- Implemented proactive cron job tools in InternalAgentSubStage for scheduling tasks.
- Created SendMessageToUserTool for sending messages to users based on cron job triggers.
- Added CreateActiveCronTool, DeleteCronJobTool, and ListCronJobsTool for cron job management.
- Introduced CronRoute for handling cron job API requests in the dashboard.
- Developed CronJobPage.vue for managing cron jobs in the dashboard UI.
- Updated SubAgentPage.vue to include persona selection for subagents.
* feat: support anthropic skills
closes: #4687
* chore: ruff
* feat: implement skills management and selection in persona configuration
* feat: enhance skills management with local environment tools and permissions
* feat: context compressor
Co-authored-by: kawayiYokami <289104862@qq.com>
* Add comprehensive tests for ContextManager and ContextTruncator
- Implemented a full test suite for ContextManager covering initialization, message processing, token-based compression, and error handling.
- Added tests for ContextTruncator focusing on message fixing, truncation by turns, dropping oldest turns, and halving.
- Ensured that both test suites validate edge cases and maintain expected behavior with various message types, including system and tool messages.
* feat: add MockProvider for LLM compression tests
* chore: remove lock
* ruff fix
* fix
* perf
* feat: enhance context compression with token tracking and logging
* feat: update logging for context compression trigger
* feat: implement context compression logic with dynamic threshold and token tracking
* fix: reorder import statements for consistency
* feat: add token_usage tracking to conversations and update related processing logic
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Co-authored-by: kawayiYokami <289104862@qq.com>
* perf: support extended thinking for Anthropic, DeepSeek reasoning mode, and Gemini text part thought signatures to improve multi-turn reasoning performance.
* chore: remove verbose
* perf
* refactor: remove special tools handling for deepseek-reasoner model in openai source
* fix: improve error handling and logging in InternalAgentSubStage processing
* refactor: remove unused reasoning content from Gemini source processing
* refactor: enhance modality determination logic in useProviderSources
Co-authored-by: kawayiYokami <289104862@qq.com>
* fix: improve error handling in tool execution to prevent infinite tool call loops
- Enhanced error handling in `call_local_llm_tool` to provide more informative exceptions for ValueError and TypeError, including detailed parameter information.
- Updated `ToolLoopAgentRunner` to yield appropriate messages for cases with no response or unsupported types, ensuring clearer communication to users.
- Improved logging and messaging consistency across tool execution processes.
* refactor: clean up unused router parameter in message retrieval functions
- Removed the unused `router` parameter from `getSessionMessages` and related function calls in `Chat.vue` and `useMessages.ts`.
- Commented out the `tool_calls` dictionary in `chat.py` for clarity, indicating it is not currently in use.
* fix: enhance exception handling in tool execution for clearer error reporting
- Improved exception handling in `call_local_llm_tool` by chaining exceptions for ValueError and TypeError, providing more context in error messages.
- Ensured that traceback information is preserved in raised exceptions for better debugging.
* perf(agent): add max step limit to prevent infinite tool call loops
* feat: implement max step limit handling in main agent runner
- Enhanced the agent runner to enforce a maximum step limit, logging a warning and forcing a final response when the limit is reached.
- Updated message handling to append a user prompt when the tool call limit is exceeded.
- Refactored tool response handling to yield appropriate messages based on the response type, including handling cases with no response or unsupported types.
- Improved conversation message formatting to ensure consistent output in the assistant's responses.
* chore: ruff format
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Co-authored-by: Soulter <905617992@qq.com>
* feat: enhance tool call handling and UI integration for tool calls render
- Added support for tool call messages in the agent runner and webchat event handling.
- Implemented JSON message component for structured tool call data.
- Updated chat route to save tool call information in message history.
- Enhanced frontend to display tool call details in a collapsible format, including status and results.
- Introduced elapsed time tracking for ongoing tool calls in the chat interface.
* fix: improve message handling in agent run utility and tool loop runner
- Refactored message sending logic in `astr_agent_run_util.py` to use `msg_chain` directly for better clarity.
- Added a check in `tool_loop_agent_runner.py` to ensure `tool_call_result_blocks` is not empty before yielding the last tool call result, preventing potential errors.
* refactor: enhance message structure and UI for chat components
- Updated message handling in `MessageList.vue` to support structured message parts, including plain text, images, audio, and files.
- Improved the `Chat.vue` component styles for better visual consistency.
- Refactored message parsing logic in `useMessages.ts` to accommodate new message formats and ensure proper rendering of embedded content.
- Removed deprecated tool call handling from the message structure, streamlining the message display process.
* chore: ruff format
* feat: implement agent statistics tracking and display in chat
- Added `AgentStats` and `TokenUsage` data classes to track agent performance metrics.
- Enhanced `ToolLoopAgentRunner` to collect and update agent statistics during execution.
- Integrated agent statistics sending to webchat for real-time updates.
- Updated chat route to save and display agent statistics in message history.
- Improved frontend components to visualize agent statistics, including token usage and duration metrics.
* fix: improve message handling in Telegram event and agent run utility
- Updated message sending logic in `astr_agent_run_util.py` to send the correct message chain for tool calls.
- Enhanced `tg_event.py` to edit messages during streaming breaks, improving message management and user experience.
- Added error handling for message editing failures to ensure robustness.
* chore: ruff format
* fix: validation error for ToolCall.extra_content in specific upstream model providers
* fix: handle missing extra_content gracefully in ToolCall serialization
* fix: omit content field for the LLM request after tool calls are completed and content is empy string or none
* chore: ruff format
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Co-authored-by: Soulter <905617992@qq.com>
* fix: assistant message validation error when tool_call exists but content not exists
* fix: enhance content validation in Message model to allow None for assistant role with tool_calls
- Implemented CozeAPIClient for file upload, image download, chat messaging, and context management.
- Developed DashscopeAgentRunner for handling requests to the Dashscope API with streaming support.
- Created DifyAgentRunner to manage interactions with the Dify API, including file uploads and workflow execution.
- Introduced DifyAPIClient for making asynchronous requests to the Dify API.
- Updated third-party agent imports to reflect new module structure.
- Added `ThirdPartyAgentSubStage` to handle interactions with third-party agent runners (Dify, Coze, Dashscope).
- Refactored `star_request.py` to ensure consistent return types in the `process` method.
- Updated `stage.py` to initialize and utilize the new `AgentRequestSubStage`.
- Modified `ProviderManager` to skip loading agent runner providers.
- Removed `Dify` source implementation as it is now handled by the new agent runner structure.
- Enhanced `DifyAPIClient` to support file uploads via both file path and file data.
- Cleaned up shared preferences handling to simplify session preference retrieval.
- Updated dashboard configuration to reflect changes in agent runner provider selection.
- Refactored conversation commands to accommodate the new agent runner structure and remove direct dependencies on Dify.
- Adjusted main application logic to ensure compatibility with the new conversation management approach.
* refactor: LLM response handling with reasoning content
- Added a `show_reasoning` parameter to `run_agent` to control the display of reasoning content.
- Updated `LLMResponse` to include a `reasoning_content` field for storing reasoning text.
- Modified `WebChatMessageEvent` to handle and send reasoning content in streaming responses.
- Implemented reasoning extraction in various provider sources (e.g., OpenAI, Gemini).
- Updated the chat interface to display reasoning content in a collapsible format.
- Removed the deprecated `thinking_filter` package and its associated logic.
- Updated localization files to include new reasoning-related strings.
* feat: add Groq chat completion provider and associated configurations
* Update astrbot/core/provider/sources/gemini_source.py
Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>
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Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>
