SDK Typescript

Overview

Strands Agents is a simple yet powerful SDK that takes a model-driven approach to building and running AI agents. The TypeScript SDK brings key features from the Python Strands framework to Node.js environments, enabling type-safe agent development for everything from simple assistants to complex workflows.

Key Features

• 🪶 Lightweight & Flexible: Simple agent loop that works seamlessly in Node.js and browser environments
• 🔒 Type-Safe Tools: Define tools easily using Zod schemas for robust input validation and type inference
• 📋 Structured Output: Get type-safe, validated responses from LLMs using Zod schemas with automatic retry on validation errors
• 🔌 Model Agnostic: First-class support for Amazon Bedrock and OpenAI, with extensible architecture for custom providers
• 🔗 Built-in MCP: Native support for Model Context Protocol (MCP) clients, enabling access to external tools and servers
• ⚡ Streaming Support: Real-time response streaming for better user experience
• 🎣 Extensible Hooks: Lifecycle hooks for monitoring and customizing agent behavior
• 💬 Conversation Management: Flexible strategies for managing conversation history and context windows
• 🤝 Multi-Agent Orchestration: Graph and Swarm patterns for coordinating multiple agents

Quick Start

Installation

Ensure you have Node.js 20+ installed, then:

npm install @strands-agents/sdk

Basic Usage

import { Agent } from '@strands-agents/sdk'

// Create agent (uses default Amazon Bedrock provider)
const agent = new Agent()

// Invoke
const result = await agent.invoke('What is the square root of 1764?')
console.log(result)

Note: For the default Amazon Bedrock model provider, you'll need AWS credentials configured and model access enabled for Claude Sonnet 4 in your region.

Core Concepts

Agents

The Agent class is the central orchestrator that manages the interaction loop between users, models, and tools.

import { Agent } from '@strands-agents/sdk'

const agent = new Agent({
  systemPrompt: 'You are a helpful assistant.',
})

Model Providers

Switch between model providers easily:

Amazon Bedrock (Default)

import { Agent, BedrockModel } from '@strands-agents/sdk'

const model = new BedrockModel({
  region: 'us-east-1',
  modelId: 'anthropic.claude-3-5-sonnet-20240620-v1:0',
  maxTokens: 4096,
  temperature: 0.7
})

const agent = new Agent({ model })

OpenAI

import { Agent } from '@strands-agents/sdk'
import { OpenAIModel } from '@strands-agents/sdk/models/openai'

// Automatically uses process.env.OPENAI_API_KEY and defaults to gpt-5.4
const model = new OpenAIModel({ api: 'chat' })

const agent = new Agent({ model })

Streaming Responses

Access responses as they are generated:

const agent = new Agent()

console.log('Agent response stream:')
for await (const event of agent.stream('Tell me a story about a brave toaster.')) {
  console.log('[Event]', event.type)
}

Tools

Tools enable agents to interact with external systems and perform actions. Create type-safe tools using Zod schemas:

import { Agent, tool } from '@strands-agents/sdk'
import { z } from 'zod'

const weatherTool = tool({
  name: 'get_weather',
  description: 'Get the current weather for a specific location.',
  inputSchema: z.object({
    location: z.string().describe('The city and state, e.g., San Francisco, CA'),
  }),
  callback: (input) => {
    // input is fully typed based on the Zod schema
    return `The weather in ${input.location} is 72°F and sunny.`
  },
})

const agent = new Agent({
  tools: [weatherTool],
})

await agent.invoke('What is the weather in San Francisco?')

