Single tool exposed to the main agent. The main agent passes a natural-language
`intent`; a focused drafter sub-LLM turns it into a full AutomationCreate JSON;
that JSON is surfaced via request_approval (action_type "automation_create") so
the user can edit/approve it on a frontend card; on approval the tool persists
via AutomationService. Three phases, one tool call.
Scope split:
- main agent sees only `intent: str` (no schema knowledge leaks into the calling
graph) — prompt fragments scoped accordingly.
- drafter sub-LLM owns the schema + few-shot intent→JSON examples — lives in
the generating graph's prompt (tools/automation/prompt.py).
Files:
- main_agent/tools/automation/{create.py, prompt.py, __init__.py}: new tool
+ drafter system prompt with two few-shot intent→JSON examples.
- system_prompt/prompts/tools/create_automation/{description.md, example.md}:
intent-only guidance for the main agent.
- main_agent/tools/index.py: add create_automation to the main-agent allowlist.
- new_chat/tools/registry.py: deferred-import factory to break the
multi_agent_chat ↔ registry cycle; one ToolDefinition entry.
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- Added a new function `_warm_agent_jit_caches` to pre-warm agent caches at startup, reducing cold invocation costs.
- Updated the `SurfSenseContextSchema` to include per-invocation fields for better state management during agent execution.
- Introduced caching mechanisms in various tools to ensure fresh database sessions are used, improving performance and reliability.
- Enhanced middleware to support new context features and improve error handling during connector and document type discovery.
- Introduced a new tool to update existing Gmail drafts, allowing users to modify draft content, recipients, and subject lines.
- Updated the Gmail tools registry to include the new update_gmail_draft tool.
- Enhanced the GmailKBSyncService to support draft ID handling during synchronization.
- Added UI components for the update draft functionality in the web application, improving user interaction with Gmail drafts.
- modify the `web_search` tool that integrates multiple live search connectors (Tavily, Linkup, Baidu) alongside the SearXNG platform for real-time information retrieval.
- Updated comments to reflect the separation of local knowledge base searches from web searches.
- Improved handling of connector types and search logic to ensure accurate routing of queries to the appropriate tools.
- Refactored existing code to streamline the search process and enhance performance.
- Deleted the migration script for adding web search columns to the database.
- Removed web search related fields from the SearchSpace model and schemas.
- Eliminated web search health check endpoint and associated service logic.
- Updated frontend components to remove web search settings management.
- Cleaned up references to SearXNG API in various modules, transitioning to a platform service approach.
- Added endpoint to list agent tools with metadata, excluding hidden tools.
- Updated NewChatRequest and RegenerateRequest schemas to include disabled tools.
- Integrated disabled tools management in the NewChatPage and Composer components.
- Improved tool instructions and visibility in the system prompt.
- Refactored tool registration to support hidden tools and default enabled states.
- Enhanced document chunk creation to handle strict zip behavior.
- Cleaned up imports and formatting across various files for consistency.
- Introduced a mechanism to identify degenerate queries that lack meaningful search signals, improving search accuracy.
- Implemented a fallback method for browsing recent documents when queries are degenerate, ensuring relevant results are returned.
- Added limits on the number of chunks fetched per document to optimize performance and prevent excessive data loading.
- Updated the ConnectorService to allow for reusable query embeddings, enhancing efficiency in search operations.
- Enhanced LLM router service to support context window fallbacks, improving robustness during context window limitations.
- Introduced dynamic character budget calculation for document formatting based on model's context window.
- Updated `format_documents_for_context` to respect character limits and improve output quality.
- Added `max_input_tokens` parameter to various functions to facilitate context-aware processing.
- Enhanced error handling for context overflow in LLM router service.
- Introduced slow callback logging in FastAPI to identify blocking calls.
- Added performance logging for agent creation and tool loading processes.
- Implemented caching for MCP tools to reduce redundant server calls.
- Enhanced sandbox management with in-process caching for improved efficiency.
- Refactored several functions for better readability and performance tracking.
- Updated tests to ensure proper functionality of new features and optimizations.
