--- title: "distributed-llama vs catai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/b4rtaz-distributed-llama-vs-withcatai-catai" tools: ["b4rtaz-distributed-llama", "withcatai-catai"] --- # distributed-llama vs catai *GraphCanon updated Sep 20, 2026* ## Verdict Pick distributed-llama if distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license; pick catai if catai, an AI assistant framework built for local deployment with Node.js, offers developers using TypeScript the ability to create and deploy AI agents through a simple API. [distributed-llama](https://github.com/b4rtaz/distributed-llama) reports 3.1k GitHub stars, 250 forks, and 48 open issues, last pushed Jul 5, 2026. [catai](https://withcatai.github.io/catai/) has 500 stars, 39 forks, and 2 open issues, last pushed Nov 16, 2025. Figures are from public GitHub metadata via [distributed-llama's repository](https://github.com/b4rtaz/distributed-llama) and [catai's repository](https://github.com/withcatai/catai). | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [catai](/tools/withcatai-catai.md) | | --- | --- | --- | | Tagline | Distributed LLM inference using home devices cluster | Run AI assistant locally with Node.js | | Stars | 3,060 | 500 | | Forks | 250 | 39 | | Open issues | 48 | 2 | | Language | C++ | TypeScript | | Adopt for | distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license. | catai, an AI assistant framework built for local deployment with Node.js, offers developers using TypeScript the ability to create and deploy AI agents through a simple API. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Inference & Serving | AI Agents, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [catai](/tools/withcatai-catai.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 76d | 307d | | Open issues (now) | 48 | 2 | | Stars delta | +48 (30d) | +2 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/b4rtaz-distributed-llama/trust.md) | [trust report](/tools/withcatai-catai/trust.md) | ## Decision facts: distributed-llama - **Adopt for:** distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license. ## Decision facts: catai - **Adopt for:** catai, an AI assistant framework built for local deployment with Node.js, offers developers using TypeScript the ability to create and deploy AI agents through a simple API. ## Choose when ### Choose distributed-llama if… - distributed-llama is primarily C++; catai is TypeScript. - Tags unique to distributed-llama: distributed-computing, llm-inference, neural-network. - When you have multiple interconnected home devices and want to maximize their combined computing power for LLM inference tasks. ### Choose catai if… - catai is primarily TypeScript; distributed-llama is C++. - Tags unique to catai: ai-assistant, chatbot, ggmlv3, llama-cpp. - Also covers AI Agents. - - When aiming to deploy a local AI assistant without reliance on cloud-based services; catai is ideal due to its focus on node-llama-cpp integration, allowing for robust offline capabilities. ## When NOT to use distributed-llama - For scenarios with fewer than two available devices, as the framework's capability to distribute and boost performance would be limited. - In professional environments that require strict data privacy controls, due to potential network vulnerabilities among home devices. ## When NOT to use catai - - For environments that strictly prohibit or limit Node.js operations on the server side, as catai is engineered to run locally via Node.js only. - - If seeking a cloud-based AI deployment solution that does not require local setup, since catai focuses solely on providing local AI capabilities through an easy-to-use API. ## Common questions ### What is the difference between distributed-llama and catai? distributed-llama: Distributed LLM inference using home devices cluster. catai: Run AI assistant locally with Node.js. See the comparison table for live GitHub stats and shared categories. ### When should I choose distributed-llama over catai? Choose distributed-llama over catai when distributed-llama is primarily C++; catai is TypeScript; Tags unique to distributed-llama: distributed-computing, llm-inference, neural-network; When you have multiple interconnected home devices and want to maximize their combined computing power for LLM inference tasks. ### When should I choose catai over distributed-llama? Choose catai over distributed-llama when catai is primarily TypeScript; distributed-llama is C++; Tags unique to catai: ai-assistant, chatbot, ggmlv3, llama-cpp; Also covers AI Agents; - When aiming to deploy a local AI assistant without reliance on cloud-based services; catai is ideal due to its focus on node-llama-cpp integration, allowing for robust offline capabilities. ### When should I avoid distributed-llama? For scenarios with fewer than two available devices, as the framework's capability to distribute and boost performance would be limited. In professional environments that require strict data privacy controls, due to potential network vulnerabilities among home devices. ### When should I avoid catai? - For environments that strictly prohibit or limit Node.js operations on the server side, as catai is engineered to run locally via Node.js only. - If seeking a cloud-based AI deployment solution that does not require local setup, since catai focuses solely on providing local AI capabilities through an easy-to-use API. ### Is distributed-llama or catai more popular on GitHub? distributed-llama has more GitHub stars (3,060 vs 500). Stars measure visibility, not whether either tool fits your constraints. ### Are distributed-llama and catai open source? Yes - both are open-source projects on GitHub (distributed-llama: MIT, catai: MIT). ### Where can I find alternatives to distributed-llama or catai? GraphCanon lists graph-backed alternatives at [distributed-llama alternatives](/tools/b4rtaz-distributed-llama/alternatives) and [catai alternatives](/tools/withcatai-catai/alternatives) ([distributed-llama markdown twin](/tools/b4rtaz-distributed-llama/alternatives.md), [catai markdown twin](/tools/withcatai-catai/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/b4rtaz-distributed-llama-vs-withcatai-catai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, distributed-llama or catai? distributed-llama: Steady. catai: Slowing. 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 distributed-llama and catai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [distributed-llama trust report](/tools/b4rtaz-distributed-llama/trust); [catai trust report](/tools/withcatai-catai/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=b4rtaz-distributed-llama`](/api/graphcanon/graph?tool=b4rtaz-distributed-llama) - 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/_