How to Run Multiple Flows in Parallel

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Parallel flow execution how-to script

Prerequisites

This guide assumes familiarity with Flows.

Parallelism is a fundamental concept in computing that enables tasks to be processed concurrently, significantly enhancing system efficiency, scalability, and overall performance.

WayFlow supports the execution of multiple Flows in parallel, using the ParallelFlowExecutionStep. This guide will show you how to:

• use ParallelFlowExecutionStep to run several tasks in parallel

• use PromptExecutionStep to summarize the outcome of the parallel tasks

To follow this guide, you need an LLM. WayFlow supports several LLM API providers. Select an LLM from the options below:

OCI GenAIvLLMOllama

from wayflowcore.models import OCIGenAIModel

if __name__ == "__main__":

    llm = OCIGenAIModel(
        model_id="provider.model-id",
        service_endpoint="https://url-to-service-endpoint.com",
        compartment_id="compartment-id",
        auth_type="API_KEY",
    )

Basic implementation

In this guide, we will create a Flow that generates a marketing message for a user. Taking the username that identifies the user as input, we will take advantage of the ParallelFlowExecutionStep to concurrently retrieve information about the user and the context, so that we can finally generate a personalized marketing welcome message.

We first define the following tools that retrieve the desired information:

• One tool that retrieves the current time;

• One tool that retrieves the user information, like name and date of birth;

• One tool that gathers the user’s purchase history;

• One tool that looks for the current list of items on sale, which could be recommended to the user.

from wayflowcore.property import DictProperty, ListProperty, StringProperty
from wayflowcore.tools.toolhelpers import DescriptionMode, tool


@tool(
    description_mode=DescriptionMode.ONLY_DOCSTRING,
    output_descriptors=[DictProperty(name="user_info", value_type=StringProperty())],
)
def get_user_information(username: str) -> dict[str, str]:
    """Retrieve information about a user"""
    return {
        "alice": {"name": "Alice", "email": "alice@email.com", "date_of_birth": "1980/05/01"},
        "bob": {"name": "Bob", "email": "bob@email.com", "date_of_birth": "1970/10/01"},
    }.get(username, {})


@tool(
    description_mode=DescriptionMode.ONLY_DOCSTRING,
    output_descriptors=[StringProperty(name="current_time")],
)
def get_current_time() -> str:
    """Return current time"""
    return "2025/10/01 10:30 PM"


@tool(
    description_mode=DescriptionMode.ONLY_DOCSTRING,
    output_descriptors=[
        ListProperty(name="user_purchases", item_type=DictProperty(value_type=StringProperty()))
    ],
)
def get_user_last_purchases(username: str) -> list[dict[str, str]]:
    """Retrieve the list of purchases made by a user"""
    return {
        "alice": [
            {"item_type": "videogame", "title": "Arkanoid", "date": "2000/10/10"},
            {"item_type": "videogame", "title": "Pacman", "date": "2002/09/09"},
        ],
        "bob": [
            {"item_type": "movie", "title": "Batman begins", "date": "2015/10/10"},
            {"item_type": "movie", "title": "The Dark Knight", "date": "2020/08/08"},
        ],
    }.get(username, [])


@tool(
    description_mode=DescriptionMode.ONLY_DOCSTRING,
    output_descriptors=[
        ListProperty(name="items_on_sale", item_type=DictProperty(value_type=StringProperty()))
    ],
)
def get_items_on_sale() -> list[dict[str, str]]:
    """Retrieve the list of items currently on sale"""
    return [
        {"item_type": "household", "title": "Broom"},
        {"item_type": "videogame", "title": "Metroid"},
        {"item_type": "movie", "title": "The Lord of the Rings"},
    ]

These tools simply gather information, therefore they can be easily parallelized. We create the flows that wrap the tools we just created, and we collect them all in a ParallelFlowExecutionStep for parallel execution.

