Top 6 Open-Source AI Agents Framework for Building Your Own Digital Workforce

Updated: May 07 2025 18:29

AI Summary: AI agents are transforming technology interaction by automating complex tasks through intelligent systems that can decide, learn, and collaborate. Building these agents in 2025 is facilitated by powerful open-source frameworks, which offer cost-effectiveness, customization, security, and community support. Key features to consider in these frameworks include memory management, tool integration, multi-agent orchestration, reasoning capabilities, ease of deployment, monitoring, and language model flexibility.

AI agents are transforming how we interact with technology, automating complex tasks that once required significant human intervention. These intelligent systems can make decisions, learn from interactions, and collaborate with humans or other agents to solve problems. But how do you actually build one?

In this comprehensive guide, we'll explore the most powerful open-source frameworks available for creating AI agents in 2025, complete with code examples and practical use cases to help you get started.


Before diving into specific frameworks, let's clarify what we mean by "AI agents." An AI agent is a software entity that can perceive its environment, make decisions, and take actions to achieve specific goals. Unlike simple AI models that respond to direct inputs, agents can:

• Maintain context and memory across interactions, enabling them to build upon past experiences and understand the nuances of ongoing conversations
• Use tools and external systems to accomplish tasks, seamlessly integrating with various APIs and resources to extend their functionalities
• Make autonomous decisions based on their programming, allowing them to navigate complex scenarios and choose the most appropriate course of action without explicit human guidance at every step
• Collaborate with humans or other agents, forming intelligent teams to tackle multifaceted problems
• Learn and improve their performance through continuous interaction with their environment and the data they process

This evolution from simple, reactive AI models to proactive, goal-oriented entities represents a significant advancement in the field, powered by the underlying technology of agentic AI, which allows these systems to operate independently with minimal ongoing human assistance. Agentic AI empowers agents to adapt to changing circumstances and address intricate challenges with notable resilience.


Open-source AI agent frameworks offer several compelling advantages:

• Cost-effectiveness: Many open-source tools are free to use, allowing for experimentation and development without significant financial outlay
• Customization: Access to source code enables complete control over over the agent's behavior, enabling extensive customization to meet specific integration and performance requirements
• Security and transparency: Open code can be audited for vulnerabilities, making it suitable for sensitive applications
• Avoiding vendor lock-in: Open standards and portability help you maintain control of your technology stack in the long term
• Community support: Active development communities provide updates, documentation, and practical insights derived from real-world applications
• Rich integrations: Most open-source frameworks offer extensive connectivity with external LLMs, tools, and APIs

Beyond these benefits, open-source frameworks promote transparency and allow for easy customization of the codebase. Their accessibility and lower costs make them particularly attractive to smaller companies and individual developers.  

The collaborative nature of open-source projects also means that multiple contributors can work on the framework, leading to continuous improvements and a more robust ecosystem.  


When evaluating which framework to use for your project, consider these essential capabilities:

• Memory management: How the agent stores and retrieves information across interactions, allowing them to maintain context and learn from past experiences
• Tool integration: The ability to connect with external systems, APIs, and resources, extending the agent's capabilities beyond mere language processing
• Multi-agent orchestration: Support for coordinating multiple specialized agents
• Reasoning capabilities: How the agent makes decisions and handles complex logic
• Ease of deployment: Tools for transitioning agents from development to production environments
• Monitoring and observability: Capabilities for tracking performance and behavior in real time, allowing for debugging and optimization
• Language model flexibility: Support for different LLMs, both open and closed-source

Beyond these, the agent architecture itself, which dictates the agent's internal organization and decision-making processes, is a fundamental aspect to consider.  

Furthermore, the communication protocols supported by the framework determine how agents interact with each other and with humans. The agent's ability to engage in perception, interpreting its environment through various data inputs, is a foundational requirement.  



LangChain has emerged as a go-to framework for developers building LLM-powered applications, simplifying the handling of complex workflows with its modular tools and robust abstractions. LangGraph, built by the creators of LangChain, is a low-level orchestration framework specifically designed for building controllable, stateful AI agents using graph-based workflows.

