This file provides guidance for AI coding agents working on this codebase. It complements CONTRIBUTING.md with agent-specific instructions. Human contributors should follow the conventions in CONTRIBUTING.md.

OGX is an API server implementing the OpenAI Responses API, Chat Completions, Embeddings, and supporting APIs (files, vector stores, batches, eval, safety). It supports multiple inference backends (OpenAI, Azure, Bedrock, vLLM, Ollama, WatsonX, etc.) through a provider architecture.

src/ogx/              # Server implementation
  core/                       # Request routing, server, storage
  providers/
    inline/                   # Built-in providers (responses, eval, vector_io, etc.)
    remote/                   # Remote provider adapters (OpenAI, Azure, vLLM, etc.)
    registry/                 # Provider registration specs
    utils/                    # Shared provider utilities (OpenAI mixin, MCP, etc.)
  distributions/              # Distribution configs (starter, ci-tests, etc.)
src/ogx_api/          # API definitions, Pydantic models, FastAPI routes
tests/
  unit/                       # Unit tests
  integration/                # Integration tests with recording/replay system

• Python 3.12 is required. Pre-commit hooks only work with 3.12.
• Use uv for all dependency management and script execution.
• Run scripts with uv run, never bare python3 or python.
• Use standard library modules when possible.
• All function signatures must have type hints. Prefer Pydantic models for validation.
• Code must pass mypy. Check the exclude list in pyproject.toml for known exceptions.
• Use def _function_name for private functions.
• Prefer explicit top-level imports over inline imports.
• Do not use exceptions as control flow.

• Comments must add value. Do not write filler comments that describe the next line.
• Add comments only to clarify surprising behavior that is not obvious from the code.
• Good variable naming and clear code organization matters more than comments.
• Do NOT remove existing comments unless they are factually wrong.
• Error messages must be prefixed with "Failed to ...".
• Use structured logging via from ogx.log import get_logger. Always use key-value style: logger.info("Processing request", model=model_id, provider=provider) instead of f-strings or %-style formatting. The pre-commit hook Block f-string logging enforces this.
• The pre-commit hook Ensure 'ogx.log' usage for logging enforces that all logging uses the project's logger, not the standard library directly.

• Always use --signoff (-s) when creating commits.
• Do not amend commits and force push during PR review. Use new commits instead.
• Use git merge main to update branches, not git rebase.
• Commit messages must use conventional commits format and full sentences, not bullet points.
• Merge upstream/main before pushing a branch to avoid CI failures from stale code.

• Unit tests: ./scripts/unit-tests.sh or uv run pytest tests/unit/ -x --tb=short
• Run pre-commit checks: uv run pre-commit run --all-files

Integration tests use a recording/replay system. Recordings are JSON files in tests/integration/*/recordings/ keyed by SHA256 hashes of HTTP request bodies. When modifying code that changes request bodies sent to providers, recordings may need to be re-recorded.

Run integration tests via the CI script (same command used in GitHub Actions):

# Replay mode (default) — uses pre-recorded responses, no API keys needed
uv run --no-sync ./scripts/integration-tests.sh \
  --stack-config server:ci-tests --setup gpt \
  --file tests/integration/responses/test_compact_responses.py

# Re-record missing recordings (requires API keys, e.g. OPENAI_API_KEY)
uv run --no-sync ./scripts/integration-tests.sh \
  --stack-config server:ci-tests --setup gpt \
  --inference-mode record-if-missing \
  --file tests/integration/responses/test_compact_responses.py

# Run a full suite
uv run --no-sync ./scripts/integration-tests.sh \
  --stack-config server:ci-tests --setup gpt --suite responses

Key flags: --stack-config (required), --setup (gpt, ollama, vllm), --inference-mode (replay, record, record-if-missing), --file (single file), --pattern (pytest -k filter), --suite (base, responses, vision).

If a test fails in replay mode with "Recording not found", re-run with --inference-mode record-if-missing and commit the new recording files. Recording locally requires an API key for the provider under test (e.g. OPENAI_API_KEY for --setup gpt). If you do not have a key, repository maintainers can trigger the record workflow on GitHub via .github/workflows/record-integration-tests.yml.

Each provider implements a protocol (e.g., Inference, Responses, VectorIO) and is registered in src/ogx/providers/registry/. Provider specs include:

• provider_type: e.g., remote::openai, inline::builtin
• module: Python module path
• config_class: Pydantic config class path
• api_dependencies: APIs this provider depends on
• deprecation_warning: For deprecated providers (triggers runtime warning)
• toolgroup_id: For tool_runtime providers that auto-register tool groups

Configuration classes must use Pydantic Field with description parameters — these generate the provider documentation automatically.

Distribution YAML files in src/ogx/distributions/ are partially auto-generated. After changing provider configs, run:

uv run ./scripts/distro_codegen.py
uv run ./scripts/provider_codegen.py

Do not edit generated files in docs/docs/providers/ manually.

When modifying or extending APIs:

1. Update models in src/ogx_api/
2. Regenerate OpenAPI specs: uv run ./scripts/run_openapi_generator.sh
3. Check for breaking changes — the pre-commit hook Check API spec for breaking changes enforces backward compatibility.
4. Include a test plan with a testing script and execution output in your PR description.

1. Add the field to the Pydantic model in src/ogx_api/
2. Thread it through the provider protocol and implementation
3. Update affected distribution configs if needed
4. Regenerate specs and docs
5. Add unit test cases covering the new parameter

Use the existing deprecation_warning field on InlineProviderSpec or RemoteProviderSpec. Search for existing examples: grep -r "deprecation_warning" src/ogx/providers/registry/

Search the codebase for existing examples of the same pattern first. Use grep to find how deprecation, validation, configuration, or aliasing is already handled elsewhere.

When making code changes, check whether the following documentation needs updating:

• README.md — the root README contains an ASCII architecture diagram showing API endpoints, inference providers, vector stores, tools/connectors, and file storage. Update it when adding or removing providers, APIs, or backend integrations.
• ARCHITECTURE.md — system overview, request flow, provider architecture, API layer, storage, configuration, and test recording system
• Module-level README.md files in key directories:
  • src/ogx/README.md, src/ogx/core/README.md
  • src/ogx/core/server/README.md, src/ogx/core/storage/README.md
  • src/ogx/core/routing_tables/README.md
  • src/ogx/providers/README.md, src/ogx/providers/inline/README.md
  • src/ogx/providers/remote/README.md, src/ogx/providers/registry/README.md
  • src/ogx/providers/utils/README.md, src/ogx/providers/utils/inference/README.md
  • src/ogx/providers/inline/agents/README.md
  • src/ogx/providers/inline/tool_runtime/README.md
  • src/ogx/providers/remote/inference/README.md
  • src/ogx/distributions/README.md
  • scripts/README.md
• tests/README.md, tests/unit/README.md, tests/integration/README.md

These files help LLMs and new contributors navigate the codebase. If your change adds, removes, or renames modules, providers, APIs, or storage backends, update the relevant documentation to stay in sync.