openrouter-typescript-sdk

name	openrouter-typescript-sdk
description	Complete reference for integrating with 300+ AI models through the OpenRouter TypeScript SDK using the callModel pattern
version	1.0.0

OpenRouter TypeScript SDK

A comprehensive TypeScript SDK for interacting with OpenRouter's unified API, providing access to 300+ AI models through a single, type-safe interface. This skill enables AI agents to leverage the callModel pattern for text generation, tool usage, streaming, and multi-turn conversations.

---

Installation

npm install @openrouter/sdk

Setup

Get your API key from openrouter.ai/settings/keys, then initialize:

import OpenRouter from "@openrouter/sdk";

const client = new OpenRouter({
  apiKey: process.env.OPENROUTER_API_KEY,
});

---

Authentication

The SDK supports two authentication methods: API keys for server-side applications and OAuth PKCE flow for user-facing applications.

API Key Authentication

The primary authentication method uses API keys from your OpenRouter account.

Obtaining an API Key

1. Visit openrouter.ai/settings/keys
2. Create a new API key
3. Store securely in an environment variable

Environment Setup

export OPENROUTER_API_KEY=sk-or-v1-your-key-here

Client Initialization

import OpenRouter from "@openrouter/sdk";

const client = new OpenRouter({
  apiKey: process.env.OPENROUTER_API_KEY,
});

The client automatically uses this key for all subsequent requests:

// API key is automatically included
const result = client.callModel({
  model: "openai/gpt-5-nano",
  input: "Hello!",
});

Get Current Key Metadata

Retrieve information about the currently configured API key:

const keyInfo = await client.apiKeys.getCurrentKeyMetadata();
console.log("Key name:", keyInfo.name);
console.log("Created:", keyInfo.createdAt);

API Key Management

Programmatically manage API keys:

// List all keys
const keys = await client.apiKeys.list();

// Create a new key
const newKey = await client.apiKeys.create({
  name: "Production API Key",
});

// Get a specific key by hash
const key = await client.apiKeys.get({
  hash: "sk-or-v1-...",
});

// Update a key
await client.apiKeys.update({
  hash: "sk-or-v1-...",
  requestBody: {
    name: "Updated Key Name",
  },
});

// Delete a key
await client.apiKeys.delete({
  hash: "sk-or-v1-...",
});

OAuth Authentication (PKCE Flow)

For user-facing applications where users should control their own API keys, OpenRouter supports OAuth with PKCE (Proof Key for Code Exchange). This flow allows users to generate API keys through a browser authorization flow without your application handling their credentials.

createAuthCode

Generate an authorization code and URL to start the OAuth flow:

const authResponse = await client.oAuth.createAuthCode({
  callbackUrl: "https://myapp.com/auth/callback",
});

// authResponse contains:
// - authorizationUrl: URL to redirect the user to
// - code: The authorization code for later exchange

console.log("Redirect user to:", authResponse.authorizationUrl);

Parameters:

Parameter	Type	Required	Description
callbackUrl	string	Yes	Your application's callback URL after user authorization

Browser Redirect:

// In a browser environment
window.location.href = authResponse.authorizationUrl;

// Or in a server-rendered app, return a redirect response
res.redirect(authResponse.authorizationUrl);

exchangeAuthCodeForAPIKey

After the user authorizes your application, they are redirected back to your callback URL with an authorization code. Exchange this code for an API key:

// In your callback handler
const code = req.query.code; // From the redirect URL

const apiKeyResponse = await client.oAuth.exchangeAuthCodeForAPIKey({
  code: code,
});

// apiKeyResponse contains:
// - key: The user's API key
// - Additional metadata about the key

const userApiKey = apiKeyResponse.key;

// Store securely for this user's future requests
await saveUserApiKey(userId, userApiKey);

Parameters:

Parameter	Type	Required	Description
code	string	Yes	The authorization code from the OAuth redirect

