AI agent stdlib that works with any AI SDK and LLM

Agentic

[!WARNING]
TODO: this project is not published yet and 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 apps that work with all of the major AI SDKs (LangChain, LlamaIndex, Vercel AI SDK, OpenAI SDK, etc).

For a simple example, stdlib clients like WeatherClient can be used normally as a fully-typed TS client:

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 this same function an LLM-based tool which works across all of the major AI SDKs via adaptors.

Here's an example using 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'),
  // this is the key line which uses the ai-sdk adaptor
  tools: createAISDKTools(weather),
  toolChoice: 'required',
  prompt: 'What is the weather in San Francisco?'
})

console.log(result.toolResults[0])

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

Here's a slightly more complex example which uses multiple clients and selects a subset of their functions using the AIFunctionSet.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 }
    }),
    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 adaptors like createDexterFunctions accept very flexible in what they accept. AIFunctionLike objects 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.

All heavy third-party imports are isolated as optional peer dependencies to keep the main @agentic/stdlib package as lightweight as possible.

Services

Service	Client	Description
Bing	BingClient	Bing web search.
Clearbit	ClearbitClient	Resolving and enriching people and company datae.
Dexa	DexaClient	Answers questions from the world's best podcasters.
Diffbot	DiffbotClient	Web page classification and scraping; person and company data enrichment.
Exa	ExaClient	Web search tailored for LLMs.
Firecrawl	FirecrawlClient	Website scraping and sanitization.
Midjourney	MidjourneyClient	Unofficial Midjourney client for generative images.
Novu	NovuClient	Sending notifications (email, SMS, in-app, push, etc).
People Data Labs	PeopleDataLabsClient	People & company data (WIP).
Perigon	PerigonClient	Real-time news API and web content data from 140,000+ sources. Structured and enriched by AI, primed for LLMs.
Polygon	PolygonClient	Stock market and company financial data.
PredictLeads	PredictLeadsClient	In-depth company data including signals like fundraising events, hiring news, product launches, technologies used, etc.
Proxycurl	ProxycurlClient	People and company data from LinkedIn & Crunchbase.
Scraper	ScraperClient	Scrapes URLs into clean html/markdown/text content (TODO: currently closed beta).
Searxng	SearxngClient	OSS meta search engine capable of searching across many providers like Reddit, Google, Brave, Arxiv, Genius, IMDB, Rotten Tomatoes, Wikidata, Wolfram Alpha, YouTube, GitHub, etc.
SerpAPI	SerpAPIClient	Lightweight wrapper around SerpAPI for Google search.
Serper	SerperClient	Lightweight wrapper around Serper for Google search.
Twitter	TwitterClient	Basic Twitter API methods for fetching users, tweets, and searching recent tweets. Includes support for plan-aware rate-limiting.
WeatherAPI	WeatherClient	Basic access to current weather data based on location.
Wikipedia	WikipediaClient	Wikipedia page search and summaries.
Wolfram Alpha	WolframAlphaClient	Wolfram Alpha LLM API client for answering computational, mathematical, and scientific questions.

Non-service Tools

• calculator
• e2b (code interpreter)
• search and scrape

AI SDKs

• OpenAI SDK
  • no need for an adaptor; use AIFunctionSet.specs or AIFunctionSet.toolSpecs
• Vercel AI SDK
  • import { createAISDKTools } from '@agentic/stdlib/ai-sdk'
• LangChain
  • import { createLangChainTools } from '@agentic/stdlib/langchain'
• LlamaIndex
  • import { createLlamaIndexTools } from '@agentic/stdlib/llamaindex'
• Firebase Genkit
  • import { createGenkitTools } from '@agentic/stdlib/genkit'
• Dexa Dexter
  • import { createDexterFunctions } from '@agentic/stdlib/dexter'

Client Goals

• clients should be as minimal as possible
• clients should use ky and zod where possible
• clients must have a strongly-typed TS DX
• clients should expose select methods via the @aiFunction(...) decorator
• 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/stdlib

TODO

• rename this repo to agentic
• sdks
  • modelfusion
• services
  • perplexity
  • replicate
  • huggingface
  • skyvern
  • unstructured
  • pull from langchain
    • provide a converter for langchain DynamicStructuredTool
  • pull from nango
  • pull from activepieces
  • general openapi support ala workgpt
• tools / chains / flows / runnables
  • market maps
• https://github.com/causaly/zod-validation-error
• investigate autotool
• investigate data connectors

License

MIT © Travis Fischer

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