AiPy, AI Beast of Burden
Help earn money, Help slack off, Help find lover, anything goes
AiPy, giving AI hands, does more than just help you think – it helps you get things done, becoming your super AI assistant! From now on, you just need to voice your ideas, and AiPy will help you think, help you plan, help you act, and deliver the final result!

Download & Installation

编组Installing in Command-Line Mode (Windows/macOS/Linux)
pip install aipyapp
编组Requirements
1. python version ≥3.92. Compatible with Windows, macOS, Linux, etc.
3. AiPy Open Source:GitHub
macOS Installer
macOS one-click installation version, comes with its own runtime environment. Requires macOS 12.7.6 or later.
Download
Windows One-Click Version
No installation required, comes with its own runtime environment,
runs and works! Run the update.bat file in the extracted package to upgrade to the latest version.
Download
more installation & usage guide

What is AiPy? It's an LLM + Python program that can operate and control everything.

AI LLM
+
Python Code
=
The most powerful AI assistant AiPy

The world will never be the same with AiPy

Learn the magic of AiPy through use cases

AI travel planning
Use AiPy to develop a full travel strategy from Chengdu to Jiuzhaigou for Qingming Festival, showing how AI can query real-time traffic schedules, fares, and accommodation information, accurately calculate budgets, and automatically fix the code when it runs wrong, and finally output a detailed travel plan in Markdown format by automatically writing a programme. The whole process rejects illusion and is efficient and intelligent!
AI Development Games
Using AiPy to develop a game of elimination game, showing how AI automatically breaks down tasks, writes HTML/JS code, calls Google API to download beautiful pictures and save them locally, and finally generates a 4×4 matrix elimination game web page. The whole process does not require human intervention, the code is automatically generated, and the file path is precisely configured.
AI Document Processing
Using AiPy to develop a game of elimination game, showing how AI automatically breaks down tasks, writes HTML/JS code, calls Google API to download beautiful pictures and save them locally, and finally generates a 4×4 matrix elimination game web page. The whole process does not require human intervention, the code is automatically generated, and the file path is precisely configured.
AI life assistant
Use AiPy to find special hotpot restaurants on Chunxi Road, showing how AI combines user needs, automatically completes the geographic location query, filtering hotpot restaurants in the neighbourhood, price filtering and result beautification, and finally generates a beautiful food recommendation webpage. The whole process from data acquisition to result presentation is automated, accurately matching life needs and making food exploration more efficient!

User Reviews

辰易
手动分析要半天的工作，几分钟就搞定了，准确率高，生成的报告还好看。
思通
整个系统框架思路还是很牛的了。
Sam
我们团队用AIPY做季度复盘，原本复杂的数据汇总和图表生成，现在一键就能完成，原先需要跨系统手动汇总的复杂数据，现在一键即可生成可视化分析报告，效率提升太明显了。
Bato
用AIPY做了员工绩效分析，不仅速度快，还能自动生成可视化报告，HR的工作效率嘎嘎提升👍👍
FANCY
AiPy导出的HTML报告模板也太专业了吧，直接就能用于客户演示，模板不仅视觉呈现简洁大气，符合现代商务审美，而且结构逻辑清晰，关键数据模块和图表展示区域都经过精心优化，值得推荐！
Joe
另外你们的大模型也很厉害，我本地的qwcode2.5一样做基础大模型，效果就差了很多。
莉莉
比openmanus好配置，哈哈。
Mike Taylor
It's too powerful. It connects to its own API. It feels more practical than MCP.
木子
昨天看到这个产品都让我失眠了，现在还没缓过劲儿来，太震撼了！
Nixtio
产品实在是太棒了，我这边已经在跑了，你们免费的 key 真是不要太爽。
Su
这个也太有想象空间了！
Tubik
I have been using it for a whole day and I simply cannot stop. It will evolve automatically. The word I made at the beginning was so ugly, but it is getting smarter and smarter. It’s great.

FAQ

Q: What is AiPy?
A: AiPy is the LLM Big Model + Python program writing + Python program running + everything the program can control.
Q: What is the difference between AiPy and the current Ai?
A: The current big model, can only Q&A, answer questions, can not actually operate the computer to help you complete specific tasks. AiPy is a task-oriented Ai system, you only need to tell him what you want to do, AiPy will help you complete. The existing big model is question and answer orientated, AiPy is task orientated.
Q: Is the AiPy a new larger model or a sleeved larger model?
A: AiPy is not a big model, but an application product based on a big model, a product that really realises the ability of general task understanding, planning, execution, and ultimately obtaining the task results through API calls from the big model.
Q: What is the AiPy paradigm and what is Python-Use? Is it an Agent-like product? How is it different from Manus, MCP, etc.?

A: AiPy is a product we developed based on a new paradigm, Python-Use, to enable more universal and rapid utilization of large models for various tasks.