Complete OAuth Flow Example

import OpenRouter from "@openrouter/sdk";
import express from "express";

const app = express();
const client = new OpenRouter({
  apiKey: process.env.OPENROUTER_API_KEY, // Your app's key for OAuth operations
});

// Step 1: Initiate OAuth flow
app.get("/auth/start", async (req, res) => {
  const authResponse = await client.oAuth.createAuthCode({
    callbackUrl: "https://myapp.com/auth/callback",
  });

  // Store any state needed for the callback
  req.session.oauthState = {
    /* ... */
  };

  // Redirect user to OpenRouter authorization page
  res.redirect(authResponse.authorizationUrl);
});

// Step 2: Handle callback and exchange code
app.get("/auth/callback", async (req, res) => {
  const { code } = req.query;

  if (!code) {
    return res.status(400).send("Authorization code missing");
  }

  try {
    const apiKeyResponse = await client.oAuth.exchangeAuthCodeForAPIKey({
      code: code as string,
    });

    // Store the user's API key securely
    await saveUserApiKey(req.session.userId, apiKeyResponse.key);

    res.redirect("/dashboard?auth=success");
  } catch (error) {
    console.error("OAuth exchange failed:", error);
    res.redirect("/auth/error");
  }
});

// Step 3: Use the user's API key for their requests
app.post("/api/chat", async (req, res) => {
  const userApiKey = await getUserApiKey(req.session.userId);

  // Create a client with the user's key
  const userClient = new OpenRouter({
    apiKey: userApiKey,
  });

  const result = userClient.callModel({
    model: "openai/gpt-5-nano",
    input: req.body.message,
  });

  const text = await result.getText();
  res.json({ response: text });
});

Security Best Practices

1. Environment Variables: Store API keys in environment variables, never in code
2. Key Rotation: Rotate keys periodically using the key management API
3. Environment Separation: Use different keys for development, staging, and production
4. OAuth for Users: Use the OAuth PKCE flow for user-facing apps to avoid handling user credentials
5. Secure Storage: Store user API keys encrypted in your database
6. Minimal Scope: Create keys with only the permissions needed

---

Core Concepts: callModel

The callModel function is the primary interface for text generation. It provides a unified, type-safe way to interact with any supported model.

Basic Usage

const result = client.callModel({
  model: "openai/gpt-5-nano",
  input: "Explain quantum computing in one sentence.",
});

const text = await result.getText();

Key Benefits

• Type-safe parameters with full IDE autocomplete
• Auto-generated from OpenAPI specs - automatically updates with new models
• Multiple consumption patterns - text, streaming, structured data
• Automatic tool execution with multi-turn support

---

Input Formats

The SDK accepts flexible input types for the input parameter:

String Input

A simple string becomes a user message:

const result = client.callModel({
  model: "openai/gpt-5-nano",
  input: "Hello, how are you?",
});

Message Arrays

For multi-turn conversations:

const result = client.callModel({
  model: "openai/gpt-5-nano",
  input: [
    { role: "user", content: "What is the capital of France?" },
    { role: "assistant", content: "The capital of France is Paris." },
    { role: "user", content: "What is its population?" },
  ],
});

Multimodal Content

Including images and text:

const result = client.callModel({
  model: "openai/gpt-5-nano",
  input: [
    {
      role: "user",
      content: [
        { type: "text", text: "What is in this image?" },
        {
          type: "image_url",
          image_url: { url: "https://example.com/image.png" },
        },
      ],
    },
  ],
});

System Instructions

Use the instructions parameter for system-level guidance:

const result = client.callModel({
  model: "openai/gpt-5-nano",
  instructions: "You are a helpful coding assistant. Be concise.",
  input: "How do I reverse a string in Python?",
});

---

Response Methods

The result object provides multiple methods for consuming the response:

