Parallel

Overview

The Parallel expression allows you to run sets of expressions at the same time concurrently. It is able to spawn branches or use a for expression within in.

There are two types of parallel expressions:

Parallel with Branches
Parallel with For Loop

Parallel with Branches

A parallel with branches is useful when you need to run a specific number of expressions simultaneously, each with their own set of actions. The number of branches for this type of parallel does not change dynamically, it’s fixed.

Parallel with For Loop

A parallel with for loop will run every expression, similar to a regular For Loop expression. However, it will run all iterations at the same time for every item in the list.

Low-Code Editor

Add a Parallel Step, select the type of parallel you need. If you select branches, you will add as many branches as you need for parallel executions of expressions. If you select for loop, bind the iterable, define loops variables, and execute iterations in parallel.

Select Branches: For static execution of paths
Set the amount of branches: Define how many tasks to run in parallel
Define expressions: Add expressions under each branch

Select For Loop: To iterate over a list of items in parallel
Set up For variables: Set up all bindings as shown in For Loop
Define expressions: Add expressions under the For Loop.

Syntax Reference

Schema

Parallel with Branches

1 parallel:
2   branches:  # List of parallel paths (at least 2)
3     - steps:  # Branch 1 expressions
4         - EXPRESSION_1  # e.g., action with output_key
5     - steps:  # Branch 2
6         - EXPRESSION_2
7     # Optional: More branches...

Parallel with For Loop

1 parallel:
2   for:
3     each: ITEM_VAR*      # str: Per-iteration binder (e.g., 'user_id')
4     index: INDEX_VAR*    # str: Optional position (e.g., 'idx')
5     in: DATA_BANK_ITERABLE*  # str: Array ref (e.g., 'data.user_ids')
6     output_key: RESULTS_VAR* 
7     steps:               # Per-iteration expressions (parallel across iterations)
8       - EXPRESSION_1

Fields

Field	Type	Mandatory	Description	Mode
`branches`	list[steps]	Yes (if Branches)	Step groups	Branches
`each`	string	Yes (if For)	item binder - accessed as `var.prop` within steps	For Loop
`index`	string	No	Index binder - provides position (e.g., `idx`)	For Loop
`in`	string	Yes (if For)	Data bank reference only (upstream outputs)	For Loop
`output_key`	list[dict]	Yes (if For)	List of per-iteration dicts: `[ {step_key: val}, ... ]`. Access: `data.results[i].step_key`.	For Loop
`steps`	list[expr]	No	Parallelized per item	For Loop

Practical Examples

Parallel with Branches

Example 1: Independent Notifications (Basic Fan-Out)

Log an event and send an email concurrently

1 parallel:
2   branches:
3     - steps:
4         - action:
5             action_name: log_event
6             input_args: 
7 							event_name: '''user_login'''
8             output_key: log_result
9     - steps:
10         - action:
11             action_name: send_email
12             input_args:
13               email: meta_info.user.email_addr
14               subject: "Login Success"
15               body: "Welcome back!"
16             output_key: email_result

Example 2: Multi-Service Sync

Update a CRM and Slack concurrently

1 parallel:
2   branches:
3     - steps:
4         - action:
5             action_name: update_crm
6             input_args: 
7 							user_id: data.user_id 
8             output_key: crm_update
9     - steps:
10         - action:
11             action_name: post_slack
12             input_args: 
13 							channel: '''#updates'''
14             output_key: slack_post

Parallel with For Loop

Send Messages in Parallel

Run actions for each in a list in parallel

1 steps:
2   - action:
3       action_name: mw.batch_get_users_by_email
4       output_key: user_results
5       input_args:
6         user_emails:
7           - '"jdoe@example.com"'
8           - '"sdom@example.com"'
9           - '"jnde@example.com"'
10  
11  - parallel:
12  		for:
13       each: user
14       index: user_index
15       in: data.user_results.user_records  # From upstream fetch
16       output_key: requested_for_notifications
17       steps:
18         - action:
19             action_name: mw.send_plaintext_chat_notification
20             output_key: notification_output
21             input_args:
22               user_record_id: user.lookup_id
23               message: '''Hi, this is a batch message!'''

Parallel Ticket Updates

Sample input argument

1 outage_tickets: [ {"id": "T1"}, {"id": "T2"} ]

Compound Action

1 parallel:
2   for:
3     each: ticket
4     index: ticket_index
5     in: data.outage_tickets
6     output_key: updated_tickets
7     steps:
8       - action:
9           action_name: update_ticket_status
10           input_args: 
11           	ticket_id: ticket.id
12 						status: '''ON_CALL'''
13           output_key: update_result
14       - action:  # Nested parallel-friendly
15           action_name: notify_owner
16           input_args: 
17 						ticket_id: ticket.id
18           output_key: notify_result

Expected Output

1 updated_tickets: [
2   {
3     "update_result": { "id": "T1", "status": "ON_CALL" },
4     "notify_result": { "sent": true }
5   },
6   {
7     "update_result": { "id": "T2", "status": "ON_CALL" },
8     "notify_result": { "sent": true }
9   }
10 ]

1	parallel:
2	branches: # List of parallel paths (at least 2)
3	- steps: # Branch 1 expressions
4	- EXPRESSION_1 # e.g., action with output_key
5	- steps: # Branch 2
6	- EXPRESSION_2
7	# Optional: More branches...

1	parallel:
2	for:
3	each: ITEM_VAR* # str: Per-iteration binder (e.g., 'user_id')
4	index: INDEX_VAR* # str: Optional position (e.g., 'idx')
5	in: DATA_BANK_ITERABLE* # str: Array ref (e.g., 'data.user_ids')
6	output_key: RESULTS_VAR*
7	steps: # Per-iteration expressions (parallel across iterations)
8	- EXPRESSION_1

1	parallel:
2	branches:
3	- steps:
4	- action:
5	action_name: log_event
6	input_args:
7	event_name: '''user_login'''
8	output_key: log_result
9	- steps:
10	- action:
11	action_name: send_email
12	input_args:
13	email: meta_info.user.email_addr
14	subject: "Login Success"
15	body: "Welcome back!"
16	output_key: email_result

1	steps:
2	- action:
3	action_name: mw.batch_get_users_by_email
4	output_key: user_results
5	input_args:
6	user_emails:
7	- '"jdoe@example.com"'
8	- '"sdom@example.com"'
9	- '"jnde@example.com"'
10
11	- parallel:
12	for:
13	each: user
14	index: user_index
15	in: data.user_results.user_records # From upstream fetch
16	output_key: requested_for_notifications
17	steps:
18	- action:
19	action_name: mw.send_plaintext_chat_notification
20	output_key: notification_output
21	input_args:
22	user_record_id: user.lookup_id
23	message: '''Hi, this is a batch message!'''

1	parallel:
2	for:
3	each: ticket
4	index: ticket_index
5	in: data.outage_tickets
6	output_key: updated_tickets
7	steps:
8	- action:
9	action_name: update_ticket_status
10	input_args:
11	ticket_id: ticket.id
12	status: '''ON_CALL'''
13	output_key: update_result
14	- action: # Nested parallel-friendly
15	action_name: notify_owner
16	input_args:
17	ticket_id: ticket.id
18	output_key: notify_result

1	updated_tickets: [
2	{
3	"update_result": { "id": "T1", "status": "ON_CALL" },
4	"notify_result": { "sent": true }
5	},
6	{
7	"update_result": { "id": "T2", "status": "ON_CALL" },
8	"notify_result": { "sent": true }
9	}
10	]