ZAM (Zusammen) 🤝

Do real work with your AI — and keep the knowledge instead of losing it.

ZAM is Bavarian for "together".

ZAM turns everyday work with your AI agent into active-recall practice, so growing automation doesn't mean growing dependence. You get the task done and you get sharper — the two stop being a trade-off.

Don't just automate. Elevate.

🌐 zam-os.org — the project website, in 7 languages.

Who ZAM is for

Anyone who works with an AI agent and doesn't want to get rusty as it takes on more — whether you're learning the field (say, a Fachinformatiker apprentice building durable competence), sharpening your craft on the job, or simply keen to keep growing while you automate. If you're pairing with Claude, Codex, Copilot & co. anyway, ZAM makes that time compound into lasting skill.

What ZAM does today

• Rides along in your AI agent. As you work a real task, ZAM breaks it into small knowledge concepts, notices which ones you're due to revisit, and weaves them into the session.
• Watches you work. Do a step well on your own and ZAM quietly marks it learned — no interruption. When no real task can show what you know, it asks a focused recall question. Both are active recall.
• Remembers what you're forgetting. Every concept is scheduled with FSRS-5 spaced repetition over a prerequisite graph, so ZAM resurfaces things right before they'd slip.
• Stays on your machine. One local SQLite database (~/.zam/zam.db), shared by the agent and the Desktop Studio. Review works offline; local LLMs (Ollama, FastFlowLM) are supported.

Two places to use ZAM

Your agent app is the main workbench. ZAM Desktop Studio is for setup, content, and focused review. They share the same local database, so progress in one shows up in the other.

1. In your AI agent — the workbench

This is where the real learning happens: turning actual tasks into practice, observing your work, and guiding you step by step. ZAM connects to the agent apps you already use:

| Agent | Connect with | |---|---| | Claude (Code / desktop app) | zam agent connect claude-code | | Codex | zam agent connect codex | | Antigravity | zam agent connect antigravity | | OpenCode | zam agent connect opencode | | GitHub Copilot (CLI / app) | zam agent connect copilot | | Goose | zam agent connect goose |

One command writes the MCP config (your agent may ask you to approve the server). For GitHub Copilot, it also installs user-scoped Studio, Recall, Graph, and Settings canvases; restart Copilot or start a new session after connecting. Then just type /zam — or say "let's do this together with ZAM" — and work normally.

2. ZAM Desktop Studio — setup, content & graph

A native app (zam ui) for the things a chat window isn't good at:

• Easier configuration — pick your language and local AI model in a settings panel, not a config file.
• Import your own material — paste notes, point ZAM at a source, or walk a structured curriculum; a guided wizard turns any of them into review cards.
• Edit your content — a real editor for concepts, questions, and prerequisites.
• See your knowledge graph — your concepts as a living map of what builds on what.
• Review — run focused active-recall rounds right in the app.

zam ui            # launch the Studio
zam ui --build    # one-time: build a native installer (needs Rust)

Review works in both places. Observation and guided task-work happen inside your agent.

Quickstart

1. Get ZAM. Grab an installer from Releases, or build from source:

git clone https://github.com/zam-os/zam.git && cd zam
npm install && npm run build

2. Set up — one guided wizard:

zam init

zam init picks a workspace, detects your hardware, offers to install a local AI runner (Ollama or FastFlowLM), initializes your database, and wires the /zam skill.

3. Connect your agent:

zam agent connect claude-code   # or codex · antigravity · opencode · copilot · goose

4. Learn while you work. Open your agent, start a real task, and type /zam. It checks what's due, plans the concepts behind the task, hands you the work, watches how it goes, and updates your schedule.

Prefer a gentler start? Run zam ui, import your training material, and do a review round in the Studio.

How it works

• Token — one atomic concept worth remembering, tagged with a Bloom level (1 remember → 5 create).
• Card — your personal spaced-repetition state for a token (FSRS-5).
• Prerequisites — a graph of what must be understood first; ZAM won't quiz a concept whose foundations you've just forgotten.
• Sessions — every work/learning episode is logged, so ratings come from real evidence.

The learning engine is an AI-agnostic kernel with zero LLM dependencies; the agent layer just drives it. See Architecture.

Documentation

• 🌐 zam-os.org — project website, in 7 languages
• Usage & maintenance · Contributing · Architecture

License

Apache 2.0 — see LICENSE.