--- title: "bisheng vs awesome-tensor-compilers" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dataelement-bisheng-vs-merrymercy-awesome-tensor-compilers" tools: ["dataelement-bisheng", "merrymercy-awesome-tensor-compilers"] --- # bisheng vs awesome-tensor-compilers *GraphCanon updated Jul 11, 2026* ## Verdict Pick bisheng when requirements: Min 16 GB RAM; Requires Docker; pick awesome-tensor-compilers when tags unique to awesome-tensor-compilers: code-generation, compiler, deep-learning, high-performance-computing. [bisheng](http://www.bisheng.ai) reports 12k GitHub stars, 1.9k forks, and 112 open issues, last pushed Jul 11, 2026. [awesome-tensor-compilers](https://github.com/merrymercy/awesome-tensor-compilers) has 2.8k stars, 327 forks, and 4 open issues, last pushed Oct 19, 2024. Figures are from public GitHub metadata via [bisheng's repository](https://github.com/dataelement/bisheng) and [awesome-tensor-compilers's repository](https://github.com/merrymercy/awesome-tensor-compilers). | | [bisheng](/tools/dataelement-bisheng.md) | [awesome-tensor-compilers](/tools/merrymercy-awesome-tensor-compilers.md) | | --- | --- | --- | | Tagline | BISHENG is an open LLM devops platform for next generation Enterprise AI applications | A list of awesome compiler projects and papers for tensor computation and deep learning. | | Stars | 11,508 | 2,762 | | Forks | 1,882 | 327 | | Open issues | 112 | 4 | | Language | TypeScript | - | | Adopt for | BISHENG is a comprehensive open-source LLM DevOps platform designed specifically for next-generation Enterprise AI applications. | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | - | | Categories | AI Agents, Data & Retrieval, Developer Tools, Evaluation & Observability, LLM Frameworks, Model Training | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [bisheng](/tools/dataelement-bisheng.md) | [awesome-tensor-compilers](/tools/merrymercy-awesome-tensor-compilers.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 630d | | Open issues (now) | 112 | 4 | | Owner type | Organization | User | | Security scan | No criticals | No lockfile | | Full report | [trust report](/tools/dataelement-bisheng/trust.md) | [trust report](/tools/merrymercy-awesome-tensor-compilers/trust.md) | ## Decision facts: bisheng - **Requirements:** Min 16 GB RAM; Requires Docker - **Adopt for:** BISHENG is a comprehensive open-source LLM DevOps platform designed specifically for next-generation Enterprise AI applications. ## Choose when ### Choose bisheng if… - Requirements: Min 16 GB RAM; Requires Docker. - Tags unique to bisheng: agent, ai, chatbot, enterprise. - Also covers AI Agents, Data & Retrieval, Developer Tools, LLM Frameworks, Model Training. - - When you need a unified solution that supports both GenAI workflows and RAG (Retrieval-Augmented Generation) capabilities, which are critical in enhancing the context understanding and response of L ### Choose awesome-tensor-compilers if… - Tags unique to awesome-tensor-compilers: code-generation, compiler, deep-learning, high-performance-computing. - Leaner open-issue backlog (4). ## When NOT to use bisheng - - If your project requires minimal resource consumption and does not demand high enterprise-level system management or advanced observability features, BISHENG might be overkill given its hardware and ## When NOT to use awesome-tensor-compilers - Last GitHub push was 630 days ago (dormant maintenance, Oct 19, 2024). Validate activity before betting a new project on awesome-tensor-compilers. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## Common questions ### What is the difference between bisheng and awesome-tensor-compilers? bisheng: BISHENG is an open LLM devops platform for next generation Enterprise AI applications. awesome-tensor-compilers: A list of awesome compiler projects and papers for tensor computation and deep learning.. See the comparison table for live GitHub stats and shared categories. ### When should I choose bisheng over awesome-tensor-compilers? Choose bisheng over awesome-tensor-compilers when Requirements: Min 16 GB RAM; Requires Docker; Tags unique to bisheng: agent, ai, chatbot, enterprise; Also covers AI Agents, Data & Retrieval, Developer Tools, LLM Frameworks, Model Training; - When you need a unified solution that supports both GenAI workflows and RAG (Retrieval-Augmented Generation) capabilities, which are critical in enhancing the context understanding and response of L. ### When should I choose awesome-tensor-compilers over bisheng? Choose awesome-tensor-compilers over bisheng when Tags unique to awesome-tensor-compilers: code-generation, compiler, deep-learning, high-performance-computing; Leaner open-issue backlog (4). ### When should I avoid bisheng? - If your project requires minimal resource consumption and does not demand high enterprise-level system management or advanced observability features, BISHENG might be overkill given its hardware and ### When should I avoid awesome-tensor-compilers? Last GitHub push was 630 days ago (dormant maintenance, Oct 19, 2024). Validate activity before betting a new project on awesome-tensor-compilers. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### Is bisheng or awesome-tensor-compilers more popular on GitHub? bisheng has more GitHub stars (11,508 vs 2,762). Stars measure visibility, not whether either tool fits your constraints. ### Are bisheng and awesome-tensor-compilers open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to bisheng or awesome-tensor-compilers? GraphCanon lists graph-backed alternatives at [bisheng alternatives](/tools/dataelement-bisheng/alternatives) and [awesome-tensor-compilers alternatives](/tools/merrymercy-awesome-tensor-compilers/alternatives) ([bisheng markdown twin](/tools/dataelement-bisheng/alternatives.md), [awesome-tensor-compilers markdown twin](/tools/merrymercy-awesome-tensor-compilers/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/dataelement-bisheng-vs-merrymercy-awesome-tensor-compilers.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, bisheng or awesome-tensor-compilers? bisheng: Very active. awesome-tensor-compilers: Dormant. 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 bisheng and awesome-tensor-compilers? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [bisheng trust report](/tools/dataelement-bisheng/trust); [awesome-tensor-compilers trust report](/tools/merrymercy-awesome-tensor-compilers/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dataelement-bisheng`](/api/graphcanon/graph?tool=dataelement-bisheng) - 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/_