Ollama Start building with open models.

Ollama: Start Building with Open Models

In a landscape crowded with model marketplaces and cloud-first tools, Ollama positions itself as a versatile, locally focused platform for working with open models. It brings together a simple command-line interface, robust integration options, REST APIs, and a thriving ecosystem of libraries, community tools, and backends. With Ollama, developers, researchers, and enthusiasts can run, test, and orchestrate language models on their own machines or in their preferred environments—without being locked into a single provider.

Download and install: choosing your path

Ollama keeps installation options flexible, so you can pick the route that fits your system and workflow. Whether you’re on macOS, Windows, or Linux, there is a straightforward path to begin exploring open-model capabilities. For developers who want containerized workloads or quick experimentation, Ollama also ships a Docker image that you can pull and run.

• macOS
• Quick start: curl -fsSL https://ollama.com/install.sh | sh
• Alternative: download manually
• Windows
• Quick start: irm https://ollama.com/install.ps1 | iex
• Alternative: download manually
• Linux
• Quick start: curl -fsSL https://ollama.com/install.sh | sh
• Manual install instructions: Manual install instructions
• Docker
• The official Ollama Docker image ollama/ollama is available on Docker Hub.
• Libraries and SDKs
• ollama-python
• ollama-js
• Community channels
• Discord
• 𝕏 (Twitter)
• Reddit

From here, you’ll be invited to start Ollama and decide whether you want to run a model directly from the CLI or connect Ollama to existing agents or applications. The experience is designed to be approachable but powerful, giving you immediate access to a suite of integrations and tools.

Get started: a guided entry into Ollama

The moment you run the Ollama CLI, you’re prompted to choose a model to start with or to hook Ollama into your existing agent ecosystems. The workflow is designed so you can explore quickly and then deepen your setup as your needs evolve.

• Coding: launch a specific integration
• Example: ollama launch claude
• Supported integrations include Claude Code, Codex, Copilot CLI, Droid, and OpenCode, among others.
• Claude Code: integration for coding-oriented interactions
• Codex: code generation and assistance
• Copilot CLI: command-line copilots for development
• Droid: another development-oriented integration
• OpenCode: open-code oriented workflows
• AI assistant: turn Ollama into a personal AI helper
• Use OpenClaw to enable Ollama as a personal AI assistant across popular messaging platforms like WhatsApp, Telegram, Slack, and Discord
• Example: ollama launch openclaw
• Chat with a model: direct, interactive conversations
• Run a model interactively, or chat with Gemma 3
• Example: ollama run gemma3

If you want a quick jump into the broader library of models and examples, the Ollama library page hosts a wide range of models and usage examples:

• See ollama.com/library for the full list
• Quickstart guide: the quickstart section provides a concise path to onboarding and first experiments

REST API: running models programmatically

Ollama exposes a REST API that enables programmatic running and management of models. This is especially valuable for building apps, dashboards, or automation around model interactions.

• Example REST call (chat endpoint)
• curl http://localhost:11434/api/chat -d '{ "model": "gemma3", "messages": [{ "role": "user", "content": "Why is the sky blue?" }], "stream": false }'
• Python usage
• pip install ollama
• from ollama import chat
• response = chat(model='gemma3', messages=[ { 'role': 'user', 'content': 'Why is the sky blue?' } ])
• print(response.message.content)
• JavaScript usage
• npm i ollama
• import ollama from "ollama"
• const response = await ollama.chat({ model: "gemma3", messages: [ { role: "user", content: "Why is the sky blue?" } ] })
• console.log(response.message.content)

Documentation and references

Ollama’s documentation is organized to help you navigate from quickstart to advanced usage:

• CLI reference
• REST API reference
• Importing models
• Modelfile reference
• Building from source

Backends and ecosystem

• Supported backends: llama.cpp
• The llama.cpp project is a foundational backend for Ollama, enabling efficient execution of large language models on a wide range of hardware.
• Documentation resources
• CLI reference
• REST API reference
• Importing models
• Modelfile reference
• Building from source

Community integrations: a broad, interconnected ecosystem

Ollama’s strength lies in its expansive ecosystem of integrations, tools, and community-driven projects. The platforms listed below illustrate the breadth of possibilities—from web-based interfaces to desktop clients, code editors, and orchestration frameworks. Each item represents a pathway to connect Ollama with your preferred tools and workflows, enabling you to tailor the experience to your needs.

