npmai 0.1.5

A framework developed for supporting in development of AI Agents without installing a lot of things and in free on cloud without signin or signup or limit hurdles by Sonu Kumar (Viral Boy Bihar).

🚀 npmai

By Sonu Kumar (Viral Boy)

npmai is a lightweight Python package designed to bridge the gap between users and open-source LLMs. Connect with Ollama and 10+ other powerful models instantly—no installation, no login, and no API keys required, and help in development of RAG Agents without installing anything locally or on cloud and it is free without sigin or signup or any type of limit.

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✨ Features

• 🔗 Zero Setup: No local Ollama installation or complex API signups needed.
• 🤖 Multi-Model Support: Execute prompts across 10+ open-source models simultaneously.
• 🧠 Built-in Memory: (New in v0.1.3) Native memory support—no need for external Agentic frameworks.
• 🕵️‍♂️🔍📑 RAG Frame-Work: no need to install Whisper or any model locally,no need to write code for the pdf,image,video,yt-video to text just use npmai
• ⚡ Framework Ready: Fully compatible with LangChain, CrewAI, and other orchestration tools.
• 🛠️ Universal API: Access via Python, JavaScript, C++, Java, or C.

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🖥️ Supported Models

Model Name	Description
llama3.2	Meta's latest powerful small model
gemma-2-instruct-9b	Google's high-performance open model
qwen-2.5-coder-7b	Alibaba's elite coding assistant
mistral-7b-instruct	Versatile and efficient instructor model
phi-3-medium	Microsoft's highly capable reasoning model
And many more...	Falcon, Baichuan-2, InternLM, Vicuna

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⚙️ Installation

Install via pip in seconds:

pip install npmai
Use code with caution.

Tip for Python 3.13+: Use py -3.13 -m pip install npmai
💡 Quick Start (Python)
python
from npmai import Ollama

# Initialize the LLM
llm = Ollama()      

# Simple invocation
response = llm.invoke("What is the future of AI?", model="llama3.2")
print(response) 

🌐 API Usage (Other Languages)
If you aren't using Python, hit our global endpoint:
POST https://npmai-api.onrender.com
🟡 JavaScript
javascript
const response = await fetch("https://npmai-api.onrender.com", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    prompt: "Hello! Who are you?",
    model: "llama3.2",
    temperature: 0.4
  })
});
const data = await response.json();
console.log(data.response);

🔵 C++
cpp
nlohmann::json payload = {
    {"prompt", "Explain quantum physics."},
    {"model", "llama3.2"},
    {"temperature", 0.4}
};
auto res = cli.Post("/llm", payload.dump(), "application/json");

🆕 Latest Update: Version 0.1.5
Just fixed some bugs and added link as a parameter in Rag class
version 0.1.4 --->> Now you do not need to write code for RAG tools like pdf,image,video,audio,yt-video to text and no need to load whisper and other requirements locally no local process everything on cloud in free without any signup or singin or key hurdles.
⚠️ Important Notes
Please star our project on Github please.
🔗 Resources
Documentation: npmai.onrender.com
API Endpoint: npmai-api.onrender.com/llm
Developed with ❤️ to make AI accessible to everyone.
Developer and Maintainer:- Sonu Kumar