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agents: | ||
- agent_name: "Financial-Analysis-Agent" | ||
model: | ||
model_name: "gpt-4" | ||
temperature: 0.1 | ||
max_tokens: 2000 | ||
system_prompt: "financial_agent_sys_prompt" | ||
max_loops: 1 | ||
autosave: true | ||
dashboard: false | ||
verbose: true | ||
dynamic_temperature_enabled: true | ||
saved_state_path: "finance_agent.json" | ||
user_name: "swarms_corp" | ||
retry_attempts: 1 | ||
context_length: 4000 | ||
return_step_meta: false | ||
output_type: "str" | ||
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" | ||
model: | ||
model_name: "gpt-4" | ||
temperature: 0.2 | ||
max_tokens: 1500 | ||
system_prompt: "stock_agent_sys_prompt" | ||
max_loops: 2 | ||
autosave: true | ||
dashboard: false | ||
verbose: true | ||
dynamic_temperature_enabled: false | ||
saved_state_path: "stock_agent.json" | ||
user_name: "stock_user" | ||
retry_attempts: 3 | ||
context_length: 4000 | ||
return_step_meta: true | ||
output_type: "json" | ||
task: "What is the best strategy for long-term stock investment?" | ||
|
||
swarm_architecture: | ||
name: "Financial-Advisory-Swarm" | ||
description: "A swarm of agents working together to provide comprehensive financial advice" | ||
swarm_type: "SequentialWorkflow" | ||
max_loops: 2 | ||
task: "Analyze ROTH IRA setup requirements and provide a comprehensive long-term investment strategy" | ||
autosave: true | ||
return_json: false | ||
rules: | | ||
1. Financial-Analysis-Agent first explains ROTH IRA setup process and requirements | ||
2. Stock-Analysis-Agent then provides specific investment strategies suitable for ROTH IRA | ||
3. Both agents should ensure advice is tax-aware and compliant with retirement account regulations | ||
4. Focus on practical, actionable steps the user can take |
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import os | ||
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from dotenv import load_dotenv | ||
from loguru import logger | ||
from swarm_models import OpenAIChat | ||
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from swarms.agents.create_agents_from_yaml import ( | ||
create_agents_from_yaml, | ||
) | ||
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# Load environment variables | ||
load_dotenv() | ||
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# Path to your YAML file | ||
yaml_file = "agents.yaml" | ||
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# Get the OpenAI API key from the environment variable | ||
api_key = os.getenv("OPENAI_API_KEY") | ||
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# Create an instance of the OpenAIChat class | ||
model = OpenAIChat( | ||
openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1 | ||
) | ||
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print(model) | ||
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try: | ||
# Create agents and run tasks (using 'both' to return agents and task results) | ||
task_results = create_agents_from_yaml( | ||
model=model, yaml_file=yaml_file, return_type="agents" | ||
) | ||
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print(task_results) | ||
logger.info(f"Results from agents: {task_results}") | ||
except Exception as e: | ||
logger.error(f"An error occurred: {e}") | ||
print(e) |
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