• 03 Mar 2023
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Article Summary

Replay allows you to run Detection & Response (D&R) rules against historical traffic.
This can be done in a few combinations of sources:

Rule Source:

  • Existing rule in the organization, by name.
  • Rule in the replay request.


  • Sensor historical traffic.
  • Local events provided during request.


Using the Replay API requires the API key to have the following permissions:

  • insight.evt.get

The returned data from the API contains the following:

  • responses: a list of the actions that would have been taken by the rule (like report, task, etc).
  • num_evals: a number of evaluation operations performed by the rule. This is a rough estimate of the performance of the rule.
  • num_events: the number of events that were replayed.
  • eval_time: the number of seconds it took to replay the data.
  "error": "",        // if an error occured.
  "stats": {
    "n_proc": 0,      // the number of events processed
    "n_shard": 0,     // the number of chunks the replay job was broken into
    "n_eval": 0,      // the number of operator evaluations performed
    "wall_time": 0    // the number of real-world seconds the job took
  "did_match": false, // indicates if the rule matched any event at all
  "results": [],      // a list of dictionaries containing the details of actions the engine would have taken
  "traces": []        // a list of trace items to help you troubleshoot where a rule failed

Python CLI

The Python CLI gives you a friendly way to replay data, and to do so across larger datasets by automatically splitting up your query into multiple queries that can run in parallel.

Sample command line to query one sensor:

limacharlie-replay --sid 9cbed57a-6d6a-4af0-b881-803a99b177d9 --start 1556568500 --end 1556568600 --rule-content ./test_rule.txt

Sample command line to query an entire organization:

limacharlie-replay --entire-org --start 1555359000 --end 1556568600 --rule-name my-rule-name

If specifying a rule as content with the --rule-content, the format should be
in JSON or YAML like:

  event: DNS_REQUEST
  op: is
  path: event/DOMAIN_NAME
  - action: report
    name: dilbert-is-here

Instead of specifying the --entire-org or --sid flags, you may use events from a local file via the --events flag.

We invite you to look at the command line usage itself, as the tool evolves.


The Replay API is available to all DataCenter locations using a per-location URL.
To get the appropriate URL for your organization, use the REST endpoint to retrieve the URLs found here named replay.

Having per-location URLs will allow us to guarantee that processing occurs within the geographical area you chose. Currently, some locations are NOT guaranteed to be in the same area due to the fact we are using the Google Cloud Run product which is not available globally. For these cases, processing is currently done in the United States, but as soon as it becomes available in your area, the processing will be moved transparently.

Authentication to this API works with the same JWTs as the main API.

For this example, we will use the experimental datacenter's URL:

The API mainly works on a per-sensor basis, on a limited amount of time. Replaying for multiple sensors (or entire org), or longer time period is done through multiple parallel API calls. This multiplexing is taken care of by the Python CLI above.

To query Replay, do a POST with a Content-Type header of application-json and with a JSON body like:

  "oid": "",             // OID this query relates to
  "rule_source": {       // rule source information (use one of "rule_name" or "rule")
    "rule_name": "",     // pre-existing rule name to run
    "rule": {            // literal rule to run
      "detect": {},
      "respond": []
  "event_source": {      // event source information (use one of "sensor_events" or "events")
    "sensor_events": {   // use historical events from sensors
      "sid": "",         // sensor id to replay from, or entire org if empty
      "start_time": 0,   // start second epoch time to replay from
      "end_time": 0      // end second epoch time to replay to
    "events": [{}]       // literal list of events to replay
  "limit_event": 0,      // optional approximate number of events to process
  "limit_eval": 0,       // optional approximate number of operator evaluations to perform
  "trace": false,        // optional, if true add trace information to response, VERY VERBOSE
  "is_dry_run": false,   // optional, if true, an estimate of the total cost of the query will be returned
  "query": ""            // optional alternative way to describe a replay query as a LimaCharlie Query Language (LCQL) query.

Like the other endpoints you can also submit a rule_name in the URL query if you want
to use an existing organization rule.

You may also specify a limit_event and limit_eval parameter as integers. They will limit the number of events evaluated and the number of rule evaluations performed (approximately). If the limits are reached, the response will contain an item named limit_eval_reached: true and limit_event_reached: true.

Finally, you may also set trace to true in the request to receive a detailed trace of the rule evaluation. This is useful in the development of new rules to find where rules are failing.


The Replay service is billed on a per event evaluated.

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