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Multi-agent Conversation Framework

AutoGen offers a unified multi-agent conversation framework as a high-level abstraction of using foundation models. It features capable, customizable and conversable agents which integrate LLMs, tools, and humans via automated agent chat. By automating chat among multiple capable agents, one can easily make them collectively perform tasks autonomously or with human feedback, including tasks that require using tools via code.

This framework simplifies the orchestration, automation and optimization of a complex LLM workflow. It maximizes the performance of LLM models and overcome their weaknesses. It enables building next-gen LLM applications based on multi-agent conversations with minimal effort.


AutoGen abstracts and implements conversable agents designed to solve tasks through inter-agent conversations. Specifically, the agents in AutoGen have the following notable features:

  • Conversable: Agents in AutoGen are conversable, which means that any agent can send and receive messages from other agents to initiate or continue a conversation

  • Customizable: Agents in AutoGen can be customized to integrate LLMs, humans, tools, or a combination of them.

The figure below shows the built-in agents in AutoGen. Agent Chat Example

We have designed a generic ConversableAgent class for Agents that are capable of conversing with each other through the exchange of messages to jointly finish a task. An agent can communicate with other agents and perform actions. Different agents can differ in what actions they perform after receiving messages. Two representative subclasses are AssistantAgent and UserProxyAgent

  • The AssistantAgent is designed to act as an AI assistant, using LLMs by default but not requiring human input or code execution. It could write Python code (in a Python coding block) for a user to execute when a message (typically a description of a task that needs to be solved) is received. Under the hood, the Python code is written by LLM (e.g., GPT-4). It can also receive the execution results and suggest corrections or bug fixes. Its behavior can be altered by passing a new system message. The LLM inference configuration can be configured via [llm_config].

  • The UserProxyAgent is conceptually a proxy agent for humans, soliciting human input as the agent's reply at each interaction turn by default and also having the capability to execute code and call functions or tools. The UserProxyAgent triggers code execution automatically when it detects an executable code block in the received message and no human user input is provided. Code execution can be disabled by setting the code_execution_config parameter to False. LLM-based response is disabled by default. It can be enabled by setting llm_config to a dict corresponding to the inference configuration. When llm_config is set as a dictionary, UserProxyAgent can generate replies using an LLM when code execution is not performed.

The auto-reply capability of ConversableAgent allows for more autonomous multi-agent communication while retaining the possibility of human intervention. One can also easily extend it by registering reply functions with the register_reply() method.

In the following code, we create an AssistantAgent named "assistant" to serve as the assistant and a UserProxyAgent named "user_proxy" to serve as a proxy for the human user. We will later employ these two agents to solve a task.

from autogen import AssistantAgent, UserProxyAgent

# create an AssistantAgent instance named "assistant"
assistant = AssistantAgent(name="assistant")

# create a UserProxyAgent instance named "user_proxy"
user_proxy = UserProxyAgent(name="user_proxy")

Tool calling

Tool calling enables agents to interact with external tools and APIs more efficiently. This feature allows the AI model to intelligently choose to output a JSON object containing arguments to call specific tools based on the user's input. A tool to be called is specified with a JSON schema describing its parameters and their types. Writing such JSON schema is complex and error-prone and that is why AutoGen framework provides two high level function decorators for automatically generating such schema using type hints on standard Python datatypes or Pydantic models:

  1. ConversableAgent.register_for_llm is used to register the function as a Tool in the llm_config of a ConversableAgent. The ConversableAgent agent can propose execution of a registered Tool, but the actual execution will be performed by a UserProxy agent.

  2. ConversableAgent.register_for_execution is used to register the function in the function_map of a UserProxy agent.

The following examples illustrates the process of registering a custom function for currency exchange calculation that uses type hints and standard Python datatypes:

  1. First, we import necessary libraries and configure models using autogen.config_list_from_json function:
from typing import Literal

from pydantic import BaseModel, Field
from typing_extensions import Annotated

import autogen

config_list = autogen.config_list_from_json(
"model": ["gpt-4", "gpt-3.5-turbo", "gpt-3.5-turbo-16k"],
  1. We create an assistant agent and user proxy. The assistant will be responsible for suggesting which functions to call and the user proxy for the actual execution of a proposed function:
llm_config = {
"config_list": config_list,
"timeout": 120,

chatbot = autogen.AssistantAgent(
system_message="For currency exchange tasks, only use the functions you have been provided with. Reply TERMINATE when the task is done.",

