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.

Overview

This server provides a sophisticated sequentialthinking tool designed for complex problem-solving. Unlike its predecessor, this version utilizes a true Multi-Agent System (MAS) architecture where:

• A Coordinating Agent (the Team object in coordinate mode) manages the workflow.
• Specialized Agents (Planner, Researcher, Analyzer, Critic, Synthesizer) handle specific sub-tasks based on their defined roles and expertise.
• Incoming thoughts are actively processed, analyzed, and synthesized by the agent team, not just logged.
• The system supports complex thought patterns, including revisions of previous steps and branching to explore alternative paths.
• Integration with external tools like Exa (via the Researcher agent) allows for dynamic information gathering.
• Robust Pydantic validation ensures data integrity for thought steps.
• Detailed logging tracks the process, including agent interactions (handled by the coordinator).

The goal is to achieve a higher quality of analysis and a more nuanced thinking process than possible with a single agent or simple state tracking by harnessing the power of specialized roles working collaboratively.

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 (Coordinate Mode)

1. Initiation: An external LLM uses the sequential-thinking-starter prompt to define the problem and initiate the process.
2. Tool Call: The LLM calls the sequentialthinking tool with the first (or subsequent) thought, structured according to the ThoughtData Pydantic model.
3. Validation & Logging: The tool receives the call, validates the input using Pydantic, logs the incoming thought, and updates the history/branch state via AppContext.
4. Coordinator Invocation: The core thought content (along with context about revisions/branches) is passed to the SequentialThinkingTeam's arun method.
5. Coordinator Analysis & Delegation: The Team (acting as Coordinator) analyzes the input thought, breaks it down into sub-tasks, and delegates these sub-tasks to the most relevant specialist agents (e.g., Analyzer for analysis tasks, Researcher for information needs).
6. Specialist Execution: Delegated agents execute their specific sub-tasks using their instructions, models, and tools (like ThinkingTools or ExaTools).
7. Response Collection: Specialists return their results to the Coordinator.
8. Synthesis & Guidance: The Coordinator synthesizes the specialists' responses into a single, cohesive output. This output may include recommendations for revision or branching based on the specialists' findings (especially from the Critic and Analyzer). It also provides guidance for the LLM on formulating the next thought.
9. Return Value: The tool returns a JSON string containing the Coordinator's synthesized response, status, and updated context (branches, history length).
10. Iteration: The calling LLM uses the Coordinator's response and guidance to formulate the next sequentialthinking tool call, potentially triggering revisions or branches as suggested.

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:

• The Coordinator agent (the Team itself).
• Multiple specialist agents (potentially Planner, Researcher, Analyzer, Critic, Synthesizer, depending on the Coordinator's delegation).

This parallel processing leads to substantially higher token usage (potentially 3-6x or more per thought step) compared to single-agent or state-tracking approaches. Budget and plan accordingly. This tool prioritizes analysis depth and quality over token efficiency.

Prerequisites

• Python 3.10+
• Access to a compatible LLM API (configured for agno). The system currently supports:
  • Groq: Requires GROQ_API_KEY.
  • DeepSeek: Requires DEEPSEEK_API_KEY.
  • OpenRouter: Requires OPENROUTER_API_KEY.
  • Configure the desired provider using the LLM_PROVIDER environment variable (defaults to deepseek).
• Exa API Key (required only if using the Researcher agent's capabilities)
  • Set via the EXA_API_KEY environment variable.
• uv package manager (recommended) or pip.

MCP Server Configuration (Client-Side)

This server runs as a standard executable script that communicates via stdio, as expected by MCP. The exact configuration method depends on your specific MCP client implementation. Consult your client's documentation for details on integrating external tool servers.

The env section within your MCP client configuration should include the API key for your chosen LLM_PROVIDER.

{
  "mcpServers": {
      "mas-sequential-thinking": {
         "command": "uvx", // Or "python", "path/to/venv/bin/python" etc.
         "args": [
            "mcp-server-mas-sequential-thinking" // Or the path to your main script, e.g., "main.py"
         ],
         "env": {
            "LLM_PROVIDER": "deepseek", // Or "groq", "openrouter"
            // "GROQ_API_KEY": "your_groq_api_key", // Only if LLM_PROVIDER="groq"
            "DEEPSEEK_API_KEY": "your_deepseek_api_key", // Default provider
            // "OPENROUTER_API_KEY": "your_openrouter_api_key", // Only if LLM_PROVIDER="openrouter"
            "DEEPSEEK_BASE_URL": "your_base_url_if_needed", // Optional: If using a custom endpoint for DeepSeek
            "EXA_API_KEY": "your_exa_api_key" // Only if using Exa
         }
      }
   }
}

