Sequential Thinking Multi-Agent System (MAS)
by 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, utilizing 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)?
The server runs as a standard executable script communicating via stdio, as expected by MCP. An external LLM uses the sequential-thinking-starter prompt to initiate the process and then calls the sequentialthinking tool iteratively with structured thoughts. The MAS processes the thought, and the coordinator synthesizes a response with guidance for the next step.
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 revisions and branching
Integration with external tools like Exa
Pydantic validation for data integrity
Detailed logging of agent interactions
Use cases of Sequential Thinking Multi-Agent System (MAS)
Complex problem-solving
Advanced data analysis
Nuanced thinking processes
Research and information gathering
AI-driven decision making
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 Python/Agno implementation uses a Multi-Agent System (MAS) architecture, while the original was a single-class state tracker.
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 input thoughts, breaks them into sub-tasks, and delegates these sub-tasks to the most relevant specialist agents.
What are the prerequisites for running this server?
What are the prerequisites for running this server?
You need Python 3.10+, access to a compatible LLM API (Groq, DeepSeek, or OpenRouter), and optionally an Exa API key if using the Researcher agent.
Why is token consumption higher in this version?
Why is token consumption higher in this version?
Due to the Multi-Agent System architecture, each sequentialthinking call invokes the Coordinator agent and multiple specialist agents, leading to substantially higher token usage compared to single-agent approaches.
How do I install the dependencies?
How do I install the dependencies?
You can use uv pip install -r requirements.txt (recommended) or pip install -r requirements.txt after cloning the repository and setting up your environment variables.