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vercel
ai
TypeScript2024–2026
vercel / ai

ai

The AI Toolkit for TypeScript. From the creators of Next.js, the AI SDK is a free open-source library for building AI-powered applications and agents

AI & Machine LearningWeb DevelopmentLibraries & SDKs
Explore on GitHubRead Hacker News discussions
10Distinct people
10Mentions
0Explicit recommenders
27.9KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

The AI Toolkit for TypeScript. From the creators of Next.js, the AI SDK is a free open-source library for building AI-powered applications and agents

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:03:16 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month10 dated mentions

Observed mentions: 2024-03-01 23:49:28 UTC to 2026-06-02 00:07:24 UTC.

0122024-03-01 00:00:00 UTC to 2024-04-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsMar 20242024-04-01 00:00:00 UTC to 2024-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-05-01 00:00:00 UTC to 2024-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-06-01 00:00:00 UTC to 2024-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-07-01 00:00:00 UTC to 2024-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-08-01 00:00:00 UTC to 2024-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-09-01 00:00:00 UTC to 2024-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-10-01 00:00:00 UTC to 2024-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-11-01 00:00:00 UTC to 2024-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-12-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2025-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-02-01 00:00:00 UTC to 2025-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-03-01 00:00:00 UTC to 2025-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-04-01 00:00:00 UTC to 2025-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsApr 20252025-05-01 00:00:00 UTC to 2025-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-06-01 00:00:00 UTC to 2025-07-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2025-07-01 00:00:00 UTC to 2025-08-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-08-01 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-01 00:00:00 UTC to 2025-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-10-01 00:00:00 UTC to 2025-11-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-11-01 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-12-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2026-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-02-01 00:00:00 UTC to 2026-03-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2026-03-01 00:00:00 UTC to 2026-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-01 00:00:00 UTC to 2026-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-05-01 00:00:00 UTC to 2026-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-06-01 00:00:00 UTC to 2026-07-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsJun 2026

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Mar 20242024-03-01 00:00:00 UTC2024-04-01 00:00:00 UTC101
Apr 20242024-04-01 00:00:00 UTC2024-05-01 00:00:00 UTC000
May 20242024-05-01 00:00:00 UTC2024-06-01 00:00:00 UTC000
Jun 20242024-06-01 00:00:00 UTC2024-07-01 00:00:00 UTC000
Jul 20242024-07-01 00:00:00 UTC2024-08-01 00:00:00 UTC000
Aug 20242024-08-01 00:00:00 UTC2024-09-01 00:00:00 UTC000
Sep 20242024-09-01 00:00:00 UTC2024-10-01 00:00:00 UTC000
Oct 20242024-10-01 00:00:00 UTC2024-11-01 00:00:00 UTC000
Nov 20242024-11-01 00:00:00 UTC2024-12-01 00:00:00 UTC000
Dec 20242024-12-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
Jan 20252025-01-01 00:00:00 UTC2025-02-01 00:00:00 UTC000
Feb 20252025-02-01 00:00:00 UTC2025-03-01 00:00:00 UTC000
Mar 20252025-03-01 00:00:00 UTC2025-04-01 00:00:00 UTC000
Apr 20252025-04-01 00:00:00 UTC2025-05-01 00:00:00 UTC101
May 20252025-05-01 00:00:00 UTC2025-06-01 00:00:00 UTC000
Jun 20252025-06-01 00:00:00 UTC2025-07-01 00:00:00 UTC211
Jul 20252025-07-01 00:00:00 UTC2025-08-01 00:00:00 UTC101
Aug 20252025-08-01 00:00:00 UTC2025-09-01 00:00:00 UTC000
Sep 20252025-09-01 00:00:00 UTC2025-10-01 00:00:00 UTC000
Oct 20252025-10-01 00:00:00 UTC2025-11-01 00:00:00 UTC101
Nov 20252025-11-01 00:00:00 UTC2025-12-01 00:00:00 UTC101
Dec 20252025-12-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
Jan 20262026-01-01 00:00:00 UTC2026-02-01 00:00:00 UTC000
Feb 20262026-02-01 00:00:00 UTC2026-03-01 00:00:00 UTC110
Mar 20262026-03-01 00:00:00 UTC2026-04-01 00:00:00 UTC000
Apr 20262026-04-01 00:00:00 UTC2026-05-01 00:00:00 UTC101
May 20262026-05-01 00:00:00 UTC2026-06-01 00:00:00 UTC000
Jun 20262026-06-01 00:00:00 UTC2026-07-01 00:00:00 UTC110

