Render Keyvalue logo

Render Keyvalue

Organization
render-oss
render-keyvalue

Provisions and configures Render Key Value (Redis-compatible Valkey 8) instances for caching, session storage, and job queues. Use when the user needs Key Value, uses Redis or Valkey, or needs a cache, session store, or job queue backend, or needs to configure maxmemory policy, ipAllowList, connection strings, or internal vs external access. Trigger terms: Key Value, Redis, Valkey, cache, session store, REDIS_URL, maxmemory, ipAllowList, allkeys-lru, noeviction.

Overview

Publisherrender-oss
Repositoryskills
Skill namerender-keyvalue
Stars
90
Forks
21
Bundled files
4
LicenseMIT
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by render-oss on GitHub. Read the source before you install it.

Installation

Install the Render Keyvalue 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.
    https://github.com/render-oss/skills/tree/main/skills/render-keyvalue
  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/render-oss/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/render-keyvalue .claude/skills/render-keyvalue
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Render Keyvalue 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 Render Keyvalue 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 Render Keyvalue 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.

Render Key Value

Render Key Value provides low-latency, Redis-compatible in-memory storage running Valkey 8. Use it as a shared cache, session store, or job queue backend. Compatible with virtually all Redis client libraries.

When to Use

  • Adding a cache or session store to a web app
  • Wiring a job queue backend for Celery, Sidekiq, BullMQ, Asynq, or Oban
  • Choosing the right maxmemory policy (cache vs queue)
  • Configuring ipAllowList in Blueprints (required field)
  • Connecting via internal vs external URLs
  • Troubleshooting auth failures or connection refused errors

For background worker setup and queue framework patterns, see render-background-workers. For Blueprint authoring, see render-blueprints.

Before configuring or troubleshooting an internal Key Value connection, read references/private-networking.md.

Key Concepts

Valkey 8 (not Redis)

New instances run Valkey 8, an open-source Redis fork. It is a drop-in replacement for Redis—existing Redis client libraries work without changes. Legacy instances (created before Feb 2025) run Redis 6.

Connection URLs

Every instance has two URLs:

URL typeWhen to useAuth required
Internal (redis://red-xxx:6379)From Render services in the same workspace and regionNo (by default)
External (rediss://red-xxx:6379)From outside Render (local dev, CI)Always

Always prefer the internal URL for production services—lower latency, no TLS overhead, communicates over the private network.

External connections are disabled by default. Enable them by adding IP ranges to the access control list in the Dashboard.

Internal authentication

By default, internal connections are unauthenticated. You can require auth for internal connections in the Dashboard for compliance or extra security. This changes the internal URL to include credentials:

redis://default:PASSWORD@red-xxx:6379

Warning: Enabling internal auth breaks existing unauthenticated connections. Migrate clients to the authenticated URL first.

Maxmemory Policy

Critical decision. Choose based on your use case:

Use casePolicyWhy
Cache (can lose data)allkeys-lruEvicts least-recently-used keys to free space
Job queue (cannot lose data)noevictionReturns error on writes when full; never drops keys
Session storeallkeys-lru or volatile-lruSessions can be regenerated; LRU is safe

All available policies:

PolicyBehaviorMemory fills up?
allkeys-lruEvict any key by LRUNo
noevictionError on writes when fullYes
volatile-lruEvict keys with TTL by LRUYes
volatile-lfuEvict keys with TTL by LFUYes
allkeys-lfuEvict any key by LFUNo
volatile-randomEvict random keys with TTLYes
allkeys-randomEvict any random keyNo
volatile-ttlEvict keys nearest to expiryYes

Blueprint Configuration

yaml
services:
  - type: keyvalue
    name: cache
    plan: starter
    region: oregon
    maxmemoryPolicy: allkeys-lru
    ipAllowList: []

ipAllowList is required

Blueprints must include ipAllowList on Key Value services. Common patterns:

