Merge branch 'main' into feature/poe-provider

docs/channels/whatsapp.md

@@ -125,7 +125,7 @@ the prefix (use `""` to remove it).
 - **DM policy**: `channels.whatsapp.dmPolicy` controls direct chat access (default: `pairing`).
   - Pairing: unknown senders get a pairing code (approve via `moltbot pairing approve whatsapp <code>`; codes expire after 1 hour).
   - Open: requires `channels.whatsapp.allowFrom` to include `"*"`.
-  - Self messages are always allowed; “self-chat mode” still requires `channels.whatsapp.allowFrom` to include your own number.
+  - Your linked WhatsApp number is implicitly trusted, so self messages skip ⁠`channels.whatsapp.dmPolicy` and `channels.whatsapp.allowFrom` checks.
 
 ### Personal-number mode (fallback)
 If you run Moltbot on your **personal WhatsApp number**, enable `channels.whatsapp.selfChatMode` (see sample above).

docs/cli/memory.md

@@ -39,3 +39,4 @@ Notes:
 - `memory status --deep` probes vector + embedding availability.
 - `memory status --deep --index` runs a reindex if the store is dirty.
 - `memory index --verbose` prints per-phase details (provider, model, sources, batch activity).
+- `memory status` includes any extra paths configured via `memorySearch.extraPaths`.

