--- title: "dograh vs OGAM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dograh-hq-dograh-vs-off-grid-ai-off-grid-ai-mobile" tools: ["dograh-hq-dograh", "off-grid-ai-off-grid-ai-mobile"] --- # dograh vs OGAM *GraphCanon updated Sep 20, 2026* ## Verdict Pick dograh if self-hosted voice AI platform with speech-to-speech, LLM integration, telephony support; pick OGAM if oGAM is a versatile offline AI toolkit that supports a wide range of functionalities including chat, vision, speech-to-text, and image generation, all of which can be performed on mobile and Mac devices without the need. [dograh](https://app.dograh.com) reports 5.7k GitHub stars, 1.4k forks, and 86 open issues, last pushed Sep 19, 2026. [OGAM](https://getoffgridai.co/pro/) has 3.1k stars, 298 forks, and 151 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [dograh's repository](https://github.com/dograh-hq/dograh) and [OGAM's repository](https://github.com/off-grid-ai/OGAM). | | [dograh](/tools/dograh-hq-dograh.md) | [OGAM](/tools/off-grid-ai-off-grid-ai-mobile.md) | | --- | --- | --- | | Tagline | Self-hosted open source voice AI platform | Swiss Army Knife of Offline AI | | Stars | 5,696 | 3,124 | | Forks | 1,426 | 298 | | Open issues | 86 | 151 | | Language | Python | TypeScript | | Adopt for | Self-hosted voice AI platform with speech-to-speech, LLM integration, telephony support | OGAM is a versatile offline AI toolkit that supports a wide range of functionalities including chat, vision, speech-to-text, and image generation, all of which can be performed on mobile and Mac devices without the need | | Persona | - | - | | Runtime | - | - | | License | BSD-2-Clause | MIT License, allowing for free use, modification, and distribution of the software. | | Categories | Inference & Serving, Speech & Audio | Computer Vision, Developer Tools, Inference & Serving, LLM Frameworks, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dograh](/tools/dograh-hq-dograh.md) | [OGAM](/tools/off-grid-ai-off-grid-ai-mobile.md) | | --- | --- | --- | | Open issues (now) | 86 | 151 | | Stars delta | +632 (30d) | +269 (30d) | | Open issues delta | +66 (30d) | +14 (30d) | | Full report | [trust report](/tools/dograh-hq-dograh/trust.md) | [trust report](/tools/off-grid-ai-off-grid-ai-mobile/trust.md) | ## Decision facts: dograh - **Adopt for:** Self-hosted voice AI platform with speech-to-speech, LLM integration, telephony support ## Decision facts: OGAM - **Pricing:** freemium - The core functionality is free, but premium features or services may be available for purchase. - **Requirements:** Min 4 GB RAM; Supports CPU, GPU, and NPU for running AI models.; Available for Android, iOS, and macOS devices. - **Adopt for:** OGAM is a versatile offline AI toolkit that supports a wide range of functionalities including chat, vision, speech-to-text, and image generation, all of which can be performed on mobile and Mac devices without the need - **License detail:** MIT License, allowing for free use, modification, and distribution of the software. ## Choose when ### Choose dograh if… - dograh is primarily Python; OGAM is TypeScript. - License: dograh is BSD-2-Clause, OGAM is MIT. - Tags unique to dograh: conversational-ai, local-llm, self-hosted, speech-to-text. - You need on-premise deployment for better security or data control ### Choose OGAM if… - OGAM is primarily TypeScript; dograh is Python. - License: OGAM is MIT, dograh is BSD-2-Clause. - Pricing: The core functionality is free, but premium features or services may be available for purchase.. - Requirements: Min 4 GB RAM; Supports CPU, GPU, and NPU for running AI models.; Available for Android, iOS, and macOS devices.. - Tags unique to OGAM: android, edge-ai, gguf, ios. - Also covers Computer Vision, Developer Tools, LLM Frameworks. - OGAM ships an MCP server manifest. - When you need a comprehensive offline AI toolkit that supports chat, vision, speech-to-text, and image generation on mobile and Mac devices. ## When NOT to use dograh - Seeking cloud-managed services without self-hosting capabilities - Need real-time collaboration with non-local models in the cloud - Preference is for tools under different licenses than BSD-2-Clause - Must integrate with platforms that lack native telephony support ## When NOT to use OGAM - If you need real-time cloud-based AI services that require internet access for data processing and model updates. - When you are working in an environment where the use of local AI models is restricted or not supported by the hardware available. - If your project requires integration with specific cloud APIs or services that OGAM does not support due to its offline nature. ## Common questions ### What is the difference between dograh and OGAM? dograh: Self-hosted open source voice AI platform. OGAM: Swiss Army Knife of Offline AI. See the comparison table for live GitHub stats and shared categories. ### When should I choose dograh over OGAM? Choose dograh over OGAM when dograh is primarily Python; OGAM is TypeScript; License: dograh is BSD-2-Clause, OGAM is MIT; Tags unique to dograh: conversational-ai, local-llm, self-hosted, speech-to-text; You need on-premise deployment for better security or data control. ### When should I choose OGAM over dograh? Choose OGAM over dograh when OGAM is primarily TypeScript; dograh is Python; License: OGAM is MIT, dograh is BSD-2-Clause; Pricing: The core functionality is free, but premium features or services may be available for purchase.; Requirements: Min 4 GB RAM; Supports CPU, GPU, and NPU for running AI models.; Available for Android, iOS, and macOS devices.; Tags unique to OGAM: android, edge-ai, gguf, ios; Also covers Computer Vision, Developer Tools, LLM Frameworks; OGAM ships an MCP server manifest; When you need a comprehensive offline AI toolkit that supports chat, vision, speech-to-text, and image generation on mobile and Mac devices. ### When should I avoid dograh? Seeking cloud-managed services without self-hosting capabilities Need real-time collaboration with non-local models in the cloud Preference is for tools under different licenses than BSD-2-Clause Must integrate with platforms that lack native telephony support ### When should I avoid OGAM? If you need real-time cloud-based AI services that require internet access for data processing and model updates. When you are working in an environment where the use of local AI models is restricted or not supported by the hardware available. If your project requires integration with specific cloud APIs or services that OGAM does not support due to its offline nature. ### Is dograh or OGAM more popular on GitHub? dograh has more GitHub stars (5,696 vs 3,124). Stars measure visibility, not whether either tool fits your constraints. ### Are dograh and OGAM open source? Yes - both are open-source projects on GitHub (dograh: BSD-2-Clause, OGAM: MIT). ### Where can I find alternatives to dograh or OGAM? GraphCanon lists graph-backed alternatives at [dograh alternatives](/tools/dograh-hq-dograh/alternatives) and [OGAM alternatives](/tools/off-grid-ai-off-grid-ai-mobile/alternatives) ([dograh markdown twin](/tools/dograh-hq-dograh/alternatives.md), [OGAM markdown twin](/tools/off-grid-ai-off-grid-ai-mobile/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/dograh-hq-dograh-vs-off-grid-ai-off-grid-ai-mobile.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, dograh or OGAM? dograh: Very active. OGAM: 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 dograh and OGAM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dograh trust report](/tools/dograh-hq-dograh/trust); [OGAM trust report](/tools/off-grid-ai-off-grid-ai-mobile/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dograh-hq-dograh`](/api/graphcanon/graph?tool=dograh-hq-dograh) - 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/_