superlinked/sie
SIE consolidates retrieval, document conversion, structured output, safety, and generation models behind one self-hosted, OpenAI-compatible API for agent stacks.
SIE is an **Apache 2.0** inference system that exposes **100+ models** for retrieval, OCR, structured output, safety, and generation through one cluster. Its gateway implements OpenAI-compatible embeddings, chat, completions, and responses endpoints.
Builders replacing several task-specific model servers can use on-demand loading and LRU eviction, then deploy with Docker or the included Kubernetes, Helm, KEDA, and Grafana setup. Existing agent frameworks and vector stores can connect through documented integrations.
SIE is an **Apache 2.0** inference system that exposes **100+ models** for retrieval, OCR, structured output, safety, and generation through one cluster. Its gateway implements OpenAI-compatible embeddings, chat, completions, and responses endpoints. Builders replacing several task-specific model servers can use on-demand loading and LRU eviction, then deploy with Docker or the included Kubernetes, Helm, KEDA, and Grafana setup. Existing agent frameworks and vector stores can connect through documented integrations. The first request for a model downloads its weights, and latency depends on the model, task, hardware, and batch size. Some dependency-incompatible families require separate images, native audio is opt-in, and anonymous environment telemetry is enabled unless disabled.
SIE consolidates many task-specific open-model servers behind familiar APIs and one elastic cluster, shifting model optionality from provider routing toward operationally managed local inference. It reduces endpoint fragmentation but does not erase serving complexity: cold downloads, eviction, hardware-sensitive latency, incompatible model families, optional audio support, and telemetry still require explicit operating choices.