Installation & Setup

Installing via Smithery

To install Sequential Thinking Multi-Agent System for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @FradSer/mcp-server-mas-sequential-thinking --client claude

Manual Installation

1. Clone the repository:

   git clone [email protected]:FradSer/mcp-server-mas-sequential-thinking.git
   cd mcp-server-mas-sequential-thinking
   

2. Set Environment Variables: Create a .env file in the project root directory or export the variables directly into your environment:

   # --- LLM Configuration ---
   # Select the LLM provider: "deepseek" (default), "groq", or "openrouter"
   LLM_PROVIDER="deepseek"
   
   # Provide the API key for the chosen provider:
   # GROQ_API_KEY="your_groq_api_key"
   DEEPSEEK_API_KEY="your_deepseek_api_key"
   # OPENROUTER_API_KEY="your_openrouter_api_key"
   
   # Optional: Base URL override (e.g., for custom DeepSeek endpoints)
   # DEEPSEEK_BASE_URL="your_base_url_if_needed"
   
   # Optional: Specify different models for Team Coordinator and Specialist Agents
   # Defaults are set within the code based on the provider if these are not set.
   # Example for Groq:
   # GROQ_TEAM_MODEL_ID="llama3-70b-8192"
   # GROQ_AGENT_MODEL_ID="llama3-8b-8192"
   # Example for DeepSeek:
   # DEEPSEEK_TEAM_MODEL_ID="deepseek-chat" # Note: `deepseek-reasoner` is not recommended as it doesn't support function calling
   # DEEPSEEK_AGENT_MODEL_ID="deepseek-chat" # Recommended for specialists
   # Example for OpenRouter:
   # OPENROUTER_TEAM_MODEL_ID="deepseek/deepseek-r1" # Example, adjust as needed
   # OPENROUTER_AGENT_MODEL_ID="deepseek/deepseek-chat" # Example, adjust as needed
   
   # --- External Tools ---
   # Required ONLY if the Researcher agent is used and needs Exa
   EXA_API_KEY="your_exa_api_key"
   

   Note on Model Selection:

   • The TEAM_MODEL_ID is used by the Coordinator (Team object). This role benefits from strong reasoning, synthesis, and delegation capabilities. Consider using a more powerful model (e.g., deepseek-chat, claude-3-opus, gpt-4-turbo) here, potentially balancing capability with cost/speed.
   • The AGENT_MODEL_ID is used by the specialist agents (Planner, Researcher, etc.). These handle focused sub-tasks. A faster or more cost-effective model (e.g., deepseek-chat, claude-3-sonnet, llama3-8b) might be suitable, depending on task complexity and budget/performance needs.
   • Defaults are provided in the code (e.g., in main.py) if these environment variables are not set. Experimentation is encouraged to find the optimal balance for your use case.

3. Install Dependencies: It's highly recommended to use a virtual environment.

   • Using uv (Recommended):

     # Install uv if you don't have it:
     # curl -LsSf https://astral.sh/uv/install.sh | sh
     # source $HOME/.cargo/env # Or restart your shell
     
     # Create and activate a virtual environment (optional but recommended)
     python -m venv .venv
     source .venv/bin/activate # On Windows use `.venv\\Scripts\\activate`
     
     # Install dependencies
     uv pip install -r requirements.txt
     # Or if a pyproject.toml exists with dependencies defined:
     # uv pip install .
     

   • Using pip:

     # Create and activate a virtual environment (optional but recommended)
     python -m venv .venv
     source .venv/bin/activate # On Windows use `.venv\\Scripts\\activate`
     
     # Install dependencies
     pip install -r requirements.txt
     # Or if a pyproject.toml exists with dependencies defined:
     # pip install .
     

Usage

Ensure your environment variables are set and the virtual environment (if used) is active.

