--- title: "agency-orchestrator vs ollama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jnmetacode-agency-orchestrator-vs-ollama-ollama" tools: ["jnmetacode-agency-orchestrator", "ollama-ollama"] --- # agency-orchestrator vs ollama *GraphCanon updated Jul 15, 2026* ## Verdict Pick agency-orchestrator when agency-orchestrator is primarily TypeScript; ollama is Go; pick ollama when ollama is primarily Go; agency-orchestrator is TypeScript. [agency-orchestrator](https://ao.aiolaola.com) reports 1.8k GitHub stars, 238 forks, and 14 open issues, last pushed Jul 15, 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 [agency-orchestrator's repository](https://github.com/jnMetaCode/agency-orchestrator) and [ollama's repository](https://github.com/ollama/ollama). | | [agency-orchestrator](/tools/jnmetacode-agency-orchestrator.md) | [ollama](/tools/ollama-ollama.md) | | --- | --- | --- | | Tagline | 🚀 One sentence → multi-AI-role collaboration → complete plan in minutes. Built on the agency-agents role library (216+ experts), zero-code YAML, web Studio + desktop app, 10 LLM providers (7 free). 基 | Get up and running with various large language models using Ollama. | | Stars | 1,790 | 175,936 | | Forks | 238 | 16,939 | | Open issues | 14 | 3,423 | | Language | TypeScript | 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 | Apache-2.0 | MIT license - permissive open-source licensing that allows for broad use of the tool. | | Categories | AI Agents, Inference & Serving, LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agency-orchestrator](/tools/jnmetacode-agency-orchestrator.md) | [ollama](/tools/ollama-ollama.md) | | --- | --- | --- | | Days since push | 0d | 1d | | Open issues (now) | 14 | 3.4k | | Owner type | User | Organization | | Full report | [trust report](/tools/jnmetacode-agency-orchestrator/trust.md) | [trust report](/tools/ollama-ollama/trust.md) | ## Shared compatibility - **Python**: [agency-orchestrator](/tools/jnmetacode-agency-orchestrator.md) - Python runtime; [ollama](/tools/ollama-ollama.md) - Python runtime ## 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 agency-orchestrator if… - agency-orchestrator is primarily TypeScript; ollama is Go. - License: agency-orchestrator is Apache-2.0, ollama is MIT. - Tags unique to agency-orchestrator: agency-agents, agent-orchestration, ai-agents, autogen-alternative. - Also covers AI Agents. - agency-orchestrator ships an MCP server manifest. ### Choose ollama if… - ollama is primarily Go; agency-orchestrator is TypeScript. - License: ollama is MIT, agency-orchestrator is Apache-2.0. - 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 agency-orchestrator - 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. ## 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 agency-orchestrator and ollama? agency-orchestrator: 🚀 One sentence → multi-AI-role collaboration → complete plan in minutes. Built on the agency-agents role library (216+ experts), zero-code YAML, web Studio + desktop app, 10 LLM providers (7 free). 基. 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 agency-orchestrator over ollama? Choose agency-orchestrator over ollama when agency-orchestrator is primarily TypeScript; ollama is Go; License: agency-orchestrator is Apache-2.0, ollama is MIT; Tags unique to agency-orchestrator: agency-agents, agent-orchestration, ai-agents, autogen-alternative; Also covers AI Agents; agency-orchestrator ships an MCP server manifest. ### When should I choose ollama over agency-orchestrator? Choose ollama over agency-orchestrator when ollama is primarily Go; agency-orchestrator is TypeScript; License: ollama is MIT, agency-orchestrator is Apache-2.0; 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 agency-orchestrator? 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. ### 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 agency-orchestrator or ollama more popular on GitHub? ollama has more GitHub stars (175,936 vs 1,790). Stars measure visibility, not whether either tool fits your constraints. ### Are agency-orchestrator and ollama open source? Yes - both are open-source projects on GitHub (agency-orchestrator: Apache-2.0, ollama: MIT). ### Where can I find alternatives to agency-orchestrator or ollama? GraphCanon lists graph-backed alternatives at [agency-orchestrator alternatives](/tools/jnmetacode-agency-orchestrator/alternatives) and [ollama alternatives](/tools/ollama-ollama/alternatives) ([agency-orchestrator markdown twin](/tools/jnmetacode-agency-orchestrator/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/jnmetacode-agency-orchestrator-vs-ollama-ollama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agency-orchestrator or ollama? agency-orchestrator: Very active. 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 agency-orchestrator and ollama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agency-orchestrator trust report](/tools/jnmetacode-agency-orchestrator/trust); [ollama trust report](/tools/ollama-ollama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jnmetacode-agency-orchestrator`](/api/graphcanon/graph?tool=jnmetacode-agency-orchestrator) - 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/_