Building with Modal and the OpenAI Agents SDK

OpenAI’s Agents SDK is presented as a framework for building customized internal agent harnesses, and the walkthrough combines it with Modal Sandboxes to create coding agents that can safely execute commands in isolated environments with optional GPU access. Beginning with an unsafe local shell-execution agent, the example moves execution into remote sandboxes, then adds persistent sessions for memory, an orchestrator-subagent model to separate high-level planning from short-lived implementation work, and asynchronous pools that allow multiple agents to run experiments in parallel. The system also introduces agent-status tracking, GPU quotas to control costs, filesystem snapshots that let new agents reuse prepared environments and artifacts, and modular skills that load task-specific instructions without hard-coding them into the general harness. Using MNIST training and OpenAI’s Parameter Golf challenge as examples, the project demonstrates how isolated compute, delegated context, parallel workers, reusable sandbox states, and configurable prompting can be composed into an autonomous research and coding workflow.

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