Frameworks

agentuity new

from llama_index.core.agent.workflow import AgentWorkflow
from llama_index.llms.openai import OpenAI
from agentuity import AgentRequest, AgentResponse, AgentContext
 
 
# Define a simple calculator tool
def multiply(a: float, b: float) -> float:
    """Useful for multiplying two numbers."""
    return a * b
 
 
# Create an agent workflow with our calculator tool
agent = AgentWorkflow.from_tools_or_functions(
    [multiply],
    llm=OpenAI(model="gpt-4o-mini"),
    system_prompt="You are a helpful assistant that can multiply two numbers.",
)
 
 
async def run(request: AgentRequest, response: AgentResponse, context: AgentContext):
    result = await agent.run(request.text or "What is 1234 * 4567?")
    return response.text(str(result))

from agentuity import AgentRequest, AgentResponse, AgentContext
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
 
llm = ChatOpenAI()
 
 
async def run(request: AgentRequest, response: AgentResponse, context: AgentContext):
    prompt = ChatPromptTemplate.from_messages(
        [
            (
                "system",
                "You are an expert in world knowledge and all things in general.",
            ),
            ("user", "{input}"),
        ]
    )
    output_parser = StrOutputParser()
    chain = prompt | llm | output_parser
    result = chain.invoke({"input": request.text})
 
    return response.text(result)

from agentuity import AgentRequest, AgentResponse, AgentContext
from agents.mycrew.crew import MyCrew
 
 
async def run(request: AgentRequest, response: AgentResponse, context: AgentContext):
    inputs = {"topic": request.text or "AI LLMs"}
    result = MyCrew().crew().kickoff(inputs=inputs)
    return response.text(str(result))

from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task
 
# If you want to run a snippet of code before or after the crew starts,
# you can use the @before_kickoff and @after_kickoff decorators
# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators
 
 
@CrewBase
class MyCrew:
    """MyCrew crew"""
 
    # Learn more about YAML configuration files here:
    # Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
    # Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
    agents_config = "config/agents.yaml"
    tasks_config = "config/tasks.yaml"
 
    # If you would like to add tools to your agents, you can learn more about it here:
    # https://docs.crewai.com/concepts/agents#agent-tools
    @agent
    def researcher(self) -> Agent:
        return Agent(config=self.agents_config["researcher"], verbose=True)
 
    @agent
    def reporting_analyst(self) -> Agent:
        return Agent(config=self.agents_config["reporting_analyst"], verbose=True)
 
    # To learn more about structured task outputs,
    # task dependencies, and task callbacks, check out the documentation:
    # https://docs.crewai.com/concepts/tasks#overview-of-a-task
    @task
    def research_task(self) -> Task:
        return Task(
            config=self.tasks_config["research_task"],
        )
 
    @task
    def reporting_task(self) -> Task:
        return Task(config=self.tasks_config["reporting_task"], output_file="report.md")
 
    @crew
    def crew(self) -> Crew:
        """Creates the MyagentCrew crew"""
        # To learn how to add knowledge sources to your crew, check out the documentation:
        # https://docs.crewai.com/concepts/knowledge#what-is-knowledge
 
        return Crew(
            agents=self.agents,  # Automatically created by the @agent decorator
            tasks=self.tasks,  # Automatically created by the @task decorator
            process=Process.sequential,
            verbose=True,
            # process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
        )