chatgpt-api/legacy/docs/usage.mdx

---
title: Usage
---

## AI SDKs

<CardGroup cols={2}>
<Card title='Vercel AI SDK' href='/sdks/ai-sdk'>
  Using Agentic with the Vercel AI SDK.
</Card>

<Card title='LangChain' href='/sdks/langchain'>
  Using Agentic with LangChain.
</Card>

<Card title='LlamaIndex' href='/sdks/llamaindex'>
  Using Agentic with LlamaIndex.
</Card>

<Card title='Firebase Genkit' href='/sdks/genkit'>
  Using Agentic with Genkit.
</Card>

<Card title='Dexa Dexter' href='/sdks/dexter'>
  Using Agentic with Dexter.
</Card>

<Card title='OpenAI' href='/sdks/openai'>
  Using Agentic with OpenAI directly.
</Card>
</CardGroup>

## Tools

### TS Tool Usage

Agentic clients like `WeatherClient` can be used as normal TS classes:

```ts
import { WeatherClient } from '@agentic/stdlib'

// Requires `process.env.WEATHER_API_KEY` (free from weatherapi.com)
const weather = new WeatherClient()

const result = await weather.getCurrentWeather({
  q: 'San Francisco'
})
console.log(result)
```

### LLM Tool Usage

Or you can use these clients as **LLM-based tools** where the LLM decides when and how to invoke the underlying functions for you.

This works across all of the major AI SDKs via adapters. Here's an example using [Vercel's AI SDK](https://github.com/vercel/ai):

```ts
// sdk-specific imports
import { openai } from '@ai-sdk/openai'
import { generateText } from 'ai'
import { createAISDKTools } from '@agentic/ai-sdk'

// sdk-agnostic imports
import { WeatherClient } from '@agentic/stdlib'

const weather = new WeatherClient()

const result = await generateText({
  model: openai('gpt-4o-mini'),
  // this is the key line which uses the `@agentic/ai-sdk` adapter
  tools: createAISDKTools(weather),
  toolChoice: 'required',
  prompt: 'What is the weather in San Francisco?'
})

console.log(result.toolResults[0])
```

You can use our standard library of thoroughly tested AI functions with your favorite AI SDK – without having to write any glue code!

### Multiple Tool Usage via AIFunctionLike

Here's a slightly more complex example which uses multiple clients and selects a subset of their functions using the `AIFunctionSet.pick` method:

```ts
// sdk-specific imports
import { ChatModel, createAIRunner } from '@dexaai/dexter'
import { createDexterFunctions } from '@agentic/dexter'

// sdk-agnostic imports
import { PerigonClient, SerperClient } from '@agentic/stdlib'

async function main() {
  // Perigon is a news API and Serper is a Google search API
  const perigon = new PerigonClient()
  const serper = new SerperClient()

  const runner = createAIRunner({
    chatModel: new ChatModel({
      params: { model: 'gpt-4o-mini', temperature: 0 }
    }),
    functions: createDexterFunctions(
      perigon.functions.pick('search_news_stories'),
      serper
    ),
    systemMessage: 'You are a helpful assistant. Be as concise as possible.'
  })

  const result = await runner(
    'Summarize the latest news stories about the upcoming US election.'
  )
  console.log(result)
}
```

Here we've exposed 2 functions to the LLM, `search_news_stories` (which comes from the `PerigonClient.searchStories` method) and `serper_google_search` (which implicitly comes from the `SerperClient.search` method).

All of the SDK adapters like `createDexterFunctions` accept very flexible `AIFunctionLike` objects, which include:

- `AIFunctionSet` - Sets of AI functions (like `perigon.functions.pick('search_news_stories')` or `perigon.functions` or `serper.functions`)
- `AIFunctionsProvider` - Client classes which expose an `AIFunctionSet` via the `.functions` property (like `perigon` or `serper`)
- `AIFunction` - Individual functions (like `perigon.functions.get('search_news_stories')` or `serper.functions.get('serper_google_search')` or AI functions created directly via the `createAIFunction` utility function)

You can pass as many of these `AIFunctionLike` objects as you'd like and you can manipulate them as `AIFunctionSet` sets via `.pick`, `.omit`, `.get`, `.map`, etc.