apm-integrations

name

apm-integrations

description

dd-trace-py integration development guide. Use when creating, modifying, or debugging contrib integrations in the Python tracer. Covers the patch module system, context_with_data, context_with_event (new), registration, testing with riot, and common anti-patterns. LLM/AI integrations should use this skill for APM-side workflow only; use llmobs-integrations for LLMObs-specific lifecycle, extraction, streaming, and VCR guidance. Pin is DEPRECATED. Triggers: "dd-trace-py", "ddtrace", "contrib", "integration", "patch.py", "trace_handlers", "PATCH_MODULES", "context_with_data", "context_with_event", "TracingEvent", "VCR", "cassette", "generative-ai", "LLM integration", "riot", "riotfile", "suitespec", "new integration", "wrap", "unwrap".

dd-trace-py APM Integrations

dd-trace-py provides automatic tracing for 90+ third-party libraries. Each integration uses monkey-patching via wrapt to intercept library calls and create spans.

Architecture

Patch Module (ddtrace/contrib/internal/)  -->  Spans + Tags
  Wraps library methods, creates events or spans through the integration's current pattern

Standard integrations use one of these patterns:

• context_with_data() + trace_handlers.py (preferred for existing): Wrappers emit events via core.context_with_data(), listeners in trace_handlers.py create spans (e.g., botocore, flask, django).
• context_with_event() + TracingEvent (NEW — preferred for new integrations): Typed event-driven pattern using core.context_with_event() with TracingEvent subclasses and TracingSubscriber. Read concrete examples such as ddtrace/contrib/internal/httpx/patch.py and ddtrace/contrib/internal/aiohttp/patch.py, plus infrastructure in ddtrace/_trace/events.py and ddtrace/_trace/subscribers/.
• Pin + tracer.trace() (DEPRECATED — do not use in new integrations): Many existing integrations use Pin.get_from() + tracer.trace() (e.g., redis, kafka, grpc). Do NOT use Pin in new code.

LLM integrations still use the APM integration workflow for contrib module layout, patch registration, and APM span tests, but LLMObs-specific span lifecycle and extraction belong in the llmobs-integrations skill.

Key Concepts

• _datadog_patch guard -- prevents double-patching on repeated patch() calls
• Pin is DEPRECATED -- many existing integrations still use Pin.get_from() and Pin().onto(), but do NOT use Pin in new integrations. Prefer context_with_event (new) or context_with_data (existing)
• config._add() -- registers integration config at module level, before patch() runs
• get_version() / _supported_versions() -- required exports for version detection
• PATCH_MODULES -- registration dict in ddtrace/_monkey.py for patch_all() discovery
• INTEGRATION_CONFIGS -- frozenset in ddtrace/internal/settings/_config.py, required for config to work
• registry.yaml -- dependency names and tested version range in scripts/integration_registry/registry.yaml

Type Annotations

dd-trace-py uses mypy for type checking. All new integration code must pass the lint skill typing check (scripts/lint typing).

Rules:

• Every function and method must have full type annotations (parameters + return type)
• patch() -> None, unpatch() -> None, get_version() -> str
• Wrapper functions use the wrapt signature: def traced_func(wrapped: Callable[..., Any], instance: Any, args: tuple[Any, ...], kwargs: dict[str, Any]) -> Any:
• Use Optional[X] for nullable parameters and return values
• Use parameterized generics: dict[str, Any], list[str], tuple[Any, ...] — not bare dict, list, tuple
• Avoid # type: ignore unless absolutely necessary (document the reason inline, e.g. # type: ignore[attr-defined] # wrapt proxy lacks stubs)
• Import types from typing where needed: Any, Callable, Optional
• Read reference integrations for correct type patterns — match their style

Reference Integrations

Always read 1-2 references of the same type before writing or modifying code.

All patch modules live in ddtrace/contrib/internal/{name}/. See Reference Integrations for canonical examples, secondary references, and pattern notes.

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LLM Integrations (LLMObs)

Use this APM skill for the shared integration work: contrib package layout, patch() / unpatch(), registration in PATCH_MODULES, scripts/integration_registry/registry.yaml, config registration, APM span tests, suitespec plumbing, and release-note/documentation expectations.

Use the llmobs-integrations skill for LLM-specific patch patterns, LlmRequestEvent, stream handlers, message/tool/token extraction, metadata sanitization, LLMObs assertions, and VCR cassette setup.

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Implementation Workflow

1. Investigate -- Read 1-2 reference integrations of the same type (see tables above)
2. Create patch module -- ddtrace/contrib/internal/{name}/patch.py with patch(), unpatch(), get_version()
3. Register -- Add to PATCH_MODULES, scripts/integration_registry/registry.yaml, and INTEGRATION_CONFIGS
4. LLM only: Create integration class -- Subclass BaseLLMIntegration in ddtrace/llmobs/_integrations/. See the llmobs-integrations skill's Implementation Guide for full patterns.
5. Write tests -- Add Venv() to riotfile.py, entries to suitespec, test files
6. Run tests -- Use the run-tests skill
7. Lint -- Use the lint skill

See Implementation Guide for detailed step-by-step.

Debugging

• DD_TRACE_DEBUG=true to see patching activity and span creation
• Missing spans -- verify _datadog_patch guard, check PATCH_MODULES entry exists
• Wrong service name -- verify config._add() at module level
• LLM-specific: see the llmobs-integrations skill for failure modes and debugging

Native Extension Build Issues

If pip install -e . fails with Rust compilation errors, target3.1* path errors, or stale cached artifacts causing builds to break, refer to Troubleshooting

This clears the Rust target directory and pip's download/build caches. Run this before retrying any pip install that fails with native extension errors.

Reference Files

• Implementation Guide -- Step-by-step new integration (all types)
• Reference Integrations -- All 13 categories with canonical examples
• Design Decisions -- Why integrations are structured this way
• Anti-Patterns -- Silent failures and common gotchas