monad-core 0.1.0

Personal AGI Operating Core

🌌 MONAD (v0.1)

Personal AGI Operating Core

🇨🇳 简体中文 (Chinese) • How It Works • Installation • Architecture

MONAD is not a chatbot or a simple tool-matcher. It is a self-learning, objective-driven autonomous rational agent core.

Unlike traditional agents that rely on a predefined, hardcoded set of tools, MONAD acts like a rational entity with basic "instincts". It has no memory and no pre-loaded knowledge of how to perform specific tasks (like checking the weather or searching the web).

Instead, it autonomously learns how to complete your tasks by writing and executing Python code on the fly, and then saving those successful experiences as reusable skills.

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🧠 Core Philosophy

• File System as Database: The system itself has no memory of past sessions. It persists all learned information (axioms, environment knowledge, learned skills, user context, and experiences) directly to local Markdown files. No vector databases, no RAG, zero external dependencies.
• Absolute Rationality: MONAD follows a strict reasoning loop (Analyze → Self-check → Learn → Execute → Reflect) to accomplish goals logically.
• Self-Learning & Self-Evolving: Instead of shipping with 100 tools, MONAD ships with only 4 basic instincts (hands 🤲, voice 🗣️, eyes 👁️, dialogue 💬). It learns everything else by generating code.
• LLM as a Command Executor: The LLM's own training data is disregarded. All factual information must be retrieved from the real world via code execution or web perception.
• Search First, Ask Later: When stuck during execution (errors, missing packages, unfamiliar tools), MONAD's first instinct is to search the web via web_fetch, never to guess. But if the user's intent is unclear, MONAD asks the user first. In short: unclear query → ask user; execution problem → search first.

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⚡ Basic Capabilities ("Instincts")

MONAD comes with only four built-in capabilities:

Capability	Metaphor	Description
🐍 python_exec	Hands 🤲	Evaluate arbitrary Python code. Process data, call APIs, read/write files, install libraries—learn to do anything.
💻 shell	Voice 🗣️	Execute shell commands on the host operating system.
👁️ web_fetch	Eyes 👁️	Perceive the internet directly. Fetch web pages with 3 modes: fast (HTTP), stealth (anti-bot), browser (JS render). Powered by Scrapling.
🙋 ask_user	Dialogue 💬	Ask the user for clarification when it truly cannot proceed independently.

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📂 Knowledge Architecture

MONAD uses Categorized Memory instead of semantic retrieval (RAG).

knowledge/
├── axioms/          # System axioms & core behavioral principles
├── environment/     # World knowledge (e.g., search engine URLs, API endpoints)
├── user/            # Categorized user context (No RAG used here)
│   ├── facts.md     #   Objective facts & preferences (e.g., prefers Python)
│   ├── mood.md      #   Current state & mood
│   └── goals.md     #   Long-term goals & ongoing projects
├── skills/          # Auto-generated reusable Python skills
├── experiences/     # Execution logs and post-task reflections
├── protocols/       # Error handling protocols
└── tools/           # Documentation for the 4 basic capabilities

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⚙️ How It Works

When you give MONAD an objective (e.g., "What is the weather in Hangzhou today?"):

1. Analyze & Self-Check: The Reasoner loads the knowledge base and checks if a get_weather skill already exists.
2. Plan & Act: If no skill exists, it writes a Python script using python_exec to call a free weather API (e.g., Open-Meteo).
3. Observe & Adjust: It runs the code, observes the standard output. If it crashes, it analyzes the error, patches the code, and retries.
4. Answer: It formats the final retrieved real-world data for the user.
5. Reflect & Learn: After the task, the Reflection module analyzes what happened. The SkillBuilder then evaluates if the logic should be extracted into a new permanent skill file.

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🚀 Installation

1. Install via pip (Recommended)

pip install monad-core

Alternatively, install from source:

git clone https://github.com/hscspring/Monad.git
cd Monad
pip install -e .

2. Configure your LLM On your first run, MONAD will initialize its workspace in ~/.monad/. Update ~/.monad/.env with your LLM Base URL, API Key, and Model name.

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💻 Usage

Interactive Terminal Mode

Start the continuous ReAct agent loop from anywhere:

monad

Self-Test

Verify all modules load correctly and the LLM connection is functioning:

monad --test

Unit Tests

Run the test suite for all tools:

python -m pytest tests/ -v

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Built with pure rational reasoning 💡