mcp-server-mas-sequential-thinking
This project involves a Multi-Agent System (MAS) implemented in Python using the Agno framework, designed for advanced problem-solving through sequential thinking. It replaces simpler state-tracking approaches by utilizing a coordinated team of specialized agents, thereby enhancing the depth and quality of analysis.
What is the main advantage of using a Multi-Agent System?
The main advantage is the ability to perform complex problem-solving by leveraging specialized agents that can handle specific sub-tasks, leading to a more nuanced and in-depth analysis.
How does the system ensure data integrity?
The system uses Pydantic validation to ensure that all thought steps and data inputs conform to predefined schemas, maintaining data integrity throughout the process.
What are the prerequisites for running this server?
You need Python 3.10+, access to a compatible LLM API, and optionally an Exa API Key if using the Researcher agent's capabilities.
How does the system handle revisions and branching?
The system supports complex thought patterns, including revisions of previous steps and branching to explore alternative paths, managed by the Coordinating Agent.
What is the impact of using this system on token consumption?
Due to the Multi-Agent System architecture, this tool consumes significantly more tokens than single-agent alternatives, as it involves multiple agents processing each thought step.