docs/cli/security.md

@@ -20,5 +20,5 @@ moltbot security audit --deep
 moltbot security audit --fix
 ```
 
-The audit warns when multiple DM senders share the main session and recommends `session.dmScope="per-channel-peer"` for shared inboxes.
+The audit warns when multiple DM senders share the main session and recommends `session.dmScope="per-channel-peer"` (or `per-account-channel-peer` for multi-account channels) for shared inboxes.
 It also warns when small models (`<=300B`) are used without sandboxing and with web/browser tools enabled.

docs/concepts/memory.md

@@ -75,8 +75,9 @@ For the full compaction lifecycle, see
 
 ## 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.
+Moltbot can build a small vector index over `MEMORY.md` and `memory/*.md` (plus
+any extra directories or files you opt in) so semantic queries can find related
+notes even when wording differs.
 
 Defaults:
 - Enabled by default.
@@ -96,6 +97,27 @@ 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`).
 
+### Additional memory paths
+
+If you want to index Markdown files outside the default workspace layout, add
+explicit paths:
+
+```json5
+agents: {
+  defaults: {
+    memorySearch: {
+      extraPaths: ["../team-docs", "/srv/shared-notes/overview.md"]
+    }
+  }
+}
+```
+
+Notes:
+- Paths can be absolute or workspace-relative.
+- Directories are scanned recursively for `.md` files.
+- Only Markdown files are indexed.
+- Symlinks are ignored (files or directories).
+
 ### Gemini embeddings (native)
 
 Set the provider to `gemini` to use the Gemini embeddings API directly:
@@ -189,14 +211,14 @@ Local mode:
 ### 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.
+- `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 allowed only when explicitly listed in `memorySearch.extraPaths`.
 - 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`).
+- File type: Markdown only (`MEMORY.md`, `memory/**/*.md`, plus any `.md` files under `memorySearch.extraPaths`).
 - 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.
+- Freshness: watcher on `MEMORY.md`, `memory/`, and `memorySearch.extraPaths` 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)

docs/concepts/session.md

@@ -11,7 +11,8 @@ Use `session.dmScope` to control how **direct messages** are grouped:
 - `main` (default): all DMs share the main session for continuity.
 - `per-peer`: isolate by sender id across channels.
 - `per-channel-peer`: isolate by channel + sender (recommended for multi-user inboxes).
-Use `session.identityLinks` to map provider-prefixed peer ids to a canonical identity so the same person shares a DM session across channels when using `per-peer` or `per-channel-peer`.
+- `per-account-channel-peer`: isolate by account + channel + sender (recommended for multi-account inboxes).
+Use `session.identityLinks` to map provider-prefixed peer ids to a canonical identity so the same person shares a DM session across channels when using `per-peer`, `per-channel-peer`, or `per-account-channel-peer`.
 
 ## Gateway is the source of truth
 All session state is **owned by the gateway** (the “master” Moltbot). UI clients (macOS app, WebChat, etc.) must query the gateway for session lists and token counts instead of reading local files.
@@ -44,6 +45,7 @@ the workspace is writable. See [Memory](/concepts/memory) and
     - Multiple phone numbers and channels can map to the same agent main key; they act as transports into one conversation.
   - `per-peer`: `agent:<agentId>:dm:<peerId>`.
   - `per-channel-peer`: `agent:<agentId>:<channel>:dm:<peerId>`.
+  - `per-account-channel-peer`: `agent:<agentId>:<channel>:<accountId>:dm:<peerId>` (accountId defaults to `default`).
   - If `session.identityLinks` matches a provider-prefixed peer id (for example `telegram:123`), the canonical key replaces `<peerId>` so the same person shares a session across channels.
 - Group chats isolate state: `agent:<agentId>:<channel>:group:<id>` (rooms/channels use `agent:<agentId>:<channel>:channel:<id>`).
   - Telegram forum topics append `:topic:<threadId>` to the group id for isolation.
@@ -94,7 +96,7 @@ Send these as standalone messages so they register.
 {
   session: {
     scope: "per-sender",      // keep group keys separate
-    dmScope: "main",          // DM continuity (set per-channel-peer for shared inboxes)
+    dmScope: "main",          // DM continuity (set per-channel-peer/per-account-channel-peer for shared inboxes)
     identityLinks: {
       alice: ["telegram:123456789", "discord:987654321012345678"]
     },

docs/gateway/configuration-examples.md

@@ -267,7 +267,8 @@ Save to `~/.clawdbot/moltbot.json` and you can DM the bot from that number.
         model: "gemini-embedding-001",
         remote: {
           apiKey: "${GEMINI_API_KEY}"
-        }
+        },
+        extraPaths: ["../team-docs", "/srv/shared-notes"]
       },
       sandbox: {
         mode: "non-main",

docs/gateway/configuration.md

@@ -2657,7 +2657,8 @@ Fields:
   - `main`: all DMs share the main session for continuity.
   - `per-peer`: isolate DMs by sender id across channels.
   - `per-channel-peer`: isolate DMs per channel + sender (recommended for multi-user inboxes).
-- `identityLinks`: map canonical ids to provider-prefixed peers so the same person shares a DM session across channels when using `per-peer` or `per-channel-peer`.
+  - `per-account-channel-peer`: isolate DMs per account + channel + sender (recommended for multi-account inboxes).
+- `identityLinks`: map canonical ids to provider-prefixed peers so the same person shares a DM session across channels when using `per-peer`, `per-channel-peer`, or `per-account-channel-peer`.
   - Example: `alice: ["telegram:123456789", "discord:987654321012345678"]`.
 - `reset`: primary reset policy. Defaults to daily resets at 4:00 AM local time on the gateway host.
   - `mode`: `daily` or `idle` (default: `daily` when `reset` is present).

docs/gateway/security/index.md

@@ -199,7 +199,7 @@ By default, Moltbot routes **all DMs into the main session** so your assistant h
 }
 ```
 
-This prevents cross-user context leakage while keeping group chats isolated. If the same person contacts you on multiple channels, use `session.identityLinks` to collapse those DM sessions into one canonical identity. See [Session Management](/concepts/session) and [Configuration](/gateway/configuration).
+This prevents cross-user context leakage while keeping group chats isolated. If you run multiple accounts on the same channel, use `per-account-channel-peer` instead. If the same person contacts you on multiple channels, use `session.identityLinks` to collapse those DM sessions into one canonical identity. See [Session Management](/concepts/session) and [Configuration](/gateway/configuration).
 
 ## Allowlists (DM + groups) — terminology
 

docs/providers/venice.md

@@ -4,9 +4,9 @@ read_when:
   - You want privacy-focused inference in Moltbot
   - You want Venice AI setup guidance
 ---
-# Venice AI (Venius highlight)
+# Venice AI (Venice highlight)
 
-**Venius** is our highlight Venice setup for privacy-first inference with optional anonymized access to proprietary models.
+**Venice** is our highlight Venice setup for privacy-first inference with optional anonymized access to proprietary models.
 
 Venice AI provides privacy-focused AI inference with support for uncensored models and access to major proprietary models through their anonymized proxy. All inference is private by default—no training on your data, no logging.