Introduction
Multi-agent systems are emerging as a powerful paradigm for building advanced LLM applications beyond single linear pipelines. Instead of one monolithic AI, multiple specialized agents can collaborate, communicate, and divide tasks to solve complex problems. Microsoft’s AutoGen framework exemplifies this approach by enabling developers to compose conversable AI agents that chat with each other (and with humans or tools) to accomplish goals.
In this article, we dive into how to create and coordinate agents in AutoGen and discuss its strengths and weaknesses compared to other orchestration frameworks such as LangGraph, CrewAI, and SmolAgents in terms of design, ease of use, extensibility, scalability, and agent coordination strategy.
Design Philosophy and Architecture of AutoGen
At its core, AutoGen treats complex workflows as dialogues among multiple agents, each capable of sending and receiving messages to drive the task forward. This design is grounded in a philosophy that coordinated agent communication can overcome individual LLM limitations and yield more robust outcomes.
AutoGen’s architecture is modular and layered for flexibility. Internally, it is organized into a hierarchy of components that developers can leverage at different abstraction levels. The foundational Core API handles low-level capabilities like message passing between agents and an event-driven runtime. Building on this is the AgentChat API, a higher-level, opinionated interface geared toward rapid prototyping of common multi-agent patterns. Finally, an Extensions API allows integration of specific tools and LLM backends to expand agent capabilities.
This layered design means you can dip down to fine-grained control or stay at a high level, depending on your needs. It also promotes extensibility: new agent types, tools, or model backends can be added without modifying the core.
AutoGen agents are intended to be capable, conversable, and customizable. For example, AssistantAgent is a built-in AI agent backed by an LLM that can read messages and generate replies and write Python code when appropriate.
In contrast, UserProxyAgent serves as a stand-in for a human user. By default it pauses for human input at each turn, but it can also execute code blocks or even delegate to an LLM if configured.
AutoGen’s agent types cover both AI and human participants, enabling flexible human-in-the-loop scenarios. Its design philosophy emphasizes composability and modularity to encourage reusability and clearer agent roles within a complex system.
Key Features of AutoGen
Multi-Agent Conversations and Orchestration Patterns
An AutoGen conversation involves a set of agents exchanging messages in a turn-by-turn fashion to work toward a solution. You can initiate a conversation by sending an initial message from one agent to another, and then let the agents automatically continue the dialogue.
For example, a UserProxyAgent can start a chat by sending the task description to an AssistantAgent, after which the two agents will chat back-and-forth autonomously. AutoGen handles the message routing and invocation logic: when one agent replies, the message is delivered to the other, possibly triggering a response, and so on until some termination condition is met.
AutoGen also supports dynamic conversation patterns that adapt as the dialogue unfolds. You can register custom auto-reply functions to an agent, enabling it to conditionally spawn new sub-conversations or invoke helpers based on message content. Because these behaviors are triggered programmatically via registered functions, the conversation topology can evolve in response to the problem context – something not possible in rigid, hardcoded workflows.
Function Calling and Tool Integration
While pure conversation can get you far, agents often need to perform actions like retrieving external information, executing calculations, or modifying the environment. AutoGen addresses this via tool integration and function calling capabilities.
In AutoGen, a tool is essentially a pre-defined function that an agent can invoke as part of its reasoning. Under the hood, AutoGen’s tool use is built on the OpenAI function-calling API paradigm. The LLM outputs a JSON payload calling one of those functions, and AutoGen will execute the function and return the result back into the conversation context.
In addition to the formal tool API, AutoGen agents can also directly generate and execute code, effectively using code as a tool. In fact, AutoGen comes with built-in support for code execution agents.
The AssistantAgent can output Python code within markdown python blocks in its response (similar to SmolAgents' CodeAgent which we discussed in our last post), which can be run by a UserProxyAgent on the fly. AutoGen’s extension modules also provide executors like DockerCommandLineCodeExecutor to isolate and run code safely.
Memory Handling and Context Management
AutoGen approaches memory in a few ways. First, the framework by default maintains an in-memory message history for each conversation. For longer contexts, it provides tools to extend or compress the context. One such utility is LLMLingua integration for text compression. This allows summarizing or encoding lengthy into shorter representations that fit more context into the LLM’s input window.
AutoGen also supports RAG patterns. An agent can be equipped with a vector store or database: when the agent needs a piece of info beyond the current context, it can query the external knowledge base.
Similarly, it encourages task decomposition strategies – breaking a complex problem into sub-tasks – which can be seen as a form of memory management: each sub-task’s result is remembered and fed into the next step by the orchestrator agent.
