Overview

The Shield360 SDK is an OpenTelemetry-native AI observability platform that enables production-ready LLM application and agent monitoring without any code changes.

It includes upstream OpenTelemetry instrumentations for HTTP frameworks, clients, and system components, delivering complete visibility across your entire AI infrastructure.

Goals

• Zero Code AI Observability - Enable production-ready LLM monitoring for your AI applications without any code changes.

• OpenTelemetry Native - Built on OpenTelemetry standards for seamless integration with existing observability stacks like Grafana, Datadog, New Relic, and Dash0.

• Drop-in Replacement - Simply replace opentelemetry-instrument with shield360-instrument to get the same functionality plus comprehensive AI capabilities.

• Complete Stack Coverage - AI instrumentations + upstream OpenTelemetry instrumentations for full-stack observability.

• End-to-End Distributed Tracing - Complete visibility: HTTP requests → framework routing → database queries → LLM calls → agent workflows → tool usage → responses

• Advanced LLM Monitoring - Real-time cost tracking, token usage optimization, performance monitoring, and evaluations scoring.

Supported instrumentations

The Shield360 SDK automatically instruments:

LLM Providers

OpenAI, Anthropic, Google, Azure OpenAI, AWS Bedrock, Ollama, Groq, Cohere, Mistral, and more

AI/Agentic Frameworks

LangChain, LlamaIndex, CrewAI, mem0, AG2, DSPy, Agno, and more

Vector Databases

ChromaDB, Pinecone, Qdrant, Milvus, Weaviate, and more

GPUs

NVIDIA and AMD

HTTP Frameworks and Clients

FastAPI, Flask, Django, Requests, HTTPX, aiohttp, urllib and more

Supported languages

Python

Complete AI observability with automatic dependency detection. Zero-code instrumentation for production Python LLM applications with real-time performance monitoring.

TypeScript/JavaScript

Full LLM monitoring support for TypeScript / JavaScript applications with distributed tracing, metrics and cost optimization.

Go

OpenTelemetry-native instrumentation for Go applications. Wrap your OpenAI and Anthropic clients for automatic tracing, token tracking, and cost monitoring.

How it works

flowchart LR
    A[Install Shield360] --> B[Instrument your application]
    B --> C[Generates Distributed Traces and metrics]
    C --> D[OpenTelemetry Export]
    D --> E[Observability Dashboard]

Option 1: Zero code instrumentation

shield360-instrument python your_app.py

Option 2: Manual instrumentation

import shield360

shield360.init()  # Enables Automatic instrumentation for AI apps and agents

Both approaches provide production-ready observability: distributed tracing, real-time cost tracking, latency monitoring, token usage optimization, and evaluation scoring.

Getting started

Select from the following guides to learn more about production AI monitoring:

Quickstart: LLM Observability

Production-ready AI monitoring setup in 2 simple steps with zero code changes

Integrations

60+ AI integrations with automatic instrumentation and performance tracking

Deploy Shield360 Platform

Deployment options for scalable LLM monitoring infrastructure

Destinations

Send telemetry to Datadog, Grafana, New Relic, Dash0, and other observability stacks

Frequently asked questions

What is the Shield360 SDK?

The Shield360 SDK is an OpenTelemetry-native library that auto-instruments LLM providers, agent frameworks, and vector databases for AI observability - tracing, cost tracking, and evaluation - without code changes.

Does the SDK require changing my code?

No. Run shield360-instrument python your_app.py for zero-code instrumentation, or call shield360.init() once if you prefer manual setup - both produce the same OpenTelemetry traces.

Can I send data to my existing observability stack?

Yes. Since the SDK emits standard OpenTelemetry data, you can export to Datadog, Grafana, New Relic, Dash0, or any OTLP-compatible backend - see Destinations.