Sequential Thinking Multi-Agent System (MAS)
by MCP-Mirror/FradSer
This project implements an advanced sequential thinking process using a Multi-Agent System (MAS) built with the Agno framework and served via MCP. It leverages coordinated specialized agents for deeper analysis and problem decomposition.
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What is Sequential Thinking Multi-Agent System (MAS)?
This server provides a sophisticated sequentialthinking tool designed for complex problem-solving. It utilizes a Multi-Agent System (MAS) architecture with a Coordinating Agent and Specialized Agents (Planner, Researcher, Analyzer, Critic, Synthesizer) to actively process, analyze, and synthesize incoming thoughts.
How to use Sequential Thinking Multi-Agent System (MAS)?
An external LLM initiates the process with a sequential-thinking-starter prompt. The LLM then calls the sequentialthinking tool iteratively, providing thoughts structured according to the ThoughtData model. The MAS processes the thought, and the Coordinator synthesizes a response with guidance for the next step. The LLM formulates the next thought based on this guidance, potentially triggering revisions or branches.
Key features of Sequential Thinking Multi-Agent System (MAS)
Multi-Agent System (MAS) architecture
Coordinating Agent for workflow management
Specialized Agents for specific sub-tasks
Active processing, analysis, and synthesis of thoughts
Support for complex thought patterns (revisions, branching)
Integration with external tools (e.g., Exa via Researcher)
Pydantic validation for data integrity
Detailed logging of agent interactions
Use cases of Sequential Thinking Multi-Agent System (MAS)
Complex problem-solving
In-depth analysis
Nuanced thinking processes
Dynamic information gathering
AI-assisted reasoning
Automated research
FAQ from Sequential Thinking Multi-Agent System (MAS)
What is the main difference between this version and the original TypeScript version?
What is the main difference between this version and the original TypeScript version?
This version utilizes a Multi-Agent System (MAS) architecture with active processing by a team of agents, while the original was a single-class state tracker with simple logging.
How does the Coordinator Agent work?
How does the Coordinator Agent work?
The Coordinator Agent (the Team object in coordinate mode) manages the workflow, analyzes incoming thoughts, breaks them into sub-tasks, and delegates these sub-tasks to the most relevant specialist agents.
Which LLM providers are supported?
Which LLM providers are supported?
The system supports Groq, DeepSeek, and OpenRouter. You need to configure the desired provider using the LLM_PROVIDER environment variable and provide the corresponding API key.
Why does this tool consume more tokens?
Why does this tool consume more tokens?
Due to the Multi-Agent System architecture, each sequentialthinking call invokes the Coordinator agent and multiple specialist agents, leading to significantly higher token usage compared to single-agent alternatives.
What is the purpose of the Researcher agent?
What is the purpose of the Researcher agent?
The Researcher agent is responsible for dynamic information gathering, and it can integrate with external tools like Exa to perform research tasks.