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Memory

Pawz has a built-in memory system that lets agents remember facts, preferences, and context across conversations.

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How it works

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Configuration

Go to Settings → Sessions to configure memory:
SettingDefaultDescription
Embedding modelnomic-embed-textModel used to create vector embeddings
Embedding base URLhttp://localhost:11434Ollama endpoint for embeddings
Embedding dimensions768Vector size (match your model)
Auto-recallOnAutomatically retrieve relevant memories
Auto-captureOnAutomatically extract facts from conversations
Recall limit5Max memories injected per message
Recall threshold0.3Minimum relevance score (0–1)

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Memory categories

Each memory is tagged with a category:
CategoryUse case
generalCatch-all
preferenceUser likes/dislikes, style preferences
instructionStanding orders, rules
contextBackground information
factConcrete facts, dates, numbers
projectProject-specific context
personInfo about people
technicalTechnical details, tools, configs
Memory retrieval uses a hybrid algorithm combining multiple signals:
  1. BM25 — text relevance via SQLite FTS5
  2. Vector cosine similarity — semantic meaning via embeddings
  3. Weighted merge — BM25 (0.4) + vector (0.6)
  4. Temporal decay — half-life of 30 days (recent memories rank higher)
  5. MMR re-ranking — λ=0.7 balances relevance vs. diversity
  6. Keyword fallback — when both BM25 and vector search return no results, the engine falls back to a keyword search using SQL LIKE on memory content. This ensures memories can always be found even when embeddings are unavailable or the FTS index misses a match.
:::tip Keyword fallback The keyword fallback is automatic — you don’t need to configure anything. If semantic search and BM25 both return empty results for a query, Pawz transparently retries with a simple substring match so you never get a false “no results found.” ::: In the Memory Palace, you can adjust:
ParameterDefaultEffect
BM25 weight0.4Text match importance
Vector weight0.6Semantic match importance
Decay half-life30 daysHow fast old memories fade
MMR lambda0.71.0 = pure relevance, 0.0 = max diversity
Threshold0.3Minimum score to include

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Memory Palace

The Memory Palace is a dedicated view for managing all stored memories. It is organized into four tabs:

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Tabs

TabPurpose
RecallSemantic search interface — enter a natural-language query and view ranked results with relevance scores
RememberManual memory creation form — set content, category, and importance then save
GraphForce-directed graph visualization of memory relationships, grouped by category with color-coded bubbles sized by importance
FilesBrowse agent personality files (IDENTITY.md, SOUL.md, etc.) stored on disk

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Recall

Type a query into the search box and press Enter (or click the search button). Results are displayed as cards showing the category tag, relevance score (percentage), content preview, and importance. Clicking a memory in the sidebar also opens it in the Recall tab.

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Remember

Manually store a new memory by filling in:
  • Content — the text to remember
  • Category — select from the standard categories (general, preference, instruction, etc.)
  • Importance — 1–10 scale
Click Save Memory to persist it. The sidebar and stats update automatically.

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Graph

Click Render to generate a bubble-chart visualization. Memories are grouped by category and laid out in a circle. Each bubble represents one memory — its size reflects importance, and its color maps to the category. This gives a quick visual overview of what your agent knows and where knowledge is concentrated.

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Files

Browse and edit the agent’s identity and personality files that live on disk. This tab is always available, even when the embedding model is not configured.

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Embedding status banner

At the top of the Memory Palace, a status banner shows whether the embedding model is loaded and operational:
StatusBanner
Ollama not running⚠️ Warning — semantic search disabled, falls back to keyword matching
Model not pulledℹ️ Info — prompts you to pull the model (~275 MB) with a one-click button
Memories need vectorsℹ️ Info — offers a Embed All button to backfill embeddings for existing memories
All good✅ Success — shows active model name (e.g. nomic-embed-text via Ollama)
:::note When embeddings are unavailable the banner tells you that memory search will fall back to keyword matching. You can still store and retrieve memories — just without semantic ranking. :::

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Recent memories sidebar

On the right side of the Memory Palace, a sidebar displays the 20 most recently stored memories for quick reference. Each card shows:
  • Category tag
  • Content preview (first 60 characters)
  • Importance score
Click any card to jump to its full details in the Recall tab. A search box at the top of the sidebar lets you filter the visible cards by keyword.

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JSON export

Click the Export button in the Memory Palace toolbar to download all memories as a JSON file. The export includes: 
{
  "exportedAt": "2026-02-20T12:00:00.000Z",
  "source": "Paw Desktop — Memory Export",
  "totalMemories": 42,
  "memories": [
    {
      "id": "...",
      "content": "User prefers dark mode",
      "category": "preference",
      "importance": 7,
      "created_at": "..."
    }
  ]
}
The file is named paw-memories-YYYY-MM-DD.json. Use this for backup or to transfer memories between machines.

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Slash commands

Quick memory operations from any chat: 
/remember <text>    Store a memory manually
/forget <id>        Delete a memory by ID
/recall <query>     Search memories and show results

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Auto-capture

When auto-capture is enabled, the engine extracts memorable facts from conversations using heuristics. It looks for:
  • User preferences (“I prefer…”, “I like…”)
  • Explicit instructions (“Always…”, “Never…”)
  • Personal context (names, locations, roles)
  • Concrete facts (dates, numbers, decisions)

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Embedding setup

Pawz auto-manages embeddings via Ollama:
  1. Checks if Ollama is reachable
  2. Auto-starts Ollama if needed
  3. Checks if the embedding model is available
  4. Auto-pulls the model if missing
  5. Tests embedding generation
If you switch embedding models, use backfill to re-embed existing memories.

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Embedding backends

The engine tries endpoints in order:
  1. Ollama /api/embed (current API)
  2. Ollama /api/embeddings (legacy API)
  3. OpenAI-compatible /v1/embeddings
This means you can use any OpenAI-compatible embedding API by changing the base URL.