The traditional classic paradigm for large model AI Agents involves developing a large number of tool agents and then relying on their collaboration to accomplish various tasks. This approach depends on the development, deployment, and installation of more and more agents. However, from a certain perspective, developing and deploying more agents actually limits the full potential of large models. In contrast, AiPy (Python-Use), a new paradigm, takes a different approach: it's a way of "enabling AI to use Python and Python to use AI." This means that the large model understands and breaks down user tasks, then achieves automatic coding and code execution through API Calling and Packages Calling. It can also continuously improve and iterate through a feedback mechanism, ultimately enabling AI to interact with the environment and complete tasks.

Therefore, we propose the concept: "The real general AI Agent is NO Agents!" AiPy (Python-Use) implements the new paradigm of "No Agents, Code is Agent." Python uses data, Python uses computers, Python uses the network, Python uses the Internet of Things, Python uses everything, ultimately achieving true AI Think Do!

The specific code related to Python-Use has already been open-sourced: https://github.com/knownsec/aipyapp

Based on this concept, we believe this is the biggest difference compared to Manus, MCP, etc.! For users:

The biggest difference between AiPy and Manus is that AiPy itself is open-source and free. Users only need to bear the cost of tokens for calling APIs of large models (of course, you can also use free large models). Because it doesn't require the invocation of numerous agents, AiPy also consumes relatively fewer tokens for the same task. Another major advantage is that AiPy supports local deployment, eliminating the need for users to upload their sensitive data and documents to the cloud. This is because AiPy is only responsible for the corresponding code generation for the task, and all data processing is done locally, offering a secure and reliable advantage for handling very large files and sensitive data.

The biggest advantage of AiPy compared to MCP Server is that users don't need to rely on various custom-developed MCP Servers for different services, nor do they need to deploy, install, or use them. They also don't need to worry about the security risks posed by unreliable MCP Server providers. AiPy can achieve the invocation of various APIs and accomplish diverse functions through real-time coding. You can see the examples shown above or experience the power of AiPy for yourselves.

In summary, AiPy offers multiple deployment options, is no longer limited by the various restrictions of cloud-based hosts, and doesn't require the development, downloading, installation, or complex configuration of various tools. All you need to do is converse with the large model.

Q: What kind of macromodels does AiPy support? Does AiPy support local big model calls? What are the recommended models?

A: AiPy theoretically supports all generic big model calls, you just need to set the API and model information of the generic big model in the configuration file to complete the call. We also support Ollama and LMStudio APIs for local big models.

Because of the Python-Use paradigm, a lot of capabilities depend on the big model itself, so the better the coding ability of the big model and other comprehensive capabilities of the big model, the better the performance of the model to achieve the task. Of course, we also need to take into account the large model API calls tokens to spend the cost of the problem, in the cost-effective point of view, we recommend the use of DeepSeek, after testing a very small amount of money can achieve most of the task execution work.

Q：What can AiPy do at the moment?
A: Theoretically speaking, AiPy can do all the tasks that can be automatically scheduled through Python. However, we are still in the early stages of development, and currently recommend trying some lightweight tasks.
Q: Can AiPy call other products and business APIs? How is it implemented? Does it support local private API?

A: Yes, AiPy supports a variety of Internet business API calls, including search, maps, trip planning, social media, weather and other API services, can be built-in can also be called to generate the code when you enter the corresponding API Key to call to use. As for the implementation of API calls we have implemented a function called ‘API Calling’, the big model estimates his understanding of the task to choose to call the corresponding API, you can also specify the way to achieve the call through the task prompt word.

Through the local deployment of AiPy is to support the local private words of the API call, you just need to write the corresponding API description and address in the configuration file.

Q: Why did AiPy choose Python over other programming languages?
A: We tried AiJava and AILua for a short time, the former was too big and bulky, and the latter was too weak in terms of capability and ecology, so we finally chose AiPython.
Q: Is AiPy an IDE? How is it different from Cursor, Windsurf, etc.?
A: AiPy is not an IDE. The biggest difference from AI IDE is that AiPy does not directly deliver code, but delivers task results. Of course, if your task is to deliver code, AiPy can also help you complete this task!
Q: Does AiPy have intelligence?
A: It depends on how intelligence is defined. If intelligence is defined as: making plans and generating actions based on one's state, environment, and goals, and giving continuous feedback during the actions, revising the plans to improve the actions, and guiding the approach to the goals, then this is intelligence. According to this definition, LLM does not have intelligence, while AiPy, according to the task goal, makes plans (writes a programme to complete the task), generates actions (runs the programme), and constantly gives feedback to improve itself (debugs the programme itself, revises the programme itself) until it achieves the goal, in this way, AiPy does have a real intelligence.
Q: What is the essence of AiPy?
A：Human use AI, AI use Python, Python use data, Python use computer, Python use network Python use IOT, Python use everything.
Q: Is the relationship between AiPy and MCP, Agent. Workflow a replacement? workflow?
A: According to the task, AiPy will write a workflow, agent, and interface transformation programme for the current task if needed, and if the current mcp and agent can be used, he will also call them directly. We think that in the future everyone will coexist.