--- title: "ollama vs open-multi-agent" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ollama-ollama-vs-open-multi-agent-open-multi-agent" tools: ["ollama-ollama", "open-multi-agent-open-multi-agent"] --- # ollama vs open-multi-agent *GraphCanon updated Jul 15, 2026* ## Verdict Pick ollama when ollama is primarily Go; open-multi-agent is TypeScript; pick open-multi-agent when open-multi-agent is primarily TypeScript; ollama is Go. [ollama](https://ollama.com) reports 176k GitHub stars, 17k forks, and 3.4k open issues, last pushed Jul 10, 2026. [open-multi-agent](https://open-multi-agent.com/?utm_source=github) has 6.6k stars, 2.4k forks, and 10 open issues, last pushed Jul 15, 2026. Figures are from public GitHub metadata via [ollama's repository](https://github.com/ollama/ollama) and [open-multi-agent's repository](https://github.com/open-multi-agent/open-multi-agent). | | [ollama](/tools/ollama-ollama.md) | [open-multi-agent](/tools/open-multi-agent-open-multi-agent.md) | | --- | --- | --- | | Tagline | Get up and running with various large language models using Ollama. | TypeScript AI agent orchestration framework with dynamic workflows. Describe the goal, not the graph: a coordinator plans the task DAG at runtime and runs it on any LLM (Claude, ChatGPT, Gemini, DeepS | | Stars | 175,936 | 6,581 | | Forks | 16,939 | 2,407 | | Open issues | 3,423 | 10 | | Language | Go | TypeScript | | 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 license - permissive open-source licensing that allows for broad use of the tool. | MIT | | Categories | Inference & Serving, LLM Frameworks | AI Agents, Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ollama](/tools/ollama-ollama.md) | [open-multi-agent](/tools/open-multi-agent-open-multi-agent.md) | | --- | --- | --- | | Days since push | 1d | 0d | | Open issues (now) | 3.4k | 10 | | Full report | [trust report](/tools/ollama-ollama/trust.md) | [trust report](/tools/open-multi-agent-open-multi-agent/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 ollama if… - ollama is primarily Go; open-multi-agent is TypeScript. - 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 ### Choose open-multi-agent if… - open-multi-agent is primarily TypeScript; ollama is Go. - Tags unique to open-multi-agent: agent-framework, agent-orchestration, agentic-ai, ai-agents. - Also covers AI Agents. ## 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. ## When NOT to use open-multi-agent - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - 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. ## Common questions ### What is the difference between ollama and open-multi-agent? ollama: Get up and running with various large language models using Ollama.. open-multi-agent: TypeScript AI agent orchestration framework with dynamic workflows. Describe the goal, not the graph: a coordinator plans the task DAG at runtime and runs it on any LLM (Claude, ChatGPT, Gemini, DeepS. See the comparison table for live GitHub stats and shared categories. ### When should I choose ollama over open-multi-agent? Choose ollama over open-multi-agent when ollama is primarily Go; open-multi-agent is TypeScript; 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 choose open-multi-agent over ollama? Choose open-multi-agent over ollama when open-multi-agent is primarily TypeScript; ollama is Go; Tags unique to open-multi-agent: agent-framework, agent-orchestration, agentic-ai, ai-agents; Also covers AI Agents. ### 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. ### When should I avoid open-multi-agent? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. 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. ### Is ollama or open-multi-agent more popular on GitHub? ollama has more GitHub stars (175,936 vs 6,581). Stars measure visibility, not whether either tool fits your constraints. ### Are ollama and open-multi-agent open source? Yes - both are open-source projects on GitHub (ollama: MIT, open-multi-agent: MIT). ### Where can I find alternatives to ollama or open-multi-agent? GraphCanon lists graph-backed alternatives at [ollama alternatives](/tools/ollama-ollama/alternatives) and [open-multi-agent alternatives](/tools/open-multi-agent-open-multi-agent/alternatives) ([ollama markdown twin](/tools/ollama-ollama/alternatives.md), [open-multi-agent markdown twin](/tools/open-multi-agent-open-multi-agent/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/ollama-ollama-vs-open-multi-agent-open-multi-agent.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ollama or open-multi-agent? ollama: Very active. open-multi-agent: 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 ollama and open-multi-agent? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ollama trust report](/tools/ollama-ollama/trust); [open-multi-agent trust report](/tools/open-multi-agent-open-multi-agent/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ollama-ollama`](/api/graphcanon/graph?tool=ollama-ollama) - 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/_