aigie 0.2.15

Enterprise-grade AI agent reliability monitoring and autonomous remediation

Aigie SDK

Production-grade Python SDK for integrating Aigie monitoring into your AI agent workflows.

✨ Features

• 🚀 Event Buffering: 10-100x performance improvement with batch uploads
• 🎯 Decorator Support: 50%+ less boilerplate code
• ⚙️ Flexible Configuration: Config class with sensible defaults
• 🔄 Automatic Retries: Exponential backoff with configurable policies
• 🔗 LangChain Integration: Seamless callback handler
• 📊 Production Ready: Handles network failures, race conditions, and more

Quick Start

Installation

pip install aigie

Basic Usage

Option 1: Context Manager (Traditional)

from aigie import Aigie

aigie = Aigie()
await aigie.initialize()

async with aigie.trace("My Workflow") as trace:
    async with trace.span("operation", type="llm") as span:
        result = await do_work()
        span.set_output({"result": result})

Option 2: Decorator (Recommended - 50% less code!)

from aigie import Aigie

aigie = Aigie()
await aigie.initialize()

@aigie.trace(name="my_workflow")
async def my_workflow():
    @aigie.span(name="operation", type="llm")
    async def operation():
        return await do_work()
    return await operation()

Option 3: With Configuration

from aigie import Aigie, Config

config = Config(
    aigie_url="https://portal.aigie.io/api",
    aigie_token="your-token",  # Required for data to be sent
    batch_size=100,  # Buffer 100 events before sending
    flush_interval=5.0  # Or flush every 5 seconds
)
aigie = Aigie(config=config)
await aigie.initialize()

Configuration

Environment Variables

export AIGIE_TOKEN=your-token-here        # Required for data to be sent
export AIGIE_URL=https://portal.aigie.io/api
export AIGIE_BATCH_SIZE=100
export AIGIE_FLUSH_INTERVAL=5.0

Config Object

from aigie import Config

config = Config(
    aigie_url="https://portal.aigie.io/api",
    aigie_token="your-token",  # Required for data to be sent
    batch_size=100,
    flush_interval=5.0,
    enable_buffering=True,  # Default: True
    max_retries=3
)

Module-level Configuration (LiteLLM-style)

import aigie

aigie.aigie_token = "your-token"  # Required for data to be sent
aigie.aigie_url = "https://portal.aigie.io/api"
aigie.init()  # Initialize with module-level settings

Performance

Before (No Buffering)

• 1000 spans = 1000+ API calls
• ~30 seconds total time
• High network overhead

After (With Buffering)

• 1000 spans = 2-10 API calls
• ~0.5 seconds total time
• 99%+ reduction in API calls

Advanced Features

OpenTelemetry Integration

Works with any OpenTelemetry-compatible tool (Datadog, New Relic, Jaeger, etc.):

from aigie import Aigie
from aigie.opentelemetry import setup_opentelemetry

aigie = Aigie()
await aigie.initialize()

# One-line setup
setup_opentelemetry(aigie, service_name="my-service")

# Now all OTel spans automatically go to Aigie!
from opentelemetry import trace
tracer = trace.get_tracer(__name__)

with tracer.start_as_current_span("operation"):
    # Automatically traced
    pass

Synchronous API

For non-async codebases:

from aigie import AigieSync

aigie = AigieSync()
aigie.initialize()  # Blocking

with aigie.trace("workflow") as trace:
    with trace.span("operation") as span:
        result = do_work()  # Sync code
        span.set_output({"result": result})

Installation

Basic

pip install aigie

With OpenTelemetry

pip install aigie[opentelemetry]

With LangChain

pip install aigie[langchain]

All Features

pip install aigie[all]

Advanced Features (Phase 3)

W3C Trace Context Propagation

Distributed tracing across microservices:

# Extract from incoming request
context = aigie.extract_trace_context(request.headers)

async with aigie.trace("workflow") as trace:
    trace.set_trace_context(context)
    
    # Propagate to downstream service
    headers = trace.get_trace_headers()
    response = await httpx.get("https://api.example.com", headers=headers)

Prompt Management

Create, version, and track prompts:

# Create prompt
prompt = await aigie.prompts.create(
    name="customer_support",
    template="You are a helpful assistant. Customer: {customer_name}",
    version="1.0"
)

# Use in trace
async with aigie.trace("support") as trace:
    trace.set_prompt(prompt)
    rendered = prompt.render(customer_name="John")
    response = await llm.ainvoke(rendered)

Evaluation Hooks

Automatic quality monitoring:

from aigie import EvaluationHook, ScoreType

hook = EvaluationHook(
    name="accuracy",
    evaluator=accuracy_evaluator,
    score_type=ScoreType.ACCURACY
)

async with aigie.trace("workflow") as trace:
    trace.add_evaluation_hook(hook)
    result = await do_work()
    await trace.run_evaluations(expected, result)

Streaming Support

Real-time span updates:

async with aigie.trace("workflow") as trace:
    async with trace.span("llm_call", stream=True) as span:
        async for chunk in llm.astream("Hello"):
            span.append_output(chunk)  # Update in real-time
            yield chunk

Documentation

• SDK Improvement Analysis - Comprehensive analysis
• Examples - Before/after code examples
• Comparison Table - Feature comparison with market leaders
• Phase 2 Features - OpenTelemetry, Sync API, Type Hints
• Phase 3 Features - W3C Context, Prompts, Evaluations, Streaming