A key strength of LangChain is its ability to build applications involving LLMs and complex workflows, easily integrating with APIs, databases, and external tools. LangGraph provides developers with fine-grained control over the flow and state of applications, making it particularly suitable for complex decision trees and iterative processes. Key Strengths of LangGraph:

• Graph-based workflow structure, where agent actions or tasks are represented as nodes, and the transitions between these actions are depicted as edges. This allows for the creation of cyclical, conditional, and non-linear workflows, providing flexibility in designing complex agent interactions
• Stateful agents with persistent context, allowing them to maintain memory and track valuable information across interactions
• Precise control over agent behavior, allowing developers to specify agent responses under various conditions
• Seamless integration with LangChain and LangSmith

LangGraph provides first-class streaming support, giving users real-time visibility into the agent's reasoning and actions. Trusted by companies like Klarna and Uber, LangGraph powers production-grade agents for various applications.

The framework also offers LangGraph Platform for deploying and scaling agents with features like memory management, thread handling, and auto-scaling task queues.

LangGraph's capabilities make it suitable for building agent systems for robotics and autonomous vehicles, creating sophisticated LLM applications, and developing stateful chatbots. Here are the list of AI agent use cases from LangGraph on Github.

Example Python Implementation: Automated Blog Post Creation Pipeline

import os
from typing import TypedDict, List, Dict, Any

from langchain_core.messages import BaseMessage, SystemMessage, HumanMessage
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import chain

import langgraph.graph as lgraph
from langchain_openai import ChatOpenAI

# Define the State
class GraphState(TypedDict):
    """
    Represents the state of our graph.

    Attributes:
        keys: Dictionary where we can store arbitrary values relevant to
        our graph.
    """
    keys: Dict[str, Any]

# Configure LLM
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
your_llm = ChatOpenAI(temperature=0.7)

def generate_outline(state: GraphState):
    """Generates a blog post outline."""
    topic = state["keys"]["topic"]

    prompt = ChatPromptTemplate.from_messages([
        ("system", "You are an expert blog post outline generator. Given a topic, you create a detailed and well-structured outline."),
        ("human", "Please generate a detailed outline for a blog post on the topic: {topic}")
    ])

    chain = prompt | your_llm
    outline = chain.invoke({"topic": topic})
    return {"keys": {"outline": outline.content}}

def write_content(state: GraphState):
    """Writes the blog post content based on the outline."""
    outline = state["keys"]["outline"]

    prompt = ChatPromptTemplate.from_messages([
        ("system", "You are an expert blog post writer. Given an outline, you write high-quality, engaging, and informative content."),
        ("human", "Please write a blog post based on the following outline:\\
{outline}")
    ])

    chain = prompt | your_llm
    content = chain.invoke({"outline": outline})
    return {"keys": {"content": content.content}}

# Define the Graph
workflow = lgraph.GraphState(GraphState)

workflow.add_node("generate_outline", generate_outline)
workflow.add_node("write_content", write_content)

workflow.set_entry_point("generate_outline")
workflow.add_edge("generate_outline", "write_content")

# Compile the graph
app = workflow.compile()

# Run the Workflow
topic = "The Future of AI in Education"
state = {"keys": {"topic": topic}}
result = app.invoke(state)

print("Blog Post Outline:", result['keys']['outline'])
print("Blog Post Content:", result['keys']['content'])

This implementation shows how LangGraph can be used to create a content generation pipeline where one agent creates an outline and another writes the full content based on that outline.



