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# Swarms CLI Documentation
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The Swarms Command Line Interface (CLI) allows you to easily manage and run your Swarms of agents from the command line. This page will guide you through the installation process and provide a breakdown of the available commands.
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## Installation
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You can install the `swarms` package using `pip` or `poetry`.
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### Using pip
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```bash
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pip3 install -U swarms
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```
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### Using poetry
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```bash
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poetry add swarms
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```
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Once installed, you can run the Swarms CLI with the following command:
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```bash
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poetry run swarms help
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```
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## Swarms CLI - Help
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When running `swarms help`, you'll see the following output:
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```
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_________
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/ _____/_ _ _______ _______ _____ ______
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\_____ \ \/ \/ /\__ \_ __ \/ \ / ___/
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/ \ / / __ \| | \/ Y Y \___ \
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/_______ / \/\_/ (____ /__| |__|_| /____ >
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\/ \/ \/ \/
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Swarms CLI - Help
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Commands:
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onboarding : Starts the onboarding process
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help : Shows this help message
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get-api-key : Retrieves your API key from the platform
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check-login : Checks if you're logged in and starts the cache
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read-docs : Redirects you to swarms cloud documentation!
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run-agents : Run your Agents from your agents.yaml
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For more details, visit: https://docs.swarms.world
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```
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### CLI Commands
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Below is a detailed explanation of the available commands:
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- **onboarding**
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Starts the onboarding process to help you set up your environment and configure your agents.
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Usage:
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```bash
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swarms onboarding
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```
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- **help**
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Displays the help message, including a list of available commands.
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Usage:
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```bash
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swarms help
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```
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- **get-api-key**
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Retrieves your API key from the platform, allowing your agents to communicate with the Swarms platform.
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Usage:
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```bash
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swarms get-api-key
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```
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- **check-login**
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Verifies if you are logged into the platform and starts the cache for storing your login session.
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Usage:
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```bash
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swarms check-login
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```
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- **read-docs**
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Redirects you to the official Swarms documentation on the web for further reading.
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Usage:
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```bash
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swarms read-docs
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```
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- **run-agents**
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Executes your agents from the `agents.yaml` configuration file, which defines the structure and behavior of your agents.
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Usage:
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```bash
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swarms run-agents
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```
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import logging
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import os
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from clusterops import (
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execute_with_cpu_cores,
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list_available_cpus,
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)
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from swarm_models import OpenAIChat
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from swarms import Agent
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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api_key = os.getenv("OPENAI_API_KEY")
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# Create an instance of the OpenAIChat class
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model = OpenAIChat(
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openai_api_key=api_key,
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model_name="gpt-4o-mini",
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temperature=0.1,
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max_tokens=2000,
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)
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# Function for the director agent
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def director_task(task: str):
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logging.info(f"Running Director agent for task: {task}")
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director = Agent(
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agent_name="Director",
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system_prompt="Directs the tasks for the workers",
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llm=model,
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max_loops=1,
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dashboard=False,
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streaming_on=True,
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verbose=True,
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stopping_token="<DONE>",
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state_save_file_type="json",
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saved_state_path="director.json",
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)
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return director.run(task)
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# Function for worker 1
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def worker1_task(task: str):
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logging.info(f"Running Worker1 agent for task: {task}")
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worker1 = Agent(
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agent_name="Worker1",
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system_prompt="Generates a transcript for a youtube video on what swarms are",
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llm=model,
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max_loops=1,
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dashboard=False,
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streaming_on=True,
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verbose=True,
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stopping_token="<DONE>",
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state_save_file_type="json",
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saved_state_path="worker1.json",
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)
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return worker1.run(task)
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# Function for worker 2
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def worker2_task(task: str):
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logging.info(f"Running Worker2 agent for task: {task}")
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worker2 = Agent(
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agent_name="Worker2",
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system_prompt="Summarizes the transcript generated by Worker1",
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llm=model,
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max_loops=1,
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dashboard=False,
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streaming_on=True,
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verbose=True,
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stopping_token="<DONE>",
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state_save_file_type="json",
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saved_state_path="worker2.json",
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)
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return worker2.run(task)
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# CPU Core Assignment Example
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def assign_tasks_to_cpus():
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# List available CPUs
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cpus = list_available_cpus()
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logging.info(f"Available CPUs: {cpus}")
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# Example: Assign Director task to 1 CPU core
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logging.info("Executing Director task using 1 CPU core")
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execute_with_cpu_cores(
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1, director_task, "Direct the creation of swarm video format"
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)
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# Example: Assign Worker1 task to 2 CPU cores
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logging.info("Executing Worker1 task using 2 CPU cores")
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execute_with_cpu_cores(
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2,
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worker1_task,
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"Generate transcript for youtube video on swarms",
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)
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# Example: Assign Worker2 task to 2 CPU cores
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logging.info("Executing Worker2 task using 2 CPU cores")
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execute_with_cpu_cores(
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2,
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worker2_task,
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"Summarize the transcript generated by Worker1",
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)
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print("finished")
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if __name__ == "__main__":
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logging.info(
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"Starting the CPU-based task assignment for agents..."
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)
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assign_tasks_to_cpus()
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from clusterops import (
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list_available_gpus,
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execute_on_gpu,
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)
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from swarms import Agent, AgentRearrange
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from swarm_models import OpenAIChat
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import os
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import logging
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from dotenv import load_dotenv
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load_dotenv()
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# Get the OpenAI API key from the environment variable
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api_key = os.getenv("OPENAI_API_KEY")
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# Create an instance of the OpenAIChat class
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model = OpenAIChat(
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openai_api_key=api_key,
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model_name="gpt-4o-mini",
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temperature=0.1,
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max_tokens=2000,
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)
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# Function for the director agent
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def director_task(task: str):
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logging.info(f"Running Director agent for task: {task}")
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director = Agent(
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agent_name="Director",
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system_prompt="Directs the tasks for the workers",
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llm=model,
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max_loops=1,
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dashboard=False,
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streaming_on=True,
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verbose=True,
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stopping_token="<DONE>",
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state_save_file_type="json",
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saved_state_path="director.json",
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)
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return director.run(task)
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# Function for worker 1
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def worker1_task(task: str):
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logging.info(f"Running Worker1 agent for task: {task}")
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worker1 = Agent(
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agent_name="Worker1",
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system_prompt="Generates a transcript for a youtube video on what swarms are",
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llm=model,
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max_loops=1,
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dashboard=False,
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streaming_on=True,
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verbose=True,
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stopping_token="<DONE>",
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state_save_file_type="json",
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saved_state_path="worker1.json",
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)
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return worker1.run(task)
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# Function for worker 2
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def worker2_task(task: str):
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logging.info(f"Running Worker2 agent for task: {task}")
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worker2 = Agent(
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agent_name="Worker2",
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system_prompt="Summarizes the transcript generated by Worker1",
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llm=model,
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max_loops=1,
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dashboard=False,
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streaming_on=True,
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verbose=True,
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stopping_token="<DONE>",
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state_save_file_type="json",
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saved_state_path="worker2.json",
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)
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return worker2.run(task)
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# GPU Assignment Example
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def assign_tasks_to_gpus():
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# List available GPUs
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gpus = list_available_gpus()
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logging.info(f"Available GPUs: {gpus}")
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# Example: Assign Director to GPU 0
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logging.info("Executing Director task on GPU 0")
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execute_on_gpu(
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0, director_task, "Direct the creation of swarm video format"
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)
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# Example: Assign Worker1 to GPU 1
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logging.info("Executing Worker1 task on GPU 1")
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execute_on_gpu(
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1,
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worker1_task,
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"Generate transcript for youtube video on swarms",
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)
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# Example: Assign Worker2 to GPU 2
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logging.info("Executing Worker2 task on GPU 2")
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execute_on_gpu(
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2,
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worker2_task,
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"Summarize the transcript generated by Worker1",
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)
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# Flow Management using AgentRearrange (optional)
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def run_agent_flow():
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# Initialize the agents
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director = Agent(
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agent_name="Director",
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system_prompt="Directs the tasks for the workers",
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llm=model,
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max_loops=1,
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dashboard=False,
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streaming_on=True,
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verbose=True,
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stopping_token="<DONE>",
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state_save_file_type="json",
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saved_state_path="director.json",
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)
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worker1 = Agent(
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agent_name="Worker1",
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system_prompt="Generates a transcript for a youtube video on what swarms are",
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llm=model,
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max_loops=1,
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dashboard=False,
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streaming_on=True,
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verbose=True,
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stopping_token="<DONE>",
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state_save_file_type="json",
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saved_state_path="worker1.json",
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)
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worker2 = Agent(
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agent_name="Worker2",
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system_prompt="Summarizes the transcript generated by Worker1",
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llm=model,
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max_loops=1,
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dashboard=False,
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streaming_on=True,
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verbose=True,
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stopping_token="<DONE>",
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state_save_file_type="json",
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saved_state_path="worker2.json",
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)
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# Define agent list and flow pattern
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agents = [director, worker1, worker2]
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flow = "Director -> Worker1 -> Worker2"
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# Use AgentRearrange to manage the flow
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agent_system = AgentRearrange(agents=agents, flow=flow)
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output = agent_system.run(
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"Create a format to express and communicate swarms of llms in a structured manner for youtube"
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)
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print(output)
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if __name__ == "__main__":
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logging.info(
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"Starting the GPU-based task assignment for agents..."
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)
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assign_tasks_to_gpus()
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logging.info("Starting the AgentRearrange task flow...")
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run_agent_flow()
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File diff suppressed because it is too large
Load Diff
@ -1,90 +1,140 @@
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import argparse
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from swarms.cli.parse_yaml import (
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create_agent_from_yaml,
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run_agent,
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list_agents,
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import os
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import time
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from rich.console import Console
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from rich.text import Text
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from swarms.cli.onboarding_process import OnboardingProcess
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from swarms.agents.create_agents_from_yaml import (
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create_agents_from_yaml,
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)
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# SWARMS_LOGO = """
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# _________
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# / _____/_ _ _______ _______ _____ ______
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# \_____ \\ \/ \/ /\__ \\_ __ \/ \ / ___/
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# / \\ / / __ \| | \/ Y Y \\___ \
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# /_______ / \/\_/ (____ /__| |__|_| /____ >
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# \/ \/ \/ \/
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# """
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console = Console()
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# RED_COLOR_CODE = "\033[91m"
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# RESET_COLOR_CODE = "\033[0m"
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ASCII_ART = """
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_________
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/ _____/_ _ _______ _______ _____ ______
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\_____ \\ \/ \/ /\__ \\_ __ \/ \ / ___/
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/ \\ / / __ \| | \/ Y Y \\___ \
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/_______ / \/\_/ (____ /__| |__|_| /____ >
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\/ \/ \/ \/
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# # print(RED_COLOR_CODE + SWARMS_LOGO + RESET_COLOR_CODE)
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"""
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def main():
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parser = argparse.ArgumentParser(
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description="""
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# Function to display the ASCII art in red
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def show_ascii_art():
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text = Text(ASCII_ART, style="bold cyan")
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console.print(text)
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Swarms CLI for managing and running swarms agents.
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# Help command
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def show_help():
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console.print(
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"""
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[bold cyan]Swarms CLI - Help[/bold cyan]
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[bold magenta]Commands:[/bold magenta]
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[bold white]onboarding[/bold white] : Starts the onboarding process
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[bold white]help[/bold white] : Shows this help message
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[bold white]get-api-key[/bold white] : Retrieves your API key from the platform
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[bold white]check-login[/bold white] : Checks if you're logged in and starts the cache
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[bold white]read-docs[/bold white] : Redirects you to swarms cloud documentation!
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[bold white]run-agents[/bold white] : Run your Agents from your agents.yaml
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For more details, visit: https://docs.swarms.world
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"""
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)
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subparsers = parser.add_subparsers(
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dest="command", help="Available commands"
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)
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# create agent command
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create_parser = subparsers.add_parser(
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"create", help="Create a new agent from a YAML file"
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# [bold white]add-agent[/bold white] : Add an agent to the marketplace under your name. Must have a Dockerfile + your agent.yaml to publish. Learn more Here: https://docs.swarms.world/en/latest/swarms_cloud/vision/
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# Fetch API key from platform
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def get_api_key():
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console.print(
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"[bold yellow]Opening the API key retrieval page...[/bold yellow]"
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)
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create_parser.add_argument(
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"agent", type=str, help="Path to the YAML file"
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# Simulating API key retrieval process by opening the website
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import webbrowser
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webbrowser.open("https://swarms.world/platform/api-keys")
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time.sleep(2)
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console.print(
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"[bold green]Your API key is available on the dashboard.[/bold green]"
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)
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# run agent command
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run_parser = subparsers.add_parser(
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"run", help="Run an agent with a specified task"
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# Redirect to docs
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def redirect_to_docs():
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console.print(
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"[bold yellow]Opening the Docs page...[/bold yellow]"
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)
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run_parser.add_argument(
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"agent_name", type=str, help="Name of the agent to run"
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# Simulating API key retrieval process by opening the website
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import webbrowser
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webbrowser.open("https://docs.swarms.world")
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time.sleep(2)
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# Check and start cache (login system simulation)
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def check_login():
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cache_file = "cache.txt"
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if os.path.exists(cache_file):
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with open(cache_file, "r") as f:
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cache_content = f.read()
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if cache_content == "logged_in":
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console.print(
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"[bold green]You are already logged in.[/bold green]"
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)
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run_parser.add_argument(
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"task", type=str, help="Task for the agent to execute"
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else:
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console.print(
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"[bold red]You are not logged in.[/bold red]"
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)
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else:
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console.print("[bold yellow]Logging in...[/bold yellow]")
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time.sleep(2)
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with open(cache_file, "w") as f:
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f.write("logged_in")
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console.print("[bold green]Login successful![/bold green]")
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# list agents command
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subparsers.add_parser("list", help="List all agents")
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# Additional help options
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||||
parser.add_argument(
|
||||
"--issue",
|
||||
action="store_true",
|
||||
help="Open an issue on GitHub: https://github.com/kyegomez/swarms/issues/new/choose",
|
||||
)
|
||||
# Main CLI handler
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Swarms Cloud CLI")
|
||||
|
||||
# Adding arguments for different commands
|
||||
parser.add_argument(
|
||||
"--community",
|
||||
action="store_true",
|
||||
help="Join our community on Discord: https://discord.com/servers/agora-999382051935506503",
|
||||
"command",
|
||||
choices=["onboarding", "help", "get-api-key", "check-login"],
|
||||
help="Command to run",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.issue:
|
||||
print(
|
||||
"Open an issue on GitHub: https://github.com/kyegomez/swarms/issues/new/choose"
|
||||
)
|
||||
elif args.community:
|
||||
print(
|
||||
"Join our community on Discord: https://discord.com/servers/agora-999382051935506503"
|
||||
show_ascii_art()
|
||||
|
||||
# Determine which command to run
|
||||
if args.command == "onboarding":
|
||||
OnboardingProcess().run()
|
||||
elif args.command == "help":
|
||||
show_help()
|
||||
elif args.command == "get-api-key":
|
||||
get_api_key()
|
||||
elif args.command == "check-login":
|
||||
check_login()
|
||||
elif args.command == "read-docs":
|
||||
redirect_to_docs()
|
||||
elif args.command == "run-agents":
|
||||
create_agents_from_yaml(
|
||||
yaml_file="agents.yaml", return_type="tasks"
|
||||
)
|
||||
elif args.command == "create":
|
||||
create_agent_from_yaml(args.agent)
|
||||
elif args.command == "run":
|
||||
run_agent(args.agent_name, args.task)
|
||||
elif args.command == "list agents":
|
||||
list_agents()
|
||||
else:
|
||||
parser.print_help()
|
||||
console.print(
|
||||
"[bold red]Unknown command! Type 'help' for usage.[/bold red]"
|
||||
)
|
||||
|
||||
|
||||
# if __name__ == "__main__":
|
||||
# main()
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
@ -1,121 +0,0 @@
|
||||
import os
|
||||
from termcolor import colored
|
||||
import json
|
||||
|
||||
welcome = """
|
||||
Swarms is the first-ever multi-agent enterpris-grade framework that enables you to seamlessly orchestrate agents!
