by ollama · Agent Tool · ★ 183.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ollama safe to install? View the security audit →
Ollama Start building with open models. Download macOS or download manually Windows or download manually Linux Manual install instructions Docker The official Ollama Docker image is available on Docker Hub. Libraries ollama-python ollama-js Community Discord 𝕏 (Twitter) Reddit Get started You'll be prompted to run a model or connect Ollama to your existing agents or applications such as , , and more. Coding To launch a specific integration: Supported integrations include Claude Code, Codex, [Droid](https://docs.ollama.com/integrati
pip install ollama
npm i ollamaTaken from the project's README on GitHub. Check the security audit before running it.
No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
| Stars | 183,124 |
| Forks | 18,283 |
| Language | Go |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 57.532110857249/100 |
| Open Issues | 4267 |
| Last Updated | 2026-10-14 |
| Created | 2023-06-26 |
| Platforms | go |
| Est. Tokens | ~20k |
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ollama is Get up and running with Kimi, GLM, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.. It is categorized as a Agent Tool with 183.1k GitHub stars.
ollama is primarily written in Go. It covers topics such as deepseek, gemma, gemma3.
The README on GitHub (github.com/ollama/ollama) gives: pip install ollama , then npm i ollama. Usage details are in the same README.
ollama is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ollama on Agent Skills Hub include unsloth, open-webui, ragflow. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: