brokle 0.2.11

Brokle Platform Python SDK for intelligent LLM routing, caching, and observability

Brokle Python SDK

Three integration patterns. One powerful platform.

The Brokle Python SDK provides intelligent routing across 250+ LLM providers, semantic caching (30-50% cost reduction), and comprehensive observability. 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: intelligent routing, semantic caching, cost optimization. OpenAI-compatible interface with Brokle extensions.

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, routing, caching, optimization

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!"}],
        routing_strategy="cost_optimized",  # Brokle extension
        cache_strategy="semantic"           # Brokle extension
    )
    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!"}],
            routing_strategy="cost_optimized",  # Smart routing
            cache_strategy="semantic",          # Semantic caching
            tags=["async", "production"]       # Analytics tags
        )
        print(f"Response: {response.choices[0].message.content}")

asyncio.run(main())

Why Choose Brokle?

• 🚀 30-50% Cost Reduction: Intelligent routing and semantic caching
• ⚡ <3ms Overhead: High-performance observability
• 🔄 250+ Providers: Route across all major LLM providers
• 📊 Complete Visibility: Real-time analytics and quality scoring
• 🛠️ Three Patterns: Start simple, scale as needed

Next Steps

• 📖 Integration Patterns Guide - Detailed examples
• ⚡ Quick Reference - Fast setup guide
• 🔧 API Reference - Complete documentation
• 💻 Examples - Pattern-based code examples

Examples

Check the examples/ directory:

• pattern1_wrapper_functions.py - Wrapper functions
• pattern2_decorator.py - Universal decorator
• pattern3_native_sdk.py - Native SDK features

Support

• Issues: GitHub Issues
• Docs: docs.brokle.com
• Email: [e-mail]

---

Simple. Powerful. Three patterns to fit your needs.