The framework’s persistence is not as opinionated as something like LangGraph’s built-in database and gives you the hooks to do it manually. There is an Agent Observability module that can help log and inspect agent states, useful for debugging and potentially for memory.
Usefully, you can resume a chat from a saved state. There are features to serialize/deserialize conversations, enabling persistent multi-session agents.
Human-in-the-Loop via User Proxy Agents
AutoGen is designed to work either fully autonomously or with humans in the loop as needed. The UserProxyAgent pretends to be the "user" in the conversation – it receives messages from assistants and can respond either by asking a human or by acting automatically. During development, you might keep a human in the loop to approve each action the AI assistant proposes. In production, you might turn to full autonomy once the system is trusted, or configure granular events for a hybrid solution.
The fact that the "user" is itself an agent means you can script the user behavior too. For instance, you could program the user agent to feed from a test script or another AI model, effectively simulating a human role. This is useful for automated testing of multi-agent setups or even chaining multiple AI agents together with one AI playing the user role for another AI.
Extensibility and Ecosystem
Through AutoGen's Extensions API, it supports a growing ecosystem of plugins and integrations. Some notable extensions include: OpenAI and Azure OpenAI, HuggingFace Transformers, a WebSurfer agent that can browse the web using a headless browser as a tool, code execution tools (Docker or cloud function executors), and connectors for other modalities (e.g. a multimodal agent could integrate vision models).
The framework also provides AutoGen Studio, a no-code GUI to visually build and run multi-agent workflows. Additionally, AutoGen Bench is available for benchmarking agent performance on tasks, which helps in evaluating and comparing different agent strategies.
Example: Creating and Coordinating Agents in AutoGen
Let’s walk through a simple example of using AutoGen to set up a pair of agents and have them collaborate on a task. We’ll create an assistant agent and a user proxy agent, then initiate a conversation where the assistant solves a problem by writing code and the user proxy executes it.
import os
from autogen import AssistantAgent, UserProxyAgent
from autogen.coding import DockerCommandLineCodeExecutor
# Set up OpenAI API key and model
openai_api_key = os.environ["OPENAI_API_KEY"]
config_list = [{"model": "gpt-4.1", "api_key": openai_api_key}]
# Create an assistant agent named "assistant" using GPT-4
assistant = AssistantAgent(name="assistant", llm_config={"config_list": config_list})
# Create a user proxy agent named "user_proxy" with a Docker-based code executor
code_executor = DockerCommandLineCodeExecutor() # Executes code in an isolated container
user_proxy = UserProxyAgent(name="user_proxy", code_execution_config={"executor": code_executor})
# Initiate a conversation by sending a question from the user_proxy to the assistant
user_proxy.initiate_chat(
assistant,
message=(
"What date is today? Which big tech stock has the largest year-to-date gain this year? "
"How much is the gain?"
)
)
In the code above, we first initialize the AssistantAgent with an OpenAI GPT-4.1 model (you could also use Azure or others by adjusting llm_config). Next, the UserProxyAgent is constructed with a DockerCommandLineCodeExecutor – this means whenever the assistant sends a code block, the user agent will run it inside a Docker container and capture the output. Finally, user_proxy.initiate_chat(assistant, message=...) kicks off the multi-agent conversation: it delivers the user’s multi-part question to the assistant.
Internally, AutoGen lets the two agents converse autonomously to resolve the query. The assistant receives the question, recognizes that to find the stock with largest YTD gain it might need to fetch current stock data. Given its prompt and tools, the AssistantAgent might decide to write a Python code snippet that fetches today’s date and stock information using an API or web scraping. It sends that as a message containing a code block.
The UserProxyAgent, upon receiving the assistant’s message, sees the code block. Since we provided a code executor, the user agent will automatically execute the code (with no human intervention, because human_input_mode is default "NEVER".
The output of the code is then used by the user agent as its reply, which goes back to the assistant. Now the assistant has the factual data and can compose a final answer in English, which it returns. The user proxy receives this answer and, finding no code and nothing requiring user input, will terminate the conversation.
Strengths and Weaknesses of AutoGen
Like any framework, AutoGen comes with its advantages and trade-offs. Let’s summarize some of its primary strengths and weaknesses:
Strengths:
Conversation-Centric Orchestration: AutoGen’s use of multi-agent conversation as the core abstraction is very powerful. It aligns well with how LLMs naturally operate (via dialogue) and makes complex workflows easier to conceptualize. The framework automates the message passing and lets agents invoke each other in flexible patterns without hard-coding sequences.
Modularity and Extensibility: AutoGen’s layered and agent class hierarchy promote reusability and customization. Developers can create new agent types or behaviors by subclassing or by plugging in custom reply handlers, without modifying the library internals.