Vended Tools: The SDK includes optional pre-built tools:

• Notebook Tool: Manage text-based notebooks for persistent note-taking
• File Editor Tool: Perform file system operations (read, write, edit files)
• HTTP Request Tool: Make HTTP requests to external APIs

Structured Output

Get type-safe, validated responses from LLMs by defining the expected output structure with Zod schemas. The agent automatically validates the LLM's response and retries on validation errors:

import { Agent } from '@strands-agents/sdk'
import { z } from 'zod'

const PersonSchema = z.object({
  name: z.string().describe('Name of the person'),
  age: z.number().describe('Age of the person'),
  occupation: z.string().describe('Occupation of the person')
})

// Configure structured output at the agent level
const agent = new Agent({ 
  structuredOutputSchema: PersonSchema 
})

const result = await agent.invoke('John Smith is a 30 year-old software engineer')

// result.structuredOutput is fully typed based on the schema
console.log(result.structuredOutput.name) // "John Smith"
console.log(result.structuredOutput.age)  // 30

Error handling: The agent automatically retries with validation feedback when the LLM provides invalid output. If validation ultimately fails, a StructuredOutputError is thrown:

import { StructuredOutputError } from '@strands-agents/sdk'

try {
  const result = await agent.invoke('Extract person info...')
  console.log(result.structuredOutput)
} catch (error) {
  if (error instanceof StructuredOutputError) {
    console.error('Validation failed:', error.message)
  }
}

MCP Integration

Seamlessly integrate Model Context Protocol (MCP) servers:

import { Agent, McpClient } from "@strands-agents/sdk";
import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";

// Create a client for a local MCP server
const documentationTools = new McpClient({
  transport: new StdioClientTransport({
    command: "uvx",
    args: ["awslabs.aws-documentation-mcp-server@latest"],
  }),
});

const agent = new Agent({
  systemPrompt: "You are a helpful assistant using MCP tools.",
  tools: [documentationTools], // Pass the MCP client directly as a tool source
});

await agent.invoke("Use a random tool from the MCP server.");

await documentationTools.disconnect();

Multi-Agent Orchestration

Coordinate multiple agents using built-in orchestration patterns.

Graph — You define a deterministic execution plan. Agents run as nodes in a directed graph, with edges controlling execution order. Parallel execution is supported, and downstream nodes run once all dependencies complete.

import { Agent, BedrockModel, Graph } from '@strands-agents/sdk'

const model = new BedrockModel({ maxTokens: 1024 })

const researcher = new Agent({
  model,
  id: 'researcher',
  systemPrompt: 'Research the topic and provide key facts.',
})

const writer = new Agent({
  model,
  id: 'writer',
  systemPrompt: 'Rewrite the research into a polished paragraph.',
})

const graph = new Graph({
  nodes: [researcher, writer],
  edges: [['researcher', 'writer']],
})

const result = await graph.invoke('What is the largest ocean?')

Swarm — The agents decide the routing. Each agent chooses whether to hand off to another agent or produce a final response, making the execution path dynamic and model-driven.

import { Agent, BedrockModel, Swarm } from '@strands-agents/sdk'

const model = new BedrockModel({ maxTokens: 1024 })

const researcher = new Agent({
  model,
  id: 'researcher',
  description: 'Researches a topic and gathers key facts.',
  systemPrompt: 'Research the answer, then hand off to the writer.',
})

const writer = new Agent({
  model,
  id: 'writer',
  description: 'Writes a polished final answer.',
  systemPrompt: 'Write the final answer. Do not hand off.',
})

const swarm = new Swarm({
  nodes: [researcher, writer],
  start: 'researcher',
  maxSteps: 4,
})

const result = await swarm.invoke('What is the largest ocean?')

Both patterns support streaming via .stream() for real-time access to handoff and node execution events. See the examples directory for complete working samples.

Documentation

For detailed guidance, tutorials, and concept overviews, please visit:

• Official Documentation: Comprehensive guides and tutorials

• API Reference: Complete API documentation

• Examples: Sample applications

  • First Agent: Basic Node.js agent
  • MCP: MCP integration example
  • Browser Agent: Browser-based agent with DOM manipulation

• Contributing Guide: Development setup and guidelines

Contributing ❤️

We welcome contributions! See our Contributing Guide for details on:

• Development setup and environment
• Testing and code quality standards
• Pull request process
• Code of Conduct
• Security issue reporting

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Security

See CONTRIBUTING for more information on reporting security issues.