Developed by Microsoft, AutoGen focuses on creating conversational AI agents that can work together to solve complex tasks. Its strength lies in enabling customizable agents that support multi-step workflows through collaboration. Key Strengths of AutoGen:

• Scalable and distributed architecture, making it suitable for handling complex and demanding applications
• Robust debugging and tracing capabilities, essential for understanding and optimizing agent behavior
• Conversational interface between agents, allowing them to exchange information and coordinate actions effectively
• OpenTelemetry support for observability into the performance and execution of agent workflows

Beyond these features, AutoGen supports both autonomous operation and human-in-the-loop scenarios, providing flexibility in how agents interact and make decisions. Microsoft also provides developer tools like AutoGen Studio, a no-code GUI for visually designing and testing agent workflows.

The framework features a layered and extensible design, comprising a Core API for low-level control, an AgentChat API for rapid prototyping, and an Extensions API for expanding functionality. AutoGen offers cross-language support for Python and .NET, broadening its applicability. It supports various LLM clients and capabilities, including code execution, enabling agents to interact with external systems and perform computational tasks.

AutoGen's versatility makes it suitable for a range of use cases, including deterministic and dynamic agentic workflows for business processes, research on multi-agent collaboration, distributed agents for multi-language applications, customer support, data analysis, and IT support. Here is a list of AutoGen examples from Microsoft.

Example Python Implementation: Create a group chat team with a web surfer agent and a user proxy agent for web browsing tasks:

# pip install -U autogen-agentchat autogen-ext[openai,web-surfer]
# playwright install
import asyncio
from autogen_agentchat.agents import UserProxyAgent
from autogen_agentchat.conditions import TextMentionTermination
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.agents.web_surfer import MultimodalWebSurfer

async def main() -> None:
    model_client = OpenAIChatCompletionClient(model="gpt-4o")
    # The web surfer will open a Chromium browser window to perform web browsing tasks.
    web_surfer = MultimodalWebSurfer("web_surfer", model_client, headless=False, animate_actions=True)
    # The user proxy agent is used to get user input after each step of the web surfer.
    # NOTE: you can skip input by pressing Enter.
    user_proxy = UserProxyAgent("user_proxy")
    # The termination condition is set to end the conversation when the user types 'exit'.
    termination = TextMentionTermination("exit", sources=["user_proxy"])
    # Web surfer and user proxy take turns in a round-robin fashion.
    team = RoundRobinGroupChat([web_surfer, user_proxy], termination_condition=termination)
    try:
        # Start the team and wait for it to terminate.
        await Console(team.run_stream(task="Find information about AutoGen and write a short summary."))
    finally:
        await web_surfer.close()
        await model_client.close()

asyncio.run(main())

This implementation demonstrates how multiple agents can collaborate to transform raw notes into structured documents with summaries and actionable to-do lists.



CrewAI specializes in multi-agent orchestration, enabling AI agents to collaborate with defined roles and shared objectives. It excels at creating team-like structures where specialized agents work together. Key Strengths of CrewAI:

• Role-based agent collaboration, allowing developers to define specific roles, expertise, and goals for each agent within a crew
• Dynamic task delegation between agents, enabling them to autonomously assign responsibilities based on their capabilities
• Production-ready framework with deep customization options to tailor agent behavior and workflows to specific requirements
• Natural, autonomous decision-making capabilities, allowing them to reason and act intelligently within their defined roles

Being a lean and lightning-fast framework built entirely from scratch, CrewAI is independent of LangChain and other agent frameworks. It empowers developers with both high-level simplicity through CrewAI Crews and precise low-level control via CrewAI Flows, which enable granular, event-driven orchestration.

CrewAI can seamlessly integrate with existing enterprise systems, data sources, and cloud infrastructure. It also offers advanced security features and real-time analytics and reporting to optimize performance and decision-making.

CrewAI's capabilities make it suitable for various use cases, including stock analysis systems, content creation for marketing, customer segmentation, coding assistance, and automating business intelligence reporting.  

Example Python Implementation: This example demonstrates the use of the CrewAI framework to automate the creation of job posting, full project source code can be found on Github.

from typing import List
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task

# Check our tools documentations for more information on how to use them
from crewai_tools import SerperDevTool, ScrapeWebsiteTool, WebsiteSearchTool, FileReadTool
from pydantic import BaseModel, Field

web_search_tool = WebsiteSearchTool()
seper_dev_tool = SerperDevTool()
file_read_tool = FileReadTool(
    file_path='job_description_example.md',
    description='A tool to read the job description example file.'
)

class ResearchRoleRequirements(BaseModel):
    """Research role requirements model"""
    skills: List[str] = Field(..., description="List of recommended skills for the ideal candidate aligned with the company's culture, ongoing projects, and the specific role's requirements.")
    experience: List[str] = Field(..., description="List of recommended experience for the ideal candidate aligned with the company's culture, ongoing projects, and the specific role's requirements.")
    qualities: List[str] = Field(..., description="List of recommended qualities for the ideal candidate aligned with the company's culture, ongoing projects, and the specific role's requirements.")

@CrewBase
class JobPostingCrew:
    """JobPosting crew"""
    agents_config = 'config/agents.yaml'
    tasks_config = 'config/tasks.yaml'

    @agent
    def research_agent(self) -> Agent:
        return Agent(
            config=self.agents_config['research_agent'],
            tools=[web_search_tool, seper_dev_tool],
            verbose=True
        )
    
    @agent
    def writer_agent(self) -> Agent:
        return Agent(
            config=self.agents_config['writer_agent'],
            tools=[web_search_tool, seper_dev_tool, file_read_tool],
            verbose=True
        )
    
    @agent
    def review_agent(self) -> Agent:
        return Agent(
            config=self.agents_config['review_agent'],
            tools=[web_search_tool, seper_dev_tool, file_read_tool],
            verbose=True
        )
    
    @task
    def research_company_culture_task(self) -> Task:
        return Task(
            config=self.tasks_config['research_company_culture_task'],
            agent=self.research_agent()
        )

    @task
    def research_role_requirements_task(self) -> Task:
        return Task(
            config=self.tasks_config['research_role_requirements_task'],
            agent=self.research_agent(),
            output_json=ResearchRoleRequirements
        )

    @task
    def draft_job_posting_task(self) -> Task:
        return Task(
            config=self.tasks_config['draft_job_posting_task'],
            agent=self.writer_agent()
        )

    @task
    def review_and_edit_job_posting_task(self) -> Task:
        return Task(
            config=self.tasks_config['review_and_edit_job_posting_task'],
            agent=self.review_agent()
        )

    @task
    def industry_analysis_task(self) -> Task:
        return Task(
            config=self.tasks_config['industry_analysis_task'],
            agent=self.research_agent()
        )

    @crew
    def crew(self) -> Crew:
        """Creates the JobPostingCrew"""
        return Crew(
            agents=self.agents,  # Automatically created by the @agent decorator
            tasks=self.tasks,  # Automatically created by the @task decorator
            process=Process.sequential,
            verbose=2,
        )

CrewAI is designed to facilitate the collaboration of role-playing AI agents. In this example, these agents work together to extract relevant information from CVs and match them to job opportunities, ensuring the best fit between candidates and job roles.




OpenAI Agents SDK is designed to facilitate lightweight orchestration of multi-agent systems, emphasizing ease of control and testing of agent interactions. The Agents SDK represents a production-ready evolution of Swarm, incorporating key improvements and ongoing maintenance by the OpenAI team. Key Strengths of OpenAI Agents SDK:

• Making agent coordination and execution efficient, highly controllable, and easily testable through the core abstractions of Agents and handoffs
• Encompasses instructions and tools, and it can transfer control to another Agent via a handoff. The Agents SDK orchestrates both single-agent and multi-agent workflows, providing a streamlined approach to building agentic applications
• Built-in tools such as web search, file search, and computer use, enabling agents to interact with the real world and perform complex tasks
• Offers handoffs for intelligently transferring control between agents based on the context of the conversation or task

The Agents SDK also provides integrated observability tools for tracing and inspecting agent workflow execution, simplifying debugging and optimization. Notably, the SDK is provider-agnostic, supporting the OpenAI Responses and Chat Completions APIs, as well as over 100 other LLMs.

