MCP consolidation:
- Route all MCP-capable connectors (Slack, Jira, Linear, ClickUp, Airtable,
Notion, Confluence, interim Gmail/Calendar, custom MCP) through a single
`mcp_discovery` subagent. Drive/OneDrive/Dropbox stay native to enrich the KB.
- Deprecate Discord/Teams/Luma: no viable official MCP server.
Google-only web search:
- Remove the main-agent `web_search` tool and the SearXNG platform service;
all public web search now flows through the `google_search` subagent via task().
- Deprecate the Tavily/SearXNG/Linkup/Baidu search connectors (HTTP 410 on
create, "Deprecated" badge); guide heavy users to the custom MCP connector.
- Remove web search from anonymous chat (pure Q&A).
- Tear SearXNG out of docker compose + install scripts; drop tavily-python
and linkup-sdk deps and their config/env vars.
Fix:
- metrics._package_version() now swallows any metadata lookup failure. A
malformed editable-install distribution with no `Version` field raised
KeyError deep in importlib.metadata, and since it runs on every
record_subagent_invoke_duration call it was crashing every task()
delegation. Verified end-to-end against live GPT-5.4.
Co-authored-by: Cursor <cursoragent@cursor.com>
- Introduced a new endpoint to check the existence of a global LLM configuration file.
- Updated the frontend to utilize this status, affecting onboarding flow and user experience.
- Added necessary atoms and types for managing global LLM config status in the application state.
- Refactored navigation to ensure proper routing based on the global config status.
- Updated database queries to check for column existence with schema context.
- Modified credit purchase quantity limits to allow up to 10,000 credits.
- Improved user interface for credit purchases, enabling custom amounts and clamping input values.
- Adjusted FAQ content to clarify credit purchasing process.
- Updated environment variables and - configurations for credit purchases via Stripe, replacing legacy page pack system.
- Introduced auto-reload feature for credit top-ups and modified database models to track credit transactions.
- Updated notification system to handle insufficient credits and auto-reload failures.
- Adjusted API routes and schemas to reflect changes in credit management.
- Add MiniMax-M3 to the model selection list (set as the new default)
- Add MiniMax-M2.7 and MiniMax-M2.7-highspeed as alternatives
- Remove deprecated MiniMax-M2.5 / M2.5-highspeed entries
- Update example config and Chinese setup docs to reference M3 (512K context)
- Added image support to the AnnouncementCard component for improved visual presentation of announcements.
- Introduced a spotlight feature in the announcement types to allow critical announcements to be displayed in a blocking dialog until acknowledged.
- Updated AnnouncementToastProvider to skip spotlight announcements to prevent duplicate notifications.
- Included a new AI automation announcement with an image in the announcements data for demonstration purposes.
- Improved the layout and readability of the run details panel by restructuring sections and adding collapsible error views.
- Introduced a new `RunErrorSection` component to present run-level errors more clearly, allowing users to toggle raw error details.
- Updated the handling of run outputs, step results, and artifacts for better user experience.
- Refactored duration calculation in `RunRow` to utilize a dedicated `formatDuration` function for consistency.
- Added a new `RunStepResult` interface to improve type safety and clarity in handling step results.
- Added model eligibility checks to ensure automations can only use billable models (premium or BYOK).
- Introduced new API endpoint to report model eligibility status for search spaces.
- Updated frontend components to display eligibility alerts and disable creation options when models are not billable.
- Enhanced automation creation forms to reflect model eligibility, preventing users from submitting invalid configurations.
- Implemented server-side logic to capture and preserve model preferences across automation edits, ensuring consistent behavior during execution.
