> ## Documentation Index > Fetch the complete documentation index at: https://cofounder.appeeky.com/llms.txt > Use this file to discover all available pages before exploring further. # Memory Vault > Browse, edit, export, and import everything the co-founder agent has learned about your apps. The **Memory Vault** is your control panel for the agent's long-term memory. It lives at `Co-founder → Vault` in the dashboard. Think of it as the answer to the question *"what does my AI co-founder actually know about me?"* — and just as importantly, *"can I correct it, back it up, or transfer it to a new app?"* *** ## The graph The vault renders the agent's memory as a force-directed graph: * Each **app** in your workspace is an anchor node, drawn with its real App Store icon. * A **Global** anchor holds knowledge that applies across every app you own. * Around each anchor sit the **facts** and **episodes** scoped to it. ### What the colors mean | Color | What it is | | ------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 🔵 **Blue** | An app anchor | | 🟣 **Purple** | The global (cross-app) anchor | | 🟢 **Green (fact)** | A fact you confirmed yourself — highest weight in the agent's prompt | | ⚪️ **Gray (fact)** | A fact the agent inferred. Useful but lower confidence — feel free to correct or supersede | | 🟠 **Orange (constraint)** | A rule the agent must respect (e.g. *"don't change the \$9.99 tier"*) | | 🔴 **Red (anti-pattern)** | A *"never do this"* — the agent will refuse to propose actions that violate it | | 🟡 **Amber (pending)** | An imported fact awaiting your approval. Invisible to the agent until you click **Approve** | | 🟢 **Green (episode +)** | An applied action whose 14-day follow-up showed an improvement | | 🔴 **Red (episode −)** | An applied action whose follow-up showed a regression. Used to avoid repeating the loser | | 🟡 **Amber (episode ?)** | Outcome unknown — either still inside the 14-day window or no clear signal | | 📄 **Source** | A knowledge source you ingested — an article, note, file, or Notion page (see below) | | 🟪 **Purple edges (animated)** | **Causal links** — show provenance. A purple edge from a fact to an episode means *"this fact was distilled from that outcome"*, and from a fact to a source means *"this belief came from that article"*. Used to audit why the agent believes what it believes | Click any node to open the detail sheet on the right with the full record, including its **kind**, **confidence**, **importance**, scope, and (for facts) any prior value the row used to hold. *** ## Adding a fact manually Click **Add fact** at the top right. Fill in: * **Scope** — *Global* (applies to every app) or a specific app * **Kind** — see the table above. Pick **anti-pattern** if it's a hard "never do this", **constraint** for a rule the agent must respect, **preference** for a soft direction, **observation** for background context * **Key** — a short snake\_case label (e.g. `brand_voice`, `target_audience`, `never_localize_to`) * **Value** — the actual content; written in plain English Manually-added facts get full confidence and start with a high importance score. They outrank anything the agent inferred on its own. ### Example seeds | Key | Value | Kind | | ------------------- | ----------------------------------------------------------- | ------------- | | `brand_voice` | *"Minimal, premium, technical. No exclamation marks."* | preference | | `target_audience` | *"Indie iOS developers 25–34, EN/DE/FR/TR markets."* | observation | | `pricing_locked` | *"Don't propose changing the \$9.99/mo subscription tier."* | constraint | | `never_localize_to` | *"ja, ko, zh — we don't have CJK support in the UI yet."* | anti\_pattern | Every one of these will show up in the next run's prompt under **What you already know**, with anti-patterns and constraints rendered as guard-rails the agent must honor. *** ## Reflect now The **Reflect now** button at the top of the page kicks off an immediate distillation pass. The agent reads every episode of yours that has a measured outcome and turns the patterns into stable facts (preferences, constraints, observations, anti-patterns) — the same job that runs automatically every Monday at 06:00 UTC. Use it right after a meaningful experiment landed (say, you just got 14-day after-data on a metadata change) and you want the agent to internalize the lesson before tomorrow's run rather than next Monday's reflection. The result lands inline on the button (`Done (3+1)` = 3 new facts, 1 refined). The full history of the last reflections is pinned in the **Recent reflections** strip above the graph — green = produced new knowledge, gray = no new evidence to distill, red = failed. *** ## Superseding facts Open any active fact and click **Supersede**. It moves to the `superseded` status — invisible to the agent but still in the database for audit. Toggle **Show superseded facts** at the top of the page to see (and graph) them. This is a soft delete by design: the history of what the agent used to believe is often as valuable as what it believes today, especially when debugging a wrong recommendation. *** ## Ingest outside knowledge The **Ingest** button (top right) is how you feed the vault knowledge that didn't come from your own runs — an article, a pasted note, a file, or a Notion page. The co-founder reads it, summarizes it, and lands the durable takeaways as **amber pending facts**. Open each one and click **Approve** to let the agent use it. Ingested sources show up as their own 📄 nodes in the graph, linked to the facts they