Its capabilities make it suitable for various applications, including customer support automation, multi-step research, content generation, code review, and sales prospecting. Here are some additional OpenAI Agents SDK examples from OpenAI on Github.

Example Python Implementation: Customer Support for an Airline

from __future__ import annotations as _annotations
import asyncio
import random
import uuid
from pydantic import BaseModel
from agents import (
    Agent,
    HandoffOutputItem,
    ItemHelpers,
    MessageOutputItem,
    RunContextWrapper,
    Runner,
    ToolCallItem,
    ToolCallOutputItem,
    TResponseInputItem,
    function_tool,
    handoff,
    trace,
)
from agents.extensions.handoff_prompt import RECOMMENDED_PROMPT_PREFIX

### CONTEXT

class AirlineAgentContext(BaseModel):
    passenger_name: str | None = None
    confirmation_number: str | None = None
    seat_number: str | None = None
    flight_number: str | None = None

### TOOLS

@function_tool(
    name_override="faq_lookup_tool", description_override="Lookup frequently asked questions."
)
async def faq_lookup_tool(question: str) -> str:
    if "bag" in question or "baggage" in question:
        return (
            "You are allowed to bring one bag on the plane. "
            "It must be under 50 pounds and 22 inches x 14 inches x 9 inches."
        )
    elif "seats" in question or "plane" in question:
        return (
            "There are 120 seats on the plane. "
            "There are 22 business class seats and 98 economy seats. "
            "Exit rows are rows 4 and 16. "
            "Rows 5-8 are Economy Plus, with extra legroom. "
        )
    elif "wifi" in question:
        return "We have free wifi on the plane, join Airline-Wifi"
    return "I'm sorry, I don't know the answer to that question."

@function_tool
async def update_seat(
    context: RunContextWrapper[AirlineAgentContext], confirmation_number: str, new_seat: str
) -> str:
    """
    Update the seat for a given confirmation number.

    Args:
        confirmation_number: The confirmation number for the flight.
        new_seat: The new seat to update to.
    """
    # Update the context based on the customer's input
    context.context.confirmation_number = confirmation_number
    context.context.seat_number = new_seat
    # Ensure that the flight number has been set by the incoming handoff
    assert context.context.flight_number is not None, "Flight number is required"
    return f"Updated seat to {new_seat} for confirmation number {confirmation_number}"

### HOOKS

async def on_seat_booking_handoff(context: RunContextWrapper[AirlineAgentContext]) -> None:
    flight_number = f"FLT-{random.randint(100, 999)}"
    context.context.flight_number = flight_number

### AGENTS

faq_agent = Agent[AirlineAgentContext](
    name="FAQ Agent",
    handoff_description="A helpful agent that can answer questions about the airline.",
    instructions=f"""{RECOMMENDED_PROMPT_PREFIX}
    You are an FAQ agent. If you are speaking to a customer, you probably were transferred to from the triage agent.
    Use the following routine to support the customer.
    # Routine
    1. Identify the last question asked by the customer.
    2. Use the faq lookup tool to answer the question. Do not rely on your own knowledge.
    3. If you cannot answer the question, transfer back to the triage agent.""",
    tools=[faq_lookup_tool],
)

seat_booking_agent = Agent[AirlineAgentContext](
    name="Seat Booking Agent",
    handoff_description="A helpful agent that can update a seat on a flight.",
    instructions=f"""{RECOMMENDED_PROMPT_PREFIX}
    You are a seat booking agent. If you are speaking to a customer, you probably were transferred to from the triage agent.
    Use the following routine to support the customer.
    # Routine
    1. Ask for their confirmation number.
    2. Ask the customer what their desired seat number is.
    3. Use the update seat tool to update the seat on the flight.
    If the customer asks a question that is not related to the routine, transfer back to the triage agent. """,
    tools=[update_seat],
)

triage_agent = Agent[AirlineAgentContext](
    name="Triage Agent",
    handoff_description="A triage agent that can delegate a customer's request to the appropriate agent.",
    instructions=(
        f"{RECOMMENDED_PROMPT_PREFIX} "
        "You are a helpful triaging agent. You can use your tools to delegate questions to other appropriate agents."
    ),
    handoffs=[
        faq_agent,
        handoff(agent=seat_booking_agent, on_handoff=on_seat_booking_handoff),
    ],
)

faq_agent.handoffs.append(triage_agent)
seat_booking_agent.handoffs.append(triage_agent)

