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

# ✅ 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()
• logtracesfeedback_scores()
• experiment.insert()

Sync (blocking, returns data):

• createdataset(), getdataset()
• createprompt(), getprompt()
• searchtraces(), searchspans()

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](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