docs/concepts/memory.md

---
summary: "How Moltbot memory works (workspace files + automatic memory flush)"
read_when:
  - You want the memory file layout and workflow
  - You want to tune the automatic pre-compaction memory flush
---
# Memory

Moltbot memory is **plain Markdown in the agent workspace**. The files are the
source of truth; the model only "remembers" what gets written to disk.

Memory search tools are provided by the active memory plugin (default:
`memory-core`). Disable memory plugins with `plugins.slots.memory = "none"`.

## Memory files (Markdown)

The default workspace layout uses two memory layers:

- `memory/YYYY-MM-DD.md`
  - Daily log (append-only).
  - Read today + yesterday at session start.
- `MEMORY.md` (optional)
  - Curated long-term memory.
  - **Only load in the main, private session** (never in group contexts).

These files live under the workspace (`agents.defaults.workspace`, default
`~/clawd`). See [Agent workspace](/concepts/agent-workspace) for the full layout.

## When to write memory

- Decisions, preferences, and durable facts go to `MEMORY.md`.
- Day-to-day notes and running context go to `memory/YYYY-MM-DD.md`.
- If someone says "remember this," write it down (do not keep it in RAM).
- This area is still evolving. It helps to remind the model to store memories; it will know what to do.
- If you want something to stick, **ask the bot to write it** into memory.

## Automatic memory flush (pre-compaction ping)

When a session is **close to auto-compaction**, Moltbot triggers a **silent,
agentic turn** that reminds the model to write durable memory **before** the
context is compacted. The default prompts explicitly say the model *may reply*,
but usually `NO_REPLY` is the correct response so the user never sees this turn.

This is controlled by `agents.defaults.compaction.memoryFlush`:

```json5
{
  agents: {
    defaults: {
      compaction: {
        reserveTokensFloor: 20000,
        memoryFlush: {
          enabled: true,
          softThresholdTokens: 4000,
          systemPrompt: "Session nearing compaction. Store durable memories now.",
          prompt: "Write any lasting notes to memory/YYYY-MM-DD.md; reply with NO_REPLY if nothing to store."
        }
      }
    }
  }
}
```

Details:
- **Soft threshold**: flush triggers when the session token estimate crosses
  `contextWindow - reserveTokensFloor - softThresholdTokens`.
- **Silent** by default: prompts include `NO_REPLY` so nothing is delivered.
- **Two prompts**: a user prompt plus a system prompt append the reminder.
- **One flush per compaction cycle** (tracked in `sessions.json`).
- **Workspace must be writable**: if the session runs sandboxed with
  `workspaceAccess: "ro"` or `"none"`, the flush is skipped.

For the full compaction lifecycle, see
[Session management + compaction](/reference/session-management-compaction).

## Vector memory search

Moltbot can build a small vector index over `MEMORY.md` and `memory/*.md` so
semantic queries can find related notes even when wording differs.

Defaults:
- Enabled by default.
- Watches memory files for changes (debounced).
- Uses remote embeddings by default. If `memorySearch.provider` is not set, Moltbot auto-selects:
  1. `local` if a `memorySearch.local.modelPath` is configured and the file exists.
  2. `openai` if an OpenAI key can be resolved.
  3. `gemini` if a Gemini key can be resolved.
  4. Otherwise memory search stays disabled until configured.
- Local mode uses node-llama-cpp and may require `pnpm approve-builds`.
- Uses sqlite-vec (when available) to accelerate vector search inside SQLite.

Remote embeddings **require** an API key for the embedding provider. Moltbot
resolves keys from auth profiles, `models.providers.*.apiKey`, or environment
variables. Codex OAuth only covers chat/completions and does **not** satisfy
embeddings for memory search. For Gemini, use `GEMINI_API_KEY` or
`models.providers.google.apiKey`. When using a custom OpenAI-compatible endpoint,
set `memorySearch.remote.apiKey` (and optional `memorySearch.remote.headers`).

### Gemini embeddings (native)

Set the provider to `gemini` to use the Gemini embeddings API directly:

```json5
agents: {
  defaults: {
    memorySearch: {
      provider: "gemini",
      model: "gemini-embedding-001",
      remote: {
        apiKey: "YOUR_GEMINI_API_KEY"
      }
    }
  }
}
```

Notes:
- `remote.baseUrl` is optional (defaults to the Gemini API base URL).
- `remote.headers` lets you add extra headers if needed.
- Default model: `gemini-embedding-001`.

If you want to use a **custom OpenAI-compatible endpoint** (OpenRouter, vLLM, or a proxy),
you can use the `remote` configuration with the OpenAI provider:

```json5
agents: {
  defaults: {
    memorySearch: {
      provider: "openai",
      model: "text-embedding-3-small",
      remote: {
        baseUrl: "https://api.example.com/v1/",
        apiKey: "YOUR_OPENAI_COMPAT_API_KEY",
        headers: { "X-Custom-Header": "value" }
      }
    }
  }
}
```

If you don't want to set an API key, use `memorySearch.provider = "local"` or set
`memorySearch.fallback = "none"`.

