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@agentic/stdlib

TODO: this is an active WIP

The goal of this project is to create a set of standard AI functions / tools which are optimized for both normal TS-usage as well as LLM-based usage across any popular AI SDK via simple adaptors.

For example, all of the stdlib tools like WeatherClient can be used both as normal, fully-typed TS SDKs:

import { WeatherClient } from '@agentic/stdlib'

const weather = new WeatherClient() // (requires `WEATHER_API_KEY` env var)

const result = await clearbit.getCurrentWeather({
  q: 'San Francisco'
})

console.log(result)

Or you can use them as a set of LLM-based functions / tools using adaptors specific to each LLM SDK. This example uses Vercel's AI SDK:

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

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

const weather = new WeatherClient()

const result = await generateText({
  model: openai('gpt-4o'),
  tools: createAISDKTools(weather),
  toolChoice: 'required',
  prompt: 'What is the weather in San Francisco?'
})

console.log(result.toolResults[0])

Let's take a slightly more complicated example which uses multiple clients and selects a subset of their functions using the pick method:

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

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

async function main() {
  const perigon = new PerigonClient()
  const serper = new SerperClient()

  const runner = createAIRunner({
    chatModel: new ChatModel({
      params: { model: 'gpt-4o', temperature: 0 }
      // debug: true
    }),
    functions: createDexterFunctions(
      perigon.functions.pick('search_news_stories'),
      serper
    ),
    systemMessage:
      'You are a helpful assistant. Be as concise as possible. Respond in markdown. Always cite your sources.'
  })

  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 corresponds to the PerigonClient.searchStories method and serper_google_search via the SerperClient.search method.

All of the SDK adaptors like createDexterFunctions are very flexible in what they accept, which is why we can pass an AIFunctionSet (like perigon.functions.pick('search_news_stories') or perigon.functions or serper.functions) or an AIFunctioProvider (like perigon or serper) or an individual AIFunction (like perigon.functions.get('search_news_stories') or serper.functions.get('serper_google_search')). You can pass as many or as few of these AIFunctionLike objects as you'd like and you can manipulate them as AIFunctionSet sets via .pick, .omit, and .map.

The SDK-specific imports are all isolated to keep the main @agentic/stdlib as lightweight as possible.

Goals

• clients should be as minimal as possible
• clients must use ky as a lightweight native fetch wrapper
• clients must have a strongly-typed TS DX
• clients should expose select methods via the @aiFunction(...) decorator
  • @aiFunction methods must use zod for input schema validation
• it should be easy to create external clients which follow the AIFunctioProvider superclass / @aiFunction decorator pattern
• common utility functions for LLM-based function calling should be exported for convenience
• clients and AIFunctions should be composable via AIFunctionSet
• clients must work with all major TS AI SDKs
  • SDK adaptors should be as lightweight as possible and be optional peer dependencies of @agentic/core
  • SDK adatptor entrypoints should all be isolated to their own top-level imports
    • @agentic/core/ai-sdk
    • @agentic/core/dexter
    • @agentic/core/genkit
    • @agentic/core/langchain

Services

• clearbit
• dexa
• diffbot
• exa
• firecrawl (WIP)
• people data labs (WIP)
• perigon
• predict leads
• proxycurl
• scraper
• searxng
• serpapi
• serper
• twitter (WIP)
• weatherapi
• wikipedia

AI SDKs

• vercel ai sdk
• dexa dexter
• firebase genkit
• langchain

TODO

• rename this repo to agentic
• change license to MIT
• sdks
  • instructor-js
  • TODO
• services
  • calculator
  • e2b
  • search-and-scrape
  • replicate
  • huggingface
  • wolfram alpha
  • midjourney
  • unstructured
  • pull from langchain
    • provide a converter for langchain DynamicStructuredTool
  • pull from other libs
  • pull from nango
• tools / chains / flows / runnables
  • market maps
• https://github.com/causaly/zod-validation-error

License

PROPRIETARY © Travis Fischer

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