typescript-sdk

# Smithery Typescript Framework [](https://badge.fury.io/js/@smithery%2Fsdk) Smithery is a Typescript framework with utilities to make it easier to connect language models (LLMs) to [Model Context Protocols](https://modelcontextprotocol.io/) (MCPs), allowing you to build agents that use resources and tools without being overwhelmed by JSON schemas. ⚠️ _This repository is work in progress and in alpha. Not recommended for production use yet._ ⚠️ **Key Features** - Connect to multiple MCPs with a single client - Adapters to transform MCP responses for OpenAI and Anthropic clients - Supports chaining tool calls until LLM completes To find our registry of MCPs, visit [https://smithery.ai/](https://smithery.ai/). # Quickstart ## Installation ```bash npm install @smithery/sdk ``` ## Usage In this example, we'll connect to Exa search capabilities using either OpenAI or Anthropic. ```bash npm install @smithery/sdk @modelcontextprotocol/sdk ``` The following code sets up the client and connects to an Exa MCP server: ```typescript import { MultiClient } from "@smithery/sdk" import { OpenAIChatAdapter } from "@smithery/sdk/integrations/llm/openai" import { AnthropicChatAdapter } from "@smithery/sdk/integrations/llm/anthropic" import { createTransport } from "@smithery/sdk/transport.js" import { OpenAI } from "openai" import Anthropic from "@anthropic-ai/sdk" // Create a new connection const exaTransport = createTransport( // Replace with your deployed MCP server URL "https://your-mcp-server.example.com" ) // Initialize a multi-client connection const client = new MultiClient() await client.connectAll({ exa: exaTransport, // You can add more connections here... }) // Configure and authenticate await client.clients.exa.request({ method: "config", params: { config: { apiKey: process.env.EXA_API_KEY, }, }, }) ``` Now you can use either OpenAI or Anthropic to interact with the tools: ```typescript // Using OpenAI const openai = new OpenAI() const openaiAdapter = new OpenAIChatAdapter(client) const openaiResponse = await openai.chat.completions.create({ model: "gpt-4o-mini", messages: [{ role: "user", content: "What AI events are happening in Singapore?" }], tools: await openaiAdapter.listTools(), }) const openaiToolMessages = await openaiAdapter.callTool(openaiResponse) ``` For more complex interactions where the LLM needs to process tool outputs and potentially make additional calls, you'll need to implement a conversation loop. Here's an example: ```typescript let messages = [ { role: "user", content: "What are some AI events happening in Singapore and how many days until the next one?", }, ] const adapter = new OpenAIChatAdapter(client) let isDone = false while (!isDone) { const response = await openai.chat.completions.create({ model: "gpt-4o-mini", messages, tools: await adapter.listTools(), }) // Handle tool calls const toolMessages = await adapter.callTool(response) // Append new messages messages.push(response.choices[0].message) messages.push(...toolMessages) isDone = toolMessages.length === 0 } ``` See a full example in the [examples](./src/examples) directory.