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| import os | |
| from swarm_models import OpenAIChat | |
| from swarms import Agent, run_agents_with_tasks_concurrently | |
| # Fetch the OpenAI API key from the environment variable | |
| api_key = os.getenv("OPENAI_API_KEY") | |
| # Create an instance of the OpenAIChat class | |
| model = OpenAIChat( | |
| openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1 | |
| ) | |
| # Initialize agents for different roles | |
| delaware_ccorp_agent = Agent( | |
| agent_name="Delaware-CCorp-Hiring-Agent", | |
| system_prompt=""" | |
| Create a comprehensive hiring description for a Delaware C Corporation, | |
| including all relevant laws and regulations, such as the Delaware General | |
| Corporation Law (DGCL) and the Delaware Corporate Law. Ensure the description | |
| covers the requirements for hiring employees, contractors, and officers, | |
| including the necessary paperwork, tax obligations, and benefits. Also, | |
| outline the procedures for compliance with Delaware's employment laws, | |
| including anti-discrimination laws, workers' compensation, and unemployment | |
| insurance. Provide guidance on how to navigate the complexities of Delaware's | |
| corporate law and ensure that all hiring practices are in compliance with | |
| state and federal regulations. | |
| """, | |
| llm=model, | |
| max_loops=1, | |
| autosave=False, | |
| dashboard=False, | |
| verbose=True, | |
| output_type="str", | |
| artifacts_on=True, | |
| artifacts_output_path="delaware_ccorp_hiring_description.md", | |
| artifacts_file_extension=".md", | |
| ) | |
| indian_foreign_agent = Agent( | |
| agent_name="Indian-Foreign-Hiring-Agent", | |
| system_prompt=""" | |
| Create a comprehensive hiring description for an Indian or foreign country, | |
| including all relevant laws and regulations, such as the Indian Contract Act, | |
| the Indian Labour Laws, and the Foreign Exchange Management Act (FEMA). | |
| Ensure the description covers the requirements for hiring employees, | |
| contractors, and officers, including the necessary paperwork, tax obligations, | |
| and benefits. Also, outline the procedures for compliance with Indian and | |
| foreign employment laws, including anti-discrimination laws, workers' | |
| compensation, and unemployment insurance. Provide guidance on how to navigate | |
| the complexities of Indian and foreign corporate law and ensure that all hiring | |
| practices are in compliance with state and federal regulations. Consider the | |
| implications of hiring foreign nationals and the requirements for obtaining | |
| necessary visas and work permits. | |
| """, | |
| llm=model, | |
| max_loops=1, | |
| autosave=False, | |
| dashboard=False, | |
| verbose=True, | |
| output_type="str", | |
| artifacts_on=True, | |
| artifacts_output_path="indian_foreign_hiring_description.md", | |
| artifacts_file_extension=".md", | |
| ) | |
| # List of agents and corresponding tasks | |
| agents = [delaware_ccorp_agent, indian_foreign_agent] | |
| tasks = [ | |
| """ | |
| Create a comprehensive hiring description for an Agent Engineer, including | |
| required skills and responsibilities. Ensure the description covers the | |
| necessary technical expertise, such as proficiency in AI/ML frameworks, | |
| programming languages, and data structures. Outline the key responsibilities, | |
| including designing and developing AI agents, integrating with existing systems, | |
| and ensuring scalability and performance. | |
| """, | |
| """ | |
| Generate a detailed job description for a Prompt Engineer, including | |
| required skills and responsibilities. Ensure the description covers the | |
| necessary technical expertise, such as proficiency in natural language processing, | |
| machine learning, and software development. Outline the key responsibilities, | |
| including designing and optimizing prompts for AI systems, ensuring prompt | |
| quality and consistency, and collaborating with cross-functional teams. | |
| """, | |
| ] | |
| # Run agents with tasks concurrently | |
| results = run_agents_with_tasks_concurrently( | |
| agents, tasks, all_cores=True, device="cpu", no_clusterops=True | |
| ) | |
| # Print the results | |
| # for result in results: | |
| # print(result) | |