cachly is a persistent AI memory platform for developers. It gives AI coding assistants like Claude Code, Cursor, GitHub Copilot and Windsurf a brain that remembers every lesson, fix, and architecture decision — forever. It connects via the MCP (Model Context Protocol) standard and includes 121 tools. Free tier available. Runs on German (EU) servers.

How does cachly work?

Run 'npx @cachly-dev/mcp-server@latest autopilot' once. The wizard auto-detects every AI editor you have installed (Claude Code, Cursor, Copilot, Windsurf, Cline, Zed) and writes the correct config for each. It then reads your entire git history with brain_from_git and loads years of team knowledge into your Brain before your first session. From that point, sessions start automatically, memory is shared across all your editors simultaneously, and a git post-commit hook teaches cachly from every commit.

Does cachly auto-detect my editors?

Yes. The cachly setup wizard automatically detects Claude Code, Cursor, GitHub Copilot, Windsurf, Cline, Zed, and Continue.dev — any editor that supports MCP. It writes the correct config file for each editor in one pass. You never manually edit JSON config files.

Is memory shared across all my AI editors?

Yes. cachly uses a single Brain that all your AI editors connect to simultaneously. A lesson remembered in Claude Code is instantly available in Cursor and GitHub Copilot. If your team uses different editors, all of you share the same persistent memory pool.

What is brain_from_git?

brain_from_git is a cachly tool that reads your entire git history before your first session and extracts lessons from every commit, PR, and revert. Your AI arrives knowing years of architectural decisions, bug fixes, and team conventions — without you writing a single line of documentation. Zero onboarding.

What is causal_trace?

causal_trace is a cachly tool that traces the history of any file or bug across your entire git history in seconds — replacing 30+ minutes of manual git blame. Describe a problem in plain English. It returns the root cause, the failure chain, and the exact fix that worked — with date, command, and file path.

What is brain_predict?

brain_predict is a cachly tool that scans your Brain for failure patterns before every deploy, migration, or dependency upgrade. It returns probability-weighted warnings based on your team's actual incident history — so you catch the next incident before it happens.

Does cachly work with Claude Code, Cursor, and GitHub Copilot?

Yes. cachly works with Claude Code, Cursor, GitHub Copilot, Windsurf, Cline, Zed, and Continue.dev — anywhere that supports MCP. Run 'npx @cachly-dev/mcp-server@latest autopilot' to configure all editors in one step. Memory is shared across all editors simultaneously.

Can cachly search memory across languages?

Yes. cachly uses semantic vector embeddings, not keyword search. A lesson stored in German appears when you search in English. A fix documented in Arabic matches a Japanese query about the same bug pattern. Supported languages include English, German, French, Spanish, Italian, Portuguese, Japanese, Chinese (Simplified and Traditional), Korean, Arabic, Hebrew, and more.

How is cachly different from mem0?

mem0 is a memory layer for Python LLM apps and chatbots — great for building AI products. cachly is built specifically for developer tooling: it connects to your AI editor via MCP, learns from your git history automatically, predicts failures before deploy, and gives your whole team shared memory. cachly runs on EU servers and is GDPR-native. For developers using Claude Code, Cursor, or Copilot, cachly is the right choice.

Is cachly GDPR compliant?

Yes. cachly runs exclusively on German servers (Hetzner). All data stays in the EU. No data is shared with third parties. cachly is fully GDPR compliant. An AVV (Auftragsverarbeitungsvertrag / Data Processing Agreement) is available for Business and Enterprise customers.

Your AI Never Forgets: Introducing Cachly Brain IDE Plugins

The Problem: AI Assistants Have Amnesia

Every time you start a new chat with Copilot, Claude, or Cursor, it starts from scratch. That bug you fixed last week? It'll research it all over again. The deploy command that finally worked? Forgotten.

This costs you: time re-explaining context every session, tokens paying for the same research twice, and sanity watching your AI make the same mistakes again.

The Solution: A Persistent Brain

Cachly Brain is a persistent memory layer for AI coding assistants. It works through the Model Context Protocol (MCP) — an open standard that lets AI tools share context.

Here's how it works:

Your AI learns — after fixing a bug, it calls learn_from_attempts("deploy:api", "success", "nohup docker compose up -d --build")
Your AI remembers — next session, it calls recall_best_solution("deploy:api") and gets the working command instantly
You see everything — our IDE plugins show lesson count, recall stats, and token savings in real-time

What the Plugins Show

Status Bar

A persistent 🧠 Brain: 13 lessons widget in your status bar. One glance tells you if your brain is healthy.

Brain Health Panel

Click the widget and see:

Metric	Value
Lessons Learned	13
Total Recalls	47
Est. Tokens Saved	~56,400
Est. Cost Saved	~$0.17

Lesson Viewer

See every lesson your AI has learned:

fix:vscode-endpoints — ✅ Changed /v1/ → /api/v1/ (recalled 3×)

infra:self-hosted-runner — ✅ Runner at /opt/actions-runner (recalled 5×)

bash:macos-lowercase — ✅ Use tr instead of ${var,,} on macOS (recalled 12×)

Each recall saves ~1,200 tokens by reusing a known solution instead of re-researching from scratch.

How Token Savings Work

When your AI starts a session, it calls session_start(). The brain returns relevant lessons for the task at hand. Without the brain, the AI would need to:

Read multiple files to understand context (~500 tokens)
Search for similar problems (~300 tokens)
Try approaches that failed before (~400 tokens)

With the brain, it gets the working solution in one call (~50 tokens). That's ~1,200 tokens saved per recall.

Over time, this compounds. A team of 5 developers with 100+ lessons can save thousands of dollars per month in API costs — and hours of wasted time.

Get Started

VS Code

Install directly from the VS Code Marketplace — search for Cachly Brain — or via the command line:

code --install-extension cachly-dev.cachly-brain

IntelliJ

Settings → Plugins → Marketplace → search Cachly Brain → Install

Configure

Get your API key from cachly.dev
Set cachly.apiKey and cachly.instanceId in settings
Done — the status bar updates automatically