How to use the Microsoft Autogen framework to Build AI Agents?

Learn how to build multi-agent AI systems using Microsoft AutoGen framework with hands-on tutorials, comparisons, and project ideas.

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Not long ago, AI assistants were limited to answering questions or generating text in isolation, they were  helpful but fundamentally passive. Then came a wave of innovation where large language models (LLMs) like GPT-4 began performing tasks with near-human reasoning. But even these powerful models had a ceiling: they worked best alone, solving one step at a time without memory, strategy, or collaboration. That’s when the idea of multi-agent workflows came back into the spotlight.

In the early days of AI, researchers imagined multiple agents working together, like teammates in a company, each with a role, responsibility, and the ability to communicate. But back then, it was mostly theory. The computation hardware, the language understanding, and the orchestration required to build multi agent applications simply weren’t there. Fast forward to today, and Microsoft’s Autogen framework brings that vision to life. How? Let us find out.

What is Microsoft’s Autogen Framework?

Microsoft’s Autogen is an open-source framework designed to simplify the orchestration of multi agent systems powered by  LLMs. Instead of building AI agents from scratch, Autogen allows AI engineers to create collaborative, task-oriented agents that can communicate, reason, and solve problems together with minimal coding effort. 

The framework is particularly useful for AI projects that involve tool usage, automation, code generation, decision-making, and other related tasks. It supports natural language interfaces, enabling different agents to interact in human-readable dialogue while executing complex workflows behind the scenes. Autogen provides both flexibility for advanced customization and convenience through out-of-the-box agent templates. Here is a LinkedIn post by Shish S., Top AI Leader 2024, highlighting their excitement about building multi-agent AI systems using Autogen and Autogen Studio.

Thus, by abstracting away the complexity of coordination, memory, and communication among agents, Autogen significantly accelerates the development of Agentic AI applications in research and production settings. Let us see what exciting features this framework has to offer.

What are the main features of autogen?

• Multi-agent Collaboration- Autogen supports both human-in-the-loop and fully autonomous agent interactions.

• LLM-as-agent abstraction- Agents are powered by LLMs like GPT-4, wrapped in interfaces that allow seamless configuration, prompting, and tool integration.

• Built-in agent roles- Includes ready-to-use agents such as AssistantAgent, UserProxyAgent, and GroupChatManager for typical collaborative tasks.

• Support for tool calling and function execution- Agents can use external tools or APIs to enhance their capabilities beyond pure text-based reasoning.

• Customizable memory and context management- Keeps track of dialogue history, tools used, and shared knowledge.

• Scalability and extensibility- Developers can define new agent types or extend existing ones to fit specific use cases.

• Interoperability- Works with different LLM providers and can be integrated with various backend services or APIs.

With these powerful features in place, let’s now explore how Autogen brings them together behind the scenes to enable dynamic, multi-agent workflows.

How does Autogen work?

Microsoft Autogen operates by managing a group of agents that interact through structured conversations to complete a task. At its core, the framework relies on a GroupChat or GroupChatManager component to orchestrate dialogue flow and message passing between agents.

Each agent is configured with a role, an LLM backend (like OpenAI’s GPT-4), and optionally, access to tools or APIs. During a session, agents receive prompts, reason over them using their LLMs, and generate responses that are passed to other agents or the user.

For example, a UserProxyAgent might act on behalf of the user, initiating a request. A CoderAgent then interprets the request, generates Python code, and passes it to a CriticAgent that checks for correctness. If needed, the agents loop through multiple iterations until a final, executable solution is reached.

The flow is driven by a conversation loop that continues until a termination condition is met (e.g., the task is complete or a user ends the session). Autogen handles the turn-taking, logging, error recovery, and even the coordination logic that determines which agent should act next.

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Microsoft Autogen Tutorial for Beginners

In this tutorial, we’ll walk through a “Hello World” agent setup using Microsoft’s open-source framework Autogen and Hugging Face’s Mistral-7B-Instruct model, all running in Google Colab.  If you have been wondering how does autogen integrate with large language models, this guide lays the foundation for building and experimenting with agent-based LLM applications.

Step 1: Install Required Packages

Start by installing the necessary libraries:

# STEP 1: Install dependencies

!pip install transformers accelerate torch autogen

This installs:

• transformers- Model loading and inference (via Hugging Face)

• autogen- The official project for building multi-agent systems

• torch + accelerate- Hardware-aware deep learning tools

Step 2: Login to Hugging Face Hub

To access private or gated models like mistralai/Mistral-7B-Instruct-v0.1, authenticate with your Hugging Face account:

# STEP 2: Log In to HuggingFace Hub

from huggingface_hub import notebook_login

notebook_login()  # Login with your HF token

Don’t have an account? Sign up at HuggingFace.co and get your access token from this page.

Step 3: Load the Mistral Model

Mistral-7B is a fast, open LLM with strong instruction-following capabilities. We’ll load it using Transformers:

# STEP 3: Load Mistral model

from transformers import AutoTokenizer, AutoModelForCausalLM

import torch

model_name = "mistralai/Mistral-7B-Instruct-v0.1"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")

This code block gives you the power of a full LLM with zero backend complexity, great for rapid prototyping and experimentation.

