- Changed response type to `RunOutput` in multimodal design and coding agent teams for improved type safety.
- Updated the `agno` package version in requirements.txt to ensure compatibility across all agents.
- Changed response type to `RunOutput` in the AI real estate and recruitment agent teams for improved type safety.
- Updated the `agno` package version in requirements.txt to ensure compatibility across all agents.
- Adjusted requirements for other dependencies to maintain consistency.
- Refactored legal agent team to utilize the new `Knowledge` class instead of `PDFKnowledgeBase`.
- Updated response type to `RunOutput` for better type safety across multiple functions.
- Changed agent model version from GPT-4.1 to GPT-5 for enhanced performance.
- Specified minimum version for the 'agno' package in requirements.txt for compatibility.
- Integrated ExaTools for competitor URL retrieval in the AI competitor intelligence agent.
- Updated response handling to utilize `RunOutput` for improved type safety.
- Specified minimum version for the 'agno' package in requirements.txt for compatibility across agents.
- Changed response type to `RunOutput` in various AI agents for improved type safety.
- Updated the `agno` package version in requirements.txt to ensure compatibility across all agents.
- Changed response type to `RunOutput` in both the 3D pygame and Tic Tac Toe agents for improved type safety.
- Updated the `agno` package version in requirements.txt to ensure compatibility.
- Introduced a new multi-agent system for analyzing landing page designs and providing expert UI/UX feedback.
- Implemented a Coordinator/Dispatcher pattern with specialized agents for visual analysis, design strategy, and visual implementation.
- Added tools for editing and generating improved landing pages, along with comprehensive reporting features.
- Introduced a multi-agent system for home renovation planning that analyzes photos and generates personalized renovation plans.
- Implemented a Coordinator/Dispatcher pattern with specialized agents for visual assessment, design planning, and project coordination.
- Added tools for generating and editing photorealistic renderings, along with budget-aware planning features.
- Updated the email generation workflow to yield progress updates during company processing.
- Refactored the database integration to use SqliteDb for better management.
- Enhanced the user interface with a more structured display of generated emails, company research, and contact information.
- Added troubleshooting tips in case of no email generation.
- Updated README with detailed features, installation instructions, and usage guide.
- Increased agno package version requirement to 2.0.4 for compatibility.
- Introduced a new AI Real Estate Agent Team with multi-agent capabilities for property search, market analysis, and valuation.
- Added comprehensive README documentation detailing features, setup instructions, and API requirements.
- Implemented the Enterprise MCP AI Agent Team for orchestrating knowledge management across local files and SaaS platforms, including specialized agents for Notion, GitHub, and Figma.
- Enhanced the overall architecture with intelligent routing and task delegation using the Model Context Protocol (MCP).
- Included necessary requirements for both teams to ensure proper functionality.
