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## This example shows how to use the workflow to recommend a PHD direction for a candidate based on their resume.
## It uses the arxiv-mcp-server to search the papers. You may find the project here: https://github.com/blazickjp/arxiv-mcp-server/tree/main
import os
from dotenv import load_dotenv
import sys
from evoagentx.models import OpenAILLMConfig, OpenAILLM
from evoagentx.workflow import WorkFlowGraph, WorkFlow
# from evoagentx.workflow.workflow_generator import WorkFlowGenerator
from evoagentx.agents import AgentManager
from evoagentx.tools.mcp import MCPToolkit
from evoagentx.tools.file_tool import FileToolkit
load_dotenv() # Loads environment variables from .env file
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
output_file = "debug/output/direction/output.md"
mcp_config_path = "examples/output/direction/mcp_direction.config"
target_directory = "examples/output/direction/"
module_save_path = "examples/output/direction/direction_demo_4o_mini.json"
def main(goal=None):
# LLM configuration
openai_config = OpenAILLMConfig(model="gpt-4o-mini", openai_key=OPENAI_API_KEY, stream=True, output_response=True, max_tokens=16000)
# Initialize the language model
llm = OpenAILLM(config=openai_config)
goal = """Read and analyze the candidate's pdf resume at examples/output/direction/test_pdf.pdf, and recommend one future PHD directions based on the resume. You should provide a list of 5 review papers about the topic for the candidate to learn more about this direction as well."""
# goal = making_goal(openai_config, goal)
helper_prompt = """The input is one parameter called "goal", and the output is a markdown report.
You should firstly read the pdf resume and summarize the background and recommend one future PHD direction based on the resume.
Then you should find 3 trending Review Papers about the topic by searching the keyword on arxiv (by searching web instead of using your out-dated training data) and provide the link of the papers.
Lastly you should summarize all the information and provide a detailed markdown report.
If you cannot find the papers, you should say "I cannot find the papers".
"""
goal += helper_prompt
## Get tools
mcp_Toolkit = MCPToolkit(config_path=mcp_config_path)
tools = mcp_Toolkit.get_toolkits()
tools.append(FileToolkit())
# ## _______________ Workflow Creation _______________
# wf_generator = WorkFlowGenerator(llm=llm, tools=tools)
# workflow_graph: WorkFlowGraph = wf_generator.generate_workflow(goal=goal)
# # [optional] save workflow
# workflow_graph.save_module(module_save_path)
## _______________ Workflow Execution _______________
#[optional] load saved workflow
workflow_graph: WorkFlowGraph = WorkFlowGraph.from_file(module_save_path)
# [optional] display workflow
# workflow_graph.display()
agent_manager = AgentManager(tools=tools)
agent_manager.add_agents_from_workflow(workflow_graph, llm_config=openai_config)
# from pdb import set_trace; set_trace()
workflow = WorkFlow(graph=workflow_graph, agent_manager=agent_manager, llm=llm)
output = workflow.execute()
## _______________ Save Output _______________
try:
# Write to file
with open(output_file, "w", encoding="utf-8") as f:
f.write(output)
print(f"Direction recommendations have been saved to {output_file}")
except Exception as e:
print(f"Error saving direction recommendations: {e}")
# from pdb import set_trace; set_trace()
print(output)
# verfiy the code
if __name__ == "__main__":
# Get custom goal from positional argument if provided
custom_goal = sys.argv[1] if len(sys.argv) > 1 else None
# Run the main function with the provided goal
main(custom_goal)
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