from wayflowcore.flow import Flow
from wayflowcore.steps import ParallelFlowExecutionStep, ToolExecutionStep

get_current_time_flow = Flow.from_steps(
    [ToolExecutionStep(name="get_current_time_step", tool=get_current_time)]
)
get_user_information_flow = Flow.from_steps(
    [ToolExecutionStep(name="get_user_information_step", tool=get_user_information)]
)
get_user_last_purchases_flow = Flow.from_steps(
    [ToolExecutionStep(name="get_user_last_purchases_step", tool=get_user_last_purchases)]
)
get_items_on_sale_flow = Flow.from_steps(
    [ToolExecutionStep(name="get_items_on_sale_steo", tool=get_items_on_sale)]
)

parallel_flow_step = ParallelFlowExecutionStep(
    name="parallel_flow_step",
    flows=[
        get_current_time_flow,
        get_user_information_flow,
        get_user_last_purchases_flow,
        get_items_on_sale_flow,
    ],
    max_workers=4,
)

The ParallelFlowExecutionStep will expose all the outputs that the different inner flows generate. We use this information to ask an LLM to generate a personalized welcome message for the user, which should also have a marketing purpose.

from wayflowcore.models import VllmModel
from wayflowcore.steps import OutputMessageStep, PromptExecutionStep

llm = VllmModel(
    model_id="LLAMA_MODEL_ID",
    host_port="LLAMA_API_URL",
)

prompt = """# Instructions

You are a marketing expert. You have to write a welcome message for a user.

The message must contain:
- A first sentence of greetings, including user's name, and personalized in case it's user's birthday
- A proposal containing something to buy

The purchase proposal must be:
- aligned with user's purchase history
- part of the list of items on sale

# User information

{{user_info}}

Note that the current time to check the birthday is: {{current_time}}

The list of items purchased by the user is:
{{user_purchases}}

# Items on sale

{{items_on_sale}}

Please write the welcome message for the user.
Do not give me the instructions to do it, I want only the final message to send.
"""

prompt_execution_step = PromptExecutionStep(
    name="prepare_marketing_message", prompt_template=prompt, llm=llm
)
output_message_step = OutputMessageStep(name="output_message_step", message_template="{{output}}")

Now that we have all the steps that compose our flow, we just put everything together to create it, and we execute it to generate our personalized message.

from wayflowcore.flow import Flow

flow = Flow.from_steps([parallel_flow_step, prompt_execution_step, output_message_step])

conversation = flow.start_conversation(inputs={"username": "bob"})
status = conversation.execute()
print(conversation.get_last_message().content)

# Expected output:
# Happy Birthday, Bob! We hope your special day is filled with excitement and joy.
# As a token of appreciation for being an valued customer, we'd like to recommend our sale on "The Lord of the Rings",
# a movie that we think you'll love, given your interest in superhero classics like "Batman Begins" and "The Dark Knight".
# It's now available at a discounted price, so don't miss out on this amazing opportunity to add it to your collection.
# Browse our sale now and enjoy!

Notes about parallelization

Not all sub-flows can be executed in parallel. The table below summarizes the limitations of parallel execution for the ParallelFlowExecutionStep:

Support	Type of flow	Examples	Remarks
FULLY SUPPORTED	Flows that do not yield and do not have any side-effect on the conversation (no variable read/write, posting to the conversation, and so on)	Embarrassingly parallel flows (simple independent operation), such as a PromptExecutionStep, ApiCallStep to post or get, and so on	N/A
SUPPORTED WITH SIDE EFFECTS	Flows that do not yield but have some side-effect on the conversation (variable read/write, posting to the conversation, and so on)	Flows with OutputMessageStep, VariableReadStep, VariableWriteStep, and so on	No guarantee in the order of operations (such as posting to the conversation), only the outputs are guaranteed in order.
NON SUPPORTED	Flows that yield. WayFlow does not support this, otherwise a user might be confused in what branch they are currently when prompted to answer.	Flows with InputMessageStep, AgentExecutionStep that can ask questions, ClientTool, and so on	It will raise an exception at instantiation time if a sub-flow can yield and step set to parallel

Note

The Global Interpreter Lock (GIL) in Python is not a problem for parallel remote requests because I/O-bound operations, such as network requests, release the GIL during their execution, allowing other threads to run concurrently while the I/O operation is in progress.

