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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.

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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.

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Web Search Sources

from agency_swarm import Agent, WebSearchTool

agent = Agent(
    name="Researcher",
    instructions="Search the web and summarize findings.",
    tools=[WebSearchTool()],
    include_web_search_sources=True,  # default
)
With include_web_search_sources=True (default), WebSearchTool calls include source URLs. Set include_web_search_sources=False to skip this.

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Conversation starters cache

Conversation starters are the suggested prompts you see in the chat UI. When enabled, cache can instantly replay the first reply without calling the LLM. 
from agency_swarm import Agent

agent = Agent(
    name="SupportAgent",
    instructions="You are helpful.",
    model="gpt-5-mini",
    conversation_starters=["Support: I need help with billing"],
    cache_conversation_starters=True,
)
In this example:
  • The UI shows the starter prompt “Support: I need help with billing”.
  • The FastAPI /get_metadata response exposes starters as conversationStarters.
  • With cache_conversation_starters=True, the first plain-text user message can replay a saved reply when it exactly matches a configured starter.
Streaming the cached reply includes events for text, tool calls, reasoning, and handoffs.
If you change key agent settings (like instructions, tools, or model), the cache for first-turn responses is rebuilt. Per-run overrides like additional_instructions, context_override, or hooks_override skip replay and call the LLM.
Cache files live under AGENCY_SWARM_CHATS_DIR (defaults to .agency_swarm) in starter_cache/. In production, point AGENCY_SWARM_CHATS_DIR at persistent storage to keep instant replies across restarts.
See also: Agent Overview, FastAPI Integration.

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Output Validation

Use output_guardrails on the Agent to validate outputs. See the detailed guide: Guardrails.

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Few‑Shot Examples

You can include few‑shot examples in instructions as plain text or pass message history to get_response / get_response_stream. 
from agency_swarm import Agent

agent = Agent(name="MyAgent", instructions="You are a helpful assistant.")

examples = [
    {"role": "user", "content": "Hi!"},
    {"role": "assistant", "content": "Hello!"},
]
new_message = {"role": "user", "content": "Can you help me write a short summary?"}

response = await agent.get_response(examples + [new_message])
See also: Few‑Shot Examples.