Grafana Agent FlowConceptsComponents


Components are the building blocks of Grafana Agent Flow. Each component is responsible for handling a single task, such as retrieving secrets or collecting Prometheus metrics.

Components are composed of two parts:

  • Arguments: settings which configure a component.
  • Exports: named values which a component exposes to other components.

Each component has a name which describes what that component is responsible for. For example, the local.file component is responsible for retrieving the contents of files on disk.

Components are specified in the config file by first providing the component’s name with a user-specified label, and then by providing arguments to configure the component:

discovery.kubernetes "pods" {
  role = "pod"

discovery.kubernetes "nodes" {
  role = "node"

Components are referenced by combining the component name with its label. For example, a local.file component labeled foo would be referenced as

The combination of a component’s name and its label must be unique within the configuration file. This means multiple instances of a component may be defined as long as each instance has a different label value.


Most arguments for a component in a config file are constant values, such setting a log_level attribute to the quoted string "debug":

log_level = "debug"

Expressions can be used to dynamically compute the value of an argument at runtime. Among other things, expressions can be used to retrieve the value of an environment variable (log_level = env("LOG_LEVEL")) or to reference an exported field of another component (log_level = local.file.log_level.content).

When a component’s argument references an exported field of another component, a dependant relationship is created: a component’s input (arguments) now depends on another component’s output (exports). The input of the component will now be re-evaluated any time the exports of the components it references get updated.

The flow of data through the set of references between components forms a pipeline.

An example pipeline may look like this:

  1. A local.file component watches a file on disk containing an API key.
  2. A prometheus.remote_write component is configured to receive metrics and forward them to an external database using the API key from the local.file for authentication.
  3. A discovery.kubernetes component discovers and exports Kubernetes Pods where metrics can be collected.
  4. A prometheus.scrape component references the exports of the previous component, and sends collected metrics to the prometheus.remote_write component.

Flow of example pipeline

The following config file represents the above pipeline:

// Get our API key from disk.
// This component has an exported field called "content", holding the content
// of the file.
// local.file.api_key will watch the file and update its exports any time the
// file changes.
local.file "api_key" {
  filename  = "/var/data/secrets/api-key"

  // Mark this file as sensitive to prevent its value from being shown in the
  // UI.
  is_secret = true

// Create a prometheus.remote_write component which other components can send
// metrics to.
// This component exports a "receiver" value which can be used by other
// components to send metrics.
prometheus.remote_write "prod" {
  endpoint {
    url = "https://prod:9090/api/v1/write"

    http_client_config {
      basic_auth {
        username = "admin"

        // Use our password file for authenticating with the production database.
        password = local.file.api_key.content

// Find Kubernetes pods where we can collect metrics.
// This component exports a "targets" value which contains the list of
// discovered pods.
discovery.kubernetes "pods" {
  role = "pod"

// Collect metrics from Kubernetes pods and send them to prod.
prometheus.scrape "default" {
  targets    = discovery.kubernetes.pods.targets
  forward_to = []