Step 4: Define a Simple Inference Wrapper

To generate completions, define a low-code function that wraps the model’s generate() method:

# STEP 4: Define inference wrapper

def mistral_completion(prompt, max_new_tokens=200):

    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

    output = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=True)

    response = tokenizer.decode(output[0], skip_special_tokens=True)

    return response[len(prompt):].strip()

This supports flexible, human-readable prompts that are perfect for writing code, answering questions, or generating design patterns.

Step 5: Wrap Mistral in an Autogen Agent

Autogen's core design is based on conversable agents: objects that can send, receive, and generate responses in multi-agent chats. Here, we define a custom MistralAgent using Hugging Face as the backend:

# STEP 5: Define MistralAgent using Autogen

from autogen import ConversableAgent

class MistralAgent(ConversableAgent):

    def generate_response(self, messages, **kwargs):

        prompt = messages[-1]['content']

        return mistral_completion(prompt)

# Create agent instance

assistant = MistralAgent(name="Assistant", system_message="You are a kind and helpful friend")

This code block mimics a low-code interface to more complex workflows that can be scaled to include:

• A critic agent (for evaluation or filtering)

• An await agent (to defer execution)

• Other agents like a UserProxyAgent, CodeExecutor, or PlannerAgent

Additionally, you can extend the ConversableAgent or import specialized classes like AssistantAgent using:

from autogen import AssistantAgent 

This line lets you use prebuilt agent templates while customizing their behavior for your specific use case.

Step 6: Try a Simple 1-Turn Chat

Let’s now interact with the assistant using a basic prompt:

# STEP 6: Simple 1-turn chat

messages = [{"role": "user", "content": "Hi! How are you?"}]

response = assistant.generate_response(messages)

print("User:", messages[0]['content'])

print("\nAssistant:", response)

This is your first working Autogen Hello World agent, using Hugging Face as the inference engine.

Bonus: Add a Multi-Turn Chat Loop

Here’s a simple way to have an interactive session with your agent:

messages = []

for _ in range(3):  # Limit to 3 rounds

    user_input = input("You: ")

    messages.append({"role": "user", "content": user_input})

    response = assistant.generate_response(messages)

    messages.append({"role": "assistant", "content": response})

    print("Assistant:", response)

This can evolve into an event-driven interface or be embedded into AutogenStudio UI, the visual playground for agent interactions.

If you are now wondering where to go from here? We suggest you explore how does Autogen handle multi-agent conversations by implementing the Agentic AI projects mentioned in the next section.

Project Ideas Using Microsoft AutoGen

Here are four practical projects that showcase Autogen’s capabilities in multi-agent setups.

1. Two-Agent Chat: Assistant and Critic

Develop a conversational system where an AssistantAgent generates responses, and a CriticAgent evaluates them, providing feedback for refinement. This setup is ideal for tasks requiring iterative improvement, such as content creation or decision-making support.

Source Code: AutoGen Example

2. Code-Generating Agent with Execution Capabilities

Create an AssistantAgent that writes code based on user prompts, and a CodeExecutorAgent that runs the generated code, returning the output. This project is perfect for automating coding tasks or building intelligent coding assistants.

Source Code: Code Execution

3. Task Planning and Delegation Agent

Implement a PlanningAgent that decomposes complex tasks into subtasks and delegates them to specialized agents. This approach is useful for project management tools or automated workflow systems.

Source Code: Task Decompositionmicrosoft.github.io+1microsoft.github.io+1

4. Cross-Language Support Agent

Build an agent capable of understanding and translating between Python, SQL, and Markdown. Such an agent can be beneficial for data analysts or developers working across different languages and documentation formats.

Source Code: Research Team Collaborationgithub.com+1github.com+1

These projects showcase the versatility of AutoGen in building complex, multi-agent systems. You may also check out these 7 Autogen Projects to Build Multi-Agent Systems, and explore Autogen GitHub for real-world examples from Penn State University, Microsoft Research, and the open-source community. By exploring these examples, you can gain hands-on experience and inspiration for your own agentic ai applications. 

As the race to build intelligent, multi-step AI systems intensifies, several open-source frameworks have emerged to help data science professionals create and manage agents that can reason, collaborate, and solve tasks. Microsoft’s Autogen is one of the most talked-about frameworks in this space, but how does it actually stack up against alternatives like LangChain, CrewAI, AgentVerse, and others? Head over to the next section to find out.

How does Autogen compare to other AI Agent frameworks?

While many AI agent frameworks offer tools to build single agents or chain together prompts, Autogen introduces a principled way to design agent networks that solve complex tasks through dialogue and collaboration. Agents in Autogen are instantiated as Python classes (such as ConversableAgent, AssistantAgent, or CriticAgent) and can hold persistent memory, communicate with other agents, and trigger functions or tools. Let us compare Autogen stands out in the crowd of other frameworks and start by looking at how does Langchain compare to autogen in terms of ease of use and other features.