|
||||
"""
|
||||
|
||||
|
||||
def print_welcome():
|
||||
print(
|
||||
colored(
|
||||
f"Welcome to the Swarms Framework! \n {welcome}",
|
||||
"cyan",
|
||||
attrs=["bold"],
|
||||
)
|
||||
)
|
||||
print(
|
||||
colored(
|
||||
"Thank you for trying out Swarms. We are excited to have you on board to enable you to get started.",
|
||||
"cyan",
|
||||
)
|
||||
)
|
||||
print()
|
||||
print(colored("Resources", "cyan", attrs=["bold"]))
|
||||
print(
|
||||
colored("GitHub: ", "cyan")
|
||||
+ colored("https://github.com/kyegomez/swarms", "magenta")
|
||||
)
|
||||
print(
|
||||
colored("Discord: ", "cyan")
|
||||
+ colored(
|
||||
"https://discord.com/servers/agora-999382051935506503",
|
||||
"magenta",
|
||||
)
|
||||
)
|
||||
print(
|
||||
colored("Documentation: ", "cyan")
|
||||
+ colored("https://docs.swarms.world", "magenta")
|
||||
)
|
||||
print(
|
||||
colored("Marketplace: ", "cyan")
|
||||
+ colored("https://swarms.world", "magenta")
|
||||
)
|
||||
print(
|
||||
colored("Submit an Issue: ", "cyan")
|
||||
+ colored(
|
||||
"https://github.com/kyegomez/swarms/issues/new/choose",
|
||||
"magenta",
|
||||
)
|
||||
)
|
||||
print(
|
||||
colored("Swarms Project Board // Roadmap ", "cyan")
|
||||
+ colored(
|
||||
"https://github.com/users/kyegomez/projects/1", "magenta"
|
||||
)
|
||||
)
|
||||
print()
|
||||
print(
|
||||
colored(
|
||||
"Let's get to know you a bit better!",
|
||||
"magenta",
|
||||
attrs=["bold"],
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def get_user_info():
|
||||
first_name = input(colored("What is your first name? ", "blue"))
|
||||
last_name = input(colored("What is your last name? ", "blue"))
|
||||
email = input(colored("What is your email? ", "blue"))
|
||||
company = input(
|
||||
colored("Which company do you work for? ", "blue")
|
||||
)
|
||||
project = input(
|
||||
colored("What are you trying to build with Swarms? ", "blue")
|
||||
)
|
||||
swarms_type = input(
|
||||
colored("What type of swarms are you building? ", "blue")
|
||||
)
|
||||
|
||||
user_info = {
|
||||
"first_name": first_name,
|
||||
"last_name": last_name,
|
||||
"email": email,
|
||||
"company": company,
|
||||
"project": project,
|
||||
"swarms_type": swarms_type,
|
||||
}
|
||||
|
||||
return user_info
|
||||
|
||||
|
||||
def save_user_info(user_info: dict = None):
|
||||
cache_dir = os.path.expanduser("~/.swarms_cache")
|
||||
if not os.path.exists(cache_dir):
|
||||
os.makedirs(cache_dir)
|
||||
|
||||
cache_file = os.path.join(cache_dir, "user_info.json")
|
||||
with open(cache_file, "w") as f:
|
||||
json.dump(user_info, f, indent=4)
|
||||
|
||||
print(
|
||||
colored(
|
||||
"Your information has been saved as a JSON file! Thank you.",
|
||||
"cyan",
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def onboard():
|
||||
print_welcome()
|
||||
user_info = get_user_info()
|
||||
