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RL environments for long horizon AI Agents
We work on benchmarking and improving the long-horizon capabilities of AI Agents. We build out specialised environments to improve the long-horizon capabilities of browser and computer use agents.
Vibrant Labs provides RL environments designed to train and evaluate AI agents on long-horizon tasks, enabling benchmarking, training, and alignment in safe, reproducible worlds. Their focus spans browser automation, customer experience conversations, and coding/infrastructure tasks, with an emphasis on long-term planning and recovery behaviors.
Offers RL-ready simulation environments to teach agents long-horizon goals, recovery behaviors, and alignment objectives. Enables benchmarking and diagnosing agent performance in complex, safe, and reproducible worlds to observe failure points, adaptation, and improvement requirements. Targets include browser-based automation, customer interactions, and automated coding/infrastructure tasks; supports evaluation framework (Ragas) and related benchmarking capabilities.
Who it’s for: AI research organizations, enterprises building long-horizon agents, and teams requiring benchmarking and simulation environments for RL and agent training (including browser automation, customer experience, and coding/infrastructure domains).
Content mentions blog, evaluation framework (Ragas) popularity, and community/Discord engagement; company claims backing and ongoing partnerships.
Jithin James (jjmachan), believes that life's purpose is found in the toil in mastering a craft and the fulfilment one gets from using it for the greater good. For him, this means excelling in software development, particularly in open-source and AI tooling, to empower fellow developers and help materialize their ambitious visions into reality. His journey is a testament to skill, dedication, and a commitment to community enrichment.
Shahul (aka ikka) discovered his fascination for AI in his sophomore year while pursuing his computer science degree. This early interest laid the foundation for his journey to becoming a Kaggle GrandMaster and led to his significant contributions to open-source AI, particularly in initiatives like OpenAssistant AI.
Deploy your LLM application with confidence.
Ragas provides an open-source evaluation and testing infrastructure for LLM-based RAG applications, enabling automated test data generation, explainable metrics, and adversarial testing to ensure production quality. It targets LLM application developers and reports broad usage across enterprises and open-source projects.
From the original launch (Mar 2024) — may be outdated.
Formerly “Exploding Gradients”, “Ragas” · why startups rename →
Environments for Long Horizon Tool Use
Agent simulation and RL for researchers
