python-sdk

# Python SDK ## Three-Layer Architecture ``` Layer 1: Public API (opik.Opik, @opik.track) ↓ Layer 2: Message Processing (queue, batching, retry) ↓ Layer 3: REST Client (OpikApi, HTTP) ``` ## Critical Gotchas ### Flush Before Exit ```python # ✅ REQUIRED for async operations client = opik.Opik() # ... tracing operations ... client.flush() # Must call before exit! ``` ### Async vs Sync Operations **Async (via message queue)** - fire-and-forget: - `trace()`, `span()` - `log_traces_feedback_scores()` - `experiment.insert()` **Sync (blocking, returns data)**: - `create_dataset()`, `get_dataset()` - `create_prompt()`, `get_prompt()` - `search_traces()`, `search_spans()` ### Lazy Imports for Integrations ```python # ✅ GOOD - integration files assume dependency exists import anthropic # Only imported when user uses integration # ❌ BAD - importing at package level from opik.integrations import anthropic # Would fail if not installed ``` ## Integration Patterns ### Pattern Selection ``` Library has callbacks? → Pure Callback (LangChain, LlamaIndex) No callbacks? → Method Patching (OpenAI, Anthropic) Callbacks unreliable? → Hybrid (ADK) ``` ### Method Patching (OpenAI, Anthropic) ```python from opik.integrations.anthropic import track_anthropic client = anthropic.Anthropic() tracked_client = track_anthropic(client) # Wraps methods ``` ### Callback-Based (LangChain) ```python from opik.integrations.langchain import OpikTracer tracer = OpikTracer() chain.invoke(input, config={"callbacks": [tracer]}) ``` ### Decorator-Based ```python @opik.track def my_function(input: str) -> str: # Auto-creates span, captures input/output return process(input) ``` ## Dependency Policy - Avoid adding new dependencies - Use conditional imports for integrations - Keep version bounds flexible: `>=2.0.0,<3.0.0` ## Batching System Messages batch together for efficiency: - Flush triggers: time (1s), size (100), memory (50MB), manual - Reduces HTTP overhead significantly ## API Method Naming ```python # CRUD: create/get/list/update/delete client.create_experiment(name="exp") client.get_dataset(name="ds") # Search for complex queries client.search_spans(project_name="proj") client.search_traces(project_name="proj") # Batch for bulk operations client.batch_create_items(...) ``` ## Reference Files - [testing.md](testing.md) - fake_backend, verifiers, test naming - [error-handling.md](error-handling.md) - Exception hierarchy, MetricComputationError - [good-code.md](good-code.md) - Access control, imports, factories, DI --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/comet-ml) — claim your Tome and manage your conversions. <!-- tomevault:4.0:skill_md:2026-04-11 -->