API Reference: initiate_chat initiate_chat is the typical method of starting a conversation in AG2. You can call this directly, as shown in the code below, and it is also the mechanism that sits within ConversableAgent’s run method. 
from autogen import ConversableAgent, LLMConfig

llm_config = LLMConfig(model="gpt-4o-mini", api_type="openai", cache_seed=None)

with llm_config:
  agent_a = ConversableAgent(name="agent_a")
  agent_b = ConversableAgent(name="agent_b")

agent_a.initiate_chat(
    recipient=agent_b,
    message="Tell me a joke",
    max_turns=2
    )
Let’s have a look at the flow of ConversableAgent.initiate_chat and then break it down. initiate_chat Breakdown of the first turn:
  1. Generate the initial message based on what is passed in. If nothing is passed in, we’ll prompt the user for input. Options for carryover also exist, though not commonly used when starting a new conversation.
  2. agent_A sends the message (“Tell me a joke”) to our recipient, agent_B, and requests a reply.
  3. We evaluate any “process_message_before_send” hooks, see Hooks, allowing functions to update the message before it gets sent to the recipient.
  4. Append the message to our messages list.
  5. Now we’re on agent-B receiving the message (API Reference: receive).
  6. The agent generates a reply given the message(s) received. This is a critical step in the process, see Generating a reply for an in-depth look at this step.
  • Evaluate our “update_agent_state”, “process_last_received_message”, and “process_all_messages_before_reply” hooks (see Hooks). These allow agent and message state to be updated before the following reply functions are evaluated.
  • Evaluate the agent’s reply functions, this is where the actual reply from an agent is generated.
    • Check termination and human reply
    • Run function and tool calls
    • Execute code blocks
    • Generate LLM response
  1. If no reply is generated, we finish this turn
  2. If a reply is generated, it is sent back to agent_A, any “process_message_before_send” hooks are evaluated, and the message is appended to our messages list.
Breakdown of turns 2 onward:
  1. Start by checking if we have reached our maximum turns, if so, summarize and end the chat.
  2. The sending agent, agent_A generates their reply to the messages (see Generating a reply).
  3. If they don’t generate a reply, summarize and end the chat.
  4. Otherwise, send their reply to agent_B and request a reply.
  5. agent_B generates a reply and, if they generate one, it gets added to the messages list. The loop then continues until the maximum turns are reached or we reach a point where we need to summarize and end.
Summarize and End: With the chat now ended, we summarize the chat using the ConversableAgent._summarize_chat method. Controlled using initiate_chat’s summary_method argument, this is typically either:
  • The last message (summary_method = “last_msg”)
  • An LLM generated summary of the messages using the senders LLM configuration (summary_method = “reflection_with_llm”)
Finally, a ChatResult object is returned. This object contains:
  • The chat history (the messages list)
  • The summary generated above
  • The cost of the conversation (based on the input and output tokens and the LLM)

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GroupChat

initiate_chat can be used to start a group chat by having the GroupChat’s GroupChatManager agent as the recipient parameter.
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