> ## Documentation Index
> Fetch the complete documentation index at: https://docs.odigos.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Overview
> Odigos will automatically apply instrumentation to your selected workloads to record observability signals from your services such as collecting traces, metrics, and logs without any code changes.
export const LanguagesCard = ({golangUrl, golangDescription, javascriptUrl, javascriptDescription, pythonUrl, pythonDescription, javaUrl, javaDescription, phpUrl, phpDescription, rubyUrl, rubyDescription, dotnetUrl, dotnetDescription}) =>
}>
{golangDescription}
}>
{javascriptDescription}
}>
{pythonDescription}
}>
{javaDescription}
}>
{phpDescription}
}>
{rubyDescription}
}>
{dotnetDescription}
;
## Supported Runtimes
Odigos provides automatic instrumentations for the following runtimes:
## Enrich with OpenTelemetry APIs
Odigos automatically generates data for open-source libraries and frameworks.
Additional trace spans, metrics datapoints and log records can be added using the OpenTelemetry APIs.
Odigos will automatically capture this data and deliver it to the chosen [destination](../backends-overview) alongside the automatically generated data.
No need to configure the OpenTelemetry SDK, Odigos will automatically
configure it for you.
Select a language to learn how to enrich your data with OpenTelemetry APIs:
## Instrumentation Flow:
1. User selects a workload to auto instrument by creating a `Source` object (per workload or per namespace).
2. Odigos `instrumentor` component watches for changes with `Source` objects, and creates a relative `InstrumentationConfig` object.
3. Odigos `odiglet` component watches for changes with the `InstrumentationConfig` objects, and runs a runtime inspection on running pods to detect the programming language for every container.
4. Odigos `instrumentor` component watches for changes with the `InstrumentationConfig` object, and adds a resource request into each pod spec of all relevant containers in the workload manifest. These resource requests are called `Instrumentation Devices`.
5. Kubernetes detects the changes in the manifest, and rollout-restarts the pods with the new `Instrumentation Devices`.
6. New pods are scheduled and started. Odiglet resolves the resource request by mounting the auto instrumentation code and relevant environment variables into the container.
7. The auto instrumentation code starts the OpenTelemetry SDK and sends telemetry data to the odigos pipeline.
In case of a failure to instrument a workload, Odigos will disable the instrumentation and rollback the workload, This behaviour can be disabled by running `bash odigos install --set autoRollback.disabled=true` or via the helm chart `autoRollback.disabled=true`