npmai
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— 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.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later (up to 3.13); cloud API endpoint must be reachable.
- Low install friction: pure Python wheel with a single runtime dependency (requests).
- Active maintenance with recent releases; last commit 2026-07-12.
License · maintenance · safety
MIT (permissive) — MIT license is permissive—you can use, modify, and distribute npmai freely in commercial or private projects with minimal restrictions.
last release 2026-05-16 (90 days) · last repo commit 2026-07-12 · 1 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,108,957 downloads/mo, #4,358 on PyPI
Alternatives
Verify before relying
pip install npmai
from npmai import Ollama
llm = Ollama()
response = llm.invoke("What is the future of AI?", model="llama3.2")
print(response)- Whether the claimed 1.2 million+ installations and 80K+ daily requests are independently verifiable or self-reported.
- Actual availability and uptime guarantees of the free cloud endpoints powering the service.
- Whether all 45+ models are equally stable or if some are experimental/unstable.
- Data retention and privacy policy for vectorized documents stored via Supabase integration.
What it is and 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—PDFs, images, video, audio, YouTube videos—converting them to text and storing them in a vectorized database, all on the cloud side with no local dependencies.
The 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 (0.1.8–0.1.9) added Supabase integration for long-term document storage and dynamic context retrieval logic to optimize RAG performance.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
npmai on PyPI
Before you install
Low install friction: pure Python wheel with a single runtime dependency (requests). Active maintenance with recent releases; last commit 2026-07-12. No known vulnerabilities.
Requires Python 3.9 or later (up to 3.13); cloud API endpoint must be reachable.
License in practice
MIT license is permissive—you can use, modify, and distribute npmai freely in commercial or private projects with minimal restrictions.
Quickstart
pip install npmai
from npmai import Ollama
llm = Ollama()
response = llm.invoke("What is the future of AI?", model="llama3.2")
print(response)Verify before relying
- Whether the claimed 1.2 million+ installations and 80K+ daily requests are independently verifiable or self-reported.
- Actual availability and uptime guarantees of the free cloud endpoints powering the service.
- Whether all 45+ models are equally stable or if some are experimental/unstable.
- Data retention and privacy policy for vectorized documents stored via Supabase integration.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <3.14,>=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagerequests |
| Maintenance | Actively maintained 90 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,108,957 / month, #4,358 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: npmai-0.1.9-py3-none-any.whl
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See also npmai-agents · llama-index · pymupdf4llm · llama-index-core · llama-index-llms-ollama · open-webui · llama-parse · llm · lightrag-hku · llama-index-cli