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Contributor Guide

This guide helps new contributors get started with the OpenAI Agents Python repository. It covers repo structure, how to test your work, available utilities, and guidelines for commits and PRs.

Location: AGENTS.md at the repository root.

Table of Contents

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

Policies & Mandatory Rules

Mandatory Skill Usage

$code-change-verification

Run $code-change-verification before marking work complete when changes affect runtime code, tests, or build/test behavior.

Run it when you change:

  • src/agents/ (library code) or shared utilities.
  • tests/ or add or modify snapshot tests.
  • examples/.
  • Build or test configuration such as pyproject.toml, Makefile, mkdocs.yml, docs/scripts/, or CI workflows.

You can skip $code-change-verification for docs-only or repo-meta changes (for example, docs/, .codex/, README.md, AGENTS.md, .github/), unless a user explicitly asks to run the full verification stack.

$openai-knowledge

When working on OpenAI API or OpenAI platform integrations in this repo (Responses API, tools, streaming, Realtime API, auth, models, rate limits, MCP, Agents SDK or ChatGPT Apps SDK), use $openai-knowledge to pull authoritative docs via the OpenAI Developer Docs MCP server (and guide setup if it is not configured).

ExecPlans

Call out potential backward compatibility or public API risks early in your plan and confirm the approach before implementing changes that could impact users.

Use an ExecPlan when work is multi-step, spans several files, involves new features or refactors, or is likely to take more than about an hour. Start with the template and rules in PLANS.md, keep milestones and living sections (Progress, Surprises & Discoveries, Decision Log, Outcomes & Retrospective) up to date as you execute, and rewrite the plan if scope shifts. If you intentionally skip an ExecPlan for a complex task, note why in your response so reviewers understand the choice.

Project Structure Guide

Overview

The OpenAI Agents Python repository provides the Python Agents SDK, examples, and documentation built with MkDocs. Use uv run python ... for Python commands to ensure a consistent environment.

Repo Structure & Important Files

  • src/agents/: Core library implementation.
  • tests/: Test suite; see tests/README.md for snapshot guidance.
  • examples/: Sample projects showing SDK usage.
  • docs/: MkDocs documentation source; do not edit translated docs under docs/ja, docs/ko, or docs/zh (they are generated).
  • docs/scripts/: Documentation utilities, including translation and reference generation.
  • mkdocs.yml: Documentation site configuration.
  • Makefile: Common developer commands.
  • pyproject.toml, uv.lock: Python dependencies and tool configuration.
  • .github/PULL_REQUEST_TEMPLATE/pull_request_template.md: Pull request template to use when opening PRs.
  • site/: Built documentation output.

Agents Core Runtime Guidelines

  • src/agents/run.py is the runtime entrypoint (Runner, AgentRunner). Keep it focused on orchestration and public flow control. Put new runtime logic under src/agents/run_internal/ and import it into run.py.
  • When run.py grows, refactor helpers into run_internal/ modules (for example run_loop.py, turn_resolution.py, tool_execution.py, session_persistence.py) and leave only wiring and composition in run.py.
  • Keep streaming and non-streaming paths behaviorally aligned. Changes to run_internal/run_loop.py (run_single_turn, run_single_turn_streamed, get_new_response, start_streaming) should be mirrored, and any new streaming item types must be reflected in src/agents/stream_events.py.
  • Input guardrails run only on the first turn and only for the starting agent. Resuming an interruption from RunState must not increment the turn counter; only actual model calls advance turns.
  • Server-managed conversation (conversation_id, previous_response_id, auto_previous_response_id) uses OpenAIServerConversationTracker in run_internal/oai_conversation.py. Only deltas should be sent. If call_model_input_filter is used, it must return ModelInputData with a list input and the tracker must be updated with the filtered input (mark_input_as_sent). Session persistence is disabled when server-managed conversation is active.
  • Adding new tool/output/approval item types requires coordinated updates across:
    • src/agents/items.py (RunItem types and conversions)
    • src/agents/run_internal/run_steps.py (ProcessedResponse and tool run structs)
    • src/agents/run_internal/turn_resolution.py (model output processing, run item extraction)
    • src/agents/run_internal/tool_execution.py and src/agents/run_internal/tool_planning.py
    • src/agents/run_internal/items.py (normalization, dedupe, approval filtering)
    • src/agents/stream_events.py (stream event names)
    • src/agents/run_state.py (RunState serialization/deserialization)
    • src/agents/run_internal/session_persistence.py (session save/rewind)
  • If the serialized RunState shape changes, bump CURRENT_SCHEMA_VERSION in src/agents/run_state.py and update serialization/deserialization accordingly.

