Bulk async coding with OpenAI Codex — batch refactors, parallel feature work, and 50% cheaper model time.
Submit bulk coding tasks and let Codex process them asynchronously for up to 50% cost savings on Batch API workloads.
Refactor large codebases with AI that understands your patterns, lint rules, and existing architecture.
Process dozens of files or repos in parallel and have the orchestrator gate-keep the results before they merge.
Codex shines on the work that does not need a human in the loop on every keystroke — broad refactors, dependency upgrades, test backfills, lint fixes across hundreds of files, scaffold generation. AI Expedite turns those one-off scripts into a repeatable workflow: declare what you want done, the orchestrator splits the job across the Codex Batch API, reviews each output, and lands the changes as a stack of small PRs the team can land at a healthy cadence.
Interactive agents are the right tool when a task requires exploration and feedback. But for fan-out work — 'add a Sentry breadcrumb to every async handler in service X', 'replace deprecated lodash imports across the monorepo', 'add a JSDoc block to every exported function in the public API' — the bottleneck is throughput, not reasoning. Codex's Batch API processes those jobs asynchronously at roughly half the unit cost of interactive use, and AI Expedite fans out the dispatch so a single 'refactor this pattern' command turns into hundreds of parallel attempts.
Every Codex-driven workflow follows the same shape: a planning agent reads the target repo, drafts the per-file change plan, and asks for your approval. Once you accept, the orchestrator submits the batch through the Codex API, polls for completion, and runs each diff through the validation pipeline — lint, tests, and an AI review pass that catches the regressions Codex's per-file context can miss. Changes that pass land as small PRs grouped by area; changes that fail come back into the queue with the failure context attached so the next run can fix forward.
If you have an OpenAI ChatGPT subscription with Codex access, AI Expedite can route work through your local Codex CLI in addition to (or instead of) the API. That matters for cost: subscription seats are flat-rate, while API calls are metered. The orchestrator picks the cheapest viable runner for each job, falling back to the API when the local CLI is busy or unavailable.
Most batch refactors fail in real codebases because the change is correct in isolation but wrong in context — a renamed function still has a stale import elsewhere, a deleted constant still has a test asserting on it. AI Expedite's planning step reads the full dependency graph (using the workspace's code analysis index) before drafting the change plan, so the batch dispatch knows about every call site. The result: refactors that actually merge.
Yes. If you connect a machine running the Codex CLI through the AI Expedite terminal app, the orchestrator will route subscription-eligible jobs through it. The Batch API stays available as a fallback when the CLI is busy.
Every diff runs through lint and existing tests before merge, and an AI review pass flags patterns that diverge from your codebase. Multi-file refactors are planned against the full dependency graph so call sites move in lockstep with the definitions they reference.
OpenAI's Batch API is roughly 50% cheaper than the synchronous API for the same model. For fan-out work like cross-codebase refactors, that adds up — and because subscription routing is flat-rate, large jobs frequently cost only the orchestration overhead.
Yes. They're complementary: Claude Code for exploratory feature work, Codex for batch and async. The orchestrator lets you set per-workflow routing so the right agent picks up the right job.
Yes. The terminal app talks to whichever Codex CLI is installed and authenticated on the machine — API key, subscription, or both. We never proxy or store your OpenAI credentials.
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