ValueMeaning
[]No external access (internal only—recommended for most apps)
[{source: "0.0.0.0/0", description: "everywhere"}]Open external access (use sparingly)
[{source: "203.0.113.0/24", description: "office"}]Specific IP ranges

Wiring to services

Use fromService with type: keyvalue and property: connectionString:

yaml
envVars:
  - key: REDIS_URL
    fromService:
      name: cache
      type: keyvalue
      property: connectionString

Available fromService properties for Key Value:

PropertyValue
connectionStringFull internal URL (redis://red-xxx:6379)
hostHostname only
portPort only (typically 6379)

Data Persistence

  • Paid instances: Disk-backed, appendfsync everysec. You may lose up to 1 second of writes on interruption.
  • Free instances: No disk persistence. Data is lost on restart or upgrade.
  • Upgrading from Free: All data is lost during the upgrade because Free instances have no disk.

Compute Plans and Changes

The compute plan determines available memory and connection limits. Use references/compute-plans.md for current Plan IDs and Key Value-specific change guidance.

Connection Examples

See references/connection-examples.md for client code in Node.js (ioredis, node-redis), Python (redis-py), Ruby (redis-rb, Sidekiq), and Go.

Common Mistakes

MistakeFix
Missing ipAllowList in BlueprintAdd ipAllowList: [] for internal-only access
Using allkeys-lru for job queuesSwitch to noeviction—LRU eviction drops queued jobs
Connecting with external URL from a Render serviceUse the internal URL for lower latency and no auth requirement
Forgetting type: keyvalue in fromServicetype is required; without it the wiring fails
Using deprecated redis type aliasPrefer keyvalue in new Blueprints (redis still works but is deprecated)

References

DocumentContents
references/compute-plans.mdCurrent Plan IDs, specifications, availability, and Key Value plan changes
references/private-networking.mdCurrent private-network scope, internal addressing, isolation, and troubleshooting
references/connection-examples.mdClient code for Node.js, Python, Ruby, Go
references/troubleshooting.mdAuth errors, connection refused, memory full, migration from Redis 6

Related Skills

  • render-background-workers — Queue consumer setup with Celery, Sidekiq, BullMQ
  • render-blueprints — Full render.yaml schema, fromService patterns
  • render-networking — Private network, internal URLs
  • render-env-vars — Wiring REDIS_URL and other connection vars

Current documentation retrieval

Whenever this skill directs you to consult current Render documentation:

  1. Retrieve the linked Markdown document directly with an available URL-fetching tool or HTTP client, such as curl. Do not substitute web-search summaries for the document.
  2. Confirm that retrieval succeeded and returned the expected document, then read its contents. Saving a file or printing its path is not sufficient.
  3. If the request fails or your tool cannot read the Markdown response, open and read the linked HTML version instead.
  4. If neither version can be retrieved, disclose that the current reference is unavailable and follow any topic-specific fallback in the skill. Use bundled guidance only for stable constraints, and do not guess at changeable platform details.

When a task requires multiple references, apply this workflow to each one and distinguish the documents you verified from those that remain unavailable.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Render Keyvalue AI skill do?

Provisions and configures Render Key Value (Redis-compatible Valkey 8) instances for caching, session storage, and job queues. Use when the user needs Key Value, uses Redis or Valkey, or needs a cache, session store, or job queue backend, or needs to configure maxmemory policy, ipAllowList, connection strings, or internal vs external access. Trigger terms: Key Value, Redis, Valkey, cache, session store, REDIS_URL, maxmemory, ipAllowList, allkeys-lru, noeviction.

Why use Render Keyvalue on TypingMind?

Because you install it once and use it with any model. Render Keyvalue 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 Render Keyvalue in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/render-oss/skills/tree/main/skills/render-keyvalue. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Render Keyvalue?

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 Render Keyvalue?

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

Is the Render Keyvalue AI skill free?

Yes. It is published on GitHub by render-oss under the MIT license. 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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