Agent Spec Exporting/Loading

You can export the assistant configuration to its Agent Spec configuration using the AgentSpecExporter.

from wayflowcore.agentspec import AgentSpecExporter

serialized_flow = AgentSpecExporter().to_json(flow)

Here is what the Agent Spec representation will look like ↓

Click here to see the assistant configuration.

JSONYAML

{
  "component_type": "Flow",
  "id": "54d4d398-5680-4ded-a594-bfd99790311a",
  "name": "flow_d2c53e52__auto",
  "description": "",
  "metadata": {
    "__metadata_info__": {}
  },
  "inputs": [
    {
      "type": "string",
      "title": "username"
    }
  ],
  "outputs": [
    {
      "type": "object",
      "additionalProperties": {
        "type": "string"
      },
      "key_type": {
        "type": "string"
      },
      "title": "user_info"
    },
    {
      "type": "array",
      "items": {
        "type": "object",
        "additionalProperties": {
          "type": "string"
        },
        "key_type": {
          "type": "string"
        }
      },
      "title": "items_on_sale"
    },
    {
      "description": "the message added to the messages list",
      "type": "string",
      "title": "output_message"
    },
    {
      "type": "string",
      "title": "current_time"
    },
    {
      "type": "array",
      "items": {
        "type": "object",
        "additionalProperties": {
          "type": "string"
        },
        "key_type": {
          "type": "string"
        }
      },
      "title": "user_purchases"
    },
    {
      "description": "the generated text",
      "type": "string",
      "title": "output"
    }
  ],
  "start_node": {
    "$component_ref": "ddcf926c-bce7-4050-a5f0-0a3da2110b52"
  },
  "nodes": [
    {
      "$component_ref": "6ce9d589-6649-4d21-992d-b9f1e76e01fa"
    },
    {
      "$component_ref": "b3642f57-f4da-4023-96dd-3916bc931b68"
    },
    {
      "$component_ref": "6594c3ef-67d2-4dfd-96a4-328a9fad8443"
    },
    {
      "$component_ref": "ddcf926c-bce7-4050-a5f0-0a3da2110b52"
    },
    {
      "$component_ref": "e8563485-edec-4ba2-ba13-4e2636e6de6c"
    }
  ],
  "control_flow_connections": [
    {
      "component_type": "ControlFlowEdge",
      "id": "eae402fb-9859-412f-9b98-d62dade4b757",
      "name": "parallel_flow_step_to_prepare_marketing_message_control_flow_edge",
      "description": null,
      "metadata": {
        "__metadata_info__": {}
      },
      "from_node": {
        "$component_ref": "6ce9d589-6649-4d21-992d-b9f1e76e01fa"
      },
      "from_branch": null,
      "to_node": {
        "$component_ref": "b3642f57-f4da-4023-96dd-3916bc931b68"
      }
    },
    {
      "component_type": "ControlFlowEdge",
      "id": "229a2cb3-80b4-4a89-9824-2dd41a59e660",
      "name": "prepare_marketing_message_to_output_message_step_control_flow_edge",
      "description": null,
      "metadata": {
        "__metadata_info__": {}
      },
      "from_node": {
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      },
      "from_branch": null,
      "to_node": {
        "$component_ref": "6594c3ef-67d2-4dfd-96a4-328a9fad8443"
      }
    },
    {
      "component_type": "ControlFlowEdge",
      "id": "d9801199-6ace-42fb-b9c4-109413041047",
      "name": "__StartStep___to_parallel_flow_step_control_flow_edge",
      "description": null,
      "metadata": {
        "__metadata_info__": {}
      },
      "from_node": {
        "$component_ref": "ddcf926c-bce7-4050-a5f0-0a3da2110b52"
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      "from_branch": null,
      "to_node": {
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      }
    },
    {
      "component_type": "ControlFlowEdge",
      "id": "9548adc6-82fb-4553-9cd9-6fd7367a93e7",
      "name": "output_message_step_to_None End node_control_flow_edge",
      "description": null,
      "metadata": {},
      "from_node": {
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      "to_node": {
        "$component_ref": "e8563485-edec-4ba2-ba13-4e2636e6de6c"
      }
    }
  ],
  "data_flow_connections": [
    {
      "component_type": "DataFlowEdge",
      "id": "04675bc3-157f-48c8-bfba-2b2045153d33",
