# Python-Use: A New AI Agent Paradigm (Agent 2.0) 🔗 [View on GitHub](https://github.com/knownsec/aipyapp)  🔗 [中文版](https://github.com/knownsec/aipyapp/blob/main/docs/README.zh.md)

<img src="aipy.jpg" alt="AIPy">

## Background: The Outdated "Prosthetic" AI Agent Model

Traditional AI (Agent 1.0) relies on Function Calling, Tools, MCP-Servers, Workflows, and plugin-based clients. These external "prosthetics" lead to high entry barriers, heavy reliance on developers, and poor coordination between tools. Worse still, most AI-generated code is locked in cloud sandboxes, unable to interact with the real environment—crippling the model’s execution potential.

We urgently need a new paradigm that reconnects AI with the real world and fully activates its native execution power—ushering in the AI Think Do era.

## A New Paradigm: Python-use = LLM + Python Interpreter

Python-Use is a task-driven, result-oriented intelligent execution paradigm. It tightly integrates LLMs with a Python interpreter to establish a complete loop:

**Task → Plan → Code → Execute → Feedback**

While this paradigm theoretically supports any language, we choose Python because:

- It has a powerful ecosystem spanning data, automation, system control, and AI;
- Its syntax is simple and readable, ideal for model generation and debugging;
- Models are naturally more proficient in Python for accurate and efficient coding.

This gives models two core capabilities:

- **API Calling**: Automatically generate and execute Python code to invoke APIs;
- **Packages Calling**: Flexibly leverage Python's ecosystem to orchestrate workflows.

Users only need to provide a task description or API key. The model handles the rest—no plugin registration, no toolchain setup, no workflow editing.

> **Important**: Python-Use is *not* a code generator or smart IDE.  
> It's a task-first, outcome-driven AI Agent.

To the user, Python-Use is simple:  
Describe a task → AI executes it → Result returned.  
No programming required.

The model autonomously understands, plans, writes, debugs, and executes code—and fixes bugs along the way. Code is just an internal implementation—not the deliverable. The real deliverable is the **result**.

## Core Principle: No Agents, Code is Agent

Python-Use introduces a radically simplified execution architecture:

**No Agents, No MCP, No Workflow, No Clients...**

It discards legacy layers and lets models use code to directly act on the environment. In short: **Code is Agent**.

With Python, the model can:

- **Python use Data**: Load, transform, analyze;
- **Python use Browser**: Automate the web;
- **Python use Computer**: Access file systems and local resources;
- **Python use IoT**: Control devices and embedded systems;
- **...** 
- **Python use Anything**: Code becomes a universal interface.

![Python-Use-Diagram](python-use-wf.png)

This means:

- **No MCP**: No standardized protocol needed—code is the protocol;
- **No Workflow**: Model plans and executes on the fly;
- **No Tools**: No plugin registrations needed—just use existing ecosystems;
- **No Agents**: Code replaces orchestration—execution becomes native.

This is the bridge that reconnects LLMs to the real digital world, unlocking their latent power.

## Single Entry Point: No Clients, Only AiPy

You don’t need multiple AI apps or UI wrappers anymore.

Just run one thing: **AiPy**, a Python-powered AI Client.

- **Unified interface**: All interaction via Python
- **Zero clutter**: No plugin mess, no bloated clients
- **AiPy**: https://www.aipy.app/

## Execution Mode Upgrade: AI Think Do = True Integration of Knowing & Doing

- **Task**: User describes intent;
- **Plan**: Model decomposes and plans a path;
- **Code**: Optimal Python strategy is generated;
- **Execute**: Direct interaction with the environment;
- **Feedback**: Output is evaluated and looped back into planning.

No external agent needed. The AI completes the full loop independently, unleashing true cognitive-action capability.

## Self-Evolution: Multi-Model Fusion

AI evolution is not just language modeling—it’s multi-modal intelligence.

- Integrates vision models for image/video understanding;
- Adds speech models for listening and speaking;
- Embeds expert models for domain reasoning;
- All fused and coordinated under a unified AI control loop.

This moves us from “chatbots” to fully embodied AI agent—on the path to true AGI.

## Vision: Free the AI, Reach AGI

Python-Use is more than a tool—it’s a future-facing AI philosophy:

**The Model is the Product → The Model is the Agent → No Agents, Code is Agent → Just Python-use → Freedom AI (AGI)**

It transforms AI from “just speaking” to “taking action,” from plugin-bound to autonomous execution. It unlocks full production power—and lights the path to general intelligence.

Join us. Let AI break free, act freely, and build the future.

**The real general AI Agent is NO Agents!** 

**No Agents, Just Python-use!**