# create a UserProxyAgent instance named "user_proxy"
user_proxy = autogen.UserProxyAgent(
is_termination_msg=lambda x: x.get("content", "") and x.get("content", "").rstrip().endswith("TERMINATE"),
  1. We define the function currency_calculator below as follows and decorate it with two decorators:
CurrencySymbol = Literal["USD", "EUR"]

def exchange_rate(base_currency: CurrencySymbol, quote_currency: CurrencySymbol) -> float:
if base_currency == quote_currency:
return 1.0
elif base_currency == "USD" and quote_currency == "EUR":
return 1 / 1.1
elif base_currency == "EUR" and quote_currency == "USD":
return 1.1
raise ValueError(f"Unknown currencies {base_currency}, {quote_currency}")

# NOTE: for Azure OpenAI, please use API version 2023-12-01-preview or later as
# support for earlier versions will be deprecated.
# For API versions 2023-10-01-preview or earlier you may
# need to set `api_style="function"` in the decorator if the default value does not work:
# `register_for_llm(description=..., api_style="function")`.
@chatbot.register_for_llm(description="Currency exchange calculator.")
def currency_calculator(
base_amount: Annotated[float, "Amount of currency in base_currency"],
base_currency: Annotated[CurrencySymbol, "Base currency"] = "USD",
quote_currency: Annotated[CurrencySymbol, "Quote currency"] = "EUR",
) -> str:
quote_amount = exchange_rate(base_currency, quote_currency) * base_amount
return f"{quote_amount} {quote_currency}"

Notice the use of Annotated to specify the type and the description of each parameter. The return value of the function must be either string or serializable to string using the json.dumps() or Pydantic model dump to JSON (both version 1.x and 2.x are supported).

You can check the JSON schema generated by the decorator chatbot.llm_config["tools"]:

[{'type': 'function', 'function':
{'description': 'Currency exchange calculator.',
'name': 'currency_calculator',
'parameters': {'type': 'object',
'properties': {'base_amount': {'type': 'number',
'description': 'Amount of currency in base_currency'},
'base_currency': {'enum': ['USD', 'EUR'],
'type': 'string',
'default': 'USD',
'description': 'Base currency'},
'quote_currency': {'enum': ['USD', 'EUR'],
'type': 'string',
'default': 'EUR',
'description': 'Quote currency'}},
'required': ['base_amount']}}}]

Python decorators are functions themselves. If you do not want to use the @chatbot.register... decorator syntax, you can call the decorators as functions:

# Register the function with the chatbot's llm_config.
currency_calculator = chatbot.register_for_llm(description="Currency exchange calculator.")(currency_calculator)

# Register the function with the user_proxy's function_map.

Alternatevely, you can also use autogen.agentchat.register_function() instead as follows:

def currency_calculator(
base_amount: Annotated[float, "Amount of currency in base_currency"],
base_currency: Annotated[CurrencySymbol, "Base currency"] = "USD",
quote_currency: Annotated[CurrencySymbol, "Quote currency"] = "EUR",
) -> str:
quote_amount = exchange_rate(base_currency, quote_currency) * base_amount
return f"{quote_amount} {quote_currency}"

description="Currency exchange calculator.",
  1. Agents can now use the function as follows:
message="How much is 123.45 USD in EUR?",


user_proxy (to chatbot):

How much is 123.45 USD in EUR?

chatbot (to user_proxy):

***** Suggested tool Call: currency_calculator *****


>>>>>>>> EXECUTING FUNCTION currency_calculator...
user_proxy (to chatbot):

***** Response from calling function "currency_calculator" *****
112.22727272727272 EUR

chatbot (to user_proxy):

123.45 USD is equivalent to approximately 112.23 EUR.


Use of Pydantic models further simplifies writing of such functions. Pydantic models can be used for both the parameters of a function and for its return type. Parameters of such functions will be constructed from JSON provided by an AI model, while the output will be serialized as JSON encoded string automatically.

The following example shows how we could rewrite our currency exchange calculator example:

# defines a Pydantic model
class Currency(BaseModel):
# parameter of type CurrencySymbol
currency: Annotated[CurrencySymbol, Field(..., description="Currency symbol")]
# parameter of type float, must be greater or equal to 0 with default value 0
amount: Annotated[float, Field(0, description="Amount of currency", ge=0)]

def currency_calculator(
base: Annotated[Currency, "Base currency: amount and currency symbol"],
quote_currency: Annotated[CurrencySymbol, "Quote currency symbol"] = "USD",
) -> Currency:
quote_amount = exchange_rate(base.currency, quote_currency) * base.amount
return Currency(amount=quote_amount, currency=quote_currency)

description="Currency exchange calculator.",

The generated JSON schema has additional properties such as minimum value encoded:

