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Runtime Cache

Organization
vercel
runtime-cache

Vercel Runtime Cache API guidance — ephemeral per-region key-value cache with tag-based invalidation. Shared across Functions, Routing Middleware, and Builds. Use when implementing caching strategies beyond framework-level caching.

Overview

Publishervercel
Repositoryvercel-plugin
Skill nameruntime-cache
Stars
286
Forks
56
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by vercel on GitHub. Read the source before you install it.

Installation

Install the Runtime Cache AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/vercel/vercel-plugin.git /tmp/vercel-plugin
mkdir -p .claude/skills
cp -r /tmp/vercel-plugin/skills/runtime-cache .claude/skills/runtime-cache
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Runtime Cache in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Runtime Cache on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Runtime Cache is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Vercel Runtime Cache API

You are an expert in the Vercel Runtime Cache — an ephemeral caching layer for serverless compute.

What It Is

The Runtime Cache is a per-region key-value store accessible from Vercel Functions, Routing Middleware, and Builds. It supports tag-based invalidation for granular cache control.

  • Regional: Each Vercel region has its own isolated cache
  • Isolated: Scoped per project AND per deployment environment (preview vs production)
  • Persistent across deployments: Cached data survives new deploys; invalidation via TTL or expireTag
  • Ephemeral: Fixed storage limit per project; LRU eviction when full
  • Framework-agnostic: Works with any framework via @vercel/functions

Key APIs

All APIs from @vercel/functions:

Basic Cache Operations

ts
import { getCache } from '@vercel/functions';

const cache = getCache();

// Store data with TTL and tags
await cache.set('user:123', userData, {
  ttl: 3600,                      // seconds
  tags: ['users', 'user:123'],    // for bulk invalidation
  name: 'user-profile',           // human-readable label for observability
});

// Retrieve cached data (returns value or undefined)
const data = await cache.get('user:123');

// Delete a specific key
await cache.delete('user:123');

// Expire all entries with a tag (propagates globally within 300ms)
await cache.expireTag('users');
await cache.expireTag(['users', 'user:123']); // multiple tags

Cache Options

ts
const cache = getCache({
  namespace: 'api',                    // prefix for keys
  namespaceSeparator: ':',             // separator (default)
  keyHashFunction: (key) => sha256(key), // custom key hashing
});

Full Example (Framework-Agnostic)

ts
import { getCache } from '@vercel/functions';

export default {
  async fetch(request: Request) {
    const cache = getCache();
    const cached = await cache.get('blog-posts');

    if (cached) {
      return Response.json(cached);
    }

    const posts = await fetch('https://api.example.com/posts').then(r => r.json());

    await cache.set('blog-posts', posts, {
      ttl: 3600,
      tags: ['blog'],
    });

    return Response.json(posts);
  },
};

Tag Expiration from Server Action

ts
'use server';
import { getCache } from '@vercel/functions';

export async function invalidateBlog() {
  await getCache().expireTag('blog');
}

CDN Cache Purging Functions

These purge across all three cache layers (CDN + Runtime Cache + Data Cache):

ts
import { invalidateByTag, dangerouslyDeleteByTag } from '@vercel/functions';

// Stale-while-revalidate: serves stale, revalidates in background
await invalidateByTag('blog-posts');

// Hard delete: next request blocks while fetching from origin (cache stampede risk)
await dangerouslyDeleteByTag('blog-posts', {
  revalidationDeadlineSeconds: 3600,
});

Important distinction:

  • cache.expireTag() — operates on Runtime Cache only
  • invalidateByTag() / dangerouslyDeleteByTag() — purges CDN + Runtime + Data caches

Next.js Integration

Next.js 16+ (use cache: remote)

ts
// next.config.ts
const nextConfig: NextConfig = { cacheComponents: true };
ts
import { cacheLife, cacheTag } from 'next/cache';

async function getData() {
  'use cache: remote'     // stores in Vercel Runtime Cache
  cacheTag('example-tag')
  cacheLife({ expire: 3600 })
  return fetch('https://api.example.com/data').then(r => r.json());
}
  • 'use cache' (no : remote) — in-memory only, ephemeral per instance
  • 'use cache: remote' — stores in Vercel Runtime Cache

