Submit an AI Tool for Workbench Review | Commune9

Submit your AI framework, library, or serving engine for independent benchmark review and latency evaluation by the Commune9 engineering community.

How can developers submit an AI tool for review on the AI Workbench?

Developers can submit machine learning tools, serving engines, vector databases, or agent libraries for review through our online submission portal or via the CLI with npx commune9 submit. Our engineering collective evaluates each submission against standardized benchmarks measuring token throughput, VRAM consumption, latency distribution, and developer API ergonomics.

Submission Category	Evaluation Criteria	Standardized Workload	Review Timeline
Serving Engine	Tokens/sec, TTFT, Memory Overhead	Llama 3 70B Continuous Batching	7 Business Days
Fine-Tuning Library	Memory Efficiency, Step Time	QLoRA 4-bit NormalFloat on 24GB VRAM	5 Business Days
Vector Database	Query Latency, QPS, Recall @ 10	1M Embedding High-Concurrency Test	7 Business Days
Agent Framework	Graph Overhead, Tool Validation	Multi-Step Cyclical Execution Graph	5 Business Days

What evaluation criteria are required for listing on the AI Workbench?

The AI Workbench evaluates developer tools across four objective criteria: 1) reproducible latency and throughput benchmarks; 2) transparent open-source licensing or enterprise privacy covenants; 3) active maintainer documentation and typed API specifications; and 4) demonstrated architectural stability under concurrent enterprise production workloads without silent failures.

Reproducibility: Runnable Docker containers or scripts enabling independent benchmark verification.
Data Privacy: Zero unauthorized telemetry or training on customer model inputs.
Developer Ergonomics: Typed APIs, comprehensive error models, and minimal dependency bloat.