### RUN

async def main():
    current_agent: Agent[AirlineAgentContext] = triage_agent
    input_items: list[TResponseInputItem] = []
    context = AirlineAgentContext()

    # Normally, each input from the user would be an API request to your app, and you can wrap the request in a trace()
    # Here, we'll just use a random UUID for the conversation ID
    conversation_id = uuid.uuid4().hex[:16]

    while True:
        user_input = input("Enter your message: ")
        with trace("Customer service", group_id=conversation_id):
            input_items.append({"content": user_input, "role": "user"})
            result = await Runner.run(current_agent, input_items, context=context)

            for new_item in result.new_items:
                agent_name = new_item.agent.name
                if isinstance(new_item, MessageOutputItem):
                    print(f"{agent_name}: {ItemHelpers.text_message_output(new_item)}")
                elif isinstance(new_item, HandoffOutputItem):
                    print(
                        f"Handed off from {new_item.source_agent.name} to {new_item.target_agent.name}"
                    )
                elif isinstance(new_item, ToolCallItem):
                    print(f"{agent_name}: Calling a tool")
                elif isinstance(new_item, ToolCallOutputItem):
                    print(f"{agent_name}: Tool call output: {new_item.output}")
                else:
                    print(f"{agent_name}: Skipping item: {new_item.__class__.__name__}")
            input_items = result.to_input_list()
            current_agent = result.last_agent


if __name__ == "__main__":
    asyncio.run(main())

This implementation demonstrates a customer support system that can intelligently route user requests to the appropriate specialized agent based on the request type.



Agno focuses on streamlining AI engineering. It's designed as a lightweight framework for building multi-modal agents with robust memory, extensive knowledge management, diverse tool integration, and advanced reasoning capabilities. It empowers developers to construct various types of agents, including Reasoning Agents, Multimodal Agents, Teams of Agents, and Agentic Workflows. Key Strengths of Agno:

• Excellent memory and conversation tracking, enabling agents to maintain coherent and context-aware interactions
• Multi-agent orchestration for complex workflows, facilitating the creation of complex workflows through the seamless collaboration of multiple specialized agents
• Built-in testing interface for rapid development, streamlining rapid development and iteration
• Deployment and monitoring tools for production use, aiding in the transition of agents from development to production environments

Beyond these features, Agno is model-agnostic, offering compatibility with over 23 different model providers, thus avoiding vendor lock-in. It boasts lightning-fast agent instantiation times and a remarkably low memory footprint, making it highly efficient.
 
Agno natively supports multiple modalities, including text, image, audio, and video, for both input and output.
It incorporates agentic RAG (Retrieval-Augmented Generation) as a default, enhancing information retrieval by searching its knowledge base for task-specific information. Agents built with Agno can return fully-typed responses using model-provided structured outputs or JSON mode.
 
Agno's versatility makes it suitable for a wide range of applications, including stock analysis AI agents, AI agents with advanced RAG for improved information retrieval, multi-agent systems for handling complex tasks, custom AI assistants tailored to specific domains, and automated data analysis and reporting.  

Its capabilities extend to automating routine IT tasks, streamlining HR processes, automating finance tasks, detecting cybersecurity threats in real time, and handling routine customer inquiries.  Here are the list of examples from Agno on Github.

Example Python Implementation: Personal Finance Agent, this example shows how to create a sophisticated financial analyst that provides comprehensive market insights using real-time data. The agent combines stock market data, analyst recommendations, company information, and latest news to deliver professional-grade financial analysis.

