On this tutorial, we demonstrated how Microsoft’s AutoGen framework empowers builders to orchestrate advanced, multi-agent workflows with minimal code. By leveraging AutoGen’s RoundRobinGroupChat and TeamTool abstractions, you possibly can seamlessly assemble specialist assistants, similar to Researchers, FactCheckers, Critics, Summarizers, and Editors, right into a cohesive “DeepDive” device. AutoGen handles the intricacies of flip‐taking, termination situations, and streaming output, permitting you to concentrate on defining every agent’s experience and system prompts somewhat than plumbing collectively callbacks or guide immediate chains. Whether or not conducting in‐depth analysis, validating information, refining prose, or integrating third‐occasion instruments, AutoGen gives a unified API that scales from easy two‐agent pipelines to elaborate, 5‐agent collaboratives.
!pip set up -q autogen-agentchat[gemini] autogen-ext[openai] nest_asyncioWe set up the AutoGen AgentChat package deal with Gemini help, the OpenAI extension for API compatibility, and the nest_asyncio library to patch the pocket book’s occasion loop, making certain you have got all of the parts wanted to run asynchronous, multi-agent workflows in Colab.
import os, nest_asyncio
from getpass import getpass
nest_asyncio.apply()
os.environ["GEMINI_API_KEY"] = getpass("Enter your Gemini API key: ")We import and apply nest_asyncio to allow nested occasion loops in pocket book environments, then securely immediate in your Gemini API key utilizing getpass and retailer it in os.environ for authenticated mannequin shopper entry.
from autogen_ext.fashions.openai import OpenAIChatCompletionClient
model_client = OpenAIChatCompletionClient(
mannequin="gemini-1.5-flash-8b",
api_key=os.environ["GEMINI_API_KEY"],
api_type="google",
)
We initialize an OpenAI‐appropriate chat shopper pointed at Google’s Gemini by specifying the gemini-1.5-flash-8b mannequin, injecting your saved Gemini API key, and setting api_type=”google”, providing you with a ready-to-use model_client for downstream AutoGen brokers.
from autogen_agentchat.brokers import AssistantAgent
researcher = AssistantAgent(identify="Researcher", system_message="Collect and summarize factual information.", model_client=model_client)
factchecker = AssistantAgent(identify="FactChecker", system_message="Confirm information and cite sources.", model_client=model_client)
critic = AssistantAgent(identify="Critic", system_message="Critique readability and logic.", model_client=model_client)
summarizer = AssistantAgent(identify="Summarizer",system_message="Condense into a quick government abstract.", model_client=model_client)
editor = AssistantAgent(identify="Editor", system_message="Polish language and sign APPROVED when executed.", model_client=model_client)We outline 5 specialised assistant brokers, Researcher, FactChecker, Critic, Summarizer, and Editor, every initialized with a role-specific system message and the shared Gemini-powered mannequin shopper, enabling them to collect data, respectively, confirm accuracy, critique content material, condense summaries, and polish language inside the AutoGen workflow.
from autogen_agentchat.groups import RoundRobinGroupChat
from autogen_agentchat.situations import MaxMessageTermination, TextMentionTermination
max_msgs = MaxMessageTermination(max_messages=20)
text_term = TextMentionTermination(textual content="APPROVED", sources=["Editor"])
termination = max_msgs | text_term
staff = RoundRobinGroupChat(
individuals=[researcher, factchecker, critic, summarizer, editor],
termination_condition=termination
)
We import the RoundRobinGroupChat class together with two termination situations, then compose a cease rule that fires after 20 complete messages or when the Editor agent mentions “APPROVED.” Lastly, it instantiates a round-robin staff of the 5 specialised brokers with that mixed termination logic, enabling them to cycle by means of analysis, fact-checking, critique, summarization, and enhancing till one of many cease situations is met.
from autogen_agentchat.instruments import TeamTool
deepdive_tool = TeamTool(staff=staff, identify="DeepDive", description="Collaborative multi-agent deep dive")WE wrap our RoundRobinGroupChat staff in a TeamTool named “DeepDive” with a human-readable description, successfully packaging the whole multi-agent workflow right into a single callable device that different brokers can invoke seamlessly.
host = AssistantAgent(
identify="Host",
model_client=model_client,
instruments=[deepdive_tool],
system_message="You've entry to a DeepDive device for in-depth analysis."
)
We create a “Host” assistant agent configured with the shared Gemini-powered model_client, grant it the DeepDive staff device for orchestrating in-depth analysis, and prime it with a system message that informs it of its means to invoke the multi-agent DeepDive workflow.
import asyncio
async def run_deepdive(subject: str):
end result = await host.run(process=f"Deep dive on: {subject}")
print("🔍 DeepDive end result:n", end result)
await model_client.shut()
subject = "Impacts of Mannequin Context Protocl on Agentic AI"
loop = asyncio.get_event_loop()
loop.run_until_complete(run_deepdive(subject))
Lastly, we outline an asynchronous run_deepdive operate that tells the Host agent to execute the DeepDive staff device on a given subject, prints the excellent end result, after which closes the mannequin shopper; it then grabs Colab’s current asyncio loop and runs the coroutine to completion for a seamless, synchronous execution.
In conclusion, integrating Google Gemini by way of AutoGen’s OpenAI‐appropriate shopper and wrapping our multi‐agent staff as a callable TeamTool provides us a robust template for constructing extremely modular and reusable workflows. AutoGen abstracts away occasion loop administration (with nest_asyncio), streaming responses, and termination logic, enabling us to iterate shortly on agent roles and total orchestration. This superior sample streamlines the event of collaborative AI programs and lays the muse for extending into retrieval pipelines, dynamic selectors, or conditional execution methods.
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Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is dedicated to harnessing the potential of Synthetic Intelligence for social good. His most up-to-date endeavor is the launch of an Synthetic Intelligence Media Platform, Marktechpost, which stands out for its in-depth protection of machine studying and deep studying information that’s each technically sound and simply comprehensible by a large viewers. The platform boasts of over 2 million month-to-month views, illustrating its recognition amongst audiences.
