AI2: Open Coding Agents

Summary

Over the past year, coding agents have transformed how developers write, test, and maintain software. These systems can debug, refactor, and even submit pull requests—fundamentally changing what software development looks like. Yet despite this progress, most coding agents share the same constraints: they're closed, expensive to train, and difficult to study or adapt to private codebases.Ai2 Open Coding Agents change that. Today we’re releasing not just a collection of strong open coding models, but a training method that makes building your own coding agent for any codebase – for example, your personal codebase or an internal codebase at your organization – remarkably accessible for tasks including code generation, code review, debugging, maintenance, and code explanation.Closed models haven't seen your internal code, so they don't know it—custom data pipelines, internal APIs, specific org conventions, and so on. Training on your private data teaches them, but generating synthetic training data from private codebases that works for agents has been challenging and cost-prohibitive. Our method makes it easy—reproducing the performance of the previously best open-source model costs ~$400 of compute, or up to $12,000 for performance that rivals the best industry models of the same size. This puts the full recipe within reach for labs and small teams. Resource constraints drove us to maximize efficiency at every stage, from data quality to inference costs to model selection. The result: we match SWE-smith, a synthetic data method, at 57× lower cost and SkyRL, an open-source reinforcement learning (RL) system, at 26× lower cost.The first release in our Open Coding Agents family is SERA (Soft-verified Efficient Repository Agents). The strongest – SERA-32B – solves 54.2% of SWE-Bench Verified problems, surpassing prior open-source state-of-the-art coding models of comparable sizes and context lengths while requiring only 40 GPU days (or fewer) to train on a cluster of 2 NV...