      "name": "parallel_flow_step_user_info_to_prepare_marketing_message_user_info_data_flow_edge",
      "description": null,
      "metadata": {
        "__metadata_info__": {}
      },
      "source_node": {
        "$component_ref": "6ce9d589-6649-4d21-992d-b9f1e76e01fa"
      },
      "source_output": "user_info",
      "destination_node": {
        "$component_ref": "b3642f57-f4da-4023-96dd-3916bc931b68"
      },
      "destination_input": "user_info"
    },
    {
      "component_type": "DataFlowEdge",
      "id": "785a09b7-2c39-48c7-a2ad-522ce7815ca7",
      "name": "parallel_flow_step_current_time_to_prepare_marketing_message_current_time_data_flow_edge",
      "description": null,
      "metadata": {
        "__metadata_info__": {}
      },
      "source_node": {
        "$component_ref": "6ce9d589-6649-4d21-992d-b9f1e76e01fa"
      },
      "source_output": "current_time",
      "destination_node": {
        "$component_ref": "b3642f57-f4da-4023-96dd-3916bc931b68"
      },
      "destination_input": "current_time"
    },
    {
      "component_type": "DataFlowEdge",
      "id": "52844a73-6b00-40b3-b714-f2f8790e0a08",
      "name": "parallel_flow_step_user_purchases_to_prepare_marketing_message_user_purchases_data_flow_edge",
      "description": null,
      "metadata": {
        "__metadata_info__": {}
      },
      "source_node": {
        "$component_ref": "6ce9d589-6649-4d21-992d-b9f1e76e01fa"
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      "source_output": "user_purchases",
      "destination_node": {
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      },
      "destination_input": "user_purchases"
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    {
      "component_type": "DataFlowEdge",
      "id": "3cd4824d-41bb-40a5-8f20-6e2aef9dcd25",
      "name": "parallel_flow_step_items_on_sale_to_prepare_marketing_message_items_on_sale_data_flow_edge",
      "description": null,
      "metadata": {
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      },
      "source_node": {
        "$component_ref": "6ce9d589-6649-4d21-992d-b9f1e76e01fa"
      },
      "source_output": "items_on_sale",
      "destination_node": {
        "$component_ref": "b3642f57-f4da-4023-96dd-3916bc931b68"
      },
      "destination_input": "items_on_sale"
    },
    {
      "component_type": "DataFlowEdge",
      "id": "f76e15fe-eb5c-47d6-b20d-de40bbd88991",
      "name": "prepare_marketing_message_output_to_output_message_step_output_data_flow_edge",
      "description": null,
      "metadata": {
        "__metadata_info__": {}
      },
      "source_node": {
        "$component_ref": "b3642f57-f4da-4023-96dd-3916bc931b68"
      },
      "source_output": "output",
      "destination_node": {
        "$component_ref": "6594c3ef-67d2-4dfd-96a4-328a9fad8443"
      },
      "destination_input": "output"
    },
    {
      "component_type": "DataFlowEdge",
      "id": "d7b0f627-aee4-4135-8052-361e63f21037",
      "name": "__StartStep___username_to_parallel_flow_step_username_data_flow_edge",
      "description": null,
      "metadata": {
        "__metadata_info__": {}
      },
      "source_node": {
        "$component_ref": "ddcf926c-bce7-4050-a5f0-0a3da2110b52"
      },
      "source_output": "username",
      "destination_node": {
        "$component_ref": "6ce9d589-6649-4d21-992d-b9f1e76e01fa"
      },
      "destination_input": "username"
    },
    {
      "component_type": "DataFlowEdge",
      "id": "48b06c96-7dad-4da2-8a94-bace2d0cac7e",
      "name": "parallel_flow_step_user_info_to_None End node_user_info_data_flow_edge",
      "description": null,
      "metadata": {},
      "source_node": {
        "$component_ref": "6ce9d589-6649-4d21-992d-b9f1e76e01fa"
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      "source_output": "user_info",
      "destination_node": {
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      },
      "destination_input": "user_info"
    },
    {
      "component_type": "DataFlowEdge",
      "id": "0c514ff2-ca9b-4f98-86e9-62d7dece421a",
      "name": "parallel_flow_step_items_on_sale_to_None End node_items_on_sale_data_flow_edge",
