What is Seq Mem

seq-mem is a Model Context Protocol (MCP) server designed for sequential thinking with memory branching. It utilizes a knowledge graph to enable AI assistants to navigate decision trees by recording thought processes, branching at low-confidence points, and backtracking to explore alternative paths.

Use cases

Use cases for seq-mem include AI assistants that need to make decisions based on uncertain information, educational platforms that guide students through problem-solving processes, and applications that require exploration of multiple solutions to a given problem.

How to use

To use seq-mem, clone the repository, create a virtual environment, and install the required dependencies. Configure the server in your Claude Desktop configuration file, specifying the command and arguments to run the server.

Key features

Key features of seq-mem include linear thinking when confidence is high (≥ 0.6), automatic branching when confidence drops below a threshold (< 0.6), path recording in an in-memory graph structure, backtracking to explore alternative branches, path visualization for complete thinking traces, and unexplored branch detection.

Where to use

seq-mem can be used in various fields such as AI development, decision-making systems, educational tools, and any application requiring complex problem-solving with memory and branching capabilities.

Sequential Memory MCP Server

A Model Context Protocol (MCP) server that combines sequential thinking with persistent memory through a knowledge graph. This enables AI assistants to explore decision trees by recording thinking traces, branching at low-confidence points, and backtracking to explore alternative paths.

Features

• Linear thinking when confidence is high (≥ 0.6)
• Automatic branching when confidence drops below threshold (< 0.6)
• Path recording in an in-memory graph structure
• Backtracking to explore alternative branches
• Path visualization to see complete thinking traces
• Unexplored branch detection to identify paths not yet taken

Installation

1. Clone this repository
2. Create a virtual environment:

   python -m venv venv
   venv\Scripts\activate  # On Windows
   # or
   source venv/bin/activate  # On Unix/MacOS
   

3. Install dependencies:

   pip install mcp
   

Configuration

Add the following to your Claude Desktop configuration file:
C:\Users\steve\AppData\Roaming\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "sequential-memory": {
      "command": "python",
      "args": [
        "-m",
        "sequential_memory.server"
      ],
      "cwd": "C:\\Users\\steve\\claude\\seq-mem\\src"
    }
  }
}

Usage

The server provides 5 main tools:

1. think

Process a thought with a confidence level.

• Parameters:
  • thought (string): The thought content
  • confidence (number): Confidence level (0.0-1.0)
• Returns: Status (continue/branch), current node ID, and whether alternatives are needed

2. select_path

Choose from alternative thoughts at a branch point.

• Parameters:
  • alternatives (array): List of alternative thoughts with confidence levels
  • selected_index (integer): Which alternative to select (0-based)
• Returns: Selected thought information and new current node ID

3. backtrack

Return to the last high-confidence node in the current path.

• Parameters: None
• Returns: Information about the node backtracked to (or no_target if none found)

4. show_current_path

Display the current thinking path from root to current node.

• Parameters: None
• Returns: Complete path with node details, total nodes, and branch points

5. get_unexplored_branches

Find all branch points with unexplored alternatives.

• Parameters: None
• Returns: List of unexplored branches with their alternatives

Example Usage

User: Let's think through a problem step by step.

Claude: I'll use sequential thinking to explore this systematically.

[thinks: "First, let me understand the problem clearly" (0.8)]
[thinks: "The key aspects seem to be X, Y, and Z" (0.9)]
[thinks: "Now I need to decide on an approach..." (0.3)]

I've reached a point of uncertainty. Let me explore different approaches:

[select_path with alternatives:
  - "Focus on aspect X first" (0.7)
  - "Start with aspect Y" (0.6)
  - "Consider aspect Z" (0.5)]

[Continue thinking on selected path...]

User: Let's backtrack and try a different approach.

Claude: [backtrack]
I've returned to "The key aspects seem to be X, Y, and Z". 
Let me explore one of the other approaches...

Testing

Run the test suite:

python -m pytest tests/test_basic.py
# or
python tests/test_basic.py

Architecture

• graph.py: Core graph data structures (Node, Edge, ThoughtGraph)
• tools.py: MCP tool implementations and definitions
• server.py: Main MCP server implementation
• test_basic.py: Comprehensive test suite

Development

The codebase is organized for clarity and extensibility:

• All graph operations are encapsulated in the ThoughtGraph class
• Tool handlers are separated in the SequentialMemoryTools class
• The server handles only MCP protocol communication
• Tests cover both unit and integration scenarios