Bedrock provider

Standalone — usable in any Node project, no @warlock.js/core required.

@warlock.js/ai-bedrock is the AWS Bedrock provider adapter for @warlock.js/ai. It speaks Bedrock’s Converse API — the model-agnostic surface — so one wire mapping covers every Bedrock-hosted family (Anthropic Claude, Amazon Nova, Meta Llama, Mistral, Cohere) and hands it all back as a vendor-neutral ModelContract. Auth uses your AWS credentials, exactly like any other AWS SDK call.

Install

npm

npm install @warlock.js/ai @warlock.js/ai-bedrock

yarn

yarn add @warlock.js/ai @warlock.js/ai-bedrock

pnpm

pnpm add @warlock.js/ai @warlock.js/ai-bedrock

Construct

One BedrockSDK holds one live BedrockRuntimeClient. Build it once per account/region and reuse it everywhere — every model and embedder it produces shares the same client, credential refresh, and retry config.

import { BedrockSDK } from "@warlock.js/ai-bedrock";

const bedrock = new BedrockSDK({
  region: "us-east-1",
  // Standard AWS SDK credential resolution — env vars, ~/.aws/credentials,
  // IAM role on EC2/ECS, SSO. Pass explicit credentials only if needed.
});

region is the one required field. The whole config object is forwarded straight to BedrockRuntimeClient, so any client option works — explicit credentials, a custom endpoint, a requestHandler, retry settings. provider relabels the upstream (defaults to "bedrock").

const bedrock = new BedrockSDK({
  region: "us-east-1",
  credentials: { accessKeyId: "AKIA...", secretAccessKey: "..." },
});

First call

Build a model, hand it to an agent, run it. execute() never throws — failures land in error as a typed AIError.

import { ai } from "@warlock.js/ai";
import { BedrockSDK } from "@warlock.js/ai-bedrock";

const bedrock = new BedrockSDK({ region: "us-east-1" });

const assistant = ai.agent({
  model: bedrock.model({ name: "anthropic.claude-sonnet-4-5-20250929-v1:0" }),
  systemPrompt: "You are a concise senior TypeScript engineer.",
});

const { text, usage, error } = await assistant.execute("Why use generics?");

if (error) {
  console.warn(error.code, error.category);
} else {
  console.log(text, usage.total);
}

Use as a model

name is the Bedrock model id or inference-profile id — Claude, Nova, Llama, and friends all go through the same call.

bedrock.model({ name: "anthropic.claude-sonnet-4-5-20250929-v1:0" });
bedrock.model({ name: "us.amazon.nova-pro-v1:0" });
bedrock.model({ name: "meta.llama3-1-8b-instruct-v1:0" });

Use as an embedder

The embedder targets the Amazon Titan Text Embeddings family (amazon.titan-embed-text-v2:0 and v1) via Bedrock’s InvokeModel.

const embedder = bedrock.embedder({ name: "amazon.titan-embed-text-v2:0" });
const { vector } = await embedder.embed("Hello world");
const { vectors } = await embedder.embedMany(["doc 1", "doc 2"]);

Titan v2 supports output truncation — pass dimensions (256 / 512 / 1024) and the adapter forwards it:

bedrock.embedder({ name: "amazon.titan-embed-text-v2:0", dimensions: 256 });

Titan has no batch endpoint. Its InvokeModel body accepts one inputText per call, so embedMany issues one request per input sequentially and aggregates token usage. For high-throughput batch embedding, reach for the OpenAI or Google adapter, which batch natively.

Capabilities

• Tool calling — vendor-neutral ToolConfigs map to Converse toolUse blocks; results round-trip back as toolResult blocks on a user turn.
• Streaming — stream() runs ConverseStream and yields text deltas, a tool-call once a toolUse block’s input JSON is complete, and a terminal done with the final finish reason and usage.
• Structured output — on by default. A root-object JSON Schema is forwarded to Converse’s native outputConfig.textFormat (Bedrock wants it as a stringified schema; the adapter handles that). Otherwise it degrades to the agent’s soft system-prompt hint plus client-side validation.
• Vision — auto-detected from the model id substring. Multimodal families covered: Anthropic Claude 3 / 3.5 / 3.7 / 4, Amazon Nova Lite/Pro/Premier, Meta Llama 3.2 (11B/90B) and Llama 4. Text-only families (Llama 3/3.1, Titan Text, Mistral 7B, Cohere Command) stay off. Override either way with the vision flag.
• Embeddings — Amazon Titan Text Embeddings, single-input (see above).

Images must be base64 bytes. Bedrock Converse has no remote-URL image source — supply inlined base64 (image/jpeg, image/png, image/gif, image/webp). Passing a { url } image raises a typed InvalidRequestError upfront rather than a downstream Bedrock fault.

Pricing and usage

Every response reports token usage (input, output, total). Bedrock supplies a pre-summed total; prompt-cache reads surface as cachedTokens when present.

Attach a pricing registry — keyed by Bedrock model id, in USD per million tokens — to turn tokens into money. SDK-level or per-model (per-model wins). With pricing set, cost rolls up through every node of the AgentReport.

const bedrock = new BedrockSDK({
  region: "us-east-1",
  pricing: {
    "anthropic.claude-sonnet-4-5-20250929-v1:0": { input: 3, output: 15 },
  },
});

Need an offline token estimate? bedrock.count(text) returns a fast character-heuristic approximation — Bedrock has no offline tokenizer and per-model tokenizers differ, so this is for budgeting, not billing.

For the agent/workflow surface these models plug into, see @warlock.js/ai. For provider-specific notes and the latest model ids, see the setup-bedrock skill.