How to Execute Agent Spec Configuration with WayFlow

Prerequisites

This guide assumes you have already defined either a Flow or an Agent using PyAgentSpec and exported its Agent Spec configuration.

• Flows

• Agents

This guide demonstrates how to:

1. Install WayFlow to use its Agent Spec adapter

2. Define a tool execution registry

3. Load an Agent Spec configuration using the WayFlow adapter

4. Run and interact with the assistant

The example uses a minimal Agent configured with a single tool for performing multiplications. The same approach can be applied to load and execute more complex assistants.

1. Installation

The execution of this guide requires to install the package wayflowcore.

pip install "wayflowcore==25.4.1"

You can find more information about the Agent Spec adapter in the WayFlow API Reference.

2. Defining the tool registry

Before loading the configuration, define a tool registry that specifies how tools are executed. This registry is required to specify how tools are executed, because the tools implementation is not included in the Agent Spec configuration of the assistants.

The example below registers a single tool that performs multiplications. In practice, this should be extended to register implementations for all tools that the assistant should use.

This guide focuses on the simplest tool type: ServerTool. For information on other types, such as ClientTool and RemoteTool, refer to the Agent Spec Language Specification.

from wayflowcore.tools import ServerTool

multiplication_tool = ServerTool(
    name="multiplication_tool",
    description="Tool that allows to compute multiplications",
    parameters={"a": {"type": "integer"}, "b": {"type": "integer"}},
    output={"title": "product", "type": "integer"},
    func=lambda a, b: a*b,
)

tool_registry = {
    "multiplication_tool": multiplication_tool,
}

3. Loading the Agent Spec configuration

The configuration of the agent is as follows. Note that the configuration first defines two components multiplication_tool and vllm_config which are then referenced in the definition of the agent itself.

JSONYAML

{
  "component_type": "Agent",
  "id": "e52d2c57-0bdc-4f25-948a-2e9d9f670008",
  "name": "Math homework assistant",
  "description": null,
  "metadata": {},
  "inputs": [],
  "outputs": [],
  "llm_config": {
    "component_type": "VllmConfig",
    "id": "vllm_config",
    "name": "llama-3.1-8b-instruct",
    "description": null,
    "metadata": {},
    "default_generation_parameters": {},
    "url": "LLAMA_PUBLIC_ENDPOINT",
    "model_id": "meta-llama/Meta-Llama-3.1-8B-Instruct"
  },
  "system_prompt": "You are an assistant for helping with math homework.",
  "tools": [
    {
      "component_type": "ServerTool",
      "id": "multiplication_tool",
      "name": "multiplication_tool",
      "description": "Tool that allows to compute multiplications",
      "metadata": {},
      "inputs": [
        {
          "title": "a",
          "type": "integer"
        },
        {
          "title": "b",
          "type": "integer"
        }
      ],
      "outputs": [
        {
          "title": "product",
          "type": "integer"
        }
      ]
    }
  ],
  "agentspec_version": "25.4.1"
}

Loading the configuration to the WayFlow executor is simple as long as the tool_registry has been defined.

from wayflowcore.agentspec import AgentSpecLoader

loader = AgentSpecLoader(tool_registry=tool_registry)
assistant = loader.load_yaml(AGENTSPEC_CONFIG)

4. Running the assistant

To start the interaction, first create a conversation. The assistant then prompts the user for input, and responses from the agent are printed continuously until the process is interrupted (e.g., via Ctrl+C). Messages of type TOOL_REQUEST and TOOL_RESULT are also displayed. These indicate when the agent invokes one of the available tools to respond to the user.

For more information on message types, refer to the WayFlow API documentation.

from wayflowcore import MessageType

if __name__ == "__main__":
    conversation = assistant.start_conversation()
    message_idx = 0
    while True:
        user_input = input("\nUSER >>> ")
        conversation.append_user_message(user_input)
        assistant.execute(conversation)
        messages = conversation.get_messages()
        for message in messages[message_idx+1:]:
            if message.message_type == MessageType.TOOL_REQUEST:
                print(f"\n{message.message_type.value} >>> {message.tool_requests}")
            else:
                print(f"\n{message.message_type.value} >>> {message.content}")
        message_idx = len(messages)

You may also want to execute a non-conversational flow. In this case the execution loop could be implemented as shown below:

if __name__ == "__main__":
    conversation = assistant.start_conversation({ "some_input_name": "some_input_value", ... })
    status = assistant.execute(conversation)
    for output_name, output_value in conversation.state.input_output_key_values.items():
        print(f"{output_name} >>> \n{output_value}")

Recap

This guide covered how to:

1. Install the Agent Spec adapter for WayFlow.

2. Define the execution of tools in a tool registry.

3. Load an Agent Spec configuration using the WayFlow adapter.

4. Run the assistant and interact with it.

Below is the complete code from this guide.

import logging
import warnings

from wayflowcore import MessageType
from wayflowcore.agentspec import AgentSpecLoader
from wayflowcore.tools import ServerTool

warnings.filterwarnings("ignore")
logging.basicConfig(level=logging.CRITICAL)

AGENTSPEC_CONFIG = """
component_type: Agent
id: e52d2c57-0bdc-4f25-948a-2e9d9f670008
name: Math homework assistant
description: null
metadata: {}
inputs: []
outputs: []
llm_config:
  component_type: VllmConfig
  id: vllm_config
  name: llama-3.1-8b-instruct
  description: null
  metadata: {}
  default_generation_parameters: {}
  url: LLAMA_PUBLIC_ENDPOINT
  model_id: meta-llama/Meta-Llama-3.1-8B-Instruct
system_prompt: You are an assistant for helping with math homework.
tools:
- component_type: ServerTool
  id: multiplication_tool
  name: multiplication_tool
  description: Tool that allows to compute multiplications
  metadata: {}
  inputs:
  - title: a
    type: integer
  - title: b
    type: integer
  outputs:
  - title: product
    type: integer
"""

multiplication_tool = ServerTool(
    name="multiplication_tool",
    description="Tool that allows to compute multiplications",
    parameters={"a": {"type": "integer"}, "b": {"type": "integer"}},
    output={"title": "product", "type": "integer"},
    func=lambda a, b: a * b,
)

tool_registry = {
    "multiplication_tool": multiplication_tool,
}

loader = AgentSpecLoader(tool_registry=tool_registry)
assistant = loader.load_yaml(AGENTSPEC_CONFIG)

if __name__ == "__main__":
    conversation = assistant.start_conversation()
    message_idx = 0
    while True:
        user_input = input("\nUSER >>> ")
        conversation.append_user_message(user_input)
        assistant.execute(conversation)
        messages = conversation.get_messages()
        for message in messages[message_idx + 1 :]:
            if message.message_type == MessageType.TOOL_REQUEST:
                print(f"\n{message.message_type.value} >>> {message.tool_requests}")
            else:
                print(f"\n{message.message_type.value} >>> {message.content}")
        message_idx = len(messages)

Next steps

To discover more advanced capabilities, refer to the WayFlow documentation and learn what you can build with Flows and Agents.