Merge pull request #745 from chethanuk/ux-cli-example
Imporve the UX of CLI and Add onboard examplespull/742/merge
commit
e97f95ef63
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agents:
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- agent_name: "Financial-Analysis-Agent"
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model:
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model_name: "gpt-4"
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temperature: 0.1
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max_tokens: 2000
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system_prompt: "financial_agent_sys_prompt"
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max_loops: 1
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autosave: true
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dashboard: false
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verbose: true
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dynamic_temperature_enabled: true
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saved_state_path: "finance_agent.json"
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user_name: "swarms_corp"
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retry_attempts: 1
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context_length: 4000
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return_step_meta: false
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output_type: "str"
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task: "How can I establish a ROTH IRA to buy stocks and get a tax break?"
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- agent_name: "Stock-Analysis-Agent"
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model:
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model_name: "gpt-4"
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temperature: 0.2
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max_tokens: 1500
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system_prompt: "stock_agent_sys_prompt"
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max_loops: 2
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autosave: true
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dashboard: false
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verbose: true
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dynamic_temperature_enabled: false
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saved_state_path: "stock_agent.json"
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user_name: "stock_user"
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retry_attempts: 3
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context_length: 4000
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return_step_meta: true
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output_type: "json"
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task: "What is the best strategy for long-term stock investment?"
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swarm_architecture:
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name: "Financial-Advisory-Swarm"
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description: "A swarm of agents working together to provide comprehensive financial advice"
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swarm_type: "SequentialWorkflow"
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max_loops: 2
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task: "Analyze ROTH IRA setup requirements and provide a comprehensive long-term investment strategy"
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autosave: true
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return_json: false
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rules: |
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1. Financial-Analysis-Agent first explains ROTH IRA setup process and requirements
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2. Stock-Analysis-Agent then provides specific investment strategies suitable for ROTH IRA
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3. Both agents should ensure advice is tax-aware and compliant with retirement account regulations
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4. Focus on practical, actionable steps the user can take
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@ -0,0 +1,37 @@
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import os
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from dotenv import load_dotenv
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from loguru import logger
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from swarm_models import OpenAIChat
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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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# Load environment variables
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load_dotenv()
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# Path to your YAML file
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yaml_file = "agents.yaml"
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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, model_name="gpt-4o-mini", temperature=0.1
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)
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print(model)
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try:
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# Create agents and run tasks (using 'both' to return agents and task results)
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task_results = create_agents_from_yaml(
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model=model, yaml_file=yaml_file, return_type="agents"
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)
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print(task_results)
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logger.info(f"Results from agents: {task_results}")
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except Exception as e:
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logger.error(f"An error occurred: {e}")
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print(e)
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