Fallbacks:
- `memorySearch.fallback` can be `openai`, `gemini`, `local`, or `none`.
- The fallback provider is only used when the primary embedding provider fails.

Batch indexing (OpenAI + Gemini):
- Enabled by default for OpenAI and Gemini embeddings. Set `agents.defaults.memorySearch.remote.batch.enabled = false` to disable.
- Default behavior waits for batch completion; tune `remote.batch.wait`, `remote.batch.pollIntervalMs`, and `remote.batch.timeoutMinutes` if needed.
- Set `remote.batch.concurrency` to control how many batch jobs we submit in parallel (default: 2).
- Batch mode applies when `memorySearch.provider = "openai"` or `"gemini"` and uses the corresponding API key.
- Gemini batch jobs use the async embeddings batch endpoint and require Gemini Batch API availability.

Why OpenAI batch is fast + cheap:
- For large backfills, OpenAI is typically the fastest option we support because we can submit many embedding requests in a single batch job and let OpenAI process them asynchronously.
- OpenAI offers discounted pricing for Batch API workloads, so large indexing runs are usually cheaper than sending the same requests synchronously.
- See the OpenAI Batch API docs and pricing for details:
  - https://platform.openai.com/docs/api-reference/batch
  - https://platform.openai.com/pricing

Config example:

```json5
agents: {
  defaults: {
    memorySearch: {
      provider: "openai",
      model: "text-embedding-3-small",
      fallback: "openai",
      remote: {
        batch: { enabled: true, concurrency: 2 }
      },
      sync: { watch: true }
    }
  }
}
```

Tools:
- `memory_search` — returns snippets with file + line ranges.
- `memory_get` — read memory file content by path.

Local mode:
- Set `agents.defaults.memorySearch.provider = "local"`.
- Provide `agents.defaults.memorySearch.local.modelPath` (GGUF or `hf:` URI).
- Optional: set `agents.defaults.memorySearch.fallback = "none"` to avoid remote fallback.

### How the memory tools work

- `memory_search` semantically searches Markdown chunks (~400 token target, 80-token overlap) from `MEMORY.md` + `memory/**/*.md`. It returns snippet text (capped ~700 chars), file path, line range, score, provider/model, and whether we fell back from local → remote embeddings. No full file payload is returned.
- `memory_get` reads a specific memory Markdown file (workspace-relative), optionally from a starting line and for N lines. Paths outside `MEMORY.md` / `memory/` are rejected.
- Both tools are enabled only when `memorySearch.enabled` resolves true for the agent.

### What gets indexed (and when)

- File type: Markdown only (`MEMORY.md`, `memory/**/*.md`).
- Index storage: per-agent SQLite at `~/.clawdbot/memory/<agentId>.sqlite` (configurable via `agents.defaults.memorySearch.store.path`, supports `{agentId}` token).
- Freshness: watcher on `MEMORY.md` + `memory/` marks the index dirty (debounce 1.5s). Sync is scheduled on session start, on search, or on an interval and runs asynchronously. Session transcripts use delta thresholds to trigger background sync.
- Reindex triggers: the index stores the embedding **provider/model + endpoint fingerprint + chunking params**. If any of those change, Moltbot automatically resets and reindexes the entire store.

### Hybrid search (BM25 + vector)

When enabled, Moltbot combines:
- **Vector similarity** (semantic match, wording can differ)
- **BM25 keyword relevance** (exact tokens like IDs, env vars, code symbols)

If full-text search is unavailable on your platform, Moltbot falls back to vector-only search.

#### Why hybrid?

Vector search is great at “this means the same thing”:
- “Mac Studio gateway host” vs “the machine running the gateway”
- “debounce file updates” vs “avoid indexing on every write”

But it can be weak at exact, high-signal tokens:
- IDs (`a828e60`, `b3b9895a…`)
- code symbols (`memorySearch.query.hybrid`)
- error strings (“sqlite-vec unavailable”)

BM25 (full-text) is the opposite: strong at exact tokens, weaker at paraphrases.
Hybrid search is the pragmatic middle ground: **use both retrieval signals** so you get
good results for both “natural language” queries and “needle in a haystack” queries.

#### How we merge results (the current design)

Implementation sketch:

1) Retrieve a candidate pool from both sides:
- **Vector**: top `maxResults * candidateMultiplier` by cosine similarity.
- **BM25**: top `maxResults * candidateMultiplier` by FTS5 BM25 rank (lower is better).

