AI agent stdlib that works with any LLM and TypeScript AI SDK.

Agentic

• Intro
• Install
  • AI SDKs
    • Vercel AI SDk
    • LangChain
    • LlamaIndex
    • Firebase Genkit
    • Dexa Dexter
    • OpenAI SDK
  • Optimized Imports
• Services
• Client Design Philosophy
• TODO
• Contributors
• License

Intro

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 and work with all of the major TS AI SDKs (LangChain, LlamaIndex, Vercel AI SDK, OpenAI SDK, etc).

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

import { WeatherClient } from '@agentic/stdlib'

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

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

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:

// 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!

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/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.

Install

npm install @agentic/stdlib @agentic/core zod

This package is ESM only and requires Node.js >= 18 or an equivalent environment (bun, deno, CF workers, etc).

AI SDKs

Each AI SDK adapter has its own package which needs to be installed.

#### Vercel AI SDk

npm install @agentic/ai-sdk ai

import { createAISDKTools } from '@agentic/ai-sdk'

See examples/ai-sdk for a full example.

#### LangChain

npm install @agentic/langchain @langchain/core langchain

import { createLangChainTools } from '@agentic/langchain'

See examples/langchain for a full example.

#### LlamaIndex

npm install @agentic/llamaindex llamaindex

import { createLlamaIndexTools } from '@agentic/llamaindex'

See examples/llamaindex for a full example.

#### Firebase Genkit

npm install @agentic/genkit @genkit-ai/ai @genkit-ai/core

import { createGenkitTools } from '@agentic/genkit'

See examples/genkit for a full example.

#### Dexa Dexter

npm install @agentic/dexter @dexaai/dexter

import { createDexterFunctions } from '@agentic/dexter'

See examples/dexter for a full example.

#### OpenAI SDK

npm install openai

There's no need for an adapter with the OpenAI SDK since all agentic tools are compatible with OpenAI by default. You can use AIFunctionSet.specs for function calling or AIFunctionSet.toolSpecs for parallel tool calling.

import { WeatherClient } from '@agentic/stdlib'
import OpenAI from 'openai'

const weather = new WeatherClient()
const openai = new OpenAI()

const messages: OpenAI.ChatCompletionMessageParam[] = [
  {
    role: 'system',
    content: 'You are a helpful assistant. Be as concise as possible.'
  },
  { role: 'user', content: 'What is the weather in San Francisco?' }
]

{
  // First call to OpenAI to invoke the weather tool
  const res = await openai.chat.completions.create({
    messages,
    model: 'gpt-4o-mini',
    temperature: 0,
    tools: weather.functions.toolSpecs,
    tool_choice: 'required'
  })
  const message = res.choices[0]?.message!
  console.log(JSON.stringify(message, null, 2))
  assert(message.tool_calls?.[0]?.function?.name === 'get_current_weather')

  const fn = weather.functions.get('get_current_weather')!
  assert(fn)

  const toolParams = message.tool_calls[0].function.arguments
  const toolResult = await fn(toolParams)

  messages.push(message)
  messages.push({
    role: 'tool',
    tool_call_id: message.tool_calls[0].id,
    content: JSON.stringify(toolResult)
  })
}

{
  // Second call to OpenAI to generate a text response
  const res = await openai.chat.completions.create({
    messages,
    model: 'gpt-4o-mini',
    temperature: 0,
    tools: weather.functions.toolSpecs
  })
  const message = res.choices?.[0]?.message
  console.log(JSON.stringify(message, null, 2))
}

See examples/openai for a full example.

See the examples directory for working examples of how to use each of these adapters.

Optimized Imports

@agentic/stdlib is just a convenience wrapper which re-exports all of the built-in AI tool packages. If you want to optimize your imports, you can replace @agentic/stdlib with the specific AI tools you want. For example:

npm install @agentic/weather @agentic/core zod

import { WeatherClient } from '@agentic/weather'

Some of these individual tool packages have peer dependencies if they depend on large, external packages. If so, you'll need to install their peer deps as well.

Take e2b, for example, which requires @e2b/code-interpreter as a peer dep:

npm install @agentic/e2b @agentic/core zod @e2b/code-interpreter

import { e2b } from '@agentic/e2b'

[!NOTE] There is no functional difference between using @agentic/stdlib versus using the individual packages directly. The only difference is if you want to optimize your install size (when running on serverless functions, for instance), in which case installing and using the individual packages directly will be more efficient. The default examples use @agentic/stdlib because it provides a simpler DX.

