ollama/ollama

Get up and running with Kimi-K2.6, GLM-5.2, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.

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Summary Information

Updated 31 minutes ago
Added to GitGenius on February 10th, 2024
Created on June 26th, 2023
Open Issues & Pull Requests: 3,691 (+1)
Number of forks: 17,404
Total Stargazers: 178,512 (+0)
Total Subscribers: 1,003 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 2.6 hours
Mean response time: 22.3 days
90th percentile: 53.9 days
Tracked items: 9,023

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 97% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. 58% of tracked open issues have had no activity in three months. Only 5% of issues opened in the past year have been closed. Three people close 53% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 2,390
New in 7 days: 48
Closed in 7 days: 40
Avg open age: 315 days
Stale 30+ days: 2,169
Stale 90+ days: 1,858

Recent activity

Opened in 7 days: 43
Closed in 7 days: 38
Comments in 7 days: 125
Events in 7 days: 293

Top labels

  • bug (5,723)
  • feature request (2,028)
  • model (757)
  • needs more info (513)
  • question (271)
  • nvidia (257)
  • windows (246)
  • amd (163)

Detailed Description

Ollama is a Go-based application that enables users to run and manage large language models locally on their machines. The project provides a straightforward way to download, install, and interact with open-source models including Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma, and other models. The application is available across multiple platforms with dedicated installers for macOS and Windows, manual installation options for Linux, and Docker support through an official Docker image on Docker Hub.

The core functionality centers on local model deployment and inference. Ollama exposes a REST API that allows developers to run and manage models programmatically, with comprehensive API documentation available. The project provides official client libraries for Python and JavaScript, enabling integration into various applications and workflows. The application supports multiple backends, with llama.cpp from the ggml-org project serving as a primary inference engine.

Ollama's ecosystem extends significantly through community integrations. The README documents dozens of third-party applications built on top of Ollama, spanning web-based chat interfaces like Open WebUI, Lobe Chat, and LibreChat, desktop applications such as AnythingLLM and Cherry Studio, mobile clients, and code editor integrations. Notable integrations include support for Claude Code, Codex, Copilot CLI, and OpenCode, allowing Ollama to function as a backend for AI-powered coding assistants. The project also supports integration with OpenClaw for creating personal AI assistants across messaging platforms including WhatsApp, Telegram, Slack, and Discord.

The repository's classification spans multiple domains including LLM infrastructure, interoperability, containerization, model customization, local deployment, self-hosting, and machine learning integration. The project overlaps with contributors from major repositories including microsoft/vscode, rust-lang/rust, and microsoft/typescript, indicating cross-pollination with significant open-source projects. Ollama's documentation includes CLI references, REST API specifications, model importing guides, Modelfile references, and build-from-source instructions, supporting both end users and developers who wish to extend the platform.