save_user_info(user_info)
|
||||
print(
|
||||
colored(
|
||||
"You're all set! Enjoy using Swarms.",
|
||||
"cyan",
|
||||
attrs=["bold"],
|
||||
)
|
||||
)
|
||||
@ -0,0 +1,188 @@
|
||||
import os
|
||||
from loguru import logger
|
||||
import json
|
||||
import time
|
||||
from typing import Dict
|
||||
from swarms_cloud.utils.log_to_swarms_database import log_agent_data
|
||||
from swarms_cloud.utils.capture_system_data import capture_system_data
|
||||
|
||||
|
||||
class OnboardingProcess:
|
||||
"""
|
||||
This class handles the onboarding process for users. It collects user data including their
|
||||
full name, first name, email, Swarms API key, and system data, then autosaves it in both a
|
||||
main JSON file and a cache file for reliability. It supports loading previously saved or cached data.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
auto_save_path: str = "user_data.json",
|
||||
cache_save_path: str = "user_data_cache.json",
|
||||
) -> None:
|
||||
"""
|
||||
Initializes the OnboardingProcess with an autosave file path and a cache path.
|
||||
|
||||
Args:
|
||||
auto_save_path (str): The path where user data is automatically saved.
|
||||
cache_save_path (str): The path where user data is cached for reliability.
|
||||
"""
|
||||
self.user_data: Dict[str, str] = {}
|
||||
self.system_data: Dict[str, str] = capture_system_data()
|
||||
self.auto_save_path = auto_save_path
|
||||
self.cache_save_path = cache_save_path
|
||||
self.load_existing_data()
|
||||
|
||||
def load_existing_data(self) -> None:
|
||||
"""
|
||||
Loads existing user data from the auto-save file or cache if available.
|
||||
"""
|
||||
if os.path.exists(self.auto_save_path):
|
||||
try:
|
||||
with open(self.auto_save_path, "r") as f:
|
||||
self.user_data = json.load(f)
|
||||
logger.info(
|
||||
"Existing user data loaded from {}",
|
||||
self.auto_save_path,
|
||||
)
|
||||
return
|
||||
except json.JSONDecodeError as e:
|
||||
logger.error(
|
||||
"Failed to load user data from main file: {}", e
|
||||
)
|
||||
|
||||
# Fallback to cache if main file fails
|
||||
if os.path.exists(self.cache_save_path):
|
||||
try:
|
||||
with open(self.cache_save_path, "r") as f:
|
||||
self.user_data = json.load(f)
|
||||
logger.info(
|
||||
"User data loaded from cache: {}",
|
||||
self.cache_save_path,
|
||||
)
|
||||
except json.JSONDecodeError as e:
|
||||
logger.error(
|
||||
"Failed to load user data from cache: {}", e
|
||||
)
|
||||
|
||||
def save_data(self, retry_attempts: int = 3) -> None:
|
||||
"""
|
||||
Saves the current user data to both the auto-save file and the cache file. If the main
|
||||
save fails, the cache is updated instead. Implements retry logic with exponential backoff
|
||||
in case both save attempts fail.
|
||||
|
||||
Args:
|
||||
retry_attempts (int): The number of retries if saving fails.
