Orchard: An open framework for scalable agentic AI

Orchard is an open-source framework for scalable and cost-effective agentic AI research, built around Orchard Env, a reusable environment service for training and evaluating agents across task domains. The same Orchard infrastructure supports software-engineering, web-navigation, and personal-assistant agents, and can train them directly inside real deployment harnesses such as Codex, OpenClaw, and ZeroClaw—letting researchers reuse environments, data pipelines, and evaluation workflows across tasks. Orchard-SWE, Orchard-GUI, and Orchard-Claw demonstrate that relatively small open-weight models can achieve strong results on complex real-world tasks. For example, Orchard-SWE reaches 69.7% on SWE-bench Verified—73.0% with value-model reranking—using only about 3 billion active parameters, approaching frontier systems using more than 10 times larger models. Alongside the models and workflows, the project releases training data and evaluation methods intended to help the broader research community build and study open agentic systems. Artificial intelligence is rapidly moving beyond static question-answering toward autonomous agents that can plan, reason, and act…