[{'type': 'function', 'function':
{'description': 'Currency exchange calculator.',
'name': 'currency_calculator',
'parameters': {'type': 'object',
'properties': {'base': {'properties': {'currency': {'description': 'Currency symbol',
'enum': ['USD', 'EUR'],
'title': 'Currency',
'type': 'string'},
'amount': {'default': 0,
'description': 'Amount of currency',
'minimum': 0.0,
'title': 'Amount',
'type': 'number'}},
'required': ['currency'],
'title': 'Currency',
'type': 'object',
'description': 'Base currency: amount and currency symbol'},
'quote_currency': {'enum': ['USD', 'EUR'],
'type': 'string',
'default': 'USD',
'description': 'Quote currency symbol'}},
'required': ['base']}}}]

For more in-depth examples, please check the following:

Multi-agent Conversations

A Basic Two-Agent Conversation Example

Once the participating agents are constructed properly, one can start a multi-agent conversation session by an initialization step as shown in the following code:

# the assistant receives a message from the user, which contains the task description
message="""What date is today? Which big tech stock has the largest year-to-date gain this year? How much is the gain?""",

After the initialization step, the conversation could proceed automatically. Find a visual illustration of how the user_proxy and assistant collaboratively solve the above task autonomously below: Agent Chat Example

  1. The assistant receives a message from the user_proxy, which contains the task description.
  2. The assistant then tries to write Python code to solve the task and sends the response to the user_proxy.
  3. Once the user_proxy receives a response from the assistant, it tries to reply by either soliciting human input or preparing an automatically generated reply. If no human input is provided, the user_proxy executes the code and uses the result as the auto-reply.
  4. The assistant then generates a further response for the user_proxy. The user_proxy can then decide whether to terminate the conversation. If not, steps 3 and 4 are repeated.

Supporting Diverse Conversation Patterns

Conversations with different levels of autonomy, and human-involvement patterns

On the one hand, one can achieve fully autonomous conversations after an initialization step. On the other hand, AutoGen can be used to implement human-in-the-loop problem-solving by configuring human involvement levels and patterns (e.g., setting the human_input_mode to ALWAYS), as human involvement is expected and/or desired in many applications.

Static and dynamic conversations

AutoGen, by integrating conversation-driven control utilizing both programming and natural language, inherently supports dynamic conversations. This dynamic nature allows the agent topology to adapt based on the actual conversation flow under varying input problem scenarios. Conversely, static conversations adhere to a predefined topology. Dynamic conversations are particularly beneficial in complex settings where interaction patterns cannot be predetermined.

  1. Registered auto-reply With the pluggable auto-reply function, one can choose to invoke conversations with other agents depending on the content of the current message and context. For example:
  • Hierarchical chat like in OptiGuide.
  • Dynamic Group Chat which is a special form of hierarchical chat. In the system, we register a reply function in the group chat manager, which broadcasts messages and decides who the next speaker will be in a group chat setting.
  • Finite state machine (FSM) based group chat which is a special form of dynamic group chat. In this approach, a directed transition matrix is fed into group chat. Users can specify legal transitions or specify disallowed transitions.
  • Nested chat like in conversational chess.
  1. LLM-Based Function Call Another approach involves LLM-based function calls, where LLM decides if a specific function should be invoked based on the conversation's status during each inference. This approach enables dynamic multi-agent conversations, as seen in scenarios like multi-user math problem solving scenario, where a student assistant automatically seeks expertise via function calls.

LLM Caching

Since version 0.2.8, a configurable context manager allows you to easily configure LLM cache, using either DiskCache or Redis. All agents inside the context manager will use the same cache.

from autogen import Cache

# Use Redis as cache
with Cache.redis(redis_url="redis://localhost:6379/0") as cache:
user.initiate_chat(assistant, message=coding_task, cache=cache)

# Use DiskCache as cache
with Cache.disk() as cache:
user.initiate_chat(assistant, message=coding_task, cache=cache)

You can vary the cache_seed parameter to get different LLM output while still using cache.

# Setting the cache_seed to 1 will use a different cache from the default one
# and you will see different output.
with Cache.disk(cache_seed=1) as cache:
user.initiate_chat(assistant, message=coding_task, cache=cache)

By default DiskCache uses .cache for storage. To change the cache directory, set cache_path_root:

with Cache.disk(cache_path_root="/tmp/autogen_cache") as cache:
user.initiate_chat(assistant, message=coding_task, cache=cache)

For backward compatibility, DiskCache is on by default with cache_seed set to 41. To disable caching completely, set cache_seed to None in the llm_config of the agent.

assistant = AssistantAgent(
"cache_seed": None,
"config_list": OAI_CONFIG_LIST,
"max_tokens": 1024,

Diverse Applications Implemented with AutoGen

The figure below shows six examples of applications built using AutoGen. Applications

Find a list of examples in this page: Automated Agent Chat Examples

For Further Reading

Interested in the research that leads to this package? Please check the following papers.