--- title: "parlor vs OGAM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fikrikarim-parlor-vs-off-grid-ai-off-grid-ai-mobile" tools: ["fikrikarim-parlor", "off-grid-ai-off-grid-ai-mobile"] --- # parlor vs OGAM *GraphCanon updated Sep 20, 2026* ## Verdict Pick parlor if parlor is an all-in-one multimodal AI solution running entirely on device for real-time voice and vision interactions; 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. [parlor](https://github.com/fikrikarim/parlor) reports 2.1k GitHub stars, 272 forks, and 1 open issues, last pushed Aug 3, 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 [parlor's repository](https://github.com/fikrikarim/parlor) and [OGAM's repository](https://github.com/off-grid-ai/OGAM). | | [parlor](/tools/fikrikarim-parlor.md) | [OGAM](/tools/off-grid-ai-off-grid-ai-mobile.md) | | --- | --- | --- | | Tagline | On-device real-time multimodal AI for voice and vision | Swiss Army Knife of Offline AI | | Stars | 2,068 | 3,124 | | Forks | 272 | 298 | | Open issues | 1 | 151 | | Language | Python | TypeScript | | Adopt for | Parlor is an all-in-one multimodal AI solution running entirely on device for real-time voice and vision interactions. | 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 | Apache-2.0 | MIT License, allowing for free use, modification, and distribution of the software. | | Categories | Computer Vision, 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._ | | [parlor](/tools/fikrikarim-parlor.md) | [OGAM](/tools/off-grid-ai-off-grid-ai-mobile.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 47d | 0d | | Open issues (now) | 1 | 151 | | Stars delta | +154 (30d) | +269 (30d) | | Open issues delta | -8 (30d) | +14 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/fikrikarim-parlor/trust.md) | [trust report](/tools/off-grid-ai-off-grid-ai-mobile/trust.md) | ## Decision facts: parlor - **Adopt for:** Parlor is an all-in-one multimodal AI solution running entirely on device for real-time voice and vision interactions. ## 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 parlor if… - parlor is primarily Python; OGAM is TypeScript. - License: parlor is Apache-2.0, OGAM is MIT. - Tags unique to parlor: apple-silicon, gemma, kokoro, litert-lm. - Need to run complex, interactive AI locally without relying on cloud services. ### Choose OGAM if… - OGAM is primarily TypeScript; parlor is Python. - License: OGAM is MIT, parlor is Apache-2.0. - 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 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 parlor - Limited by Python 3.12 requirement and need for specialized hardware such as Apple Silicon. - Insufficient ~3 GB RAM makes it unsuitable for environments with tight memory constraints. ## 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 parlor and OGAM? parlor: On-device real-time multimodal AI for voice and vision. OGAM: Swiss Army Knife of Offline AI. See the comparison table for live GitHub stats and shared categories. ### When should I choose parlor over OGAM? Choose parlor over OGAM when parlor is primarily Python; OGAM is TypeScript; License: parlor is Apache-2.0, OGAM is MIT; Tags unique to parlor: apple-silicon, gemma, kokoro, litert-lm; Need to run complex, interactive AI locally without relying on cloud services. ### When should I choose OGAM over parlor? Choose OGAM over parlor when OGAM is primarily TypeScript; parlor is Python; License: OGAM is MIT, parlor is Apache-2.0; 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 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 parlor? Limited by Python 3.12 requirement and need for specialized hardware such as Apple Silicon. Insufficient ~3 GB RAM makes it unsuitable for environments with tight memory constraints. ### 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 parlor or OGAM more popular on GitHub? OGAM has more GitHub stars (3,124 vs 2,068). Stars measure visibility, not whether either tool fits your constraints. ### Are parlor and OGAM open source? Yes - both are open-source projects on GitHub (parlor: Apache-2.0, OGAM: MIT). ### Where can I find alternatives to parlor or OGAM? GraphCanon lists graph-backed alternatives at [parlor alternatives](/tools/fikrikarim-parlor/alternatives) and [OGAM alternatives](/tools/off-grid-ai-off-grid-ai-mobile/alternatives) ([parlor markdown twin](/tools/fikrikarim-parlor/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/fikrikarim-parlor-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, parlor or OGAM? parlor: Steady. 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 parlor and OGAM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [parlor trust report](/tools/fikrikarim-parlor/trust); [OGAM trust report](/tools/off-grid-ai-off-grid-ai-mobile/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=fikrikarim-parlor`](/api/graphcanon/graph?tool=fikrikarim-parlor) - 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/_