Autogen vs LangChain

Compared to LangChain, which emphasizes modularity and tool integration across LLMs, Autogen takes a more conversation-first approach. LangChain is incredibly versatile with a strong ecosystem of integrations (like vector stores, APIs, databases, and tools), but its default agent behavior tends to be monolithic. Autogen, on the other hand, encourages building systems composed of multiple agents, each with a defined role and behavior pattern. It’s particularly well-suited for scenarios like code writing and review, where multiple rounds of iteration, feedback, and delegation are needed.

Autogen vs CrewAI

CrewAI also brings multi-agent coordination into the picture, but it leans more heavily into “task orchestration” than back-and-forth agent conversation. Where CrewAI might define a sequence of tasks for multiple agents, Autogen’s model is more event-driven and dynamic, allowing agents to respond in real-time to changing context. This makes Autogen more natural for building emergent workflows where agents adapt their strategies based on outcomes and shared state.

Autogen vs MetaGPT

In contrast, frameworks like AgentVerse and MetaGPT often package more opinionated templates or abstractions, focusing on preset behaviors. These are great for rapid prototyping, but Autogen gives developers fine-grained control over agent logic and communication pathways, making it better suited for experimentation, research, and long-term system design.

Another unique aspect of Autogen is its compatibility with code execution, tool use, and human feedback loops out-of-the-box. You can have an assistant agent generate code, pass it to a code executor, let a critic agent evaluate the results, and continue improving, all within the same event loop. This positions Autogen not just as a programming framework, but as a framework for building intelligent development workflows.

Autogen vs. MCP

While both Autogen and Microsoft’s Multi-Agent Collaboration Platform (MCP) focus on agent-based systems, they operate at different layers of abstraction and serve different goals. Autogen is a Python-first, open-source development toolkit that excels at designing, simulating, and debugging agentic workflows in a controlled environment. In contrast, MCP is a deployment-grade orchestration platform optimized for running agent-based solutions across distributed systems in production environments. Think of Autogen as the "R&D lab" where you build and iterate on agents, and MCP as the "operations center" where those agents are deployed at scale with monitoring, governance, and enterprise integration. The two are complementary, many organizations may prototype in Autogen and then scale using MCP.

Finally, with the introduction of Autogen Studio, Microsoft is pushing toward a low-code, visual interface for designing and testing agents. This could make Autogen even more accessible to non-engineers and cross-functional teams looking to build AI systems without writing extensive boilerplate code.

Note that Autogen also benefits from an expanding ecosystem of community extensions: tools, templates, and wrappers contributed by developers to speed up development and integrate with external platforms like LangChain, VS Code, or HuggingFace. But, Autogen’s core strength lies in its scalability, modular design, and multi-agent philosophy. While other frameworks may offer easier integrations or lighter abstractions, Autogen is ideal for those building complex, intelligent workflows where agents must interact, reason, revise, and collaborate, just like humans working together on a task.

If you're curious about mastering frameworks like AutoGen and building real-world AI agent applications, consider exploring ProjectPro. It’s a subscription-based platform offering access to a rich repository of solved projects in GenAI, NLP, Machine Learning, Data Science, and Big Data. Each project is designed by industry experts with production-grade code and business context, helping you move beyond tutorials to real deployments. Plus, with features like an AI Interview Coach and guided project walkthroughs, ProjectPro equips you not just to learn, but to land top data roles. It’s perfect for hands-on learners aiming for depth and career advancement.

FAQs

1) What are the main differences between crewai and autogen?

CrewAI focuses on task orchestration with sequential workflows, while Autogen emphasizes dynamic, event-driven multi-agent conversations, enabling agents to interact, adapt, and collaborate in real-time for complex tasks.

2) What are the main differences between autogen v0.2 and v0.4?

Autogen v0.4 introduces improved multi-agent coordination, better tool integrations, enhanced memory management, and the launch of Autogen Studio—a low-code interface—offering more flexibility and easier agent workflow design compared to v0.2.

3) Is AutoGen owned by Microsoft?

Yes, Autogen is an open-source framework developed and maintained by Microsoft, designed to build multi-agent AI workflows and intelligent automation.

4) Is AutoGen worth it?

Autogen is worth it if you need a flexible, scalable framework for building multi-agent AI systems with rich inter-agent communication, code execution, and iterative feedback loops, especially suited for research and complex workflows.

5) What is the difference between AutoGen and MCP?

Autogen is a development toolkit for prototyping multi-agent workflows, while MCP is Microsoft’s enterprise platform for deploying and orchestrating these agents at scale with governance and monitoring. Autogen focuses on creation; MCP on production deployment.

About the Author

Manika

Manika Nagpal is a versatile professional with a strong background in both Physics and Data Science. As a Senior Analyst at ProjectPro, she leverages her expertise in data science and writing to create engaging and insightful blogs that help businesses and individuals stay up-to-date with the

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