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For-Sunny

Galaxy Brain

by For-Sunny
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Python
Hybrid

Galaxy Brain

Think. Do. Done.

Sequential Thinking + Sequential Doing = Complete Cognitive Loop

MIT License Python 3.10+ MCP

What is this?

Galaxy Brain is an MCP server that combines two powerful concepts:

  1. Sequential Thinking (from Anthropic's MCP) - structured reasoning with revision and branching

  2. Sequential Doing - batch execution with variable piping between operations

Together they form a complete cognitive loop: think through a problem, convert thoughts to actions, execute, done.

PROBLEM │ ▼ ┌─────────────┐ │ THINK │ ← reason step by step │ │ ← revise if wrong │ │ ← branch to explore └──────┬──────┘ │ ▼ ┌─────────────┐ │ BRIDGE │ ← convert thoughts to operations └──────┬──────┘ │ ▼ ┌─────────────┐ │ DO │ ← execute sequentially │ │ ← pipe results between ops └──────┬──────┘ │ ▼ DONE

Installation

Quick Install (PowerShell)

git clone https://github.com/For-Sunny/galaxy-brain.git cd galaxy-brain .\scripts\install.ps1

Quick Install (Bash)

git clone https://github.com/For-Sunny/galaxy-brain.git cd galaxy-brain chmod +x scripts/install.sh ./scripts/install.sh

Manual Install

pip install galaxy-brain

Then add to your Claude Desktop config (%APPDATA%\Claude\claude_desktop_config.json on Windows):

{ "mcpServers": { "galaxy-brain": { "command": "python", "args": ["-m", "galaxy_brain.server"] } } }

Usage

The Galaxy Brain Move: think_and_do

One tool to rule them all:

think_and_do({ "problem": "I need to read a config file, parse it, and count the keys", "thoughts": [ "First I need to read the config file", "Then parse it as JSON", "Finally count the number of keys" ], "operations": [ { "service": "file", "method": "read", "params": { "path": "config.json" } }, { "service": "transform", "method": "json_parse", "params": { "content": "$results[0].result.content" } }, { "service": "python", "method": "eval", "params": { "expression": "len($results[1].result)" } } ] })

See that $results[0].result.content? That's variable piping - each operation can reference results from previous operations.

Thinking Tools

Start a thinking session and reason step by step:

# Start thinking start_thinking({ "problem": "How should I refactor this authentication system?", "initial_estimate": 5 }) # Returns: { "session_id": "think_abc123..." } # Add thoughts think({ "session_id": "think_abc123...", "thought": "The current system uses session cookies...", "confidence": 0.8 }) # Realize you were wrong? Revise! revise({ "session_id": "think_abc123...", "revises_thought": 2, "revised_content": "Actually, we should use JWTs because...", "reason": "Stateless is better for our scale" }) # Want to explore an alternative? Branch! branch({ "session_id": "think_abc123...", "branch_from": 3, "branch_name": "oauth_approach", "first_thought": "What if we used OAuth2 instead?" }) # Done thinking conclude({ "session_id": "think_abc123...", "conclusion": "We should migrate to JWT with refresh tokens", "confidence": 0.9 })

Doing Tools

Execute operations with variable piping:

execute_batch({ "batch_name": "process_data", "operations": [ { "service": "shell", "method": "run", "params": { "command": "curl -s https://api.example.com/data" } }, { "service": "transform", "method": "json_parse", "params": { "content": "$results[0].result.stdout" } }, { "service": "file", "method": "write", "params": { "path": "output.json", "content": "$results[1].result" } } ] })

Available Services

Service

Methods

Description

python

execute, eval

Run Python code or evaluate expressions

shell

run

Execute shell commands

file

read, write, exists

File operations

transform

json_parse, json_stringify, extract, template

Data transformations

Bridge Tools

Convert thinking sessions to action plans:

# Generate plan from concluded session generate_plan({ "session_id": "think_abc123..." }) # Execute the generated plan execute_plan({ "plan_id": "plan_xyz789..." })

Variable Piping Syntax

Reference previous results using $results[N].path.to.value:

$results[0] # Full result of operation 0 $results[0].result # The result field $results[0].result.content # Nested access $results[1].result.data[0] # Array access (in path format)

Variables are resolved before each operation executes, so you can build pipelines:

operations = [ # Op 0: Read a file { "service": "file", "method": "read", "params": { "path": "input.txt" } }, # Op 1: Use content from op 0 { "service": "python", "method": "execute", "params": { "code": "print(len('$results[0].result.content'))" } }, # Op 2: Use stdout from op 1 { "service": "file", "method": "write", "params": { "path": "count.txt", "content": "$results[1].result.stdout" } } ]

Configuration

Create galaxy-brain.json in your working directory:

{ "thinking": { "max_thoughts": 50, "max_branches": 10, "max_revisions_per_thought": 5 }, "doing": { "max_operations": 50, "default_timeout": 30, "max_timeout": 300, "stop_on_error": true }, "bridge": { "auto_execute": false, "validate_before_execute": true }, "log_level": "INFO" }

Or use environment variables:

  • GALAXY_BRAIN_LOG_LEVEL

  • GALAXY_BRAIN_MAX_THOUGHTS

  • GALAXY_BRAIN_MAX_OPERATIONS

Why "Galaxy Brain"?

Because when you combine structured thinking with chained execution, you're operating on a whole other level.

Think. Do. Done. Big brain energy. Cosmic efficiency.

Credits

License

MIT License - Do whatever you want with it.

Think. Do. Done.

Built by

Website | Store | GitHub | glass@cipscorps.io

Enterprise memory infrastructure for AI systems: CASCADE Enterprise, PyTorch Memory, Hebbian Mind, and the full CIPS Stack.

Copyright (c) 2025-2026 C.I.P.S. LLC

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