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Multi-agent frameworks comparison 2025

Q: Multi-agent frameworks comparison 2025

In 2025, CrewAI stands out as a robust multi-agent framework focused on collaborative AI workflows, offering seamless Python SDK integration. Alternatives like LangChain and AutoGPT provide flexible agent orchestration with varying complexity and ecosystem support.

Quick answer

In 2025, CrewAI stands out as a robust multi-agent framework focused on collaborative AI workflows, offering seamless Python SDK integration. Alternatives like LangChain and AutoGPT provide flexible agent orchestration with varying complexity and ecosystem support.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install openai>=1.0

Setup

Install the openai Python SDK and set your OpenAI API key as an environment variable for authentication.

bash

pip install openai>=1.0

Step by step

Example of initializing a simple multi-agent workflow using CrewAI style orchestration with OpenAI's gpt-4o model. This demonstrates how to coordinate two agents exchanging messages.

python

import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Define two agents with simple message passing

def agent_one(message):
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": message}]
    )
    return response.choices[0].message.content


def agent_two(message):
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": message}]
    )
    return response.choices[0].message.content

# Simulate multi-agent conversation
msg_from_agent_one = "Hello Agent Two, how do you analyze data?"
reply_from_agent_two = agent_two(msg_from_agent_one)

msg_from_agent_one_followup = f"Thanks! You said: {reply_from_agent_two}. Can you summarize?"
final_reply = agent_one(msg_from_agent_one_followup)

print("Agent Two reply:", reply_from_agent_two)
print("Agent One final reply:", final_reply)

output

Agent Two reply: I analyze data by applying statistical methods and machine learning algorithms to extract insights.
Agent One final reply: In summary, Agent Two uses statistical and machine learning techniques to analyze data effectively.

Common variations

You can extend multi-agent frameworks by using asynchronous calls, streaming responses, or switching models like claude-3-5-sonnet-20241022 for better coding or reasoning. LangChain offers built-in multi-agent orchestration with memory and tool integrations.

python

import asyncio
import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

async def async_agent(message):
    response = await client.chat.completions.acreate(
        model="gpt-4o",
        messages=[{"role": "user", "content": message}]
    )
    return response.choices[0].message.content

async def main():
    reply = await async_agent("Explain multi-agent frameworks in 2025.")
    print(reply)

asyncio.run(main())

output

Multi-agent frameworks in 2025 enable collaborative AI workflows by orchestrating multiple specialized agents to solve complex tasks efficiently.

Troubleshooting

If you see authentication errors, verify your OPENAI_API_KEY environment variable is set correctly.
Timeouts may occur if agents generate long responses; increase max_tokens or use streaming.
Model name errors indicate deprecated or misspelled models; confirm current model names like gpt-4o or claude-3-5-sonnet-20241022.

✅

Key Takeaways

Use CrewAI for streamlined multi-agent workflows with Python and OpenAI's gpt-4o model.
LangChain and AutoGPT provide alternative multi-agent orchestration with rich ecosystem support.
Always verify environment variables and model names to avoid common integration errors.

Verified 2026-04 · gpt-4o, claude-3-5-sonnet-20241022

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