      "description": null,
      "metadata": {},
      "source_node": {
        "$component_ref": "6ce9d589-6649-4d21-992d-b9f1e76e01fa"
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      "name": "output_message_step_output_message_to_None End node_output_message_data_flow_edge",
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      "metadata": {},
      "source_node": {
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      },
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    {
      "component_type": "DataFlowEdge",
      "id": "aa31f23e-3ba0-41be-8214-dc32d9365401",
      "name": "parallel_flow_step_current_time_to_None End node_current_time_data_flow_edge",
      "description": null,
      "metadata": {},
      "source_node": {
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      "name": "parallel_flow_step_user_purchases_to_None End node_user_purchases_data_flow_edge",
      "description": null,
      "metadata": {},
      "source_node": {
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      "destination_input": "output"
    }
  ],
  "$referenced_components": {
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      "component_type": "ExtendedParallelFlowNode",
      "id": "6ce9d589-6649-4d21-992d-b9f1e76e01fa",
      "name": "parallel_flow_step",
      "description": "",
      "metadata": {
        "__metadata_info__": {}
      },
      "inputs": [
        {
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          "title": "username"
        }
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      "outputs": [
        {
          "type": "string",
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        },
        {
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          },
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      ],
      "branches": [
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      "input_mapping": {},
      "output_mapping": {},
      "flows": [
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      },
      "inputs": [
        {
          "description": "\"user_info\" input variable for the template",
          "type": "string",
          "title": "user_info"
        },
        {
          "description": "\"current_time\" input variable for the template",
          "type": "string",
          "title": "current_time"
        },
        {
          "description": "\"user_purchases\" input variable for the template",
          "type": "string",
          "title": "user_purchases"
        },
        {
          "description": "\"items_on_sale\" input variable for the template",
          "type": "string",
          "title": "items_on_sale"
        }
      ],
      "outputs": [
        {
          "description": "the generated text",
          "type": "string",
          "title": "output"
        }
      ],
      "branches": [
        "next"
      ],
      "llm_config": {
        "component_type": "VllmConfig",
        "id": "deb3fd81-b199-4611-925d-01e22192f5c7",
        "name": "llm_c9adb3b0__auto",
        "description": null,
        "metadata": {
          "__metadata_info__": {}
        },
        "default_generation_parameters": null,
        "url": "LLAMA_API_URL",
        "model_id": "LLAMA_MODEL_ID"
      },
      "prompt_template": "# Instructions\n\nYou are a marketing expert. You have to write a welcome message for a user.\n\nThe message must contain:\n- A first sentence of greetings, including user's name, and personalized in case it's user's birthday\n- A proposal containing something to buy\n \nThe purchase proposal must be:\n- aligned with user's purchase history\n- part of the list of items on sale\n\n# User information\n\n{{user_info}}\n\nNote that the current time to check the birthday is: {{current_time}}\n\nThe list of items purchased by the user is:\n{{user_purchases}}\n\n# Items on sale\n\n{{items_on_sale}}\n\nPlease write the welcome message for the user.\nDo not give me the instructions to do it, I want only the final message to send.  \n"
    },
    "6594c3ef-67d2-4dfd-96a4-328a9fad8443": {
      "component_type": "PluginOutputMessageNode",
      "id": "6594c3ef-67d2-4dfd-96a4-328a9fad8443",