Run the server. Choose one of the following methods:

1. Using uv run (Recommended):

   uv --directory /path/to/mcp-server-mas-sequential-thinking run mcp-server-mas-sequential-thinking
   

2. Directly using Python:

   python main.py
   

The server will start and listen for requests via stdio, making the sequentialthinking tool available to compatible MCP clients configured to use it.

sequentialthinking Tool Parameters

The tool expects arguments matching the ThoughtData Pydantic model:

# Simplified representation from src/models.py
class ThoughtData(BaseModel):
    thought: str                 # Content of the current thought/step
    thoughtNumber: int           # Sequence number (>=1)
    totalThoughts: int           # Estimated total steps (>=1, suggest >=5)
    nextThoughtNeeded: bool      # Is another step required after this?
    isRevision: bool = False     # Is this revising a previous thought?
    revisesThought: Optional[int] = None # If isRevision, which thought number?
    branchFromThought: Optional[int] = None # If branching, from which thought?
    branchId: Optional[str] = None # Unique ID for the new branch being created
    needsMoreThoughts: bool = False # Signal if estimate is too low before last step

Interacting with the Tool (Conceptual Example)

An LLM would interact with this tool iteratively:

1. LLM: Uses a starter prompt (like sequential-thinking-starter) with the problem definition.
2. LLM: Calls sequentialthinking tool with thoughtNumber: 1, the initial thought (e.g., "Plan the analysis..."), an estimated totalThoughts, and nextThoughtNeeded: True.
3. Server: MAS processes the thought. The Coordinator synthesizes responses from specialists and provides guidance (e.g., "Analysis plan complete. Suggest researching X next. No revisions recommended yet.").
4. LLM: Receives the JSON response containing coordinatorResponse.
5. LLM: Formulates the next thought based on the coordinatorResponse (e.g., "Research X using available tools...").
6. LLM: Calls sequentialthinking tool with thoughtNumber: 2, the new thought, potentially updated totalThoughts, nextThoughtNeeded: True.
7. Server: MAS processes. The Coordinator synthesizes (e.g., "Research complete. Findings suggest a flaw in thought #1's assumption. RECOMMENDATION: Revise thought #1...").
8. LLM: Receives the response, notes the recommendation.
9. LLM: Formulates a revision thought.
10. LLM: Calls sequentialthinking tool with thoughtNumber: 3, the revision thought, isRevision: True, revisesThought: 1, nextThoughtNeeded: True.
11. ... and so on, potentially branching or extending the process as needed.

Tool Response Format

The tool returns a JSON string containing:

{
  "processedThoughtNumber": int,          // The thought number that was just processed
  "estimatedTotalThoughts": int,          // The current estimate of total thoughts
  "nextThoughtNeeded": bool,              // Whether the process indicates more steps are needed
  "coordinatorResponse": "...",           // Synthesized output from the agent team, including analysis, findings, and guidance for the next step.
  "branches": ["main", "branch-id-1"],  // List of active branch IDs
  "thoughtHistoryLength": int,          // Total number of thoughts processed so far (across all branches)
  "branchDetails": {
    "currentBranchId": "main",            // The ID of the branch the processed thought belongs to
    "branchOriginThought": null | int,    // The thought number where the current branch diverged (null for 'main')
    "allBranches": {                      // Count of thoughts in each active branch
      "main": 5,
      "branch-id-1": 2
     }
  },
  "isRevision": bool,                     // Was the processed thought a revision?
  "revisesThought": null | int,           // Which thought number was revised (if isRevision is true)
  "isBranch": bool,                       // Did this thought start a new branch?
  "status": "success | validation_error | failed", // Outcome status
  "error": null | "Error message..."     // Error details if status is not 'success'
}

Logging

• Logs are written to ~/.sequential_thinking/logs/sequential_thinking.log by default. (Configuration might be adjustable in the logging setup code).
• Uses Python's standard logging module.
• Includes a rotating file handler (e.g., 10MB limit, 5 backups) and a console handler (typically INFO level).
• Logs include timestamps, levels, logger names, and messages, including structured representations of thoughts being processed.

Development

1. Clone the repository: (As in Installation)

   git clone [email protected]:FradSer/mcp-server-mas-sequential-thinking.git
   cd mcp-server-mas-sequential-thinking
   

2. Set up Virtual Environment: (Recommended)

   python -m venv .venv
   source .venv/bin/activate # On Windows use `.venv\\Scripts\\activate`
   

3. Install Dependencies (including dev): Ensure your requirements-dev.txt or pyproject.toml specifies development tools (like pytest, ruff, black, mypy).

   # Using uv
   uv pip install -r requirements.txt
   uv pip install -r requirements-dev.txt # Or install extras if defined in pyproject.toml: uv pip install -e ".[dev]"
   
   # Using pip
   pip install -r requirements.txt
   pip install -r requirements-dev.txt # Or install extras if defined in pyproject.toml: pip install -e ".[dev]"
   

4. Run Checks: Execute linters, formatters, and tests (adjust commands based on your project setup).

   # Example commands (replace with actual commands used in the project)
   ruff check . --fix
   black .
   mypy .
   pytest
   

5. Contribution: (Consider adding contribution guidelines: branching strategy, pull request process, code style).

License

MIT