Agents-OSS
EN
Submit
← Back to all projects

Ollama

Run a published AI model on your own machine with one command

OfficialMIT
Stars
180k
Forks
17.6k
Open issues
3.8k
Last commit
28 Aug 2026

What is Ollama?

Pulls a quantised model — one compressed to run in less memory — and starts a local HTTP API in front of it, so running a model is a single command. The obvious starting point for building against a model on your own hardware. For serving many people at once, a server built for throughput will do considerably better.

What can you do with Ollama?

  • Run a model with one command — ollama run gemma4 fetches what it needs and drops you straight into a conversation. The models you can run are listed at ollama.com/library.
  • Choose between your own hardware and their cloud — The same commands either run the model on your machine or, for models too large for it, run them on Ollama's cloud instead.
  • Call it from your own app — Send JSON to http://localhost:11434/api/chat and a reply comes back. Official Python and JavaScript clients install with pip install ollama or npm i ollama.
  • Point a coding assistant at your own model — ollama launch claude starts Claude Code against a model running on your machine, and the same command covers Codex, Copilot CLI, OpenCode, Droid and DeepSeek Harness.
  • Reach it from the chat apps you use — ollama launch openclaw puts your local model behind WhatsApp, Telegram, Slack and Discord as a personal assistant.

Before you choose Ollama

  • Downloading a model can fail its integrity check and have to start over. The report has been open since October 2023 with more than 150 comments, described as uncommon but never closed.Reported in#941

Star history

15 Aug to 28 Aug · +1.1k

179k180k

Frequently asked questions

Is Ollama free for commercial use?

Ollama is released under the MIT licence — OSI-approved open source, which permits commercial use.

How can Ollama be deployed?

Ollama is available as Runs locally / Self-hosted.

Documentation

Reproduced from the ollama/ollama README, published under MIT. Read the original ↗

Ollama

Start building with open models.

Download

macOS

curl -fsSL https://ollama.com/install.sh | sh

or download manually

Windows

irm https://ollama.com/install.ps1 | iex

or download manually

Linux

curl -fsSL https://ollama.com/install.sh | sh

Manual install instructions

Docker

The official Ollama Docker image ollama/ollama is available on Docker Hub.

Libraries

Community

Get started

ollama

You’ll be prompted to run a model or connect Ollama to your existing agents or applications such as Claude Code, OpenClaw, OpenCode , Codex, Copilot, and more.

Coding

To launch a specific integration:

ollama launch claude

Supported integrations include Claude Code, Codex, Copilot CLI, DeepSeek Harness, Droid, and OpenCode.

AI assistant

Use OpenClaw to turn Ollama into a personal AI assistant across WhatsApp, Telegram, Slack, Discord, and more:

ollama launch openclaw

Chat with a model

Run and chat with Gemma 4:

ollama run gemma4

See ollama.com/library for the full list.

See the quickstart guide for more details.

REST API

Ollama has a REST API for running and managing models.

curl http://localhost:11434/api/chat -d '{
  "model": "gemma4",
  "messages": [{
    "role": "user",
    "content": "Why is the sky blue?"
  }],
  "stream": false
}'

See the API documentation for all endpoints.

Python

pip install ollama
from ollama import chat

response = chat(model='gemma4', messages=[
  {
    'role': 'user',
    'content': 'Why is the sky blue?',
  },
])
print(response.message.content)

JavaScript

npm i ollama
import ollama from "ollama";

const response = await ollama.chat({
  model: "gemma4",
  messages: [{ role: "user", content: "Why is the sky blue?" }],
});
console.log(response.message.content);

Supported backends

  • llama.cpp project founded by Georgi Gerganov.

Documentation

Community Integrations

Want to add your project? Open a pull request.

Chat Interfaces

Web

Desktop

  • Dify.AI - LLM app development platform
  • AnythingLLM - All-in-one AI app for Mac, Windows, and Linux
  • Maid - Cross-platform mobile and desktop client
  • Witsy - AI desktop app for Mac, Windows, and Linux
  • Cherry Studio - Multi-provider desktop client
  • Ollama App - Multi-platform client for desktop and mobile
  • PyGPT - AI desktop assistant for Linux, Windows, and Mac
  • Alpaca - GTK4 client for Linux and macOS
  • SwiftChat - Cross-platform including iOS, Android, and Apple Vision Pro
  • Enchanted - Native macOS and iOS client
  • RWKV-Runner - Multi-model desktop runner
  • Ollama Grid Search - Evaluate and compare models
  • macai - macOS client for Ollama and ChatGPT
  • AI Studio - Multi-provider desktop IDE
  • Reins - Parameter tuning and reasoning model support
  • ConfiChat - Privacy-focused with optional encryption
  • LLocal.in - Electron desktop client
  • MindMac - AI chat client for Mac
  • Msty - Multi-model desktop client
  • BoltAI for Mac - AI chat client for Mac
  • IntelliBar - AI-powered assistant for macOS
  • Kerlig AI - AI writing assistant for macOS
  • Hillnote - Markdown-first AI workspace
  • Perfect Memory AI - Productivity AI personalized by screen and meeting history

Mobile

SwiftChat, Enchanted, Maid, Ollama App, Reins, and ConfiChat listed above also support mobile platforms.

Code Editors & Development

Libraries & SDKs

Frameworks & Agents

RAG & Knowledge Bases

  • RAGFlow - RAG engine based on deep document understanding
  • R2R - Open-source RAG engine
  • MaxKB - Ready-to-use RAG chatbot
  • Minima - On-premises or fully local RAG
  • Chipper - AI interface with Haystack RAG
  • ARGO - RAG and deep research on Mac/Windows/Linux
  • Archyve - RAG-enabling document library
  • Casibase - AI knowledge base with RAG and SSO
  • BrainSoup - Native client with RAG and multi-agent automation

Bots & Messaging

Terminal & CLI

Productivity & Apps

Observability & Monitoring

  • Opik - Debug, evaluate, and monitor LLM applications
  • OpenLIT - OpenTelemetry-native monitoring for Ollama and GPUs
  • Lunary - LLM observability with analytics and PII masking
  • Langfuse - Open source LLM observability
  • HoneyHive - AI observability and evaluation for agents
  • MLflow Tracing - Open source LLM observability

Database & Embeddings

Infrastructure & Deployment

Cloud

Package Managers

Ollama
Ask AI
GitHub

Other options in Model Serving

Compare side by side:llama.cpp vs Ollama

llama.cppModel Serving

The quantisation work here is what put usable models on hardware without a datacentre GPU, and much of the local-model ecosystem is built on top of it. Working with it directly means dealing with build flags and hardware specifics that higher-level wrappers hide from you.

OfficialMIT126k+1.1k
vLLMModel Serving

Paged attention and continuous batching let one GPU serve far more concurrent requests than a naive loop, which matters because agents are unusually chatty. The OpenAI-compatible endpoint means most agent frameworks point at it with a base-URL change. Expect real operational work around GPU memory sizing and model loading times.

OfficialApache-2.090.3k+686
LiteLLMModel Serving

The library half lets one function call reach any supported provider in the OpenAI request and response shape. The gateway half is the reason most teams arrive: a self-hosted server that issues virtual keys with their own budgets and rate limits, falls back when a provider fails, spreads load across deployments, and records cost per key. The price is an extra component in the request path that you now operate, and code under the enterprise directory is not covered by the MIT grant.

OfficialMIT57.5k+553