Install and initialize Wolbarg Workspace in your project with @wolbarg/workspace.

Install

Published on npm as @wolbarg/workspace.

Run inside your project folder (not global):

npm install @wolbarg/workspace
npx @wolbarg/workspace init

A short wizard asks for storage and an embedding provider. Press Enter to keep the defaults (SQLite + Ollama, fully local).

Skip the wizard:

npx @wolbarg/workspace init --yes

Embedding provider

Workspace needs an embedding model to search memory. The default is Ollama:

ollama pull nomic-embed-text

The init wizard also offers OpenAI, Voyage AI, Gemini, LM Studio, OpenRouter, or any OpenAI-compatible endpoint. API keys go in .env.wolbarg — never commit that file.

Config

init writes:

File	What it is
`.wolbarg/workspace/config.json`	Storage + embedding settings
`.wolbarg/workspace/memory.db`	Local SQLite database (default)
`.env.wolbarg`	API keys — do not commit

Defaults: SQLite storage, Ollama embeddings, 90-day retention. Teams can switch to Postgres in the wizard. Re-run init any time to change providers.

Check that it works

npx @wolbarg/workspace doctor
npx @wolbarg/workspace status
npx @wolbarg/workspace review

Connect an agent

npx @wolbarg/workspace connect claude
npx @wolbarg/workspace connect cursor
npx @wolbarg/workspace connect codex

Restart the agent afterwards. See Connectors for host-specific details.

Seed project memory (first session)

After init + connect, paste this into Claude Code, Cursor, or Codex. The agent should walk the repo and write present truth into Workspace (SQLite or Postgres — whichever you chose in init) via memory_write.

You are seeding Wolbarg Workspace for this project.

Goal: explore the codebase and store the current project truth into Wolbarg Workspace memory (the configured SQLite or Postgres store). Do not invent facts. Prefer evidence from files, tests, and docs.

Steps:
1. Confirm Wolbarg MCP tools are available. Call `memory_load` first.
2. Map the project: package manifests, README, docs/, architecture notes, main entrypoints, app/package layout, CI configs, env examples (names only — never store secret values).
3. Extract present truth only — not a chat transcript. Cover:
   - architecture.*  — how the system is structured, key modules, data flow
   - decision.*      — settled product/tech choices and why
   - constraint.*    — hard rules (Node version, no force-push, local-first, etc.)
   - convention.*    — naming, folder layout, commit/PR norms
   - glossary.*      — project-specific terms
   - task.*          — known open work (if clearly documented)
   - bug.*           — known open bugs (if clearly documented)
   - finding.*       — important discoveries that future agents should not re-investigate
4. For each fact, call `memory_write` with:
   - kind: one of architecture | decision | constraint | convention | glossary | task | bug | finding
   - key: stable namespaced id, e.g. decision.auth.session-strategy
   - title + statement (statement ≤ 240 chars, present tense)
   - detail when useful
   - evidence: file/doc/test/command refs with paths
   - scope.paths / scope.components when relevant
   - confidence 0–1 based on how certain the evidence is
5. Batch related observations (up to 50 per `memory_write`). Prefer fewer high-signal facts over dumping every file.
6. Never store secrets, API keys, tokens, .env values, passwords, or raw logs.
7. When done, call `memory_reconcile` with mode "deep", then `memory_load` and summarize what was stored.

Start now. Explore the repo thoroughly, then write memory.

After it finishes, check:

npx @wolbarg/workspace status
npx @wolbarg/workspace review

Install

Install

Embedding provider

Config

Check that it works

Connect an agent

Seed project memory (first session)

On this page