Tokio Prompt Orchestrator

Example Use Cases

• 1

  Scalable, resilient orchestration of LLM inference requests with backpressure and failure handling.

• 2

  Integration with multiple LLM providers through a unified async pipeline.

• 3

  A self-tuning, multi-stage inference pipeline with observability and rate limiting.

Description

Tokio Prompt Orchestrator is a high-performance, asynchronous pipeline designed to manage large language model (LLM) inference workflows efficiently. It features a five-stage DAG pipeline with built-in resilience layers such as request deduplication, circuit breakers, rate limiting, and dead-letter queues to ensure robustness. The orchestrator supports multiple LLM providers including Anthropic, OpenAI, and local models, and offers optional autonomous self-tuning control loops for optimized performance. It provides both a terminal REPL and a web API for integration, making it suitable for interactive and programmatic use.