2) Convert BM25 rank into a 0..1-ish score:
- `textScore = 1 / (1 + max(0, bm25Rank))`

3) Union candidates by chunk id and compute a weighted score:
- `finalScore = vectorWeight * vectorScore + textWeight * textScore`

Notes:
- `vectorWeight` + `textWeight` is normalized to 1.0 in config resolution, so weights behave as percentages.
- If embeddings are unavailable (or the provider returns a zero-vector), we still run BM25 and return keyword matches.
- If FTS5 can’t be created, we keep vector-only search (no hard failure).

This isn’t “IR-theory perfect”, but it’s simple, fast, and tends to improve recall/precision on real notes.
If we want to get fancier later, common next steps are Reciprocal Rank Fusion (RRF) or score normalization
(min/max or z-score) before mixing.

Config:

```json5
agents: {
  defaults: {
    memorySearch: {
      query: {
        hybrid: {
          enabled: true,
          vectorWeight: 0.7,
          textWeight: 0.3,
          candidateMultiplier: 4
        }
      }
    }
  }
}
```

### Embedding cache

Moltbot can cache **chunk embeddings** in SQLite so reindexing and frequent updates (especially session transcripts) don't re-embed unchanged text.

Config:

```json5
agents: {
  defaults: {
    memorySearch: {
      cache: {
        enabled: true,
        maxEntries: 50000
      }
    }
  }
}
```

### Session memory search (experimental)

You can optionally index **session transcripts** and surface them via `memory_search`.
This is gated behind an experimental flag.

```json5
agents: {
  defaults: {
    memorySearch: {
      experimental: { sessionMemory: true },
      sources: ["memory", "sessions"]
    }
  }
}
```

Notes:
- Session indexing is **opt-in** (off by default).
- Session updates are debounced and **indexed asynchronously** once they cross delta thresholds (best-effort).
- `memory_search` never blocks on indexing; results can be slightly stale until background sync finishes.
- Results still include snippets only; `memory_get` remains limited to memory files.
- Session indexing is isolated per agent (only that agent’s session logs are indexed).
- Session logs live on disk (`~/.clawdbot/agents/<agentId>/sessions/*.jsonl`). Any process/user with filesystem access can read them, so treat disk access as the trust boundary. For stricter isolation, run agents under separate OS users or hosts.

Delta thresholds (defaults shown):

```json5
agents: {
  defaults: {
    memorySearch: {
      sync: {
        sessions: {
          deltaBytes: 100000,   // ~100 KB
          deltaMessages: 50     // JSONL lines
        }
      }
    }
  }
}
```

### SQLite vector acceleration (sqlite-vec)

When the sqlite-vec extension is available, Moltbot stores embeddings in a
SQLite virtual table (`vec0`) and performs vector distance queries in the
database. This keeps search fast without loading every embedding into JS.

Configuration (optional):

```json5
agents: {
  defaults: {
    memorySearch: {
      store: {
        vector: {
          enabled: true,
          extensionPath: "/path/to/sqlite-vec"
        }
      }
    }
  }
}
```

Notes:
- `enabled` defaults to true; when disabled, search falls back to in-process
  cosine similarity over stored embeddings.
- If the sqlite-vec extension is missing or fails to load, Moltbot logs the
  error and continues with the JS fallback (no vector table).
- `extensionPath` overrides the bundled sqlite-vec path (useful for custom builds
  or non-standard install locations).

### Local embedding auto-download

- Default local embedding model: `hf:ggml-org/embeddinggemma-300M-GGUF/embeddinggemma-300M-Q8_0.gguf` (~0.6 GB).
- When `memorySearch.provider = "local"`, `node-llama-cpp` resolves `modelPath`; if the GGUF is missing it **auto-downloads** to the cache (or `local.modelCacheDir` if set), then loads it. Downloads resume on retry.
- Native build requirement: run `pnpm approve-builds`, pick `node-llama-cpp`, then `pnpm rebuild node-llama-cpp`.
- Fallback: if local setup fails and `memorySearch.fallback = "openai"`, we automatically switch to remote embeddings (`openai/text-embedding-3-small` unless overridden) and record the reason.

### Custom OpenAI-compatible endpoint example

```json5
agents: {
  defaults: {
    memorySearch: {
      provider: "openai",
      model: "text-embedding-3-small",
      remote: {
        baseUrl: "https://api.example.com/v1/",
        apiKey: "YOUR_REMOTE_API_KEY",
        headers: {
          "X-Organization": "org-id",
          "X-Project": "project-id"
        }
      }
    }
  }
}
```

Notes:
- `remote.*` takes precedence over `models.providers.openai.*`.
- `remote.headers` merge with OpenAI headers; remote wins on key conflicts. Omit `remote.headers` to use the OpenAI defaults.