serializable	PY support	Downloads	Version
ChatBedrockConverse	@langchain/aws	✅	✅

Model features

See the links in the table headers below for guides on how to use specific features.

Setup

To access Bedrock models you'll need to create an AWS account, set up the Bedrock API service, get an access key ID and secret key, and install the @langchain/aws integration package.

Credentials

Head to the AWS docs to sign up for AWS and setup your credentials. You'll also need to turn on model access for your account, which you can do by following these instructions.

Set your Bedrock credentials in the environment:

export BEDROCK_AWS_REGION="us-east-1"
export BEDROCK_AWS_ACCESS_KEY_ID="your-access-key-id"
export BEDROCK_AWS_SECRET_ACCESS_KEY="your-secret-access-key"

Alternatively, set AWS_BEARER_TOKEN_BEDROCK for API key authentication. See the AWS Bedrock API key docs.

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"

Installation

The LangChain ChatBedrockConverse integration lives in the @langchain/aws package:

npm install @langchain/aws @langchain/core

yarn add @langchain/aws @langchain/core

pnpm add @langchain/aws @langchain/core

Instantiation

Now we can instantiate our model object and generate chat completions.

There are a few different ways to authenticate with AWS - the below examples rely on an access key, secret access key and region set in your environment variables:

import { ChatBedrockConverse } from "@langchain/aws";

const llm = new ChatBedrockConverse({
  model: "us.anthropic.claude-sonnet-4-6",
  region: process.env.BEDROCK_AWS_REGION,
  credentials: {
    accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID!,
    secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY!,
  },
});

Invocation

const aiMsg = await llm.invoke([
  [
    "system",
    "You are a helpful assistant that translates English to French. Translate the user sentence.",
  ],
  ["human", "I love programming."],
])
aiMsg

AIMessage {
  "id": "f5dc5791-224e-4fe5-ba2e-4cc51d9e7795",
  "content": "J'adore la programmation.",
  "additional_kwargs": {},
  "response_metadata": {
    "$metadata": {
      "httpStatusCode": 200,
      "requestId": "f5dc5791-224e-4fe5-ba2e-4cc51d9e7795",
      "attempts": 1,
      "totalRetryDelay": 0
    },
    "metrics": {
      "latencyMs": 584
    },
    "stopReason": "end_turn",
    "usage": {
      "inputTokens": 29,
      "outputTokens": 11,
      "totalTokens": 40
    }
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 29,
    "output_tokens": 11,
    "total_tokens": 40
  }
}

console.log(aiMsg.content)

J'adore la programmation.

Tool calling

Tool calling with Bedrock models works in a similar way to other models, but note that not all Bedrock models support tool calling. Please refer to the AWS model documentation for more information.

API reference

For detailed documentation of all ChatBedrockConverse features and configurations head to the API reference.