"""
Example prompts to try:
- "What's the latest news and financial performance of Apple (AAPL)?"
- "Give me a detailed analysis of Tesla's (TSLA) current market position"
- "How are Microsoft's (MSFT) financials looking? Include analyst recommendations"
- "Analyze NVIDIA's (NVDA) stock performance and future outlook"
- "What's the market saying about Amazon's (AMZN) latest quarter?"

Run: `pip install openai yfinance agno` to install the dependencies
"""
from textwrap import dedent
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.yfinance import YFinanceTools

finance_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    tools=[
        YFinanceTools(
            stock_price=True,
            analyst_recommendations=True,
            stock_fundamentals=True,
            historical_prices=True,
            company_info=True,
            company_news=True,
        )
    ],
    instructions=dedent("""\\
        You are a seasoned Wall Street analyst with deep expertise in market analysis! 📊

        Follow these steps for comprehensive financial analysis:
        1. Market Overview
           - Latest stock price
           - 52-week high and low
        2. Financial Deep Dive
           - Key metrics (P/E, Market Cap, EPS)
        3. Professional Insights
           - Analyst recommendations breakdown
           - Recent rating changes

        4. Market Context
           - Industry trends and positioning
           - Competitive analysis
           - Market sentiment indicators

        Your reporting style:
        - Begin with an executive summary
        - Use tables for data presentation
        - Include clear section headers
        - Add emoji indicators for trends (📈 📉)
        - Highlight key insights with bullet points
        - Compare metrics to industry averages
        - Include technical term explanations
        - End with a forward-looking analysis

        Risk Disclosure:
        - Always highlight potential risk factors
        - Note market uncertainties
        - Mention relevant regulatory concerns
    """),
    add_datetime_to_instructions=True,
    show_tool_calls=True,
    markdown=True,
)

# Example usage with detailed market analysis request
finance_agent.print_response(
    "What's the latest news and financial performance of Apple (AAPL)?", stream=True
)

# Semiconductor market analysis example
finance_agent.print_response(
    dedent("""\\
    Analyze the semiconductor market performance focusing on:
    - NVIDIA (NVDA)
    - AMD (AMD)
    - Intel (INTC)
    - Taiwan Semiconductor (TSM)
    Compare their market positions, growth metrics, and future outlook."""),
    stream=True,
)

# Automotive market analysis example
finance_agent.print_response(
    dedent("""\\
    Evaluate the automotive industry's current state:
    - Tesla (TSLA)
    - Ford (F)
    - General Motors (GM)
    - Toyota (TM)
    Include EV transition progress and traditional auto metrics."""),
    stream=True,
)

# More example prompts to explore:
"""
Advanced analysis queries:
1. "Compare Tesla's valuation metrics with traditional automakers"
2. "Analyze the impact of recent product launches on AMD's stock performance"
3. "How do Meta's financial metrics compare to its social media peers?"
4. "Evaluate Netflix's subscriber growth impact on financial metrics"
5. "Break down Amazon's revenue streams and segment performance"

Industry-specific analyses:
Semiconductor Market:
1. "How is the chip shortage affecting TSMC's market position?"
2. "Compare NVIDIA's AI chip revenue growth with competitors"
3. "Analyze Intel's foundry strategy impact on stock performance"
4. "Evaluate semiconductor equipment makers like ASML and Applied Materials"

Automotive Industry:
1. "Compare EV manufacturers' production metrics and margins"
2. "Analyze traditional automakers' EV transition progress"
3. "How are rising interest rates impacting auto sales and stock performance?"
4. "Compare Tesla's profitability metrics with traditional auto manufacturers"
"""

MetaGPT takes a unique approach by simulating entire software development teams. It assigns LLM agents to specific roles like product manager, architect, and engineer, enabling the generation of complete software artifacts through a single interface. Key Strengths of MetaGPT:

• Role-based simulation of software development teams, mirroring how human teams collaborate on software projects
• Ability to generate full-stack prototypes
• Production of comprehensive documentation, diagrams, and production-ready code with integrated testing
• Pre-defined agents for specific software roles, streamlining the process of building complex software with minimal human oversight

Its capabilities make it ideal for generating user stories, conducting competitive analysis, defining requirements and data structures, designing APIs, and producing various software development documents. MetaGPT can also be used for website and game development, as well as for rapid prototyping of digital products. Here is the list of use case examples from MetaGPT.

