ZAM Knowledge Base
Bundle OKF 0.1 · 12 conceitos · zam-os/zam
Open source Repository Open in the app JSON README (API)
About
# ZAM Knowledge Base
Living reference knowledge for this repository in
[Open Knowledge Format](https://github.com/GoogleCloudPlatform/knowledge-catalog).
Current truth only — the *why* behind it lives in [../adr/](../adr/)
(ADR 2026-07-17). Do not edit by hand: write through the
`zam_okf_upsert` MCP tool.
## algorithm
- [FSRS-6 Scheduling](fsrs-scheduling.md) — ZAM schedules reviews with a deterministic FSRS-6 kernel, persisted same-day learning steps, per-learner workload controls, and sibling-aware queues and burial.
## architecture
- [Kernel and CLI Architecture](kernel-architecture.md) — ZAM is split into an AI-agnostic learning kernel and a thin CLI orchestration layer; all learning logic lives in the kernel, all LLM/HTTP code in the CLI.
- [Local AI Runtimes](local-ai-runtimes.md) — Local text and image generation is offered only on accelerated hardware - Foundry Local for text, Ollama for images - because CPU generation is too slow to review with; embeddings are the excepti
Details
- Kind
- OKF bundles
- Topic
- AI, RAG & memory
- Publisher
- zam-os
- Origin
- okf_github
- Category
- dados
- Version
- 0.1
- Stars
- 8
- Forks
- 1
- Open pull requests
- 2
- Last push
- 2026-09-11T06:15:24Z
- Repository state
- ativo
- Language
- TypeScript
- License
- NOASSERTION
- Added
- 2026-09-09 05:03:59
- Updated
- 2026-09-13 14:03:47
- Origin id
zam-os/zam:docs/okf/index.md
README
# 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](https://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-6 spaced repetition, including short learning and relearning steps, over a prerequisite graph. - **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` | | **Hermes** | `zam agent connect hermes` | 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. #### Portable Agent Plugin The repository and published npm package also follow the vendor-neutral [Agent Plugins v1.0.0](https://agent-plugins.org/) format. Compatible clients can load the package root to discover the ZAM skill and stdio MCP server together. A source checkout needs `npm ci && npm run build` first; published packages already contain the runtime. See [ZAM Agent Plugin](docs/AGENT_PLUGIN.md) for the layout and validation. ### 2. ZAM Desktop Studio — *setup, content & graph* A native app (`zam ui`) for the things a chat window isn't good at: - **Guided setup** — the first start connects your AI model, agent, and workspace one page at a time; everything stays editable in Settings later. - **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. ```bash 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.** One line installs the desktop app and the `zam` CLI: ```bash # macOS · Linux curl -fsSL https://zam-os.org/install.sh | sh ``` ```powershell # Windows · PowerShell irm https://zam-os.org/install.ps1 | iex ``` Or grab an installer from [Releases](https://github.com/zam-os/zam/releases), or build from source (`git clone` → `npm install && npm run build`). **2. Open ZAM.** The first start walks you through setup, one page at a time: who you're learning as, your AI model (cloud or local), your agent, your workspace, and your first learning content — from a curriculum, your own sources, a project, or a goal you name. Every page can be skipped and finished later; the dashboard keeps the remaining steps as a checklist. No terminal required. **3. 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? Import material and do a review round right in the Studio. Multi-device (a server database and mobile pairing) is an optional later upgrade in Settings — the first run stays fully local. ### Prefer the terminal? The same setup runs as commands — `zam init` is the guided wizard, CLI-style: ```bash zam init # workspace · AI model · database · /zam skill zam agent connect claude-code # or codex · antigravity · opencode · copilot · goose · hermes ``` --- ## 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-6). - **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](docs/ARCHITECTURE.md). --- ## Documentation - 🌐 [zam-os.org](https://zam-os.org) — project website, in 7 languages - [Usage & maintenance](docs/USAGE.md) · [Contributing](CONTRIBUTING.md) · [Architecture](docs/ARCHITECTURE.md) ## License Apache 2.0 — see [LICENSE](LICENSE).