{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"npmai provides a Python interface to access open-source LLMs like Ollama and 45+ other models through a cloud API, plus RAG (retrieval-augmented generation) tools for processing documents, images, and video without local installation.","skillfed_tags":["llm-inference","rag","cloud-api"],"use_cases":["Prototype or demo an LLM-powered application without downloading and running Ollama locally.","Build a RAG chatbot over your own documents (PDFs, images, videos) without writing file-processing code.","Integrate multiple open-source models into a LangChain or CrewAI workflow via a single Python API.","Experiment with different model outputs (Llama, Gemma, Qwen, Mistral) by switching a model parameter.","Access LLM inference from non-Python code (JavaScript, C++, Java) via the HTTP endpoint."],"what_it_does":"npmai is a Python wrapper around a cloud-hosted LLM inference service that lets you call open-source models (Ollama, Llama, Gemma, Qwen, Mistral, and others) without installing them locally or managing API keys. It's designed to eliminate setup friction for developers wanting to experiment with or integrate multiple LLMs into applications. The package includes a RAG (retrieval-augmented generation) module that handles document ingestion\u2014PDFs, images, video, audio, YouTube videos\u2014converting them to text and storing them in a vectorized database, all on the cloud side with no local dependencies.\n\nThe service is free and claims to handle significant throughput (80K+ requests per 24 hours) without charging users or developers. It integrates with LangChain and other orchestration frameworks, and offers both a Python SDK and HTTP API endpoints for use from JavaScript, C++, Java, or C. Recent versions (0.1.8\u20130.1.9) added Supabase integration for long-term document storage and dynamic context retrieval logic to optimize RAG performance.","worth_installing":"Yes, with conditions. Install if you want zero-setup access to multiple open-source LLMs and RAG tooling without local compute or API keys. The low install friction, permissive license, and active maintenance make it a reasonable experiment. However, verify uptime guarantees and data privacy for the cloud-hosted vectorized documents before using in production; the free tier's long-term reliability is unproven."},"id":"npmai","links":{"html":"https://skillfed.io/packages/npmai","md":"https://skillfed.io/packages/npmai.md","pypi":"https://pypi.org/project/npmai/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-16","license_spdx":null,"license_treatment":"permissive","name":"npmai","python_support":"supports_current","summary":"npmai is a lightweight Python package designed to bridge the gap between users and open-source LLMs. Connect with Ollama and 45+ other powerful models instantly\u2014 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."},"popularity":{"monthly_downloads":1108957,"position":4358,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.1.9"}