      "name": "output_message_step",
      "description": "",
      "metadata": {
        "__metadata_info__": {}
      },
      "inputs": [
        {
          "description": "\"output\" input variable for the template",
          "type": "string",
          "title": "output"
        }
      ],
      "outputs": [
        {
          "description": "the message added to the messages list",
          "type": "string",
          "title": "output_message"
        }
      ],
      "branches": [
        "next"
      ],
      "message": "{{output}}",
      "input_mapping": {},
      "output_mapping": {},
      "message_type": "AGENT",
      "rephrase": false,
      "llm_config": null,
      "expose_message_as_output": true,
      "component_plugin_name": "NodesPlugin",
      "component_plugin_version": "26.1.0.dev0"
    },
    "ddcf926c-bce7-4050-a5f0-0a3da2110b52": {
      "component_type": "StartNode",
      "id": "ddcf926c-bce7-4050-a5f0-0a3da2110b52",
      "name": "__StartStep__",
      "description": "",
      "metadata": {
        "__metadata_info__": {}
      },
      "inputs": [
        {
          "type": "string",
          "title": "username"
        }
      ],
      "outputs": [
        {
          "type": "string",
          "title": "username"
        }
      ],
      "branches": [
        "next"
      ]
    },
    "e8563485-edec-4ba2-ba13-4e2636e6de6c": {
      "component_type": "EndNode",
      "id": "e8563485-edec-4ba2-ba13-4e2636e6de6c",
      "name": "None End node",
      "description": "End node representing all transitions to None in the WayFlow flow",
      "metadata": {},
      "inputs": [
        {
          "type": "object",
          "additionalProperties": {
            "type": "string"
          },
          "key_type": {
            "type": "string"
          },
          "title": "user_info"
        },
        {
          "type": "array",
          "items": {
            "type": "object",
            "additionalProperties": {
              "type": "string"
            },
            "key_type": {
              "type": "string"
            }
          },
          "title": "items_on_sale"
        },
        {
          "description": "the message added to the messages list",
          "type": "string",
          "title": "output_message"
        },
        {
          "type": "string",
          "title": "current_time"
        },
        {
          "type": "array",
          "items": {
            "type": "object",
            "additionalProperties": {
              "type": "string"
            },
            "key_type": {
              "type": "string"
            }
          },
          "title": "user_purchases"
        },
        {
          "description": "the generated text",
          "type": "string",
          "title": "output"
        }
      ],
      "outputs": [
        {
          "type": "object",
          "additionalProperties": {
            "type": "string"
          },
          "key_type": {
            "type": "string"
          },
          "title": "user_info"
        },
        {
          "type": "array",
          "items": {
            "type": "object",
            "additionalProperties": {
              "type": "string"
            },
            "key_type": {
              "type": "string"
            }
          },
          "title": "items_on_sale"
        },
        {
          "description": "the message added to the messages list",
          "type": "string",
          "title": "output_message"
        },
        {
          "type": "string",
          "title": "current_time"
        },
        {
          "type": "array",
          "items": {
            "type": "object",
            "additionalProperties": {
              "type": "string"
            },
            "key_type": {
              "type": "string"
            }
          },
          "title": "user_purchases"
        },
        {
          "description": "the generated text",
          "type": "string",
          "title": "output"
        }
      ],
      "branches": [],
      "branch_name": "next"
    }
  },
  "agentspec_version": "25.4.2"
}

