Python Sdk

Sync (blocking, returns data):

• create_dataset(), get_dataset()
• create_prompt(), get_prompt()
• search_traces(), search_spans()

Lazy Imports for Integrations

# ✅ 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)

from opik.integrations.anthropic import track_anthropic

client = anthropic.Anthropic()
tracked_client = track_anthropic(client)  # Wraps methods

Callback-Based (LangChain)

from opik.integrations.langchain import OpikTracer

tracer = OpikTracer()
chain.invoke(input, config={"callbacks": [tracer]})

Decorator-Based

@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

# 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 - fake_backend, verifiers, test naming
• error-handling.md - Exception hierarchy, MetricComputationError
• good-code.md - Access control, imports, factories, DI