Method	Purpose
getText()	Get complete text after all tools complete
getResponse()	Full response object with token usage
getTextStream()	Stream text deltas as they arrive
getReasoningStream()	Stream reasoning tokens (for o1/reasoning models)
getToolCallsStream()	Stream tool calls as they complete

getText()

const result = client.callModel({
  model: "openai/gpt-5-nano",
  input: "Write a haiku about coding",
});

const text = await result.getText();
console.log(text);

getResponse()

const result = client.callModel({
  model: "openai/gpt-5-nano",
  input: "Hello!",
});

const response = await result.getResponse();
console.log("Text:", response.text);
console.log("Token usage:", response.usage);

getTextStream()

const result = client.callModel({
  model: "openai/gpt-5-nano",
  input: "Write a short story",
});

for await (const delta of result.getTextStream()) {
  process.stdout.write(delta);
}

---

Tool System

Create strongly-typed tools using Zod schemas for automatic validation and type inference.

Defining Tools

import { tool } from "@openrouter/sdk";
import { z } from "zod";

const weatherTool = tool({
  name: "get_weather",
  description: "Get current weather for a location",
  inputSchema: z.object({
    location: z.string().describe("City name"),
    units: z.enum(["celsius", "fahrenheit"]).optional().default("celsius"),
  }),
  outputSchema: z.object({
    temperature: z.number(),
    conditions: z.string(),
    humidity: z.number(),
  }),
  execute: async (params) => {
    // Implement weather fetching logic
    return {
      temperature: 22,
      conditions: "Sunny",
      humidity: 45,
    };
  },
});

Using Tools with callModel

const result = client.callModel({
  model: "openai/gpt-5-nano",
  input: "What is the weather in Paris?",
  tools: [weatherTool],
});

const text = await result.getText();
// The SDK automatically executes the tool and continues the conversation

Tool Types

Regular Tools

Standard execute functions that return a result:

const calculatorTool = tool({
  name: "calculate",
  description: "Perform mathematical calculations",
  inputSchema: z.object({
    expression: z.string(),
  }),
  execute: async ({ expression }) => {
    return { result: eval(expression) };
  },
});

Generator Tools

Yield progress events using eventSchema:

const searchTool = tool({
  name: "web_search",
  description: "Search the web",
  inputSchema: z.object({ query: z.string() }),
  eventSchema: z.object({
    type: z.literal("progress"),
    message: z.string(),
  }),
  outputSchema: z.object({ results: z.array(z.string()) }),
  execute: async function* ({ query }) {
    yield { type: "progress", message: "Searching..." };
    yield { type: "progress", message: "Processing results..." };
    return { results: ["Result 1", "Result 2"] };
  },
});

Manual Tools

Set execute: false to handle tool calls yourself:

const manualTool = tool({
  name: "user_confirmation",
  description: "Request user confirmation",
  inputSchema: z.object({ message: z.string() }),
  execute: false,
});

---

Multi-Turn Conversations with Stop Conditions

Control automatic tool execution with stop conditions:

import { stepCountIs, maxCost, hasToolCall } from "@openrouter/sdk";

const result = client.callModel({
  model: "openai/gpt-5.2",
  input: "Research this topic thoroughly",
  tools: [searchTool, analyzeTool],
  stopWhen: [
    stepCountIs(10), // Stop after 10 turns
    maxCost(1.0), // Stop if cost exceeds $1.00
    hasToolCall("finish"), // Stop when 'finish' tool is called
  ],
});

Available Stop Conditions

Condition	Description
stepCountIs(n)	Stop after n turns
maxCost(amount)	Stop when cost exceeds amount
hasToolCall(name)	Stop when specific tool is called

Custom Stop Conditions

const customStop = (context) => {
  return context.messages.length > 20;
};

const result = client.callModel({
  model: "openai/gpt-5-nano",
  input: "Complex task",
  tools: [myTool],
  stopWhen: customStop,
});