You can then load the configuration back to an assistant using the AgentSpecLoader.

from wayflowcore.agentspec import AgentSpecLoader

tool_registry = {
    "get_user_information": get_user_information,
    "get_current_time": get_current_time,
    "get_user_last_purchases": get_user_last_purchases,
    "get_items_on_sale": get_items_on_sale,
}
flow = AgentSpecLoader(tool_registry=tool_registry).load_json(serialized_flow)

Note

This guide uses the following extension/plugin Agent Spec components:

• ExtendedParallelFlowNode

See the list of available Agent Spec extension/plugin components in the API Reference

Next steps

Having learned how to perform generic parallel operations in WayFlow, you may now proceed to How to Do Map and Reduce Operations in Flows.

Full code

Click on the card at the top of this page to download the full code for this guide or copy the code below.

# Copyright © 2025 Oracle and/or its affiliates.
#
# This software is under the Apache License 2.0
# %%[markdown]
# WayFlow Code Example - How to Run Multiple Flows in Parallel
# ------------------------------------------------------------

# How to use:
# Create a new Python virtual environment and install the latest WayFlow version.
# ```bash
# python -m venv venv-wayflowcore
# source venv-wayflowcore/bin/activate
# pip install --upgrade pip
# pip install "wayflowcore==26.1" 
# ```

# You can now run the script
# 1. As a Python file:
# ```bash
# python howto_parallelflowexecution.py
# ```
# 2. As a Notebook (in VSCode):
# When viewing the file,
#  - press the keys Ctrl + Enter to run the selected cell
#  - or Shift + Enter to run the selected cell and move to the cell below# (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0) or Universal Permissive License
# (UPL) 1.0 (LICENSE-UPL or https://oss.oracle.com/licenses/upl), at your option.



# %%[markdown]
## Define the tools

# %%
from wayflowcore.property import DictProperty, ListProperty, StringProperty
from wayflowcore.tools.toolhelpers import DescriptionMode, tool


@tool(
    description_mode=DescriptionMode.ONLY_DOCSTRING,
    output_descriptors=[DictProperty(name="user_info", value_type=StringProperty())],
)
def get_user_information(username: str) -> dict[str, str]:
    """Retrieve information about a user"""
    return {
        "alice": {"name": "Alice", "email": "alice@email.com", "date_of_birth": "1980/05/01"},
        "bob": {"name": "Bob", "email": "bob@email.com", "date_of_birth": "1970/10/01"},
    }.get(username, {})


@tool(
    description_mode=DescriptionMode.ONLY_DOCSTRING,
    output_descriptors=[StringProperty(name="current_time")],
)
def get_current_time() -> str:
    """Return current time"""
    return "2025/10/01 10:30 PM"


@tool(
    description_mode=DescriptionMode.ONLY_DOCSTRING,
    output_descriptors=[
        ListProperty(name="user_purchases", item_type=DictProperty(value_type=StringProperty()))
    ],
)
def get_user_last_purchases(username: str) -> list[dict[str, str]]:
    """Retrieve the list of purchases made by a user"""
    return {
        "alice": [
            {"item_type": "videogame", "title": "Arkanoid", "date": "2000/10/10"},
            {"item_type": "videogame", "title": "Pacman", "date": "2002/09/09"},
        ],
        "bob": [
            {"item_type": "movie", "title": "Batman begins", "date": "2015/10/10"},
            {"item_type": "movie", "title": "The Dark Knight", "date": "2020/08/08"},
        ],
    }.get(username, [])


