- Implemented a new dependency pipeline for plugins to manage Python package dependencies, including conflict detection and automatic installation of missing dependencies.
- Introduced an HTML rendering service that utilizes existing browsers to render HTML content as PNG images, with support for various configurations and error handling.
- Introduced a new method `validate_plugin_config` in `PluginRuntimeManager` to validate plugin configurations at runtime.
- Implemented the `_normalize_plugin_config` method in `PluginRunner` to normalize and persist plugin configurations.
- Enhanced the `PluginRunner` to handle configuration validation requests and return normalized configurations.
- Updated the WebUI routes to utilize runtime validation for plugin configurations, ensuring that configurations are validated and normalized before saving.
- Added tests for runtime configuration validation and normalization processes, including handling of invalid configurations.
- Added a new `tooling` module to define a unified model for tool declarations, invocations, and execution results, facilitating compatibility between plugins, legacy actions, and MCP tools.
- Implemented `ToolProvider` interface for various tool providers including built-in tools, MCP tools, and plugin runtime tools.
- Enhanced `MCPManager` and `MCPConnection` to support unified tool invocation and execution results.
- Updated `ComponentRegistry` and related classes to accommodate the new tool specifications and descriptions.
- Refactored existing components to utilize the new tooling system, ensuring backward compatibility with legacy actions.
- Improved error handling and logging for tool invocations across different providers.
- Refactored model fetching logic to support various authentication methods for OpenAI-compatible APIs.
- Introduced new data models for LLM service requests and responses to standardize interactions across layers.
- Added an adapter base class for unified request execution across different providers.
- Implemented utility functions for building OpenAI-compatible client configurations and request overrides.
- Updated the PluginMeta class to utilize a strongly typed PluginManifest, improving type safety and clarity.
- Refactored dependency extraction logic to streamline the handling of plugin dependencies.
- Modified the PluginLoader to accommodate new manifest versioning and validation processes.
- Enhanced the PluginRunner to work with a dictionary for external available plugins, allowing for version mapping.
- Updated built-in plugins' manifest files to version 2, adding URLs and SDK versioning for better integration and documentation.
- Improved error handling and logging for plugin loading and dependency resolution processes.
- Updated `calculate_session_id` method in `SessionUtils` to include optional `account_id` and `scope` parameters for more granular session ID generation.
- Added new environment variables in `plugin_runtime` for external plugin dependencies and global configuration snapshots.
- Introduced methods in `RuntimeComponentManagerProtocol` for loading and reloading plugins globally, accommodating external dependencies.
- Enhanced `PluginRunnerSupervisor` to manage external available plugin IDs during plugin reloads.
- Implemented dependency extraction and management in `PluginRuntimeManager` to handle cross-supervisor dependencies.
- Added tests for session ID calculation and message registration in `ChatManager` to ensure correct behavior with new parameters.
- Add test for fallback to bot account in platform IO route metadata when context message is absent.
- Improve PlatformIOManager to avoid duplicate driver entries and streamline fallback driver handling.
- Refactor OutboundTracker to support tracking by both internal message ID and driver ID, enhancing the uniqueness of pending records.
- Introduce dynamic API capabilities in RuntimeComponent, allowing plugins to replace their dynamic API lists.
- Update APIRegistry to manage dynamic APIs more effectively, including registration and toggling of API statuses.
- Implement authorization checks for dynamic API capabilities to ensure proper permissions.
- Restrict direct calls to certain host RPC methods from plugins for enhanced security.
- Refactor send_service to ensure fallback to current platform account when no context message is available.
- Added APIEntry and APIRegistry classes for managing plugin APIs.
- Updated PluginRunnerSupervisor to include API registry and methods for invoking APIs.
- Enhanced PluginRuntimeManager to support API registration and invocation.
- Created tests for API registration, invocation, and visibility between plugins.
- Refactored component handling to distinguish between runtime components and APIs.
- Removed unused core action mirror functionality from PluginRunnerSupervisor.
- Simplified action and command execution logic in send_service.py.
- Introduced ComponentQueryService for unified component querying in plugin runtime.
- Enhanced message component handling with new binary component support.
- Improved message sequence construction and detection of outbound message flags.
- Updated methods for sending messages to streamline the process and improve readability.
- Added module_name parameter to PluginMeta for better module tracking.
- Improved documentation for PluginMeta and PluginLoader methods.
- Introduced methods for managing loaded plugins: set_loaded_plugin, remove_loaded_plugin, and purge_plugin_modules.
- Enhanced dependency resolution in PluginLoader with resolve_dependencies method.
- Implemented candidate discovery and loading in PluginLoader.
- Added support for plugin reloading with _reload_plugin_by_id in PluginRunner.
- Improved error handling and logging throughout the RPCClient and PluginRunner.
- Added support for handling hook invocations in PluginRunner.
- Refactored plugin registration and unregistration processes for clarity and efficiency.
