AI-Powered Python & Python-Powered AI
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
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
- Poor coordination between tools
- Most AI-generated code locked in cloud sandboxes, unable to interact with the real environment
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.
Python-Use provides the entire Python execution environment to LLM. Imagine LLM sitting in front of a computer, typing various commands into the Python command-line interpreter, pressing Enter to execute, observing the results, and then typing and executing more code.
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.
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.
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
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
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.
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.
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/
AiPy has two running modes:
Very simple and easy to use—just input your task. Suitable for users unfamiliar with Python.
Suitable for users familiar with Python, allowing both task input and Python commands. Ideal for advanced users.
Create ~/.aipyapp/aipyapp.toml:
[llm.deepseek]
type = "deepseek"
api_key = "Your DeepSeek API Key"pip install aipyappaipy🚀 Python use - AIPython (0.1.22) [https://aipy.app]
>>> Get the latest posts from Reddit r/LocalLLaMA
......
>>> /done
aipy --pythonAutomatic task processing:
Python use - AIPython (Quit with 'exit()')
>>> ai("Get the title of Google's homepage")
>>> ai("Use psutil to list all processes on MacOS")
📦 LLM requests to install third-party packages: ['psutil']
If you agree and have installed, please enter 'y [y/n] (n): y
~/.aipyapp/aipyapp.toml:
[llm.deepseek]
type = "deepseek"
api_key = "Your DeepSeek API Key"uv run aipy
>>> Get the latest posts from Reddit r/LocalLLaMA
......
......
>>> /done
pip install aipyapp and run with aipy
-> % aipy
🚀 Python use - AIPython (0.1.22) [https://aipy.app]
>> Get the latest posts from Reddit r/LocalLLaMA
......
>>
Automatic task processing:
>>> ai("Get the title of Google's homepage")
Python use - AIPython (Quit with 'exit()')
>>> ai("Use psutil to list all processes on MacOS")
📦 LLM requests to install third-party packages: ['psutil']
If you agree and have installed, please enter 'y [y/n] (n): y
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!
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 agents—on the path to true AGI.
- Hei Ge: Product manager/senior user/chief tester
- Sonnet 3.7: Generated the first version of the code, which was almost ready to use without modification
- ChatGPT: Provided many suggestions and code snippets, especially for the command-line interface
- Codeium: Intelligent code completion
- Copilot: Code improvement suggestions
Python-Use: The Future of AI Agents