produced — so you can always trace a belief back to the article it came from. The full flow (all the ways to feed it, plus the audit) lives in **[Second Brain](../capabilities/second-brain)**. ### Fact badges Open any fact and the detail sheet shows its state at a glance: * 🟢 **Verified** — active and in use on every run. * 🟡 **Pending review** — ingested or imported, waiting for your approval; the agent can't see it yet. * **Confirmed by you** — you typed or approved it yourself (highest weight). ### Source badges & "Review due" Each source wears a badge showing whether it still holds up: * 🟢 **Self-maintained** — audited recently and consistent with your playbook. * 🟡 **Review due** — new, flagged after an anomaly on that app, or last audited over 30 days ago. * 🔴 **Needs changes** — the last audit found a conflict. Click **Audit against my workflow** on a source to re-check it against your current facts; that clears a "Review due" flag. The co-founder also re-checks flagged sources on its own — see [Second Brain → self-maintenance](../capabilities/second-brain). *** ## Export Click **Export** to download everything (or just one app's slice if a filter is active) as either: * **JSON** — the canonical format. Re-importable, lossless. * **Markdown** — a human-readable summary for sharing or archiving. Use it for backups, sharing knowledge with another founder you trust, or migrating between environments. *** ## Import This is where the vault stops being a viewer and starts being a force multiplier. Click **Import**, paste a JSON payload (the same shape you get from Export), pick a **Mode**, and optionally **remap to a target app**. ### Mode * **Pending** *(recommended default)* — every imported fact lands with `status='pending'`. The agent **completely ignores pending facts** until you open each one in the vault and click *"Approve — let the agent use this"*. This is the safe option: even a malicious or careless file can't influence the next run without you OK'ing it row by row. * **Active** — apply the import immediately. Use this only for trusted backups you exported yourself. ### Remap to a target app Pick a specific app and **every per-app fact and episode in the file is rewritten to that app**. This is what makes seeding possible: * Spin up a new app and import the memory pack from your existing one in one click. * Build a domain-specific pack ("photo-editor app ASO learnings") once and reuse it across every app in that category. * Recover a corrupted vault from a backup. ### Built-in safety rails Every import is wrapped in: * **Schema validation** — every row is checked (key length, value length, kind whitelist, app id presence). One bad row aborts the whole import; you never end up with a partial write. * **Owner enforcement** — the file's claimed `owner_email` is ignored. Imports always land under the calling user's account. * **Confidence demotion** — imported facts are tagged with 50% confidence, sitting between agent guesses (40%) and your hand-typed facts (100%). Even an active import can't silently override knowledge you confirmed yourself. * **Batch tracking** — every import gets a unique batch id stamped on every row. The success banner shows the id and a one-click **Undo this import** button. * **Hard cap** of 5,000 rows per request. ### Undo Every import is fully reversible: * Facts that **overwrote** a previous value have that value restored from the snapshot the importer took. * Facts that were **freshly inserted** by this import are deleted outright. * Episodes from this import are deleted. You can undo any time from the import dialog right after the import (one click), or via the API by `DELETE /cofounder/memory/import/`. *** ## Example import pack Here's a starter pack you can paste into the import dialog to test the flow. Set Mode to **Pending** and either Remap to a specific app or remove the `episodes` block (the seed `app_id` placeholder isn't a real id). ```json theme={"theme":{"light":"github-light","dark":"github-dark"}} { "version": 1, "facts": [ { "key": "brand_voice", "value": "Minimal, premium, technical. No exclamation marks.", "kind": "preference", "importance": 0.85 }, { "key": "target_audience", "value": "Indie iOS developers 25–34, EN/DE/FR/TR markets.", "kind": "observation", "importance": 0.75 }, { "key": "never_localize_to", "value": "ja, ko, zh — we don't have CJK support in the UI yet.", "kind": "anti_pattern", "importance": 0.95 }, { "key": "pricing_locked", "value": "Do not propose changing the $9.99/mo subscription tier.", "kind": "constraint", "importance": 0.9 }, { "key": "tr_localization_works","value": "TR localization on similar apps consistently lifted non-US downloads by ~22% in 14 days.", "kind": "observation", "importance": 0.8 } ] } ``` After importing, open each amber pending node, read the value, and click **Approve** to let the agent start using it. From the next run on, anti-patterns will appear at the top of the prompt under **HARD STOPS — never propose these**. *** ## How vault edits affect runs Any change in the vault takes effect on the **next run** — there's no manual sync. * Adding a fact → it's eligible for prompt injection on the next run. * Approving a pending fact → same as above. * Superseding a fact → the agent stops seeing it on the next run. * Importing → pending rows are inert until approved; active rows participate immediately. * Undoing an import → instant; the next run sees the original state. If you want the agent to act on a vault change *right now*, trigger a manual run from `Co-founder → Runs → Run now`.