---

Dynamic Parameters

Compute parameters based on conversation context:

const result = client.callModel({
  model: (ctx) =>
    ctx.numberOfTurns > 3 ? "openai/gpt-4" : "openai/gpt-4o-mini",
  temperature: (ctx) => (ctx.numberOfTurns > 1 ? 0.3 : 0.7),
  input: "Hello!",
});

Context Object Properties

Property	Type	Description
numberOfTurns	number	Current turn count
messages	array	All messages so far
instructions	string	Current system instructions
totalCost	number	Accumulated cost

---

nextTurnParams: Context Injection

Tools can modify parameters for subsequent turns, enabling skills and context-aware behavior:

const skillTool = tool({
  name: "load_skill",
  description: "Load a specialized skill",
  inputSchema: z.object({
    skill: z.string().describe("Name of the skill to load"),
  }),
  nextTurnParams: {
    instructions: (params, context) => {
      const skillInstructions = loadSkillInstructions(params.skill);
      return `${context.instructions}\n\n${skillInstructions}`;
    },
  },
  execute: async ({ skill }) => {
    return { loaded: skill };
  },
});

Use Cases for nextTurnParams

• Skill Systems: Dynamically load specialized capabilities
• Context Accumulation: Build up context over multiple turns
• Mode Switching: Change model behavior mid-conversation
• Memory Injection: Add retrieved context to instructions

---

Generation Parameters

Control model behavior with these parameters:

const result = client.callModel({
  model: "openai/gpt-5-nano",
  input: "Write a creative story",
  temperature: 0.7, // Creativity (0-2, default varies by model)
  maxOutputTokens: 1000, // Maximum tokens to generate
  topP: 0.9, // Nucleus sampling parameter
  frequencyPenalty: 0.5, // Reduce repetition
  presencePenalty: 0.5, // Encourage new topics
  stop: ["\n\n"], // Stop sequences
});

---

Streaming

All streaming methods support concurrent consumers from a single result object:

const result = client.callModel({
  model: "openai/gpt-5-nano",
  input: "Write a detailed explanation",
});

// Consumer 1: Stream text to console
const textPromise = (async () => {
  for await (const delta of result.getTextStream()) {
    process.stdout.write(delta);
  }
})();

// Consumer 2: Get full response simultaneously
const responsePromise = result.getResponse();

// Both run concurrently
const [, response] = await Promise.all([textPromise, responsePromise]);
console.log("\n\nTotal tokens:", response.usage.totalTokens);

Streaming Tool Calls

const result = client.callModel({
  model: "openai/gpt-5-nano",
  input: "Search for information about TypeScript",
  tools: [searchTool],
});

for await (const toolCall of result.getToolCallsStream()) {
  console.log(`Tool called: ${toolCall.name}`);
  console.log(`Arguments: ${JSON.stringify(toolCall.arguments)}`);
  console.log(`Result: ${JSON.stringify(toolCall.result)}`);
}

---

Format Conversion

Convert between ecosystem formats for interoperability:

OpenAI Format

import { fromChatMessages, toChatMessage } from "@openrouter/sdk";

// OpenAI messages → OpenRouter format
const result = client.callModel({
  model: "openai/gpt-5-nano",
  input: fromChatMessages(openaiMessages),
});

// Response → OpenAI chat message format
const response = await result.getResponse();
const chatMsg = toChatMessage(response);

Claude Format

import { fromClaudeMessages, toClaudeMessage } from "@openrouter/sdk";

// Claude messages → OpenRouter format
const result = client.callModel({
  model: "anthropic/claude-3-opus",
  input: fromClaudeMessages(claudeMessages),
});

// Response → Claude message format
const response = await result.getResponse();
const claudeMsg = toClaudeMessage(response);

---

Responses API Message Shapes

The SDK uses the OpenResponses format for messages. Understanding these shapes is essential for building robust agents.

