- Implement SAAsWorkers class in a new file
src/workers.py
- Add asynchronous execution capabilities for parallel processing
- Integrate SAAsWorkers with the existing Orchestrator class
- Improve the
_generate_main_prompt
method insrc/orchestrator.py
for better task breakdown - Implement logic for the MAIN_ASSISTANT to handle tasks without subtask decomposition
- Enhance prompt writing capabilities for SUB_ASSISTANT tasks
- Develop a parallel research system in
/src/plugins/comparative_analysis.py
- Refine the create_assistant function to accept adding new necessary tools for web scraping and data processing
- Write the prompt system that will instruct the MAIN_ASSISTANT to run a deep research objective
- Extend the CLI in
src/main.py
to support new SAAsWorkers functionality - Implement a plugin system using
pluggy
for extending use cases - Create commands for custom prompts and plugin selection
- Update existing tests in
tests/test_orchestrator.py
for new functionality - Add new tests for SAAsWorkers in
tests/test_workers.py
- Create tests for the plugin system and CLI in
tests/test_plugins_and_custom_commands.py
andtests/test_main.py
- Enhance error handling and logging throughout the project
- Update the README.md with new features and usage instructions
- Create example scripts for using plugins and custom prompts
- Document the SAAsWorkers implementation and integration
- Add documentation for the plugin system and how to create new plugins
- Optimize parallel execution in SAAsWorkers
- Conduct end-to-end testing of the entire system
- Address any remaining bugs or issues
- Prepare for deployment (if applicable)
- Implement additional use cases as plugins (e.g., content creation, autocomplete)
- Enhance the configuration management system
- Implement a full-featured FastAPI-based API
- Develop strategies for handling larger workloads and scaling
- Integrate additional Phidata tools and features
- Implement long-term memory and knowledge base systems
- Create a more advanced UI with data visualization capabilities
- Implement a caching system for frequently used prompts or responses
- Add support for more LLM providers
- Develop a system for chaining multiple plugins or use cases
- Implement a feedback loop for continuous improvement of assistant responses