How LlamaIndex uses Temporal to scale reliable document orchestration

LlamaIndex describes adopting Temporal, an open-source durable workflow execution platform, to replace increasingly complex RabbitMQ-based orchestration for LlamaParse, its document-parsing service that handles more than 130 file types and processes documents with varied requirements such as text extraction, OCR, vision models, and distributed CPU/GPU workloads. As document sizes and processing diversity grew, the previous system required custom mechanisms for state tracking, retries, heartbeats, fairness, backpressure, rate limits, and recovery from failed jobs, creating operational complexity and requiring overprovisioning. Temporal provides durable workflows and activities whose execution state persists across failures, enabling work to resume reliably and allowing workflows to coordinate through unique IDs, signals, leases, and resource-locking patterns. LlamaIndex built workflow-based semaphores and dedicated concurrency-limiter workers to manage permits, project-level job limits, and shared resources without relying on separate caches, databases, or limiter services. The company reports that the transition consolidated several supporting layers and enabled LlamaParse to scale to tens of millions of pages processed daily for its Batch API while allowing engineering teams to focus more directly on document-processing logic.

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