---
title: Turborepo Caching
category: product
entity_type: skill
price: $15
canonical: https://forgehouse.ai/skills/turborepo-caching/
lang: en
hreflang_alt: https://forgehouse.ai/tr/skiller/turborepo-caching/
last_updated: 2026-06-20
---

# Turborepo Caching

> Configure Turborepo for efficient monorepo builds with local and remote caching.

A production playbook for configuring Turborepo so your monorepo builds only what changed and caches everything else, locally and across CI. It covers task graph design with dependsOn, content-addressable caching, affected-package filtering, and remote caching on Vercel or a self-hosted server, turning repeated builds into near-instant cache hits.

## Use cases
- Setting up turbo.json pipelines for a fresh monorepo
- Cutting CI minutes by building only affected packages
- Wiring remote caching shared between CI and local machines
- Standing up a self-hosted remote cache server
- Debugging unexpected cache misses with dry-run and summarize
- Scoping builds with package filters and workspace protocol

## Benefits
- Cached tasks complete in zero milliseconds instead of full rebuilds
- CI cost drops as only changed packages and their dependents rebuild
- Build pipelines run in correct dependency order with no race conditions
- Cache keys stay precise so unrelated edits don't invalidate builds

## What’s included
- Complete turbo.json with build, test, lint, typecheck, and dev pipelines
- Remote caching setup for Vercel plus a self-hosted Express cache server
- Affected-analysis filter recipes ('...[origin/main]', HEAD^1 and more)
- Explicit inputs/outputs rules to avoid phantom hits and false misses
- GitHub Actions CI workflow with TURBO_TOKEN and team config
- Cache debugging toolkit: dry-run, verbose hashes, graph, force, summarize

## Who it’s for
Teams running a JavaScript/TypeScript monorepo who want fast, predictable builds and lower CI spend through intelligent caching.

## How it runs
A cache hit rate under 80 percent is a configuration bug, not bad luck. Taking a Turborepo from cold builds to shared remote hits comes down to six moves:
1. Model the task graph in turbo.json: build depends on upstream builds, outputs declared explicitly (.next output minus its cache folder), inputs declared explicitly so test files and markdown never invalidate the build key.
2. Use the workspace protocol everywhere ('workspace:*' for internal packages) so local source always resolves; pinned versions on internal packages are the most common monorepo cache trap.
3. Connect the remote cache: turbo login and link for Vercel, or a self-hosted artifact server, with TURBO_TOKEN and TURBO_TEAM in CI so the pipeline and every laptop pull from the same cache.
4. Filter in CI instead of building the world: the filter '...[origin/main]' builds only changed packages and their dependents on each PR.
5. Order the pipeline fail-fast: deploy depends on build, test and lint together; dev servers are marked persistent and never cached.
6. Debug misses with dry-run, summarize and verbose hashing output; a cache hit rate under roughly 80 to 90 percent means inputs or globalDependencies are defined too broadly.

## FAQ
### Our monorepo is on another tool: is migration covered, and does this apply outside JavaScript?
Migrating from other monorepo tools is one of the covered scenarios, with a complete turbo.json for build, test, lint, typecheck and dev pipelines. The playbook is written for JavaScript/TypeScript workspaces; Turborepo can technically run other tasks, but the recipes here assume that ecosystem.

### How do I know a cache hit is safe and not serving stale output?
Caching is content-addressable: the hash covers declared inputs, so the explicit inputs/outputs rules in the playbook exist exactly to prevent phantom hits and false misses. When something looks wrong, the debugging toolkit: dry-run, verbose hashes, graph, force and summarize, shows you what went into the key.

### Will caching rescue a monorepo with a tangled dependency graph?
No. Tasks only skip when their inputs are unchanged and their dependsOn chain is correct, so a graph where everything depends on everything keeps rebuilding everything. The task-graph design section helps, but untangling the dependencies is work the cache cannot do for you.

## Price
$15, one-time, no subscription. VAT included.

Related guide: [How to run a marketing agency with AI automation](https://forgehouse.ai/guides/ai-marketing-agency-automation/)