- Detect x-openclaw flag in OpenAPI schema and init dedicated config
- Implement multimodal input/output (image download, compress, base64)
- Add OpenClaw connection test and status validation in tool service
- Fix auth_config token check to support both api_key and bearer_token
- Inject runtime context (user_id, conversation_id, files) in chat services
1. Handling the storage of multimodal messages and adapting to the loading of historical messages for multi-round conversations;
2. Obtain the interface for retrieving the voice status of the reply;
3. File Information Retrieval Interface
1. Historical multimodal message writing is incorporated into the conversation context;
2. Resolve the issues where csv, json, and txt files cannot be recognized due to encoding problems;
3. File quantity limit;
4. Error details
1.Voice output is generated in a streaming manner.
2.Multimodal file storage type repair;
3.Adding features to the configuration of the sub-agents in the multi-agent system
1. Increase support for visual models and multimodal models;
2. The application and workflow can input various multimodal files such as images, documents, audio, and videos.
1. Add the "Skills" module;
2. The loading of the model square has been modified to be controlled through environment variables;
3. Dynamic scheduling of the skill binding tool;
4. Agent Integration Skills
* [fix]Fix the interface for statistics of recent activities and applications
* [changes]Modify the code based on the AI review
1.Use the boolean auxiliary methods provided by SQLAlchemy instead of using == True in the is_active filter.
2.The calculation of the "PROJECT_ROOT" has now been hardcoded with five levels of nested os.path.dirname calls.
* [fix]Fix the interface for statistics of recent activities and applications
* [changes]Modify the code based on the AI review
1.Use the boolean auxiliary methods provided by SQLAlchemy instead of using == True in the is_active filter.
2.The calculation of the "PROJECT_ROOT" has now been hardcoded with five levels of nested os.path.dirname calls.
* refactor(celery): optimize task routing and worker configuration
- Simplify Celery queue configuration with single default 'io_tasks' queue
- Implement task routing strategy separating IO-bound and CPU-bound tasks
- Add Flower monitoring support with task event tracking enabled
- Add summary node search optimization to only retrieve summary nodes
- Clean up unused imports and reorganize import statements for consistency
- Update docker-compose configuration to support multi-queue worker setup
* chore(celery): simplify flower configuration and add gevent dependency
* chore(dependencies): add gevent dependency to requirements
- Add gevent==24.11.1 to api/requirements.txt
- Gevent is required for async worker support in Celery
- Complements existing flower and celery configuration
* refactor(celery): simplify async event loop handling and reorganize task queues
- Replace complex nest_asyncio and manual event loop management with asyncio.run() in read_message_task, write_message_task, regenerate_memory_cache, and workspace_reflection_task
- Rename task queues from io_tasks/cpu_tasks to memory_tasks/document_tasks for better semantic clarity
- Update task routing configuration to reflect new queue names for memory agent tasks and document processing tasks
- Remove redundant exception handling comments and simplify error handling logic
- Update README with improved community support section including GitHub Issues, Pull Requests, Discussions, and WeChat community links
- Simplifies event loop management by leveraging asyncio.run() which handles loop creation and cleanup automatically, reducing code complexity and potential race conditions
