Codex Knowledge Base
Designing Workflows for Codex-Spark: When Inference Becomes Near-Instant
At 65–70 tokens per second, a 400-token code response takes roughly 6 seconds — a noticeable pause.
GPT-5.1-Codex-Max: Long-Horizon Tasks, Native Compaction and 24-Hour Sessions
Most Codex tasks complete in minutes. But some tasks — a full microservice migration, a large-scale refactor across 50+ files, a days-long debugging session.
Codex CLI + GitHub Actions: The Complete Integration Guide
The openai/codex-action GitHub Action lets you run the Codex CLI inside any GitHub Actions workflow — gating PRs on AI-driven code review, auto-fixing.
Mid-Turn Steering in Codex CLI: Redirecting Agents in Flight
The default posture for working with agentic tools has always been: write a careful prompt, send it, wait, evaluate the output, and iterate.
Codex CLI for Jupyter Notebooks and Scientific Python
Jupyter notebooks are the lingua franca of scientific Python, yet the .ipynb format is one of the environments where Codex CLI historically performs least.
Codex CLI Incident Response: Automating On-Call with Agents
Wire PagerDuty/Datadog alerts into Codex CLI agentic workflows for AI-driven incident investigation and patch generation.
Codex CLI in 2027: Reading the Roadmap
OpenAI does not publish a product roadmap for Codex CLI. What it does publish — obsessively, in sub-weekly releases — is a changelog.
Advanced AGENTS.md Patterns for Monorepos
Monorepos concentrate a large surface area of code under a single Git root, and that concentration creates a fundamental tension for AI coding agents.
Test-First Development with Codex: Using TDD as the Agent Feedback Loop
The single biggest problem with autonomous agents is knowing when theyre done. A human developer can feel when code feels right. An agent cannot.
Spec-Driven Development with Codex: Writing Specifications Before Code
Test-Driven Development (TDD) tells the agent when it is done. Spec-Driven Development (SDD) tells it what to build in the first place. The two approaches.