Message Roles

Messages contain a role property that determines the message type:

Role	Description
user	User-provided input
assistant	Model-generated responses
system	System instructions
developer	Developer-level directives
tool	Tool execution results

Text Message

Simple text content from user or assistant:

interface TextMessage {
  role: "user" | "assistant";
  content: string;
}

Multimodal Message (Array Content)

Messages with mixed content types:

interface MultimodalMessage {
  role: "user";
  content: Array<
    | { type: "input_text"; text: string }
    | {
        type: "input_image";
        imageUrl: string;
        detail?: "auto" | "low" | "high";
      }
    | {
        type: "image";
        source: {
          type: "url" | "base64";
          url?: string;
          media_type?: string;
          data?: string;
        };
      }
  >;
}

Tool Function Call Message

When the model requests a tool execution:

interface ToolCallMessage {
  role: "assistant";
  content?: null;
  tool_calls?: Array<{
    id: string;
    type: "function";
    function: {
      name: string;
      arguments: string; // JSON-encoded arguments
    };
  }>;
}

Tool Result Message

Result returned after tool execution:

interface ToolResultMessage {
  role: "tool";
  tool_call_id: string;
  content: string; // JSON-encoded result
}

Non-Streaming Response Structure

The complete response object from getResponse():

interface OpenResponsesNonStreamingResponse {
  output: Array<ResponseMessage>;
  usage?: {
    inputTokens: number;
    outputTokens: number;
    cachedTokens?: number;
  };
  finishReason?: string;
  warnings?: Array<{
    type: string;
    message: string;
  }>;
  experimental_providerMetadata?: Record<string, unknown>;
}

Response Message Types

Output messages in the response array:

// Text/content message
interface ResponseOutputMessage {
  type: "message";
  role: "assistant";
  content: string | Array<ContentPart>;
  reasoning?: string; // For reasoning models (o1, etc.)
}

// Tool result in output
interface FunctionCallOutputMessage {
  type: "function_call_output";
  call_id: string;
  output: string;
}

Parsed Tool Call

When tool calls are parsed from the response:

interface ParsedToolCall {
  id: string;
  name: string;
  arguments: unknown; // Validated against inputSchema
}

Tool Execution Result

After a tool completes execution:

interface ToolExecutionResult {
  toolCallId: string;
  toolName: string;
  result: unknown; // Validated against outputSchema
  preliminaryResults?: unknown[]; // From generator tools
  error?: Error;
}

Step Result (for Stop Conditions)

Available in custom stop condition callbacks:

interface StepResult {
  stepType: "initial" | "continue";
  text: string;
  toolCalls: ParsedToolCall[];
  toolResults: ToolExecutionResult[];
  response: OpenResponsesNonStreamingResponse;
  usage?: {
    inputTokens: number;
    outputTokens: number;
    cachedTokens?: number;
  };
  finishReason?: string;
  warnings?: Array<{ type: string; message: string }>;
  experimental_providerMetadata?: Record<string, unknown>;
}

TurnContext

Available to tools and dynamic parameter functions:

interface TurnContext {
  numberOfTurns: number; // Turn count (1-indexed)
  turnRequest?: OpenResponsesRequest; // Current request being made
  toolCall?: OpenResponsesFunctionToolCall; // Current tool call (in tool context)
}

---

Event Shapes

The SDK provides multiple streaming methods that yield different event types.

Response Stream Events

The getFullResponsesStream() method yields these event types:

type EnhancedResponseStreamEvent =
  | ResponseCreatedEvent
  | ResponseInProgressEvent
  | OutputTextDeltaEvent
  | OutputTextDoneEvent
  | ReasoningDeltaEvent
  | ReasoningDoneEvent
  | FunctionCallArgumentsDeltaEvent
  | FunctionCallArgumentsDoneEvent
  | ResponseCompletedEvent
  | ToolPreliminaryResultEvent;

