SWE Agent Data Collection and Execution Environment

3 posts · Sep 2025 – Mar 2026

What this trail covers

This post details the integration of Cerebras inference with Docker Compose to create isolated and reproducible environments for AI-generated code. It introduces DevDuck, a multi-agent system that leverages Cerebras for code generation and Docker Compose for orchestrating components and sandboxing code execution. This enables developers to build and evaluate new code quickly while keeping experiments isolated and repeatable, balancing speed, cost, and safety. The post also details the transformation of the execution environment to support agentic workflows.

All Cerebras trails

Mar 2026CerebrasThis post introduces Codex Spark, a variant of Codex served on Cerebras WSE hardware, capable of generating over 1,200 tokens/second. It details how this speed enables real-time collaboration and rapid iteration for developers, allowing them to bake validation steps (diffs, reviews, unit tests, QA) into every commit cycle. The post provides practical tips for leveraging this speed, including treating the AI as a pair programmer, making validation cheap, exploring more implementation paths, running fewer sessions, managing context windows effectively, externalizing state via Git, using the right Codex model for the task, and implementing tighter permissions and restrictions. It also highlights the integration with tools like Ralph loops for automated iteration.
Nov 2025CerebrasThis post details the technical journey in transforming OpenHands from a SWE-Bench evaluation tool into a general-purpose RL training platform. Key contributions include a refactored evaluation workflow for dynamic loading of arbitrary Docker images and runtime metadata parsing, and a unified rollout architecture that maintains a persistent runtime context to eliminate container duplication overhead, reducing instance setup time by approximately 95%. These advancements enable multi-turn interaction support, fast rollout generation, sparse reward evaluation, and comprehensive trajectory logging for training code-repair agents via Reinforcement Learning.
Nov 2025CerebrasThis post details the methodology for constructing a high-quality, diverse corpus of real-world software projects for training SWE agents. It describes sourcing repositories from three pipelines: PyPI-linked projects (107,000 instances), high-impact GitHub repositories (50,000 instances), and multimodal codebases (3,500 instances). The post also outlines the architecture of the code execution environment, which automates end-to-end differential testing of Python repositories by setting up isolated workspaces, analyzing repository structure and dependencies, generating optimized Dockerfiles, building environments with LLM-based fixes for build failures, performing robust test discovery with iterative auto-correction, executing baseline and patched tests in isolated Docker runs, and comparing results to classify tests as FAIL_TO_PASS or PASS_TO_PASS. A reversal phase rechecks classifications by undoing the main patch, ensuring accuracy. The system produces comprehensive artifacts including logs, Docker files, summaries, and detailed JSON results.
Sep 2025Cerebras and Docker: Speed Meets SafetyThis post introduces DevDuck, a multi-agent system that demonstrates the integration of Cerebras inference with Docker Compose for isolated and reproducible AI code execution. It highlights how Docker Compose can orchestrate agentic applications, including Cerebras agents and local models, within sandboxed containers. The post details the setup process, including cloning a demo repository, configuring API keys, and running Docker Compose to initialize agents and manage sandbox environments. It emphasizes the security benefits of network isolation within containers and the speed of code generation and execution provided by Cerebras inference.
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