|
||||
"""
|
||||
attempt = 0
|
||||
backoff_time = 1 # Starting backoff time (in seconds)
|
||||
|
||||
while attempt < retry_attempts:
|
||||
try:
|
||||
combined_data = {**self.user_data, **self.system_data}
|
||||
log_agent_data(combined_data)
|
||||
# threading.Thread(target=log_agent_data(combined_data)).start()
|
||||
with open(self.auto_save_path, "w") as f:
|
||||
json.dump(combined_data, f, indent=4)
|
||||
# logger.info(
|
||||
# "User and system data successfully saved to {}",
|
||||
# self.auto_save_path,
|
||||
# )
|
||||
with open(self.cache_save_path, "w") as f:
|
||||
json.dump(combined_data, f, indent=4)
|
||||
# logger.info(
|
||||
# "User and system data successfully cached in {}",
|
||||
# self.cache_save_path,
|
||||
# )
|
||||
return # Exit the function if saving was successful
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"Error saving user data (Attempt {}): {}",
|
||||
attempt + 1,
|
||||
e,
|
||||
)
|
||||
|
||||
# Retry after a short delay (exponential backoff)
|
||||
time.sleep(backoff_time)
|
||||
attempt += 1
|
||||
backoff_time *= (
|
||||
2 # Double the backoff time for each retry
|
||||
)
|
||||
|
||||
logger.error(
|
||||
"Failed to save user data after {} attempts.",
|
||||
retry_attempts,
|
||||
)
|
||||
|
||||
def ask_input(self, prompt: str, key: str) -> None:
|
||||
"""
|
||||
Asks the user for input, validates it, and saves it in the user_data dictionary.
|
||||
Autosaves and caches after each valid input.
|
||||
|
||||
Args:
|
||||
prompt (str): The prompt message to display to the user.
|
||||
key (str): The key under which the input will be saved in user_data.
|
||||
|
||||
Raises:
|
||||
ValueError: If the input is empty or only contains whitespace.
|
||||
"""
|
||||
try:
|
||||
response = input(prompt)
|
||||
if response.strip().lower() == "quit":
|
||||
logger.info(
|
||||
"User chose to quit the onboarding process."
|
||||
)
|
||||
exit(0)
|
||||
if not response.strip():
|
||||
raise ValueError(
|
||||
f"{key.capitalize()} cannot be empty."
|
||||
)
|
||||
self.user_data[key] = response.strip()
|
||||
self.save_data()
|
||||
except ValueError as e:
|
||||
logger.warning(e)
|
||||
self.ask_input(prompt, key)
|
||||
except KeyboardInterrupt:
|
||||
logger.warning(
|
||||
"Onboarding process interrupted by the user."
|
||||
)
|
||||
exit(1)
|
||||
|
||||
def collect_user_info(self) -> None:
|
||||
"""
|
||||
Initiates the onboarding process by collecting the user's full name, first name, email,
|
||||
Swarms API key, and system data.
|
||||
"""
|
||||
logger.info("Initiating swarms cloud onboarding process...")
|
||||
self.ask_input(
|
||||
"Enter your first name (or type 'quit' to exit): ",
|
||||
"first_name",
|
||||
)
|
||||
self.ask_input(
|
||||
"Enter your Last Name (or type 'quit' to exit): ",
|
||||
"last_name",
|
||||
)
|
||||
self.ask_input(
|
||||
"Enter your email (or type 'quit' to exit): ", "email"
|
||||
)
|
||||
self.ask_input(
|
||||
"Enter your Swarms API key (or type 'quit' to exit): Get this in your swarms dashboard: https://swarms.world/platform/api-keys ",
|
||||
"swarms_api_key",
|
||||
)
|
||||
logger.success("Onboarding process completed successfully!")
|
||||
|
||||
def run(self) -> None:
|
||||
"""
|
||||
Main method to run the onboarding process. It handles unexpected errors and ensures
|
||||
proper finalization.
|
||||
"""
|
||||
try:
|
||||
self.collect_user_info()
|
||||
except Exception as e:
|
||||
logger.error("An unexpected error occurred: {}", e)
|
||||
finally:
|
||||
logger.info("Finalizing the onboarding process.")
|
||||
|
||||
|
||||
# if __name__ == "__main__":
|
||||
# onboarding = OnboardingProcess()
|
||||
# onboarding.run()
|
||||
Loading…
Reference in new issue