Next.js 16 Invalidation APIs

FunctionContextBehavior
updateTag(tag)Server Actions onlyImmediate expiration, read-your-own-writes
revalidateTag(tag, 'max')Server Actions + Route HandlersStale-while-revalidate (recommended)
revalidateTag(tag, { expire: 0 })Route Handlers (webhooks)Immediate expiration from external triggers

Important: Single-argument revalidateTag(tag) is deprecated in Next.js 16. Always pass a cacheLife profile as the second argument.

Runtime Cache vs ISR Isolation

  • Runtime Cache tags do NOT apply to ISR pages
  • cache.expireTag does NOT invalidate ISR cache
  • Next.js revalidatePath / revalidateTag does NOT invalidate Runtime Cache
  • To manage both, use same tag and purge via invalidateByTag (hits all cache layers)

CLI Cache Commands

bash
# Purge all cached data
vercel cache purge                    # CDN + Data cache
vercel cache purge --type cdn         # CDN only
vercel cache purge --type data        # Data cache only
vercel cache purge --yes              # skip confirmation

# Invalidate by tag (stale-while-revalidate)
vercel cache invalidate --tag blog-posts,user-profiles

# Hard delete by tag (blocks until revalidated)
vercel cache dangerously-delete --tag blog-posts
vercel cache dangerously-delete --tag blog-posts --revalidation-deadline-seconds 3600

# Image invalidation
vercel cache invalidate --srcimg /images/hero.jpg

Note: --tag and --srcimg cannot be used together.

CDN Cache Tags

Add tags to CDN cached responses for later invalidation:

ts
import { addCacheTag } from '@vercel/functions';

// Via helper
addCacheTag('product-123');

// Via response header
return Response.json(product, {
  headers: {
    'Vercel-CDN-Cache-Control': 'public, max-age=86400',
    'Vercel-Cache-Tag': 'product-123,products',
  },
});

Limits

PropertyLimit
Item size2 MB
Tags per Runtime Cache item64
Tags per CDN item128
Max tag length256 bytes
Tags per bulk REST API call16

Tags are case-sensitive and cannot contain commas.

Observability

Monitor hit rates, invalidation patterns, and storage usage in the Vercel Dashboard under Observability → Runtime Cache. The CDN dashboard (March 5, 2026) provides a unified view of global traffic distribution, cache performance metrics, a redesigned purging interface, and project-level routing — update response headers or rewrite to external APIs without triggering a new deployment. Project-level routes are available on all plans and take effect instantly.

When to Use

  • Caching API responses or computed data across functions in a region
  • Tag-based invalidation when content changes (CMS webhook → expire tag)
  • Reducing database load for frequently accessed data
  • Cross-function data sharing within a region

When NOT to Use

  • Framework-level page caching → use Next.js Cache Components ('use cache')
  • Persistent storage → use a database (Neon, Upstash)
  • CDN-level full response caching → use Cache-Control / Vercel-CDN-Cache-Control headers
  • Cross-region shared state → use a database
  • User-specific data that differs per request

References

Frequently asked questions

What does the Runtime Cache AI skill do?

Vercel Runtime Cache API guidance — ephemeral per-region key-value cache with tag-based invalidation. Shared across Functions, Routing Middleware, and Builds. Use when implementing caching strategies beyond framework-level caching.

Why use Runtime Cache on TypingMind?

Because you install it once and use it with any model. Runtime Cache is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Runtime Cache in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vercel/vercel-plugin/tree/main/skills/runtime-cache. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Runtime Cache?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Runtime Cache?

As many as you like. As long as a model supports skills, you can use Runtime Cache with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Runtime Cache AI skill free?

It is published on GitHub by vercel. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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