Hierarchical Process: Manager Agent

from crewai import Agent, Task, Crew, Process
from langchain_openai import ChatOpenAI

# Specialist agents (no manager role needed — CrewAI creates the manager)
pharmacologist = Agent(
    role="Clinical Pharmacologist",
    goal="Analyze drug mechanisms and clinical profiles",
    backstory="Board-certified pharmacologist with 20 years in clinical research",
    verbose=True,
)

regulatory_expert = Agent(
    role="Regulatory Affairs Specialist",
    goal="Analyze drug regulatory status and compliance requirements",
    backstory="Former FDA reviewer with expertise in drug approvals",
    verbose=True,
)

medical_writer = Agent(
    role="Medical Writer",
    goal="Produce accurate, clear drug information documents",
    backstory="Experienced medical writer for pharmaceutical companies",
    verbose=True,
)

# Tasks — the manager decides which agent handles each
pharmacology_task = Task(
    description="Provide a comprehensive pharmacological profile of {drug_name}",
    expected_output="Pharmacological report: mechanism, PK/PD, indications, side effects, interactions",
    agent=pharmacologist,
)

regulatory_task = Task(
    description="Provide the regulatory status of {drug_name} in the US and EU",
    expected_output="Regulatory report: approval dates, indications, black box warnings, REMS if applicable",
    agent=regulatory_expert,
)

synthesis_task = Task(
    description=(
        "Write a comprehensive drug monograph for {drug_name} "
        "incorporating the pharmacological and regulatory information."
    ),
    expected_output=(
        "Drug monograph (800 words): Clinical Overview, Pharmacology, Regulatory Status, "
        "Prescribing Considerations, Patient Counseling Points"
    ),
    agent=medical_writer,
)

crew = Crew(
    agents=[pharmacologist, regulatory_expert, medical_writer],
    tasks=[pharmacology_task, regulatory_task, synthesis_task],
    process=Process.hierarchical,
    manager_llm=ChatOpenAI(model="gpt-4o", temperature=0),  # Manager's brain
    verbose=True,
)

result = crew.kickoff(inputs={"drug_name": "Rivaroxaban"})
print(result.raw)

from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic

# Option 1: OpenAI GPT-4o (default choice for most use cases)
crew = Crew(
    ...
    process=Process.hierarchical,
    manager_llm=ChatOpenAI(model="gpt-4o", temperature=0),
)

# Option 2: Anthropic Claude (strong reasoning for complex delegation)
crew = Crew(
    ...
    process=Process.hierarchical,
    manager_llm=ChatAnthropic(model="claude-opus-4-7", temperature=0),
)

from crewai import Agent

custom_manager = Agent(
    role="Drug Information Director",
    goal=(
        "Coordinate specialist agents to produce comprehensive, accurate drug information. "
        "Ensure quality by reviewing outputs before finalizing."
    ),
    backstory=(
        "20 years managing medical information departments at pharmaceutical companies. "
        "Expert at identifying gaps in drug documentation and directing specialists."
    ),
    llm=ChatOpenAI(model="gpt-4o", temperature=0),
    verbose=True,
)

crew = Crew(
    agents=[pharmacologist, regulatory_expert, medical_writer],
    tasks=[pharmacology_task, regulatory_task, synthesis_task],
    process=Process.hierarchical,
    manager_agent=custom_manager,  # Use instead of manager_llm
    verbose=True,
)

from crewai import Agent, Task, Crew, Process
from langchain_openai import ChatOpenAI

# Research team
literature_researcher = Agent(
    role="Medical Literature Researcher",
    goal="Find and synthesize relevant clinical trials and pharmacological data",
    backstory="Research pharmacist with expertise in systematic reviews and meta-analyses",
    verbose=True,
)

safety_analyst = Agent(
    role="Drug Safety Analyst",
    goal="Identify and analyze adverse events, drug interactions, and safety signals",
    backstory="Pharmacovigilance expert, formerly at the FDA Adverse Event Reporting System",
    verbose=True,
)

clinical_advisor = Agent(
    role="Clinical Practice Advisor",
    goal="Translate research into practical clinical recommendations",
    backstory="Hospital pharmacist with 15 years of direct patient care experience",
    verbose=True,
)

document_specialist = Agent(
    role="Medical Documentation Specialist",
    goal="Produce publication-ready pharmaceutical documentation",
    backstory="Medical writer with experience in drug package inserts and clinical guidelines",
    verbose=True,
)

# Tasks
research_task = Task(
    description=(
        "Research the clinical evidence for {drug_name} in {indication}. "
        "Include pivotal trials, meta-analyses, and recent real-world evidence."
    ),
    expected_output=(
        "Evidence summary: key trials, effect sizes, number needed to treat, "
        "quality of evidence (GRADE), and research gaps"
    ),
    agent=literature_researcher,
)

safety_task = Task(
    description=(
        "Analyze the complete safety profile of {drug_name}. "
        "Focus on: frequency of adverse events, drug interactions, contraindications, "
        "and any recent safety updates or black box warnings."
    ),
    expected_output=(
        "Safety report: common adverse events (with frequency %), "
        "serious adverse events, drug interactions (list at least 10), "
        "contraindicated populations, monitoring requirements"
    ),
    agent=safety_analyst,
)

clinical_task = Task(
    description=(
        "Based on the evidence and safety data, develop practical clinical recommendations "
        "for prescribing {drug_name} in {indication}."
    ),
    expected_output=(
        "Clinical recommendations: patient selection criteria, dosing guidance, "
        "monitoring parameters, when to avoid, patient counseling points"
    ),
    agent=clinical_advisor,
)

final_document_task = Task(
    description=(
        "Produce a comprehensive drug information monograph for {drug_name} "
        "in {indication} using all research, safety, and clinical data."
    ),
    expected_output=(
        "Complete drug monograph (1200 words): "
        "Indications, Clinical Evidence (with citations), "
        "Pharmacology, Safety Profile, Prescribing Recommendations, "
        "Patient Counseling, References"
    ),
    agent=document_specialist,
)

crew = Crew(
    agents=[literature_researcher, safety_analyst, clinical_advisor, document_specialist],
    tasks=[research_task, safety_task, clinical_task, final_document_task],
    process=Process.hierarchical,
    manager_llm=ChatOpenAI(model="gpt-4o", temperature=0),
    verbose=True,
)

result = crew.kickoff(inputs={
    "drug_name": "Apixaban",
    "indication": "atrial fibrillation stroke prevention",
})

print(result.raw)