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).
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).
-`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.
This package is [ESM only](https://gist.github.com/sindresorhus/a39789f98801d908bbc7ff3ecc99d99c) and requires `Node.js >= 18` or an equivalent environment (bun, deno, CF workers, etc).
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.
`@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:
```sh
npm install @agentic/weather@agentic/core zod
```
```ts
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:
> 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.
| [Clearbit](https://dashboard.clearbit.com/docs) | `@agentic/clearbit` | `ClearbitClient` | Resolving and enriching people and company datae. |
| [Dexa](https://dexa.ai) | `@agentic/dexa` | `DexaClient` | Answers questions from the world's best podcasters. |
| [Diffbot](https://docs.diffbot.com) | `@agentic/diffbot` | `DiffbotClient` | Web page classification and scraping; person and company data enrichment. |
| [E2B](https://e2b.dev) | `@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](https://docs.exa.ai) | `@agentic/exa` | `ExaClient` | Web search tailored for LLMs. |
| [Jina](https://jina.ai/reader) | `@agentic/jina` | `JinaClient` | Clean URL reader and web search + URL top result reading with a generous free tier. |
| [People Data Labs](https://www.peopledatalabs.com) | `@agentic/people-data-labs` | `PeopleDataLabsClient` | People & company data (WIP). |
| [Perigon](https://www.goperigon.com/products/news-api) | `@agentic/perigon` | `PerigonClient` | Real-time news API and web content data from 140,000+ sources. Structured and enriched by AI, primed for LLMs. |
| [Polygon](https://polygon.io) | `@agentic/polygon` | `PolygonClient` | Stock market and company financial data. |
| [PredictLeads](https://predictleads.com) | `@agentic/predict-leads` | `PredictLeadsClient` | In-depth company data including signals like fundraising events, hiring news, product launches, technologies used, etc. |
| [Proxycurl](https://nubela.co/proxycurl) | `@agentic/proxycurl` | `ProxycurlClient` | People and company data from LinkedIn & Crunchbase. |
| [Searxng](https://docs.searxng.org) | `@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](https://docs.searxng.org/user/configured_engines.html#configured-engines). |
| [SerpAPI](https://serpapi.com/search-api) | `@agentic/serpapi` | `SerpAPIClient` | Lightweight wrapper around SerpAPI for Google search. |
| [Serper](https://serper.dev) | `@agentic/serper` | `SerperClient` | Lightweight wrapper around Serper for Google search. |
| [SocialData](https://socialdata.tools) | `@agentic/social-data` | `SocialDataClient` | Unofficial Twitter / X client (readonly) which is much cheaper than the official Twitter API. |
| [Tavily](https://tavily.com) | `@agentic/tavily` | `TavilyClient` | Web search API tailored for LLMs. |
| [Twilio](https://www.twilio.com/docs/conversations/api) | `@agentic/twilio` | `TwilioClient` | Twilio conversation API to send and receive SMS messages. |
| [Twitter](https://developer.x.com/en/docs/twitter-api) | `@agentic/twitter` | `TwitterClient` | Basic Twitter API methods for fetching users, tweets, and searching recent tweets. Includes support for plan-aware rate-limiting. Uses [Nango](https://www.nango.dev) for OAuth support. |
| [Weather](https://www.weatherapi.com) | `@agentic/weather` | `WeatherClient` | Basic access to current weather data based on location. |
| [Wikipedia](https://www.mediawiki.org/wiki/API) | `@agentic/wikipedia` | `WikipediaClient` | Wikipedia page search and summaries. |
| [Wolfram Alpha](https://products.wolframalpha.com/llm-api/documentation) | `@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.