Chat Interfaces (Web)

• Open WebUI: an extensible, self-hosted AI interface
• Onyx: connected AI workspace
• LibreChat: multi-provider support, enhanced ChatGPT clone
• Lobe Chat: modern chat framework with plugin ecosystem
• NextChat: cross-platform ChatGPT UI
• Perplexica: AI-powered search engine (open-source Perplexity alternative)
• big-AGI: AI suite for professionals
• Lollms WebUI: multi-model web interface
• ChatOllama: chatbot with knowledge bases
• Bionic GPT: on-premise AI platform
• Chatbot UI: ChatGPT-style web interface
• Hollama: minimal web interface
• Chatbox: desktop and web AI client
• chat: chat web app for teams
• Ollama RAG Chatbot: RAG-style chat with multiple PDFs
• Tkinter-based client: Python desktop client for Ollama
• Ollama-gui: GUI desktop client for Ollama

Desktop

• Dify.AI, AnythingLLM, Maid: cross-platform AI app clients
• Witsy, Cherry Studio: multi-provider desktop clients
• Ollama App: multi-platform desktop/mobile client
• PyGPT, Alpaca: AI desktop assistants
• SwiftChat, Enchanted: native cross-platform clients
• Reins, ConfiChat: privacy-focused or encrypted experiences
• LLocal.in: Electron-based desktop client
• MindMac: AI chat client for Mac
• Msty, BoltAI for Mac: Mac-focused clients
• IntelliBar: AI-powered assistant for macOS
• Kerlig AI: AI writing assistant for macOS
• Hillnote: Markdown-first AI workspace
• Perfect Memory AI: AI productivity personalized by screen/meeting history

Mobile

• Ollama Android Chat: one-click Ollama on Android
• Note: SwiftChat, Enchanted, Maid, Ollama App, Reins, and ConfiChat also offer mobile support

Code Editors & Development

• Cline: VS Code extension for multi-file/whole-repo coding
• Continue: open-source AI code assistant for any IDE
• Void: open source AI code editor
• Copilot for Obsidian: AI assistant for Obsidian
• twinny: Copilot-like experiences
• gptel Emacs client: Emacs LLM client
• Ollama Copilot: Ollama integration for Copilot-like features
• Obsidian Local GPT: local AI for Obsidian
• Ellama Emacs client: Emacs LLM tools
• orbiton: config-free text editor with Ollama tab completion
• AI ST Completion: OpenAI-style completion in Sublime Text
• VT Code: Rust-based terminal coding agent

Libraries & SDKs

• LiteLLM: unified API for 100+ LLM providers
• Semantic Kernel (Microsoft): Python connectors for Ollama
• LangChain4j, LangChainGo, LangChain (Python/JS): integrations with Ollama
• Ollama for Ruby, Ollama for .NET (OllamaSharp), LangChainRust
• Java, Python, Go, Dart, Rust, and more language ecosystems
• Data frameworks and toolkits (LlamaIndex, Haystack, etc.)
• Ollama-hpp for C++, Ollama4j for Java, Ollama for Laravel, Ollama for Swift
• RAG frameworks and vector DB integrations (chromem-go, pgai, Kangaroo, etc.)
• Open-source wrappers and clients for various languages

Frameworks & Agents

• AutoGPT, crewAI, Strands Agents, Cheshire Cat, any-agent, Stakpak, Hexabot
• These frameworks enable autonomous agents or orchestrations that can leverage Ollama as a local model backend

RAG & Knowledge Bases

• RAGFlow, R2R, MaxKB, Minima, Chipper, ARGO, Archyve, Casibase, BrainSoup
• Tools to connect Ollama with document stores, knowledge bases, and retrieval-augmented generation workflows

Bots & Messaging

• LangBot, AstrBot, Discord-Ollama Chat Bot, Ollama Telegram Bot, LLM Telegram Bot
• Bots built around Ollama for cross-platform messaging experiences

