MEMORY.md is now loaded into context at session start, ensuring the
agent has access to curated long-term memory without requiring
embedding-based semantic search.
Previously, MEMORY.md was only accessible via the memory_search tool,
which requires an embedding provider (OpenAI/Gemini API key or local
model). When no embedding provider was configured, the agent would
claim memories were empty even though MEMORY.md existed and contained
data.
This change:
- Adds DEFAULT_MEMORY_FILENAME constant
- Includes MEMORY.md in WorkspaceBootstrapFileName type
- Loads MEMORY.md in loadWorkspaceBootstrapFiles()
- Does NOT add MEMORY.md to subagent allowlist (keeps user data private)
- Does NOT auto-create MEMORY.md template (user creates as needed)
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
* docs: Add Oracle Cloud (OCI) platform guide
- Add comprehensive guide for Oracle Cloud Always Free tier (ARM)
- Cover VCN security, Tailscale Serve setup, and why traditional hardening is unnecessary
- Update vps.md to list Oracle as top provider option
- Update digitalocean.md to link to official Oracle guide instead of community gist
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* Keep community gist link, remove unzip
* Fix step order: lock down VCN after Tailscale is running
* Move VCN lockdown to final step (after verifying everything works)
* docs: make Oracle/Tailscale guide safer + tone down DO copy
* docs: fix Oracle guide step numbering
* docs: tone down VPS hub Oracle blurb
* docs: add Oracle Cloud guide (#2333) (thanks @hirefrank)
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: Pocket Clawd <pocket@Pockets-Mac-mini.local>
- DeepSeek V3.1: reasoning false (not a reasoning model)
- GPT OSS 120B: reasoning true
- Remove deprecated GLM 4.6 model
- Update hints to 'Private and verifiable inference'
- Fix alias to match default model (GLM-4.7)
- Add near-ai-api-key to preferred-provider mapping
- Add NEAR AI to docs/providers/index.md and models.md
- Add note about model list changes + link to cloud.near.ai/models
Plugin commands can return buttons in channelData.telegram.buttons,
but deliverReplies() was ignoring them. Now we:
1. Extract buttons from reply.channelData?.telegram?.buttons
2. Build inline keyboard using buildInlineKeyboard()
3. Pass reply_markup to sendMessage()
Buttons are attached to the first text chunk when text is chunked.
Plugin commands were added to setMyCommands menu but didn't have
bot.command() handlers registered. This meant /flow-start and other
plugin commands would fall through to the general message handler
instead of being dispatched to the plugin command executor.
Now we register bot.command() handlers for each plugin command,
with full authorization checks and proper result delivery.
