https://github.com/smithery-ai/typescript-sdk
Smithery connects language models to Model Context Protocols, allowing you to build agents that use resources and tools without being overwhelmed by JSON schemas.
https://github.com/smithery-ai/typescript-sdk
agentic modelcontextprotocol
Last synced: 24 days ago
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Smithery connects language models to Model Context Protocols, allowing you to build agents that use resources and tools without being overwhelmed by JSON schemas.
- Host: GitHub
- URL: https://github.com/smithery-ai/typescript-sdk
- Owner: smithery-ai
- License: agpl-3.0
- Created: 2024-12-03T11:52:38.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-03-01T04:42:23.000Z (about 1 month ago)
- Last Synced: 2025-03-01T05:24:59.392Z (about 1 month ago)
- Topics: agentic, modelcontextprotocol
- Language: TypeScript
- Homepage: https://smithery.ai
- Size: 187 KB
- Stars: 77
- Watchers: 1
- Forks: 9
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- my-awesome-list - typescript-sdk - ai | 158 | (TypeScript)
README
# 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.