# Odigos — Zero-Code Distributed Tracing for Kubernetes

> Automatically instruments applications running in Kubernetes with OpenTelemetry using eBPF, requiring no code changes to generate distributed traces, metrics, and logs.

## Install

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# Odigos — Zero-Code Distributed Tracing for Kubernetes

## Quick Use
```bash
# Install the Odigos CLI
brew install odigos-io/homebrew-odigos-cli/odigos
# Deploy Odigos into your Kubernetes cluster
odigos install
# Select applications to instrument via the UI
odigos ui
```

## Introduction
Odigos automatically instruments applications running in Kubernetes to produce distributed traces, metrics, and logs without any code changes. It uses eBPF and OpenTelemetry auto-instrumentation to detect running services and generate telemetry, then routes data to your preferred observability backend.

## What Odigos Does
- Auto-detects applications running in Kubernetes pods and their programming languages
- Instruments services using eBPF and OpenTelemetry with zero code modifications
- Generates distributed traces, metrics, and logs from instrumented workloads
- Routes telemetry to backends like Jaeger, Datadog, Grafana, New Relic, and others
- Manages the full OpenTelemetry Collector pipeline within the cluster

## Architecture Overview
Odigos deploys as a set of Kubernetes controllers. An instrumentor component detects running pods and their languages, then injects the appropriate OpenTelemetry auto-instrumentation (eBPF for compiled languages, language agents for interpreted ones). A scheduler manages OpenTelemetry Collectors as a pipeline, handling sampling, batching, and export to configured destinations.

## Self-Hosting & Configuration
- Install via CLI (brew or curl) and deploy to any Kubernetes cluster
- Select which namespaces and workloads to instrument through the UI or CRDs
- Configure observability destinations (Jaeger, Datadog, Grafana, etc.) via the dashboard
- Adjust sampling rates and collection pipelines through Odigos CRDs
- Supports both managed Kubernetes (EKS, GKE, AKS) and self-hosted clusters

## Key Features
- Zero-code instrumentation requiring no application changes or SDK integration
- eBPF-based instrumentation for Go, C++, and other compiled languages
- Multi-destination routing to send telemetry to multiple backends simultaneously
- Language auto-detection for Python, Java, Node.js, .NET, Go, and more
- OpenTelemetry-native pipeline managed entirely within the cluster

## Comparison with Similar Tools
- **OpenTelemetry Operator** — Requires manual configuration per service; Odigos automates detection and instrumentation
- **Pixie** — eBPF-based but stores data in-cluster; Odigos exports to external backends
- **Datadog Agent** — Vendor-specific; Odigos is vendor-neutral and OpenTelemetry-native
- **Jaeger** — A tracing backend only; Odigos handles instrumentation and collection

## FAQ
**Q: Does Odigos add performance overhead?**
A: eBPF instrumentation adds minimal overhead. Language-agent instrumentation has typical OpenTelemetry auto-instrumentation costs.

**Q: Which languages does Odigos support?**
A: Python, Java, Node.js, .NET, Go, and C++ via eBPF, with the list expanding.

**Q: Can I use Odigos outside Kubernetes?**
A: Currently Odigos is designed for Kubernetes environments and relies on pod-level detection.

**Q: Is Odigos free?**
A: The open-source edition is free. An enterprise edition adds advanced features like pipeline management and SSO.

## Sources
- https://github.com/odigos-io/odigos
- https://docs.odigos.io/

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Source: https://tokrepo.com/en/workflows/asset-e0606081
Author: AI Open Source