Example Python Implementation: Email summary and response by using DataInterpreter for viewing emails, quickly reviewing, and automatically replying to them.

import os
from metagpt.roles.di.data_interpreter import DataInterpreter

async def main():
    email_account = "your_email_account"
    # your password will stay only on your device and not go to LLM api
    os.environ["email_password"] = "your_email_password"

    ### Prompt for automatic email reply, uncomment to try this too ###
    # prompt = f"""I will give you your Outlook email account ({email_account}) and password (email_password item in the environment variable). You need to find the latest email in my inbox with the sender's suffix @gmail.com and reply "Thank you! I have received your email~"""""

    ### Prompt for automatic email summary ###
    prompt = f"""I will give you your Outlook email account ({email_account}) and password (email_password item in the environment variable).
            Firstly, Please help me fetch the latest 5 senders and full letter contents.
            Then, summarize each of the 5 emails into one sentence (you can do this by yourself, no need to import other models to do this) and output them in a markdown format."""
    di = DataInterpreter()
    await di.run(prompt)

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())

With so many options available, selecting the right framework depends on your specific requirements:

• For projects involving complex, multi-agent systems, frameworks like AutoGen and CrewAI offer robust orchestration capabilities.
• Specific use cases often align well with certain frameworks; for instance, MetaGPT is tailored for software development automation, while CrewAI and OpenAI Agents SDK are well-suited for customer service applications.
• For tasks involving extensive data analysis, Agno and AutoGen provide strong features.
• If memory management is paramount, Agno offer specialized capabilities.
• Frameworks like LangGraph and Agno are known for their strong reasoning capabilities, while OpenAI Agents SDK often provide a smoother deployment experience.
• The programming language preference of the development team is another critical factor, with most frameworks primarily supporting Python, although some, like AutoGen, also offer JavaScript/TypeScript support.
• Community support and the availability of comprehensive documentation can significantly impact the development process, especially for teams new to AI agents. Frameworks like LangChain/LangGraph, AutoGen, CrewAI, and Agno boast active communities and extensive resources.
• Projects with demanding scalability and performance requirements might lean towards frameworks like Agno, which is designed to handle large-scale deployments.
• The OpenAI Agents SDK provides a straightforward way to leverage OpenAI's powerful models and built-in tools for various agentic applications.

As we look toward the future of AI agent development, several trends are emerging:

• Advanced reasoning capabilities: Frameworks are increasingly focusing on enhancing advanced reasoning capabilities in agents through methods like chain-of-thought reasoning and reflection, enabling them to tackle more complex problems.
• Enhanced multi-agent collaboration: More sophisticated protocols for agent communication and cooperation that will facilitate intricate team structures capable of addressing multifaceted challenges.
• Improved long-term memory: Better techniques for for storing and retrieving contextual information promising to create more consistent and reliable agent behavior over extended interactions.
• Specialized domain agents: We'll see the rise of agents specifically optimized for domains like legal, medical, and financial services
• Better observability and control: Tools for monitoring, explaining, and controlling agent behavior will become more sophisticated
• Deployment flexibility: There's a growing interest in more flexible deployment options, including the ability to run open-source models locally or on various cloud platforms.

Open-source AI agent frameworks are democratizing access to autonomous AI systems, enabling developers to build sophisticated agents without starting from scratch. As you embark on your own AI agent development journey, consider starting with simpler implementations and gradually incorporating more advanced features. The open-source nature of these frameworks means you'll have access to active communities, extensive documentation, and continuous improvements to support your projects.