Event Type Reference

Event Type	Description	Payload
response.created	Response object initialized	{ response: ResponseObject }
response.in_progress	Generation has started	{}
response.output_text.delta	Text chunk received	{ delta: string }
response.output_text.done	Text generation complete	{ text: string }
response.reasoning.delta	Reasoning chunk (o1 models)	{ delta: string }
response.reasoning.done	Reasoning complete	{ reasoning: string }
response.function_call_arguments.delta	Tool argument chunk	{ delta: string }
response.function_call_arguments.done	Tool arguments complete	{ arguments: string }
response.completed	Full response complete	{ response: ResponseObject }
tool.preliminary_result	Generator tool progress	{ toolCallId: string; result: unknown }

Text Delta Event

interface OutputTextDeltaEvent {
  type: "response.output_text.delta";
  delta: string;
}

Reasoning Delta Event

For reasoning models (o1, etc.):

interface ReasoningDeltaEvent {
  type: "response.reasoning.delta";
  delta: string;
}

Function Call Arguments Delta Event

interface FunctionCallArgumentsDeltaEvent {
  type: "response.function_call_arguments.delta";
  delta: string;
}

Tool Preliminary Result Event

From generator tools that yield progress:

interface ToolPreliminaryResultEvent {
  type: "tool.preliminary_result";
  toolCallId: string;
  result: unknown; // Matches the tool's eventSchema
}

Response Completed Event

interface ResponseCompletedEvent {
  type: "response.completed";
  response: OpenResponsesNonStreamingResponse;
}

Tool Stream Events

The getToolStream() method yields:

type ToolStreamEvent =
  | { type: "delta"; content: string }
  | { type: "preliminary_result"; toolCallId: string; result: unknown };

Example: Processing Stream Events

const result = client.callModel({
  model: "openai/gpt-5-nano",
  input: "Analyze this data",
  tools: [analysisTool],
});

for await (const event of result.getFullResponsesStream()) {
  switch (event.type) {
    case "response.output_text.delta":
      process.stdout.write(event.delta);
      break;

    case "response.reasoning.delta":
      console.log("[Reasoning]", event.delta);
      break;

    case "response.function_call_arguments.delta":
      console.log("[Tool Args]", event.delta);
      break;

    case "tool.preliminary_result":
      console.log(`[Progress: ${event.toolCallId}]`, event.result);
      break;

    case "response.completed":
      console.log("\n[Complete]", event.response.usage);
      break;
  }
}

Message Stream Events

The getNewMessagesStream() yields OpenResponses format updates:

type MessageStreamUpdate =
  | ResponsesOutputMessage // Text/content updates
  | OpenResponsesFunctionCallOutput; // Tool results

Example: Tracking New Messages

const result = client.callModel({
  model: "openai/gpt-5-nano",
  input: "Research this topic",
  tools: [searchTool],
});

const allMessages: MessageStreamUpdate[] = [];

for await (const message of result.getNewMessagesStream()) {
  allMessages.push(message);

  if (message.type === "message") {
    console.log("Assistant:", message.content);
  } else if (message.type === "function_call_output") {
    console.log("Tool result:", message.output);
  }
}

---

API Reference

Client Methods

Beyond callModel, the client provides access to other API endpoints:

const client = new OpenRouter({
  apiKey: process.env.OPENROUTER_API_KEY,
});

// List available models
const models = await client.models.list();

// Chat completions (alternative to callModel)
const completion = await client.chat.send({
  model: "openai/gpt-5-nano",
  messages: [{ role: "user", content: "Hello!" }],
});

// Legacy completions format
const legacyCompletion = await client.completions.generate({
  model: "openai/gpt-5-nano",
  prompt: "Once upon a time",
});

// Usage analytics
const activity = await client.analytics.getUserActivity();

// Credit balance
const credits = await client.credits.getCredits();

// API key management
const keys = await client.apiKeys.list();