Operation Guide

Prerequisites

  • Python 3.9+.
  • uv installed for dependency management (uv sync) and uv run for Python commands.
  • make available to run repository tasks.

Development Workflow

  1. Sync with main and create a feature branch: 
    git checkout -b feat/<short-description>
  2. If dependencies changed or you are setting up the repo, run make sync.
  3. Implement changes and add or update tests alongside code updates.
  4. Highlight backward compatibility or API risks in your plan before implementing breaking or user-facing changes.
  5. Build docs when you touch documentation: 
    make build-docs
  6. When $code-change-verification applies, run it to execute the full verification stack before marking work complete.
  7. Commit with concise, imperative messages; keep commits small and focused, then open a pull request.
  8. When reporting code changes as complete (after substantial code work), invoke $pr-draft-summary to generate the required PR summary block with change summary, PR title, and draft description.

Testing & Automated Checks

Before submitting changes, ensure relevant checks pass and extend tests when you touch code.

When $code-change-verification applies, run it to execute the required verification stack from the repository root. Rerun the full stack after applying fixes.

Unit tests and type checking

  • Run the full test suite: 
    make tests
  • Run a focused test: 
    uv run pytest -s -k <pattern>
  • Type checking: 
    make mypy

Snapshot tests

Some tests rely on inline snapshots; see tests/README.md for details. Re-run make tests after updating snapshots.

  • Fix snapshots: 
    make snapshots-fix
  • Create new snapshots: 
    make snapshots-create

Coverage

  • Generate coverage (fails if coverage drops below threshold): 
    make coverage

Formatting, linting, and type checking

  • Formatting and linting use ruff; run make format (applies fixes) and make lint (checks only).
  • Type hints must pass make mypy.
  • Write comments as full sentences ending with a period.
  • Imports are managed by Ruff and should stay sorted.

Mandatory local run order

When $code-change-verification applies, run the full sequence in order (or use the skill scripts):

make format
make lint
make mypy
make tests

Utilities & Tips

  • Install or refresh development dependencies: 
    make sync
  • Run tests against Python 3.9 in an isolated environment: 
    make old_version_tests
  • Documentation workflows: 
    make build-docs      # build docs after editing docs
make serve-docs      # preview docs locally
make build-full-docs # run translations and build
  • Snapshot helpers: 
    make snapshots-fix
make snapshots-create
  • Use examples/ to see common SDK usage patterns.
  • Review Makefile for common commands and use uv run for Python invocations.
  • Explore docs/ and docs/scripts/ to understand the documentation pipeline.
  • Consult tests/README.md for test and snapshot workflows.
  • Check mkdocs.yml to understand how docs are organized.

Pull Request & Commit Guidelines

  • Use the template at .github/PULL_REQUEST_TEMPLATE/pull_request_template.md; include a summary, test plan, and issue number if applicable.
  • Add tests for new behavior when feasible and update documentation for user-facing changes.
  • Run make format, make lint, make mypy, and make tests before marking work ready.
  • Commit messages should be concise and written in the imperative mood. Small, focused commits are preferred.

Review Process & What Reviewers Look For

  • ✅ Checks pass (make format, make lint, make mypy, make tests).
  • ✅ Tests cover new behavior and edge cases.
  • ✅ Code is readable, maintainable, and consistent with existing style.
  • ✅ Public APIs and user-facing behavior changes are documented.
  • ✅ Examples are updated if behavior changes.
  • ✅ History is clean with a clear PR description.