All parameters inside the Agent class follow the same structure as the OpenAI Agents SDK Agent. However, there are a few advanced parameters that require more explanation.

​
Parallel Tool Calls

Controls whether tools execute in parallel or sequentially for a given agent. 
from agency_swarm import Agent, ModelSettings

agent = Agent(
    name="MyAgent",
    instructions="...",
    model_settings=ModelSettings(parallel_tool_calls=False),
)
To force sequential execution for a specific tool regardless of that setting, set one_call_at_a_time = True in the tool’s ToolConfig. See Advanced Tool Configuration. If your files_folder ends with _vs_<vector_store_id>, Agency Swarm automatically associates files with that Vector Store and adds FileSearchTool to the Agent. The include_search_results behavior can be toggled via the Agent’s include_search_results flag. Note: in v0.x you could configure file_search directly on the Agent (Assistants API). In v1.x prefer the files_folder + Vector Store convention.

​
File Search Configuration (v0.x)

Parameters:
  • max_num_results: The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive. Note that the file search tool may output fewer than max_num_results results.
  • ranking_options: The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0. See the file search tool documentation for more information.

​
Output Validation

v1.x (Agents SDK): Use output_guardrails on the Agent to validate outputs. See the detailed guide: Output Validation. For a complete working example, see: examples/response_validation.py. v0.x (Response Validator): You can validate responses before sending them to the user or other agents by defining a response_validator method on the Agent. This method should raise an error if the response is invalid, allowing the agent to correct itself.

​
Few‑Shot Examples

Few‑shot examples are supported and appended to instructions at initialization. The format follows the message object format. 
from agency_swarm import Agent

examples = [
    {"role": "user", "content": "Hi!"},
    {"role": "assistant", "content": "Hello!"},
]

agent = Agent(name="MyAgent", examples=examples)
See also: Few‑Shot Examples.