pathwaycom/pathway

Real-time data movement is genuinely hard to get right, and most tools force you to choose between the full Kafka ecosystem or gluing together scripts that run on a schedule. This framework sits in between: a Python-native ETL layer designed to handle streaming data, incremental computation, and LLM pipeline orchestration in a unified programming model. You write standard Python, define your data transformations, and the engine handles the continuous update logic under the hood. The LLM and RAG angle is more than a marketing label. It addresses a real production problem: keeping context windows fed with fresh data without rebuilding indexes from scratch on every batch run. Connectors for common data sources are included, and the incremental processing model means downstream AI components see updates with low latency rather than waiting for a full refresh cycle. The honest reservation is that the abstraction layer introduces its own learning curve, and debugging streaming pipelines is never trivial regardless of the framework. -> Best for: ML engineers and backend founders building production RAG systems or AI features that require continuously updated, low-latency data pipelines.