Terminal & CLI

• aichat, oterm, gollama, tlm, tenere, ParLlama, llm-ollama, ShellOracle
• Command-line and terminal-first interfaces for interacting with Ollama

Productivity & Apps

• AppFlowy, Screenpipe, Vibe, Page Assist
• Notion-like collaboration and AI-assisted productivity tools
• NativeMind, Ollama Fortress, 1Panel, Writeopia
• AI-assisted editing, browsing, and workflow automation

Observability & Monitoring

• Opik, OpenLIT, Lunary, Langfuse, HoneyHive, MLflow Tracing
• Monitoring, observability, and debugging for LLM apps

Database & Embeddings

• pgai (PostgreSQL as a vector database)
• MindsDB, chromem-go (embeddable Ollama embeddings)
• Kangaroo (AI-powered SQL client)

Infrastructure & Deployment

Cloud

• Google Cloud (GPU Ollama tutorials)
• Fly.io, Koyeb, Harbor
• Containerized LLM toolkit with Ollama as the default backend

Package Managers

• Pacman (Arch Linux), Homebrew, Nix, Helm Chart, Gentoo, Flox, Guix channel
• Package managers that provide Ollama or Ollama-related tooling for different environments

A thriving, evolving ecosystem

The Ollama ecosystem is a tapestry of projects, contributors, and communities. It covers the full spectrum of use cases—from local experimentation and research to production-grade deployments and developer tooling. Whether you’re building a local-first AI assistant, a knowledge-aware chat interface, or an integrated development workflow with AI copilots, Ollama provides the backbone to run models locally while still connecting to external tools and services when you need them.

What makes Ollama compelling

• Open models, local control: Ollama emphasizes open-model ecosystems, giving you control over how models run, how data is managed, and how you connect with other tools.
• Flexible installation: With macOS, Windows, Linux, and Docker options, you can set up Ollama in a way that aligns with your hardware, preferences, and CI/CD environments.
• Rich integration surface: The platform is designed to plug into a wide array of agents, assistants, browsers, editors, and cloud services, enabling you to orchestrate models in a way that suits your workflow.
• REST and SDKs: The REST API, along with Python and JavaScript SDKs, lowers the barrier to building apps and automations that leverage local models.
• Community-driven: A large ecosystem of community projects, aliases, and connectors means you can reuse and adapt existing work rather than starting from scratch.

Getting involved

If you’re excited about local AI and open-model ecosystems, Ollama invites you to explore, contribute, and connect with others who are building at the edge of what’s possible. You can participate through the official channels, browse the library of models, or contribute integrations and tooling via pull requests and community discussions. The breadth of tools and integrations means there’s likely a path that fits your current project, whether you’re tinkering on a personal machine, prototyping an app, or contributing to a broader AI workflow framework.

How to approach your first project with Ollama

• Start with the basics: install Ollama on your preferred platform using the macOS, Windows, or Linux instructions, then run a simple model through the CLI or the REST API.
• Experiment with integrations: pick a development-focused integration (such as Claude Code, Codex, or Copilot CLI) to accelerate coding tasks, or try OpenClaw to turn Ollama into an ambient AI assistant across messaging apps.
• Explore the library: visit ollama.com/library to see a broad catalog of models and example usage. The quickstart guide is a useful companion for getting up to speed.
• Build with the REST API: leverage the curl example, and then port the interaction into Python or JavaScript to embed model interactions into your apps.
• Tap into the community: join the Discord, X (Twitter), and Reddit channels to share experiences, ask questions, and find collaborators who are building similar things.

In closing

Ollama is more than a single tool; it’s an ecosystem that invites you to run open models locally, while still providing the flexibility to connect with a wide range of external services, frameworks, and communities. Whether you’re a researcher exploring the capabilities of new models, a developer integrating AI into software, or a hobbyist building personal AI assistants, Ollama serves as a solid foundation for experimentation, development, and deployment. The combination of straightforward installation options, a rich REST API, diverse integrations, and a sprawling community makes Ollama a compelling choice for those who value both openness and versatility in their AI tooling.

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