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Brokle Platform Python SDK for OpenTelemetry-native LLM observability and tracing

Project description

Brokle Python SDK

OpenTelemetry-native observability for AI applications.

The Brokle Python SDK provides comprehensive observability, tracing, and auto-instrumentation for LLM applications. Choose your integration level:

🎯 Three Integration Patterns

Pattern 1: Wrapper Functions Wrap existing SDK clients (OpenAI, Anthropic) for automatic observability and platform features.

Pattern 2: Universal Decorator Framework-agnostic @observe() decorator with automatic hierarchical tracing. Works with any AI library.

Pattern 3: Native SDK (Sync & Async) Full platform capabilities with OpenAI-compatible interface. Context manager support with automatic resource cleanup.

Installation

pip install brokle

Setup

export BROKLE_API_KEY="bk_your_api_key_here"
export BROKLE_HOST="http://localhost:8080"

Quick Start

Pattern 1: Wrapper Functions

# Wrap existing SDK clients for automatic observability
from openai import OpenAI
from anthropic import Anthropic
from brokle import wrap_openai, wrap_anthropic

# OpenAI wrapper
openai_client = wrap_openai(
    OpenAI(api_key="sk-..."),
    tags=["production"],
    session_id="user_session_123"
)

# Anthropic wrapper
anthropic_client = wrap_anthropic(
    Anthropic(api_key="sk-ant-..."),
    tags=["claude", "analysis"]
)

response = openai_client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)
# ✅ Automatic Brokle observability and tracing

Pattern 2: Universal Decorator

# Automatic hierarchical tracing with just @observe()
from brokle import observe
import openai

client = openai.OpenAI()

@observe(name="parent-workflow")
def main_workflow(data: str):
    # Parent span automatically created
    result1 = analyze_data(data)
    result2 = summarize_findings(result1)
    return f"Final result: {result1} -> {result2}"

@observe(name="data-analysis")
def analyze_data(data: str):
    # Child span automatically linked to parent
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": f"Analyze: {data}"}]
    )
    return response.choices[0].message.content

@observe(name="summarization")
def summarize_findings(analysis: str):
    # Another child span automatically linked to parent
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": f"Summarize: {analysis}"}]
    )
    return response.choices[0].message.content

# Automatic hierarchical tracing - no manual workflow management needed
result = main_workflow("User behavior data from Q4 2024")
# ✅ Complete span hierarchy: parent -> analyze_data + summarize_findings

Pattern 3: Native SDK

Sync Client:

from brokle import Brokle

# Context manager (recommended)
with Brokle(
    api_key="bk_...",
    host="http://localhost:8080"
) as client:
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": "Hello!"}],
        tags=["production"]  # Analytics tags
    )
    print(f"Response: {response.choices[0].message.content}")

Async Client:

from brokle import AsyncBrokle
import asyncio

async def main():
    async with AsyncBrokle(
        api_key="bk_...",
    ) as client:
        response = await client.chat.completions.create(
            model="gpt-4",
            messages=[{"role": "user", "content": "Hello!"}],
            tags=["async", "production"]  # Analytics tags
        )
        print(f"Response: {response.choices[0].message.content}")

asyncio.run(main())

Why Choose Brokle?

  • ⚡ <3ms Overhead: High-performance observability
  • 📊 Complete Visibility: Real-time analytics and quality scoring
  • 🔧 OpenTelemetry Native: Standards-based tracing and metrics
  • 🛠️ Three Patterns: Start simple, scale as needed

Next Steps

Examples

Check the examples/ directory:

Support


Simple. Powerful. OpenTelemetry-native observability for AI.

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