CrewAI

from crewai import LLM, Agent, Task, Crew
from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL

llm = LLM(
    model="gpt-4o",
    base_url=PORTKEY_GATEWAY_URL,
    api_key="dummy",  # Portkey handles auth via virtual key
    extra_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        virtual_key="YOUR_VIRTUAL_KEY"
    )
)

agent = Agent(
    role="Researcher",
    goal="Research AI topics",
    backstory="An expert researcher with deep knowledge of AI systems.",
    llm=llm
)

task = Task(
    description="Summarize the key benefits of AI gateway routing.",
    expected_output="A concise bullet-point summary.",
    agent=agent
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

from crewai import Agent, Task, Crew
from langchain_openai import ChatOpenAI
from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL

llm = ChatOpenAI(
    model="gpt-4o",
    base_url=PORTKEY_GATEWAY_URL,
    api_key="dummy",
    default_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        virtual_key="YOUR_VIRTUAL_KEY"
    )
)

agent = Agent(
    role="Researcher",
    goal="Research AI topics",
    backstory="An expert researcher with deep knowledge of AI systems.",
    llm=llm
)

from crewai import Agent, Task, Crew, Process
from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
from crewai import LLM

# GPT-4o for strategic reasoning
gpt_llm = LLM(
    model="gpt-4o",
    base_url=PORTKEY_GATEWAY_URL,
    api_key="dummy",
    extra_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        virtual_key="YOUR_OPENAI_VIRTUAL_KEY"
    )
)

# Claude Sonnet for writing tasks
anthropic_llm = LLM(
    model="claude-3-5-sonnet-latest",
    base_url=PORTKEY_GATEWAY_URL,
    api_key="dummy",
    extra_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        virtual_key="YOUR_ANTHROPIC_VIRTUAL_KEY",
        config="YOUR_PORTKEY_CONFIG_ID",
        trace_id="crew-run-001"
    )
)

product_manager = Agent(
    role="Product Manager",
    goal="Define clear requirements for a software product",
    backstory="An experienced PM skilled in writing concise, developer-ready specs.",
    llm=gpt_llm
)

developer = Agent(
    role="Software Developer",
    goal="Develop software based on the provided requirements",
    backstory="A skilled developer proficient in building robust applications.",
    llm=anthropic_llm
)

task1 = Task(
    description="Define the key requirements for a classic ping-pong game.",
    expected_output="A clear, concise list of requirements.",
    agent=product_manager
)

task2 = Task(
    description="Based on the requirements, write Python code for the ping-pong game.",
    expected_output="Complete, runnable Python code.",
    agent=developer
)

crew = Crew(
    agents=[product_manager, developer],
    tasks=[task1, task2],
    process=Process.sequential,
    verbose=True
)

result = crew.kickoff()
print(result)

from crewai import Agent, Task, Crew, Process
from crewai import LLM
import json

config = {
    "retry": {"attempts": 5},
    "cache": {"mode": "simple"}
}

llm = LLM(
    model="gpt-4o-mini",
    base_url="http://localhost:8787/v1",
    api_key="sk-***",
    extra_headers={
        "x-portkey-config": json.dumps(config)
    }
)

researcher = Agent(
    role="Senior Research Analyst",
    goal="Uncover cutting-edge developments in AI",
    backstory="You work at a leading tech think tank. Your expertise lies in identifying emerging trends.",
    llm=llm
)

writer = Agent(
    role="Tech Content Strategist",
    goal="Craft compelling content on tech advancements",
    backstory="You are a renowned Content Strategist known for insightful articles on technology.",
    llm=llm
)

research_task = Task(
    description="Conduct a comprehensive analysis of the latest advancements in AI in 2025.",
    expected_output="Full analysis report in bullet points.",
    agent=researcher
)

write_task = Task(
    description="Compose an insightful article on the AI advancements from the research.",
    expected_output="A 4-paragraph article formatted as markdown.",
    agent=writer
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential
)

result = crew.kickoff()
print(result)