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Meet SETA: Open Source Training Reinforcement Learning Environments for Terminal Agents with 400 Tasks and CAMEL Toolkit

Researchers from CAMEL AI, Eigent AI and partners released SETA, an open‑source stack that couples a terminal‑focused toolkit with 400 synthetic reinforcement‑learning tasks. The framework delivers state‑of‑the‑art results on the Terminal Bench benchmark, hitting 46.5% accuracy on version 2.0 with a Claude Sonnet 4.5 backbone and 35% on version 1.0 using GPT‑4.1. SETA also bundles a Note‑Taking Toolkit for persistent memory and a structured logging system that simplifies debugging and evaluation across training and benchmark pipelines.