Services

Service	Package	Named export	Description
Bing	@agentic/bing	BingClient	Bing web search.
Calculator	@agentic/calculator	calculator	Basic calculator for simple mathematical expressions.
Clearbit	@agentic/clearbit	ClearbitClient	Resolving and enriching people and company datae.
Dexa	@agentic/dexa	DexaClient	Answers questions from the world's best podcasters.
Diffbot	@agentic/diffbot	DiffbotClient	Web page classification and scraping; person and company data enrichment.
E2B	@agentic/e2b	e2b	Hosted Python code intrepreter sandbox which is really useful for data analysis, flexible code execution, and advanced reasoning on-the-fly. (peer dep @e2b/code-interpreter)
Exa	@agentic/exa	ExaClient	Web search tailored for LLMs.
Firecrawl	@agentic/firecrawl	FirecrawlClient	Website scraping and sanitization.
HackerNews	@agentic/hacker-news	HackerNewsClient	Official HackerNews API.
Hunter	@agentic/hunter	HunterClient	Email finder, verifier, and enrichment.
Jina	@agentic/jina	JinaClient	Clean URL reader and web search + URL top result reading with a generous free tier.
Midjourney	@agentic/midjourney	MidjourneyClient	Unofficial Midjourney client for generative images.
Novu	@agentic/novu	NovuClient	Sending notifications (email, SMS, in-app, push, etc).
People Data Labs	@agentic/people-data-labs	PeopleDataLabsClient	People & company data (WIP).
Perigon	@agentic/perigon	PerigonClient	Real-time news API and web content data from 140,000+ sources. Structured and enriched by AI, primed for LLMs.
Polygon	@agentic/polygon	PolygonClient	Stock market and company financial data.
PredictLeads	@agentic/predict-leads	PredictLeadsClient	In-depth company data including signals like fundraising events, hiring news, product launches, technologies used, etc.
Proxycurl	@agentic/proxycurl	ProxycurlClient	People and company data from LinkedIn & Crunchbase.
Searxng	@agentic/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	@agentic/serpapi	SerpAPIClient	Lightweight wrapper around SerpAPI for Google search.
Serper	@agentic/serper	SerperClient	Lightweight wrapper around Serper for Google search.
Slack	@agentic/slack	SlackClient	Send and receive Slack messages.
SocialData	@agentic/social-data	SocialDataClient	Unofficial Twitter / X client (readonly) which is much cheaper than the official Twitter API.
Tavily	@agentic/tavily	TavilyClient	Web search API tailored for LLMs.
Twilio	@agentic/twilio	TwilioClient	Twilio conversation API to send and receive SMS messages.
Twitter	@agentic/twitter	TwitterClient	Basic Twitter API methods for fetching users, tweets, and searching recent tweets. Includes support for plan-aware rate-limiting. Uses Nango for OAuth support.
Weather	@agentic/weather	WeatherClient	Basic access to current weather data based on location.
Wikidata	@agentic/wikidata	WikidataClient	Basic Wikidata client.
Wikipedia	@agentic/wikipedia	WikipediaClient	Wikipedia page search and summaries.
Wolfram Alpha	@agentic/wolfram-alpha	WolframAlphaClient	Wolfram Alpha LLM API client for answering computational, mathematical, and scientific questions.

Note that you can import any of these AI tools from @agentic/stdlib OR from their individual packages. Installing and importing from their individual packages is more efficient, but it's less convenient so it isn't the default.

Client Design Philosophy

• clients should be as minimal as possible
• clients should use ky and zod where possible
• clients should have a strongly-typed TS DX
• clients should expose select methods via the @aiFunction(...) decorator
  • inputSchema zod schemas should be as minimal as possible with descriptions prompt engineered specifically for use with LLMs
• clients and AIFunctions should be composable via AIFunctionSet
• clients should work with all major TS AI SDKs

TODO

• services
  • browserbase
  • brave search
  • phantombuster
  • apify
  • perplexity
  • valtown
  • replicate
  • huggingface
  • skyvern
  • pull from clay
  • pull from langchain
    • provide a converter for langchain DynamicStructuredTool
  • pull from nango
  • pull from activepieces
  • general openapi support ala workgpt
• compound tools / chains / flows / runnables
  • market maps
• incorporate zod-validation-error
• investigate autotool
• investigate alt search engines
• investigate data connectors
• add unit tests for individual providers

Contributors

• Travis Fischer
• David Zhang
• Philipp Burckhardt
• And all of the amazing OSS contributors!

License

MIT © Travis Fischer

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