@tool(
    description_mode=DescriptionMode.ONLY_DOCSTRING,
    output_descriptors=[
        ListProperty(name="items_on_sale", item_type=DictProperty(value_type=StringProperty()))
    ],
)
def get_items_on_sale() -> list[dict[str, str]]:
    """Retrieve the list of items currently on sale"""
    return [
        {"item_type": "household", "title": "Broom"},
        {"item_type": "videogame", "title": "Metroid"},
        {"item_type": "movie", "title": "The Lord of the Rings"},
    ]



# %%[markdown]
## Create the flows to be run in parallel

# %%
from wayflowcore.flow import Flow
from wayflowcore.steps import ParallelFlowExecutionStep, ToolExecutionStep

get_current_time_flow = Flow.from_steps(
    [ToolExecutionStep(name="get_current_time_step", tool=get_current_time)]
)
get_user_information_flow = Flow.from_steps(
    [ToolExecutionStep(name="get_user_information_step", tool=get_user_information)]
)
get_user_last_purchases_flow = Flow.from_steps(
    [ToolExecutionStep(name="get_user_last_purchases_step", tool=get_user_last_purchases)]
)
get_items_on_sale_flow = Flow.from_steps(
    [ToolExecutionStep(name="get_items_on_sale_steo", tool=get_items_on_sale)]
)

parallel_flow_step = ParallelFlowExecutionStep(
    name="parallel_flow_step",
    flows=[
        get_current_time_flow,
        get_user_information_flow,
        get_user_last_purchases_flow,
        get_items_on_sale_flow,
    ],
    max_workers=4,
)

# %%[markdown]
## Generate the marketing message

# %%
from wayflowcore.models import VllmModel
from wayflowcore.steps import OutputMessageStep, PromptExecutionStep

llm = VllmModel(
    model_id="LLAMA_MODEL_ID",
    host_port="LLAMA_API_URL",
)

prompt = """# Instructions

You are a marketing expert. You have to write a welcome message for a user.

The message must contain:
- A first sentence of greetings, including user's name, and personalized in case it's user's birthday
- A proposal containing something to buy

The purchase proposal must be:
- aligned with user's purchase history
- part of the list of items on sale

# User information

{{user_info}}

Note that the current time to check the birthday is: {{current_time}}

The list of items purchased by the user is:
{{user_purchases}}

# Items on sale

{{items_on_sale}}

Please write the welcome message for the user.
Do not give me the instructions to do it, I want only the final message to send.
"""

prompt_execution_step = PromptExecutionStep(
    name="prepare_marketing_message", prompt_template=prompt, llm=llm
)
output_message_step = OutputMessageStep(name="output_message_step", message_template="{{output}}")

# %%[markdown]
## Create and test the final flow

# %%
from wayflowcore.flow import Flow

flow = Flow.from_steps([parallel_flow_step, prompt_execution_step, output_message_step])

conversation = flow.start_conversation(inputs={"username": "bob"})
status = conversation.execute()
print(conversation.get_last_message().content)

# Expected output:
# Happy Birthday, Bob! We hope your special day is filled with excitement and joy.
# As a token of appreciation for being an valued customer, we'd like to recommend our sale on "The Lord of the Rings",
# a movie that we think you'll love, given your interest in superhero classics like "Batman Begins" and "The Dark Knight".
# It's now available at a discounted price, so don't miss out on this amazing opportunity to add it to your collection.
# Browse our sale now and enjoy!

# %%[markdown]
## Export config to Agent Spec

# %%
from wayflowcore.agentspec import AgentSpecExporter

serialized_flow = AgentSpecExporter().to_json(flow)

# %%[markdown]
## Load Agent Spec config

# %%
from wayflowcore.agentspec import AgentSpecLoader

tool_registry = {
    "get_user_information": get_user_information,
    "get_current_time": get_current_time,
    "get_user_last_purchases": get_user_last_purchases,
    "get_items_on_sale": get_items_on_sale,
}
flow = AgentSpecLoader(tool_registry=tool_registry).load_json(serialized_flow)