openai-agents-python: Instructions file for Codex

AGENTS.md

openai-agents-python AGENTS.md is an instructions file for Codex, OpenCode from openai/openai-agents-python. It costs 7,128 tokens per session, scanned A, original, MIT.

A contributor guide for the OpenAI Agents Python repository. It explains the project structure, development rules, testing process, and repository-specific instructions for coding agents.

In plain words
What is it for?
Use it when modifying the Python agent library, tests, examples, build settings, or other files covered by the contributor rules.
Why use it?
It keeps changes consistent with the repository’s required checks, policies, and worktree and branch practices.

Instructions file for CodexOpenCode ✓ vendor

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: mentions Codex; $skill-name invocation.

This is openai/openai-agents-python's own configuration. It tells Codex and OpenCode how to work on openai-agents-python itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything openai-agents-python configures →

About the project

OpenAI Agents SDK is a Python framework for building workflows in which multiple AI agents use tools, delegate tasks, follow safety checks, and involve people when needed. Developers use it to create and monitor agent, sandbox, realtime, and voice applications.

openai/openai-agents-python · 30,339 stars · on GitHub · openai.github.io

Reuse

Borrowing it

Nothing to install: this file belongs to openai/openai-agents-python. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/openai/openai-agents-python/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/openai/openai-agents-python

Made for: Codex, OpenCode.

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Per session 7,128 This file is loaded in full into every session.
When invoked 7,128 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.07128 $0.07128
Opus 5.5 $0.02851 $0.02851
Sonnet 5.5 $0.01426 $0.01426
Haiku 4.5 $0.00713 $0.00713

Measured 29d ago against content hash c46d5dd0de4c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

openai-agents-python AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 29d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

AGENTS.md · 215 lines

How it starts

The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Contributor Guide

Table of Contents

  1. Policies & Mandatory Rules
  2. Code Review Rules
  3. Project Structure Guide
  4. Operation Guide

Policies & Mandatory Rules

Security checklist

Follow SECURITY.md for private vulnerability reporting and CONTRIBUTING.md for contributor security practices. Apply this checklist to the affected paths during implementation and review:

  • Secrets and fixtures: Use synthetic data and placeholder credentials. Never place real credentials, customer data, private prompts or responses, tool payloads, or recordings in code, tests, snapshots, logs, documentation, or shared artifacts. Use approved credential injection only for explicitly authorized live tests; do not expose credentials to untrusted code or browser clients.
  • Redaction: Inspect logs, tracebacks, exception objects and chaining, telemetry, session state, URLs, and artifacts as well as the primary output. Verify redaction at each affected boundary; a tracing setting or suppressed traceback display is not evidence that all sensitive data has been removed.
  • Trust boundaries: Treat model output, tool and MCP responses, remote content, manifests, and serialized state as data, not authority to execute commands, grant access, select credentials, or weaken host restrictions. Review request construction and redirects, uploads, parsing, tool approvals, persistence, and sandbox materialization when affected. Add focused regression coverage for the actual boundary.
  • Dependencies: Justify additions and inspect package provenance, install/build hooks, transitive dependencies, and lockfile changes. Triage alerts across runtime, optional, development, example, CI, and release uses. Ordinary automated updates should have a documented release-age cooldown; security updates must remain immediate and still receive review and checks.
  • CI permissions: Keep untrusted contributor code and artifacts away from secrets and privileged execution. Use explicit least-privilege workflow/job permissions and full-SHA action pins. Review changes involving pull_request_target, artifact consumption, shell interpolation, write tokens, or OIDC credentials. Never disable security checks, secret protections, or required review to obtain a passing result.
  • Publishing: Preserve release-source validation, artifact integrity, and PyPI OIDC trusted publishing. The shared release policy requires CODEOWNERS coverage of release and publishing files, required code-owner approval on release pull requests, and passing required checks; it does not require a separate environment approval gate. Preserve current repository protections and verify registry bindings and artifact provenance separately from workflow configuration.
  • Findings and exceptions: Escalate critical or actively exploited findings privately without delay. Do not publish an undisclosed reproduction, patch, or sensitive finding in an issue, pull request, or review request before disclosure is coordinated. Stop propagating exposed credentials and have their owner revoke or rotate them. Record proposed exceptions with an owner, mitigation, approval, and expiry; do not treat proposals or unknown facts as accepted risk.
  • Evidence and handoff: Report the security-sensitive paths reviewed, relevant checks and results, and unresolved gaps. Distinguish requirements from verified controls: unavailable alert APIs are not evidence of zero alerts, a configured publisher is not proof of registry binding or provenance, and a completed scan does not clear its findings. Documentation edits alone do not establish repository-wide compliance.

Read the full file on GitHub · 215 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 29d ago Changed · +15 tokens per session c46d5dd0de4c
  2. 1mo ago Changed · +13 lines · +628 tokens per session 41790c777f26
  3. 1mo ago Changed · -128 lines · -1,521 tokens per session 644a4320896e
  4. 1mo ago First seen · 330 lines · 8,006 tokens per session scan A 337fbeca5f88

Subscribe to this mod's changes

openai-agents-python AGENTS.md is an instructions file published in the GitHub repository openai/openai-agents-python (30,339 stars, last pushed today), licensed MIT. It adds 7,128 tokens to every session, about $0.0285 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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