Sequential Thinking Multi-Agent System (MAS)

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This project implements an advanced sequential thinking process using a Multi-Agent System (MAS) built with the Agno framework and served via MCP. It represents a significant evolution from simpler state-tracking approaches by leveraging coordinated, specialized agents for deeper analysis and problem decomposition.

What is This?

This is an MCP server - not a standalone application. It runs as a background service that extends your LLM client (like Claude Desktop) with sophisticated sequential thinking capabilities. The server provides a sequentialthinking tool that processes thoughts through multiple specialized AI agents, each examining the problem from a different cognitive angle.

Core Architecture: Multi-Dimensional Thinking Agents

The system employs 6 specialized thinking agents, each focused on a distinct cognitive perspective:

1. Factual Agent

• Focus: Objective facts and verified data
• Approach: Analytical, evidence-based reasoning
• Capabilities:
  • Web research for current facts (via ExaTools)
  • Data verification and source citation
  • Information gap identification
• Time allocation: 120 seconds for thorough analysis

2. Emotional Agent

• Focus: Intuition and emotional intelligence
• Approach: Gut reactions and feelings
• Capabilities:
  • Quick intuitive responses (30-second snapshots)
  • Visceral reactions without justification
  • Emotional pattern recognition
• Time allocation: 30 seconds (quick reaction mode)

3. Critical Agent

• Focus: Risk assessment and problem identification
• Approach: Logical scrutiny and devil's advocate
• Capabilities:
  • Research counterexamples and failures (via ExaTools)
  • Identify logical flaws and risks
  • Challenge assumptions constructively
• Time allocation: 120 seconds for deep analysis

4. Optimistic Agent

• Focus: Benefits, opportunities, and value
• Approach: Positive exploration with realistic grounding
• Capabilities:
  • Research success stories (via ExaTools)
  • Identify feasible opportunities
  • Explore best-case scenarios logically
• Time allocation: 120 seconds for balanced optimism

5. Creative Agent

• Focus: Innovation and alternative solutions
• Approach: Lateral thinking and idea generation
• Capabilities:
  • Cross-industry innovation research (via ExaTools)
  • Divergent thinking techniques
  • Multiple solution generation
• Time allocation: 240 seconds (creativity needs time)

6. Synthesis Agent

• Focus: Integration and metacognitive orchestration
• Approach: Holistic synthesis and final answer generation
• Capabilities:
  • Integrate all perspectives into coherent response
  • Answer the original question directly
  • Provide actionable, user-friendly insights
• Time allocation: 60 seconds for synthesis
• Note: Uses enhanced model, does NOT include ExaTools (focuses on integration)

AI-Powered Intelligent Routing

The system uses AI-driven complexity analysis to determine the optimal thinking sequence:

Processing Strategy:

• Single fixed strategy: full_exploration is mandatory for all requests
• No legacy modes: single/double/triple routing paths are removed
• Complexity analysis retained: metrics are still generated for observability

The AI analyzer still evaluates:

• Problem complexity and semantic depth
• Primary problem type (factual, emotional, creative, philosophical, etc.)
• Required thinking modes for observability and diagnostics
• Model behavior metadata (Enhanced vs Standard usage)

AI Routing Flow Diagram

flowchart TD
    A[Input Thought] --> B[AI Complexity Analyzer]
    B --> C[Complexity Metadata Stored]
    C --> D[Fixed Strategy: full_exploration]
    D --> E[Step 1: Initial Synthesis]
    E --> F[Step 2: Parallel Specialist Agents]
    F --> G[Step 3: Final Synthesis]
    G --> H[Unified Response]

Key Insights:

• Deterministic behavior: every request runs the same full multi-step path
• Parallel execution: non-synthesis agents still run simultaneously
• Synthesis integration: orchestration and final answer are both synthesis-driven

Research Capabilities (ExaTools Integration)

4 out of 6 agents are equipped with web research capabilities via ExaTools:

• Factual Agent: Search for current facts, statistics, verified data
• Critical Agent: Find counterexamples, failed cases, regulatory issues
• Optimistic Agent: Research success stories, positive case studies
• Creative Agent: Discover innovations across different industries
• Emotional & Synthesis Agents: No ExaTools (focused on internal processing)

Research is optional - requires EXA_API_KEY environment variable. The system works perfectly without it, using pure reasoning capabilities.

Model Intelligence

Dual Model Strategy:

• Enhanced Model: Used for Synthesis agent (complex integration tasks)
• Standard Model: Used for individual thinking agents
• AI Selection: System automatically chooses the right model based on task complexity

Supported Providers:

• DeepSeek (default) - High performance, cost-effective
• Groq - Ultra-fast inference
• OpenRouter - Access to multiple models
• GitHub Models - OpenAI models via GitHub API
• Anthropic - Claude models with prompt caching
• Ollama - Local model execution

Key Differences from Original Version (TypeScript)

This Python/Agno implementation marks a fundamental shift from the original TypeScript version:

| Feature/Aspect | Python/Agno Version (Current) | TypeScript Version (Original) | | :------------------ | :------------------------------------------------------------------- | :--------------------------------------------------- | | Architecture | Multi-Agent System (MAS); Active processing by a team of agents. | Single Class State Tracker; Simple logging/storing. | | Intelligence | Distributed Agent Logic; Embedded in specialized agents & Coordinator. | External LLM Only; No internal intelligence. | | Processing | Active Analysis & Synthesis; Agents act on the thought. | Passive Logging; Merely recorded the thought. | | Frameworks | Agno (MAS) + FastMCP (Server); Uses dedicated MAS library. | MCP SDK only. | | Coordination | Explicit Team Coordination Logic (Team in coordinate mode). | None; No coordination concept. | | Validation | Pydantic Schema Validation; Robust data validation. | Basic Type Checks; Less reliable. | | External Tools | Integrated (Exa via Researcher); Can perform research tasks. | None. | | Logging | Structured Python Logging (File + Console); Configurable. | Console Logging with Chalk; Basic. | | Language & Ecosystem | Python; Leverages Python AI/ML ecosystem. | TypeScript/Node.js. |

In essence, the system evolved from a passive thought recorder to an active thought processor powered by a collaborative team of AI agents.

How it Works (Multi-Dimensional Processing)

1. Initiation: An external LLM uses the sequentialthinking tool to define the problem and initiate the process.
2. Tool Call: The LLM calls the sequentialthinking tool with the current thought, structured according to the ThoughtData model.
3. AI Complexity Analysis: The system still performs AI-powered analysis to capture complexity metadata and diagnostic signals.
4. Fixed Strategy Execution: The system always runs the mandatory full_exploration multi-step sequence.
5. Parallel Processing: Multiple thinking agents process the thought simultaneously from their specialized perspectives:

• Factual agents gather objective data (with optional web research)
• Critical agents identify risks and problems
• Optimistic agents explore opportunities and benefits
• Creative agents generate innovative solutions
• Emotional agents provide intuitive insights

6. Research Integration: Agents equipped with ExaTools conduct targeted web research to enhance their analysis.
7. Synthesis & Integration: The Synthesis agent integrates all perspectives into a coherent, actionable response using enhanced models.
8. Response Generation: The system returns a comprehensive analysis with guidance for next steps.
9. Iteration: The calling LLM uses the synthesized response to formulate the next thinking step or conclude the process.

Token Consumption Warning

High Token Usage: Due to the Multi-Agent System architecture, this tool consumes significantly more tokens than single-agent alternatives or the previous TypeScript version. Each sequentialthinking call invokes multiple specialized agents simultaneously, leading to substant