ollama/ollama

What is Ollama?

Ollama is a self‑hosted platform for running large language models (LLMs) locally. It bundles a command‑line interface, a REST API, and a set of integrations that let you:

• Download and run open‑source models (e.g., Gemma 4) on your own machine (macOS, Windows, Linux, Docker).
• Interact with those models via a simple ollama run <model> command, a chat‑style REPL, or programmatically through HTTP or language‑specific SDKs.
• Connect the local models to other tools – code assistants, AI chat bots, RAG pipelines, IDE extensions, and more – via the provided integrations.

The project is built on top of llama.cpp, a fast CPU‑only inference engine, and ships its own Docker image for containerised use.

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Why it matters

• Privacy & control – Because the models run on your hardware, no data leaves your device.
• Cost‑effective – No cloud‑API fees; you only need the compute resources you already have.
• Open ecosystem – A growing list of community integrations (web UIs, desktop apps, VS Code extensions, RAG frameworks, etc.) lets you plug Ollama into virtually any workflow.

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Getting started (quick steps)

1. Install – Run the platform‑specific script (macOS/ Linux) or PowerShell command (Windows). A Docker image is also available.
2. Run a model – ollama run gemma4 starts the Gemma 4 model and opens a chat prompt.
3. Use the API – Send a POST to http://localhost:11434/api/chat with a JSON payload containing model, messages, and optional stream flag.
4. Integrate – Choose a ready‑made integration (e.g., ollama launch claude for Claude Code, ollama launch openclaw for a multi‑messenger assistant) or call the SDKs in Python, JavaScript, Rust, etc.

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Core features (as described in the README)

Feature	Description
Cross‑platform binaries	Installers for macOS, Windows, Linux, plus a Docker image.
CLI	ollama command lets you list, pull, run, and manage models.
REST API	HTTP endpoints for chat, completions, embeddings, model management.
Model library	Access to a public catalog (e.g., Gemma 4) via ollama run <model>.
Integrations	Pre‑built launch commands for Claude Code, Codex, Copilot CLI, OpenClaw, etc.
Language SDKs	Official Python (ollama-python) and JavaScript (ollama-js) packages; many community SDKs for Go, Rust, .NET, Ruby, etc.
Backend	Uses llama.cpp for fast CPU inference; can also run on GPU‑enabled builds.
Extensive ecosystem	Hundreds of community projects: web UIs, desktop clients, VS Code extensions, RAG frameworks, agents, monitoring tools, and deployment scripts.

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Who should use Ollama?

• Developers who want to prototype or ship AI features without relying on external APIs.
• Researchers & hobbyists interested in experimenting with open‑source LLMs locally.
• Enterprises needing on‑premise AI for data‑privacy or regulatory compliance.
• Tool builders looking for a simple, uniform backend to power chat interfaces, code assistants, or retrieval‑augmented generation pipelines.

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Where to find more information

• Official docs – CLI reference, API spec, model import guide, and development notes are linked from the README.
• Community – Discord, X (Twitter), Reddit, and a long list of third‑party integrations (web UI, desktop apps, IDE plugins, agent frameworks, etc.).
• Source code – The repository includes a docs/development.md for building Ollama from source.

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TL;DR

Ollama is a practical, open‑source way to run LLMs on your own hardware, offering a CLI, REST API, and a rich ecosystem of integrations that let you turn local models into chat assistants, code helpers, RAG pipelines, and more—without sending data to the cloud.