Skip to content

Latest commit

 

History

History
236 lines (186 loc) · 19.2 KB

readme.md

File metadata and controls

236 lines (186 loc) · 19.2 KB
Raw

Agentic

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

Build Status NPM MIT License Prettier Code Formatting

Agentic

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

For example, stdlib clients like WeatherClient can be used as normal TS classes:

import { WeatherClient } from '@agentic/stdlib'

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

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 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 `@agentic/stdlib/ai-sdk` adaptor
  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/stdlib/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', 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.

Install

npm install @agentic/stdlib

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

Note

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

Depending on the AI SDK and tool you want to use, you'll also need to install the required peer dependencies.

Services

Service Client Description
Bing BingClient Bing web search.
Calculator calculator Basic calculator for simple mathematical expressions.
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.
E2B e2b Hosted Python code intrepreter sandbox which is really useful for data analysis, flexible code execution, and advanced reasoning on-the-fly.
Exa ExaClient Web search tailored for LLMs.
Firecrawl FirecrawlClient Website scraping and sanitization.
HackerNews HackerNewsClient Official HackerNews API.
Hunter HunterClient Email finder, verifier, and enrichment.
Jina JinaClient Clean URL reader and web search + URL top result reading with a generous free tier.
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.
Slack SlackClient Send and receive Slack messages.
SocialData SocialDataClient Unofficial Twitter / X client (readonly) which is much cheaper than the official Twitter API.
Tavily TavilyClient Web search API tailored for LLMs.
Twilio TwilioClient Twilio conversation API to send and receive SMS messages.
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.
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.

Note that many of these clients expose multiple AI functions.

Compound Tools

  • SearchAndCrawl

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'

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

Client Goals

  • 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
    • SDK adaptors should be as lightweight as possible and be optional peer dependencies of @agentic/stdlib

TODO

Contributors

License

MIT © Travis Fischer

To stay up to date or learn more, follow @transitive_bs on Twitter.