# a_initiate_group_chat
## ``autogen.agentchat.a_initiate_group_chat`async`[
```
a_initiate_group_chat(pattern, messages, max_rounds=20, safeguard_policy=None, safeguard_llm_config=None, mask_llm_config=None)
```
Initialize and run a group chat using a pattern for configuration, asynchronously.
| PARAMETER | DESCRIPTION |
|--------------------------|-------------------------------------------------------------------------------------------------|
| `pattern` | Pattern object that encapsulates the chat configuration.
**TYPE:**`Pattern` |
| `messages` | Initial message(s).
**TYPE:**`list[dict[str, Any]] | str` |
| `max_rounds` | Maximum number of conversation rounds.
**TYPE:**`int`**DEFAULT:**`20` |
| `safeguard_policy` | Optional safeguard policy dict or path to JSON file.
**TYPE:**`dict[str, Any] | str | None`**DEFAULT:**`None` |
| `safeguard_llm_config` | Optional LLM configuration for safeguard checks.
**TYPE:**`LLMConfig | None`**DEFAULT:**`None` |
| `mask_llm_config` | Optional LLM configuration for masking.
**TYPE:**`LLMConfig | None`**DEFAULT:**`None` |
| RETURNS | DESCRIPTION |
|---------------------------|-------------------------------------------------------------------------------------------------|
| `ChatResult` | Conversations chat history.
**TYPE:**`ChatResult` |
| `ContextVariables` | Updated Context variables.
**TYPE:**`ContextVariables` |
| `Agent` | "ConversableAgent": Last speaker. |
Source code in `autogen/agentchat/group/multi_agent_chat.py`
| | |
| --- | --- |
| ```
@export_module("autogen.agentchat")
async def a_initiate_group_chat(
pattern: "Pattern",
messages: list[dict[str, Any]] | str,
max_rounds: int = 20,
safeguard_policy: dict[str, Any] | str | None = None,
safeguard_llm_config: LLMConfig | None = None,
mask_llm_config: LLMConfig | None = None,
) -> tuple[ChatResult, ContextVariables, "Agent"]:
"""Initialize and run a group chat using a pattern for configuration, asynchronously.
Args:
pattern: Pattern object that encapsulates the chat configuration.
messages: Initial message(s).
max_rounds: Maximum number of conversation rounds.
safeguard_policy: Optional safeguard policy dict or path to JSON file.
safeguard_llm_config: Optional LLM configuration for safeguard checks.
mask_llm_config: Optional LLM configuration for masking.
Returns:
ChatResult: Conversations chat history.
ContextVariables: Updated Context variables.
"ConversableAgent": Last speaker.
"""
# Let the pattern prepare the group chat and all its components
# Only passing the necessary parameters that aren't already in the pattern
(
_, # agents,
_, # wrapped_agents,
_, # user_agent,
context_variables,
_, # initial_agent,
_, # group_after_work,
_, # tool_execution,
_, # groupchat,
manager,
processed_messages,
last_agent,
_, # group_agent_names,
_, # temp_user_list,
) = pattern.prepare_group_chat(
max_rounds=max_rounds,
messages=messages,
)
# Apply safeguards if provided
if safeguard_policy:
from .safeguards import apply_safeguard_policy
apply_safeguard_policy(
groupchat_manager=manager,
policy=safeguard_policy,
safeguard_llm_config=safeguard_llm_config,
mask_llm_config=mask_llm_config,
)
# Start or resume the conversation
if len(processed_messages) > 1:
last_agent, last_message = await manager.a_resume(messages=processed_messages)
clear_history = False
else:
last_message = processed_messages[0]
clear_history = True
if last_agent is None:
raise ValueError("No agent selected to start the conversation")
chat_result = await last_agent.a_initiate_chat(
manager,
message=last_message, # type: ignore[arg-type]
clear_history=clear_history,
summary_method=pattern.summary_method,
)
# Recalculate cost to include ALL agents in the group chat
# initiate_chat only gathers cost from [sender, recipient],
# but in group chat we need to include all participating agents
all_agents = list(manager.groupchat.agents) + [manager]
chat_result.cost = cast(CostDict, gather_usage_summary(all_agents))
cleanup_temp_user_messages(chat_result)
return chat_result, context_variables, manager.last_speaker
``` | ``` |