@langchain/aws

This package contains the LangChain.js integrations for AWS through their SDK.

Installation

npm install @langchain/aws

This package, along with the main LangChain package, depends on @langchain/core. If you are using this package with other LangChain packages, you should make sure that all of the packages depend on the same instance of @langchain/core. You can do so by adding appropriate fields to your project's package.json like this:

{
  "name": "your-project",
  "version": "0.0.0",
  "dependencies": {
    "@langchain/aws": "^0.0.1",
    "@langchain/core": "^0.3.0"
  },
  "resolutions": {
    "@langchain/core": "^0.3.0"
  },
  "overrides": {
    "@langchain/core": "^0.3.0"
  },
  "pnpm": {
    "overrides": {
      "@langchain/core": "^0.3.0"
    }
  }
}

The field you need depends on the package manager you're using, but we recommend adding a field for the common yarn, npm, and pnpm to maximize compatibility.

Chat Models

This package contains the ChatBedrockConverse class, which is the recommended way to interface with the AWS Bedrock Converse series of models.

To use, install the requirements, and configure your environment following the traditional authentication methods.

export BEDROCK_AWS_REGION=
export BEDROCK_AWS_SECRET_ACCESS_KEY=
export BEDROCK_AWS_ACCESS_KEY_ID=

Alternatively, set the AWS_BEARER_TOKEN_BEDROCK environment variable locally for API Key authentication. For additional API key details, refer to docs.

export BEDROCK_AWS_REGION=
export AWS_BEARER_TOKEN_BEDROCK=

Then initialize

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

const model = new ChatBedrockConverse({
  region: process.env.BEDROCK_AWS_REGION ?? "us-east-1",
  credentials: {
    secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY,
    accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID,
  },
});

const response = await model.invoke(new HumanMessage("Hello world!"));

Service Tiers

AWS Bedrock supports service tiers that control latency, cost, and capacity. You can set the tier at construction time or per-call.

• Supported values: priority, default, flex, reserved.
• See AWS docs: https://docs.aws.amazon.com/bedrock/latest/userguide/service-tiers-inference.html

Set at construction time:

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

const llm = new ChatBedrockConverse({
  region: process.env.BEDROCK_AWS_REGION ?? "us-east-1",
  credentials: {
    secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY!,
    accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID!,
  },
  serviceTier: "priority",
});

Override per invocation (takes precedence over constructor):

const res = await llm.invoke("Translate this", { serviceTier: "flex" });

serviceTier affects the request sent to Bedrock Converse ({ serviceTier: { type: "..." } }). If not provided, AWS uses the default tier.

Using Application Inference Profiles

AWS Bedrock Application Inference Profiles allow you to define custom endpoints that can route requests across regions or manage traffic for your models.

You can use an inference profile ARN by passing it to the applicationInferenceProfile parameter. When provided, this ARN will be used for the actual inference calls instead of the model ID:

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

const model = new ChatBedrockConverse({
  region: process.env.BEDROCK_AWS_REGION ?? "us-east-1",
  model: "anthropic.claude-haiku-4-5-20251001-v1:0",
  applicationInferenceProfile:
    "arn:aws:bedrock:eu-west-1:123456789102:application-inference-profile/fm16bt65tzgx",
  credentials: {
    secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY,
    accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID,
  },
});

const response = await model.invoke(new HumanMessage("Hello world!"));

Important: You must still provide the model parameter with the actual model ID (e.g., "anthropic.claude-haiku-4-5-20251001-v1:0"), even when using an inference profile. This ensures proper metadata tracking in tools like LangSmith, including accurate cost and latency measurements per model. The applicationInferenceProfile ARN will override the model ID only for the actual inference API calls.

Note: AWS does not currently provide an API to programmatically retrieve the underlying model from an inference profile ARN, so it's the user's responsibility to ensure the model parameter matches the model configured in the inference profile.

Streaming

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

const model = new ChatBedrockConverse({
  region: process.env.BEDROCK_AWS_REGION ?? "us-east-1",
  credentials: {
    secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY,
    accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID,
  },
});

const response = await model.stream(new HumanMessage("Hello world!"));

Development

To develop the AWS package, you'll need to follow these instructions:

Install dependencies

pnpm install

Build the package

pnpm build

Or from the repo root:

pnpm build --filter @langchain/aws

Run tests

Test files should live within a tests/ file in the src/ folder. Unit tests should end in .test.ts and integration tests should end in .int.test.ts:

$ pnpm test
$ pnpm test:int

Lint & Format

Run the linter & formatter to ensure your code is up to standard:

pnpm lint && pnpm format

Adding new entrypoints

If you add a new file to be exported, either import & re-export from src/index.ts, or add it to the exports field in the package.json file and run pnpm build to generate the new entrypoint.

Publishing

After running pnpm build, publish a new version with:

$ npm publish