What Hacker News says

10 sources
nlawalker·Apr 3, 2026
Relative to making docs accessible to AI via filesystem tools, I've been looking around to see what kinds of patterns SDK authors are using to get AI coding agents to use the freshest documentation, and Vercel is doing something interesting with their AI SDK that I haven't seen elsewhere (although maybe I just haven't looked hard enough). The "ai" npm package includes a root-level docs folder containing .mdx versions of the docs from their site, specific to the version of the package. Their intended AI-assisted developer experience is that people discover and install their ai-sdk skill (via their npx skills tool, which supports discovery and install of skills from most any provider, not just Vercel). The SKILL.md instructs the agent to explicitly ignore all knowledge that may have been trained into its model, and to first use grep to look
Read the rest
for docs in node_modules/ai/docs/ before searching the website. https://github.com/vercel/ai/blob/main/skills/use-ai-sdk/SKI...
In We replaced RAG with a virtual filesystem for our AI documentation assistant
koakuma-chan·Nov 18, 2025
> Can anyone direct me towards how to ... make one? https://hamel.dev/blog/posts/evals/ > What are "obvious" things that are important to get right - temperature set to 0? At least ~10 or 20 attempts at the same problem for each llm? LLMs are actually pretty deterministic, so there is no need to do more than one attempt with the exact same data. > Finally, any known/commonly used frameworks to do this, or any tooling that can call different LLMs would be enough? https://github.com/vercel/ai https://github.com/mattpocock/evalite
In Thoughts on Evals
AaronFriel·Oct 13, 2025
Oh, this is quite similar to an online parser I'd written a few years ago[1]. I have some worked examples on how to use it with the now-standard Chat Completions API for LLMs to stream and filter structured outputs (aka JSON). This is the underlying technology for a "Copilot" or "AI" application I worked on in my last role. Like yours, I'm sure, these incremental or online parser libraries are orders of magnitude faster[2] than alternatives for parsing LLM tool calls for the very simple reason that alternative approaches repeatedly parse the entire concatenated response, which requires buffering the entire payload, repeatedly allocating new objects, and for an N token response, you parse the first token N times! All of the "industry standard" approaches here are quadratic, which is going to scale quite poorly as LLMs generate larger and l
Read the rest
arger responses to meet application needs, and users want low latency outputs. One of the most useful features of this approach is filtering LLM tool calls on the server and passing through a subset of the parse events to the client. This makes it relatively easy to put moderation, metadata capture, and other requirements in a single tool call, while still providing low latency streaming UI. It also avoids the problem with many moderation APIs where for cost or speed reasons, one might delegate to a smaller, cheaper model to generate output in a side-channel of the normal output stream. This not only doesn't scale, but it also means the more powerful model is unaware of these requirements, or you end up with a "flash of unapproved content" due to moderation delays, etc. I found that it was extremely helpful to work at the level of parse events, but recognize that building partial values is also important, so I'm working on something similar in Rust[3], but taking a more holistic view and building more of an "AI SDK" akin to Vercel's, but written in Rust. [1] https://github.com/aaronfriel/fn-stream [2] https://github.com/vercel/ai/pull/1883 [3] https://github.com/aaronfriel/jsonmodem (These are my own opinions, not those of my employer, etc. etc.)
In JSON River – Parse JSON incrementally as it streams in
cauchyk·Jun 29, 2025Submission

Show HN: A modern C++20 AI SDK (GPT‑4o, Claude 3.5, tool‑calling)

Hi all, I’m hacking on new features for the ClickHouse native client and wanted the same “just call the model” ergonomics JavaScript and Python now enjoy. It didn’t exist for modern C++, so I wrote one. ai‑sdk‑cpp (Apache‑2.0) gives you: - Unified calls to OpenAI (GPT‑4o) and Anthropic (Claude 3.5) with a single C++20 API. - Streaming, multi‑turn chat, error handling—all std::optional/std::variant, no macros. - Tool calling (function‑calling) so the model can hit real APIs; sync or async, runs in parallel. The tricky bit: C++ still lacks real reflection, so mapping plain functions → JSON schemas isn’t as automatic as, say, TypeScript decorators. I’d love fresh eyes on that part. Try the examples and tell me where it feels clunky. This is inspired by Vercel's AI SDK [1], and litellm [2]. Repo live here: https://github.com/ClickHouse/ai-s
Read the rest
dk-cpp, feedback welcome! [1] https://github.com/vercel/ai [2] https://github.com/BerriAI/litellm
56 points · Open discussion
MaxLeiter·Mar 1, 2024
The AI is calling a function (https://platform.openai.com/docs/guides/function-calling) that is mapped to developer-controlled logic. The easiest thing to do is render specific UIs for every function call, but you can render however you'd like. We kept the code snippets in the blog post brief, but you can see the full code for the demo here: https://github.com/vercel/ai/tree/main/examples/next-ai-rsc
In AI SDK 3.0: Stream React Components from LLMs to deliver richer user experiences

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