PraisonAI Agents

pip install praisonaiagents
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx

from praisonaiagents import Agent

# Instructions IS the task - no prompt needed
agent = Agent(instructions="Research the latest AI trends and summarize them")
result = agent.start()  # Uses instructions as task
print(result)

from praisonaiagents import Agent

# Agent with role, prompt overrides
agent = Agent(
    name="Assistant",
    instructions="You are a helpful research assistant"
)
result = agent.start("What are the latest AI developments?")
print(result)

from praisonaiagents import Agent, AgentTeam

# Create agents with instructions
research_agent = Agent(instructions="Research about AI trends")
summary_agent = Agent(instructions="Summarize the research findings")

# Run multi-agent workflow
agents = AgentTeam(agents=[research_agent, summary_agent])
result = agents.start()  # Verbose output by default
print(result)

from praisonaiagents import Agent, Task, AgentTeam

# Create agents
researcher = Agent(
    name="Researcher",
    role="Senior Research Analyst",
    goal="Uncover cutting-edge developments in AI and data science",
    backstory="""You are an expert at a technology research group, 
    skilled in identifying trends and analyzing complex data.""",
    llm="gpt-4o"
)

writer = Agent(
    name="Writer",
    role="Tech Content Strategist",
    goal="Craft compelling content on tech advancements",
    backstory="""You are a content strategist known for 
    making complex tech topics interesting and easy to understand.""",
    llm="gpt-4o"
)

# Create tasks
research_task = Task(
    description="Research the latest developments in AI and data science",
    expected_output="A comprehensive report on recent AI trends and breakthroughs",
    agent=researcher
)

writing_task = Task(
    description="Create an engaging blog post about the research findings",
    expected_output="A well-structured blog post explaining AI developments",
    agent=writer,
    context=[research_task]  # This task depends on research_task output
)

# Create and run the agents
agents = AgentTeam(
    agents=[researcher, writer],
    tasks=[research_task, writing_task]
)

result = agents.start()
print(result)

# Verbose output (default in terminal)
result = agent.start()

# Force silent mode
result = agent.start(output="silent")

# For production scripts, use .run()
result = agent.run()  # Always silent

framework: praisonai
topic: Refactor the auth module

config:
  acp: true
  lsp: true

roles:
  refactorer:
    role: Code Refactorer
    goal: Tighten auth.py for readability and safety
    backstory: Staff engineer, security-minded.
    tasks:
      refactor:
        description: Refactor the auth module in {topic}
        expected_output: Diff applied via the agent-centric tools.

pip install agentops
# AgentOps tracking is automatically enabled

from duckduckgo_search import DDGS

def search_tool(query: str) -> list:
    """
    Perform a web search using DuckDuckGo and return relevant results.

    Args:
        query (str): The search query string to look up information about.

    Returns:
        list: A list of dictionaries containing search results with the following keys:
            - title (str): The title of the search result
            - url (str): The URL of the search result
            - snippet (str): A brief excerpt or description of the search result
    """
    results = []
    ddgs = DDGS()
    for result in ddgs.text(keywords=query, max_results=10):
        results.append({
            "title": result.get("title", ""),
            "url": result.get("href", ""),
            "snippet": result.get("body", ""),
        })
    return results

agent = Agent(
    name="Researcher",
    tools=[search_tool]
)

def task_completed(output):
    print(f"Task completed: {output.description}")
    print(f"Output: {output.raw}")

task = Task(
    description="Analysis task",
    callback=task_completed
)

task1 = Task(
    description="Research task 1",
    async_execution=True
)

task2 = Task(
    description="Research task 2",
    async_execution=True
)

final_task = Task(
    description="Compile results",
    context=[task1, task2]  # Waits for both tasks
)

agent = Agent(
    name="Analyst",
    memory=True,  # Enable memory
    memory={
        "type": "short_term",
        "max_tokens": 4000
    }
)

Framework 'praisonai' was requested but is not installed.
Install it with:
    pip install praisonaiagents