Registering Handoffs to a nested chat

In addition to transferring to an agent, you can also trigger a nested chat by using a handoff with OnCondition. This is a useful way to perform sub-tasks without that work becoming part of the broader swarm’s messages and agents.

Configuring the nested chat is similar to establishing a nested chat for an agent.

Nested chats are a set of sequential chats and these are defined like so:

nested_chats = [
 {
        "recipient": my_first_agent,
        "summary_method": "reflection_with_llm",
        "summary_prompt": "Summarize the conversation into bullet points.",
 },
 {
        "recipient": poetry_agent,
        "message": "Write a poem about the context.",
        "max_turns": 1,
        "summary_method": "last_msg",
 },
]

New to nested chats within swarms is the ability to carryover some context from the swarm chat into the nested chat. This is done by adding a carryover configuration. If you’re not using carryover, then no messages from the swarm chat will be brought into the nested chat.

The carryover is applicable only to the first chat in the nested chats and works together with that nested chat’s “message” value, if any.

my_carryover_config = {
    "summary_method": "reflection_with_llm",
    "summary_args": {"summary_prompt": "Summarise the conversation into bullet points."}
 }

The summary_method can be (with messages referring to the swarm chat’s messages):

• "all" - messages will be converted to a new-line concatenated string, e.g. [first nested chat message]\nContext: \n[swarm message 1]\n[swarm message 2]\n...
• "last_msg" - the latest message will be added, e.g. [first nested chat message]\nContext: \n[swarm's latest message]
• "reflection_with_llm" - utilizes an LLM to interpret the messages and its resulting response will be added, e.g. [first nested chat message]\nContext: \n[llm response]
• Callable - a function that returns the full message (this will not concatenate with the first nested chat’s message, it will replace it entirely).

The signature of the summary_method callable is: def my_method(agent: ConversableAgent, messages: List[Dict[str, Any]], summary_args: Dict) -> str:

Both the “reflection_with_llm” and Callable will be able to utilise the summary_args if they are included.

With your configuration available, you can add it to the first chat in the nested chat:

nested_chats = [
 {
        "recipient": my_first_agent,
        "summary_method": "reflection_with_llm",
        "summary_prompt": "Summarize the conversation into bullet points.",
        "carryover_config": my_carryover_config,
 },
 {
        "recipient": poetry_agent,
        "message": "Write a poem about the context.",
        "max_turns": 1,
        "summary_method": "last_msg",
 },
]

Finally, we add the nested chat as a handoff in the same way as we do to an agent:

register_hand_off(
    agent=agent_1,
    hand_to=[OnCondition(
        target={
            "chat_queue": [nested_chats],
            "config": Any,
            "reply_func_from_nested_chats": None,
            "use_async": False
        },
        condition="condition_1")
])

See the documentation on registering a nested chat for further information on the parameters reply_func_from_nested_chats, use_async, and config.

Once a nested chat is complete, the resulting output from the last chat in the nested chats will be returned as the agent that triggered the nested chat’s response.