Built-in Tool and Code Execution Support: Unlike some frameworks that leave tool integration to the user, AutoGen has first-class support for function calling tools and code execution loops. It leverages OpenAI’s function call interface for a standardized way of adding tools. At the same time, it allows full code generation and execution cycles (with safety measures like Docker sandboxes) as part of agent dialogues.
Human-in-the-Loop Flexibility: AutoGen makes it easy to include or exclude human participation at will, thanks to the UserProxyAgent design and configurable human input modes.
Weaknesses:
Steep Learning Curve for Complex Scenarios: While simple use cases are straightforward, mastering AutoGen for complex multi-agent workflows can have a learning curve. The framework introduces its own abstractions (agents, group chats, reply functions, etc.) which developers must learn to use effectively.
Overhead and Performance Considerations: By orchestrating agents via messaging, AutoGen can incur overhead in latency and API calls. Each turn in a conversation is essentially a new LLM inference. If agents bounce messages many times, this can be slower and costlier than a single-shot prompt that does everything (if that were possible).
Opinionated Structure: AutoGen’s conversation-first approach may not suit every problem. Some tasks might be more naturally represented as a directed acyclic graph or a linear pipeline rather than a chat among agents. While you can force those into AutoGen’s model (e.g. a chain of agents passing state is like a conversation), it might feel indirect.
Ecosystem Maturity: Although rapidly improving, AutoGen’s ecosystem is still newer and smaller than, say, LangChain’s. Fewer pre-built integrations or community-contributed agents exist at the moment (as of mid 2025). If you need a very domain-specific tool or connector, you might have to implement it yourself.
Complexity of Multi-Agent Debugging: This is a general weakness of multi-agent systems, not unique to AutoGen, but worth noting. When multiple LLMs interact, unpredictable behaviors can emerge (e.g. agents getting stuck in loops asking each other irrelevant questions or echoing each other’s errors). AutoGen provides some tools to mitigate this like termination conditions (and you can always insert a human to intervene), but ensuring reliability is difficult.
Comparison with LangGraph, CrewAI, and SmolAgents
LangGraph offers a low-level, highly controllable approach with explicit graph-defined workflows and built-in long-term state – excellent for complex applications requiring custom logic and persistence, but at the cost of simplicity.
CrewAI takes a more managed approach, simplifying common multi-agent patterns (especially for business process workflows) by leveraging LangChain under the hood; it’s easy to use but less flexible if you stray from provided patterns.
SmolAgents is all about minimalism – it gives you just enough to empower an LLM with tool use (particularly via code execution), making it very accessible, though you sacrifice advanced orchestration features and multi-agent capabilities (unless you craft them yourself).
AutoGen sits somewhat between these extremes. It provides higher-level abstractions than LangGraph, but it is more flexible and open-ended than CrewAI’s fixed templates. Unlike SmolAgents, AutoGen is inherently multi-agent from the ground up – conversations between agents are first-class – which makes it suitable when collaboration or role-play between agents is needed.
When considering scalability and production deployment, AutoGen and LangGraph both are aimed at robust solutions – LangGraph with its persistent state and explicit control, AutoGen with its async runtime and integration hooks. CrewAI is also aiming for production (with an enterprise platform available), but it’s more a specialized solution for orchestrating LLM "teams" in a predefined manner. SmolAgents is newer and lighter-weight – great for experimentation and integrating into existing Python workflows, but not (yet) a full-fledged orchestration platform.
Conclusion
Microsoft’s AutoGen framework represents a significant step toward next-generation LLM applications powered by multiple agents in conversation. Its design philosophy centers on modular, conversable agents that can be mixed and matched to tackle tasks collectively – a "team of AIs" approach that leverages the strengths of large language models while compensating for their weaknesses via collaboration and tool use.
AutoGen’s strengths lie in its powerful conversation-driven orchestration, extensibility, and the rich feature set for building autonomous (or semi-autonomous) agent systems. It does come with a learning curve and some complexity, as any sophisticated framework does, and it isn’t the only solution in town.
We compared it with LangGraph, CrewAI, and SmolAgents – each framework has its niche, from LangGraph’s explicit graphs to SmolAgents’ simplicity. AutoGen distinguishes itself by offering a middle path: high-level enough to get started quickly with multi-agent chats, yet flexible enough to implement custom workflows and integrate new tools or models as needed.
For advanced developers looking to build AI agents that coordinate with each other (and with humans) in solving complex tasks, AutoGen is a promising framework to consider. It provides the tools required with support backed by Microsoft while also being completely open source.
.jpeg)
