--- title: "graphrag-rs vs ollama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automataia-graphrag-rs-vs-ollama-ollama" tools: ["automataia-graphrag-rs", "ollama-ollama"] --- # graphrag-rs vs ollama *GraphCanon updated Jul 11, 2026* ## Verdict Pick graphrag-rs when graphrag-rs is primarily Rust; ollama is Go; pick ollama when ollama is primarily Go; graphrag-rs is Rust. [graphrag-rs](https://automataia.github.io/graphrag-rs/) reports 518 GitHub stars, 47 forks, and 0 open issues, last pushed Jun 2, 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 [graphrag-rs's repository](https://github.com/automataIA/graphrag-rs) and [ollama's repository](https://github.com/ollama/ollama). | | [graphrag-rs](/tools/automataia-graphrag-rs.md) | [ollama](/tools/ollama-ollama.md) | | --- | --- | --- | | Tagline | GraphRAG-rs is a high-performance, state-of-the-art Rust implementation of GraphRAG (Graph-based Retrieval Augmented Generation) that builds knowledge graphs from documents and enables natural languag | Get up and running with various large language models using Ollama. | | Stars | 518 | 175,936 | | Forks | 47 | 16,939 | | Open issues | 0 | 3,423 | | Language | Rust | 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 | Inference & Serving, LLM Frameworks, Vector Databases | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [graphrag-rs](/tools/automataia-graphrag-rs.md) | [ollama](/tools/ollama-ollama.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 38d | 1d | | Open issues (now) | 0 | 3.4k | | Owner type | User | Organization | | Security scan | No lockfile | 52 low (52 low) | | Full report | [trust report](/tools/automataia-graphrag-rs/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 graphrag-rs if… - graphrag-rs is primarily Rust; ollama is Go. - Tags unique to graphrag-rs: ai, embeddings, entity-extraction, graphrag. - Also covers Vector Databases. ### Choose ollama if… - ollama is primarily Go; graphrag-rs is Rust. - 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 graphrag-rs - 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. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## 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 graphrag-rs and ollama? graphrag-rs: GraphRAG-rs is a high-performance, state-of-the-art Rust implementation of GraphRAG (Graph-based Retrieval Augmented Generation) that builds knowledge graphs from documents and enables natural languag. 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 graphrag-rs over ollama? Choose graphrag-rs over ollama when graphrag-rs is primarily Rust; ollama is Go; Tags unique to graphrag-rs: ai, embeddings, entity-extraction, graphrag; Also covers Vector Databases. ### When should I choose ollama over graphrag-rs? Choose ollama over graphrag-rs when ollama is primarily Go; graphrag-rs is Rust; 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 graphrag-rs? 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. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### 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 graphrag-rs or ollama more popular on GitHub? ollama has more GitHub stars (175,936 vs 518). Stars measure visibility, not whether either tool fits your constraints. ### Are graphrag-rs and ollama open source? Yes - both are open-source projects on GitHub (graphrag-rs: MIT, ollama: MIT). ### Where can I find alternatives to graphrag-rs or ollama? GraphCanon lists graph-backed alternatives at [graphrag-rs alternatives](/tools/automataia-graphrag-rs/alternatives) and [ollama alternatives](/tools/ollama-ollama/alternatives) ([graphrag-rs markdown twin](/tools/automataia-graphrag-rs/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/automataia-graphrag-rs-vs-ollama-ollama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, graphrag-rs or ollama? graphrag-rs: 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 graphrag-rs and ollama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [graphrag-rs trust report](/tools/automataia-graphrag-rs/trust); [ollama trust report](/tools/ollama-ollama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=automataia-graphrag-rs`](/api/graphcanon/graph?tool=automataia-graphrag-rs) - 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/_