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Ollama

by ollamaGo

Run large language models locally to reduce inference costs and latency.

Toolexperimental

Maturity: experimental because latest release v0.32.5 is pre 1.0. Derived from release and commit history, not a rating.

Stars
178k
Forks
17k
Downloads / mo
—
Last commit
2026-07-31
License
MIT
Open issues
3.6k

Market and trust evidence

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In practice

Written by AI from this repository’s README · high confidence

Running a local model normally means compiling an inference engine and managing weights and serving yourself.

Use it when

Use it when you want to pull and chat with open models on your own machine and expose them to tools over a local API.

Not the right pick when

Model quality and speed depend on your own hardware, since everything runs locally rather than on a hosted endpoint.

Capabilities

  • run and chat with models from the command line
  • REST API for running and managing models
  • Python and JavaScript client libraries
  • launch integrations for coding agents
  • official Docker image on Docker Hub
  • macOS, Windows and Linux installers

Cost: Free and open source

CompareRepository ↗Homepage ↗

Video walkthroughs

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What the repository ships

Ships CLAUDE.mdHas docsDocker imageSecurity policyCI configured

Detected from the actual files in the repository root.

Latest release v0.32.5

Published 2026-07-27

What's Changed

  • Fixed an MLX Metal bug that could reduce output quality for NVFP4 models, particularly Laguna.

Full Changelog: https://github.com/ollama/ollama/compare/v0.32.4...v0.32.5

Tags

Cheaper-Model Agentslocal-llminferencecost-savingsopen-source

README

<p align="center">

<a href="https://ollama.com">

<img src="https://github.com/ollama/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7" alt="ollama" width="200"/>

</a>

</p>

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, 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 -

Truncated. Read the full README on GitHub ↗

Related tools

AiderAider-AIAI pair-programming CLI with model routing and cost-effective multi-model editing strategies.llama-cpp-pythonabetlenPython bindings for llama.cpp enabling low cost local large language model inference.llama.cppggerganovEfficient C plus plus inference of LLaMA models enabling cheaper on device execution.llamafileMozilla-OchoSingle file executables that run LLMs locally without heavy infrastructure or API cost.ExLlamaV2turboderpFast inference library for running quantized LLMs on consumer GPUs at lower cost.OpenLLMbentomlPlatform for running and deploying open source LLMs cost effectively in production.