---

Error Handling

The SDK provides specific error types with actionable messages:

try {
  const result = await client.callModel({
    model: "openai/gpt-5-nano",
    input: "Hello!",
  });
  const text = await result.getText();
} catch (error) {
  if (error.statusCode === 401) {
    console.error("Invalid API key - check your OPENROUTER_API_KEY");
  } else if (error.statusCode === 402) {
    console.error("Insufficient credits - add credits at openrouter.ai");
  } else if (error.statusCode === 429) {
    console.error("Rate limited - implement backoff retry");
  } else if (error.statusCode === 503) {
    console.error("Model temporarily unavailable - try again or use fallback");
  } else {
    console.error("Unexpected error:", error.message);
  }
}

Error Status Codes

Code	Meaning	Action
400	Bad request	Check request parameters
401	Unauthorized	Verify API key
402	Payment required	Add credits
429	Rate limited	Implement exponential backoff
500	Server error	Retry with backoff
503	Service unavailable	Try alternative model

---

Complete Example: Agent with Tools

import OpenRouter, { tool, stepCountIs } from "@openrouter/sdk";
import { z } from "zod";

const client = new OpenRouter({
  apiKey: process.env.OPENROUTER_API_KEY,
});

// Define tools
const searchTool = tool({
  name: "web_search",
  description: "Search the web for information",
  inputSchema: z.object({
    query: z.string().describe("Search query"),
  }),
  outputSchema: z.object({
    results: z.array(
      z.object({
        title: z.string(),
        snippet: z.string(),
        url: z.string(),
      }),
    ),
  }),
  execute: async ({ query }) => {
    // Implement actual search
    return {
      results: [
        {
          title: "Example",
          snippet: "Example result",
          url: "https://example.com",
        },
      ],
    };
  },
});

const finishTool = tool({
  name: "finish",
  description: "Complete the task with final answer",
  inputSchema: z.object({
    answer: z.string().describe("The final answer"),
  }),
  execute: async ({ answer }) => ({ answer }),
});

// Run agent
async function runAgent(task: string) {
  const result = client.callModel({
    model: "openai/gpt-5-nano",
    instructions:
      "You are a helpful research assistant. Use web_search to find information, then use finish to provide your final answer.",
    input: task,
    tools: [searchTool, finishTool],
    stopWhen: [stepCountIs(10), hasToolCall("finish")],
  });

  // Stream progress
  for await (const toolCall of result.getToolCallsStream()) {
    console.log(`[${toolCall.name}] ${JSON.stringify(toolCall.arguments)}`);
  }

  return await result.getText();
}

// Usage
const answer = await runAgent(
  "What are the latest developments in quantum computing?",
);
console.log("Final answer:", answer);

---

Best Practices

1. Prefer callModel Over Direct API Calls

The callModel pattern provides automatic tool execution, type safety, and multi-turn handling.

2. Use Zod for Tool Schemas

Zod provides runtime validation and excellent TypeScript inference:

import { z } from "zod";

const schema = z.object({
  name: z.string().min(1),
  age: z.number().int().positive(),
});

3. Implement Stop Conditions

Always set reasonable limits to prevent runaway costs:

stopWhen: [stepCountIs(20), maxCost(5.0)];

4. Handle Errors Gracefully

Implement retry logic for transient failures:

async function callWithRetry(params, maxRetries = 3) {
  for (let i = 0; i < maxRetries; i++) {
    try {
      return await client.callModel(params).getText();
    } catch (error) {
      if (error.statusCode === 429 || error.statusCode >= 500) {
        await sleep(Math.pow(2, i) * 1000);
        continue;
      }
      throw error;
    }
  }
}

5. Use Streaming for Long Responses

Streaming provides better UX and allows early termination:

for await (const delta of result.getTextStream()) {
  // Process incrementally
}

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Additional Resources

• API Keys: openrouter.ai/settings/keys
• Model List: openrouter.ai/models
• GitHub Issues: github.com/OpenRouterTeam/typescript-sdk/issues

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SDK Status: Beta - Report issues on GitHub