--- title: "obsidian-llm-wiki-local vs ollama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kytmanov-obsidian-llm-wiki-local-vs-ollama-ollama" tools: ["kytmanov-obsidian-llm-wiki-local", "ollama-ollama"] --- # obsidian-llm-wiki-local vs ollama *GraphCanon updated Jul 15, 2026* ## Verdict Pick obsidian-llm-wiki-local when obsidian-llm-wiki-local is primarily Python; ollama is Go; pick ollama when ollama is primarily Go; obsidian-llm-wiki-local is Python. [obsidian-llm-wiki-local](https://github.com/kytmanov/obsidian-llm-wiki-local) reports 771 GitHub stars, 122 forks, and 2 open issues, last pushed May 26, 2026. [ollama](https://ollama.com) has 176k stars, 17k forks, and 3.4k open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [obsidian-llm-wiki-local's repository](https://github.com/kytmanov/obsidian-llm-wiki-local) and [ollama's repository](https://github.com/ollama/ollama). | | [obsidian-llm-wiki-local](/tools/kytmanov-obsidian-llm-wiki-local.md) | [ollama](/tools/ollama-ollama.md) | | --- | --- | --- | | Tagline | Karpathy’s LLM Wiki, 100% local with Ollama. Drop Markdown notes → AI extracts concepts → your Obsidian wiki auto-links and grows. Zero sharing. Your notes stay yours. | Get up and running with various large language models using Ollama. | | Stars | 771 | 175,936 | | Forks | 122 | 16,939 | | Open issues | 2 | 3,423 | | Language | Python | Go | | Adopt for | - | Ollama is a Go-based platform that provides tools for deploying and managing large language models (LLMs) like Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma using docker images, package managers, cloud and | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT license - permissive open-source licensing that allows for broad use of the tool. | | Categories | Data & Retrieval, Inference & Serving, LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [obsidian-llm-wiki-local](/tools/kytmanov-obsidian-llm-wiki-local.md) | [ollama](/tools/ollama-ollama.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 50d | 1d | | Open issues (now) | 2 | 3.4k | | Owner type | User | Organization | | Full report | [trust report](/tools/kytmanov-obsidian-llm-wiki-local/trust.md) | [trust report](/tools/ollama-ollama/trust.md) | ## Decision facts: ollama - **Hosting:** self hosted - Ollama supports self-hosted and cloud-deployable models using Docker, Helm charts, and various package managers. - **Adopt for:** Ollama is a Go-based platform that provides tools for deploying and managing large language models (LLMs) like Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma using docker images, package managers, cloud and - **License detail:** MIT license - permissive open-source licensing that allows for broad use of the tool. ## Choose when ### Choose obsidian-llm-wiki-local if… - obsidian-llm-wiki-local is primarily Python; ollama is Go. - Tags unique to obsidian-llm-wiki-local: git-based-wiki, karpathy, knowledge-base, llm-knowledge-base. - Also covers Data & Retrieval. ### Choose ollama if… - ollama is primarily Go; obsidian-llm-wiki-local is Python. - Ollama supports self-hosted and cloud-deployable models using Docker, Helm charts, and various package managers. - Tags unique to ollama: deepseek, gemma, glm, go. - ollama ships Docker support for self-hosted deployment. - Use Ollama when you require a multi-model platform supporting several large language models such as Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and intend to deploy in various cloud or ## When NOT to use obsidian-llm-wiki-local - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use ollama - Avoid using Ollama if you are only interested in a single LLM deployment and seek simplified, model-specific solutions with tailored support rather than a comprehensive multi-model platform. ## Common questions ### What is the difference between obsidian-llm-wiki-local and ollama? obsidian-llm-wiki-local: Karpathy’s LLM Wiki, 100% local with Ollama. Drop Markdown notes → AI extracts concepts → your Obsidian wiki auto-links and grows. Zero sharing. Your notes stay yours.. ollama: Get up and running with various large language models using Ollama.. See the comparison table for live GitHub stats and shared categories. ### When should I choose obsidian-llm-wiki-local over ollama? Choose obsidian-llm-wiki-local over ollama when obsidian-llm-wiki-local is primarily Python; ollama is Go; Tags unique to obsidian-llm-wiki-local: git-based-wiki, karpathy, knowledge-base, llm-knowledge-base; Also covers Data & Retrieval. ### When should I choose ollama over obsidian-llm-wiki-local? Choose ollama over obsidian-llm-wiki-local when ollama is primarily Go; obsidian-llm-wiki-local is Python; Ollama supports self-hosted and cloud-deployable models using Docker, Helm charts, and various package managers; Tags unique to ollama: deepseek, gemma, glm, go; ollama ships Docker support for self-hosted deployment; Use Ollama when you require a multi-model platform supporting several large language models such as Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and intend to deploy in various cloud or. ### When should I avoid obsidian-llm-wiki-local? Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid ollama? Avoid using Ollama if you are only interested in a single LLM deployment and seek simplified, model-specific solutions with tailored support rather than a comprehensive multi-model platform. ### Is obsidian-llm-wiki-local or ollama more popular on GitHub? ollama has more GitHub stars (175,936 vs 771). Stars measure visibility, not whether either tool fits your constraints. ### Are obsidian-llm-wiki-local and ollama open source? Yes - both are open-source projects on GitHub (obsidian-llm-wiki-local: MIT, ollama: MIT). ### Where can I find alternatives to obsidian-llm-wiki-local or ollama? GraphCanon lists graph-backed alternatives at [obsidian-llm-wiki-local alternatives](/tools/kytmanov-obsidian-llm-wiki-local/alternatives) and [ollama alternatives](/tools/ollama-ollama/alternatives) ([obsidian-llm-wiki-local markdown twin](/tools/kytmanov-obsidian-llm-wiki-local/alternatives.md), [ollama markdown twin](/tools/ollama-ollama/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/kytmanov-obsidian-llm-wiki-local-vs-ollama-ollama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, obsidian-llm-wiki-local or ollama? obsidian-llm-wiki-local: Steady. ollama: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for obsidian-llm-wiki-local and ollama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [obsidian-llm-wiki-local trust report](/tools/kytmanov-obsidian-llm-wiki-local/trust); [ollama trust report](/tools/ollama-ollama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kytmanov-obsidian-llm-wiki-local`](/api/graphcanon/graph?tool=kytmanov-obsidian-llm-wiki-local) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_