Agent - Agno

Parameters

Parameter	Type	Default	Description
`model`	`Optional[Model]`	`None`	Model to use for this Agent
`name`	`Optional[str]`	`None`	Agent name
`agent_id`	`Optional[str]`	`None`	Agent UUID (autogenerated if not set)
`agent_data`	`Optional[Dict[str, Any]]`	`None`	Metadata associated with this agent
`introduction`	`Optional[str]`	`None`	Agent introduction. This is added to the chat history when a run is started.
`user_id`	`Optional[str]`	`None`	ID of the user interacting with this agent
`user_data`	`Optional[Dict[str, Any]]`	`None`	Metadata associated with the user interacting with this agent
`session_id`	`Optional[str]`	`None`	Session UUID (autogenerated if not set)
`session_name`	`Optional[str]`	`None`	Session name
`session_state`	`Optional[Dict[str, Any]]`	`None`	Session state (stored in the database to persist across runs)
`context`	`Optional[Dict[str, Any]]`	`None`	Context available for tools and prompt functions
`add_context`	`bool`	`False`	If True, add the context to the user prompt
`resolve_context`	`bool`	`True`	If True, resolve the context (i.e. call any functions in the context) before running the agent
`memory`	`Optional[Memory]`	`None`	Agent Memory
`add_history_to_messages`	`bool`	`False`	Add chat history to the messages sent to the Model
`num_history_runs`	`int`	`3`	Number of historical runs to include in the messages.
`search_previous_sessions_history`	`bool`	`False`	Set this to `True` to allow searching through previous sessions.
`num_history_sessions`	`int`	`2`	Specify the number of past sessions to include in the search. It's advisable to keep this number to 2 or 3 for now, as a larger number might fill up the context length of the model, potentially leading to performance issues.
`knowledge`	`Optional[AgentKnowledge]`	`None`	Agent Knowledge
`knowledge_filters`	`Optional[Dict[str, Any]]`	`None`	Knowledge filters to apply to the knowledge base
`enable_agentic_knowledge_filters`	`bool`	`False`	Enable agentic knowledge filters
`add_references`	`bool`	`False`	Enable RAG by adding references from AgentKnowledge to the user prompt
`retriever`	`Optional[Callable[..., Optional[List[Dict]]]]`	`None`	Function to get references to add to the user_message
`references_format`	`Literal["json", "yaml"]`	`"json"`	Format of the references
`storage`	`Optional[AgentStorage]`	`None`	Agent Storage
`extra_data`	`Optional[Dict[str, Any]]`	`None`	Extra data stored with this agent
`tools`	`Optional[List[Union[Toolkit, Callable, Function]]]`	`None`	A list of tools provided to the Model
`show_tool_calls`	`bool`	`False`	Show tool calls in Agent response
`tool_call_limit`	`Optional[int]`	`None`	Maximum number of tool calls allowed for a single run
`tool_choice`	`Optional[Union[str, Dict[str, Any]]]`	`None`	Controls which (if any) tool is called by the model
`reasoning`	`bool`	`False`	Enable reasoning by working through the problem step by step
`reasoning_model`	`Optional[Model]`	`None`	Model to use for reasoning
`reasoning_agent`	`Optional[Agent]`	`None`	Agent to use for reasoning
`reasoning_min_steps`	`int`	`1`	Minimum number of reasoning steps
`reasoning_max_steps`	`int`	`10`	Maximum number of reasoning steps
`read_chat_history`	`bool`	`False`	Add a tool that allows the Model to read the chat history
`search_knowledge`	`bool`	`True`	Add a tool that allows the Model to search the knowledge base
`update_knowledge`	`bool`	`False`	Add a tool that allows the Model to update the knowledge base
`read_tool_call_history`	`bool`	`False`	Add a tool that allows the Model to get the tool call history
`system_message`	`Optional[Union[str, Callable, Message]]`	`None`	Provide the system message as a string or function. This overrides `description`, `goal`, `instructions`, etc. and sends the provided system message as-is.
`system_message_role`	`str`	`"system"`	Role for the system message
`create_default_system_message`	`bool`	`True`	If True, create a default system message using agent settings
`description`	`Optional[str]`	`None`	A description of the Agent that is added to the start of the system message
`goal`	`Optional[str]`	`None`	The goal of this task
`success_criteria`	`Optional[str]`	`None`	Success criteria for the agent
`instructions`	`Optional[Union[str, List[str], Callable]]`	`None`	List of instructions for the agent
`expected_output`	`Optional[str]`	`None`	Provide the expected output from the Agent
`additional_context`	`Optional[str]`	`None`	Additional context added to the end of the system message
`markdown`	`bool`	`False`	If markdown=true, add instructions to format the output using markdown
`add_name_to_instructions`	`bool`	`False`	If True, add the agent name to the instructions
`add_datetime_to_instructions`	`bool`	`False`	If True, add the current datetime to the system message
`add_location_to_instructions`	`bool`	`False`	If True, add the current location to the system message
`add_state_in_messages`	`bool`	`False`	If True, add the session state variables in messages
`add_messages`	`Optional[List[Union[Dict, Message]]]`	`None`	A list of extra messages added after the system message
`user_message`	`Optional[Union[List, Dict, str, Callable, Message]]`	`None`	Provide the user message
`user_message_role`	`str`	`"user"`	Role for the user message
`create_default_user_message`	`bool`	`True`	If True, create a default user message
`retries`	`int`	`0`	Number of retries to attempt
`delay_between_retries`	`int`	`1`	Delay between retries
`exponential_backoff`	`bool`	`False`	If True, the delay between retries is doubled each time
`response_model`	`Optional[Type[BaseModel]]`	`None`	Provide a response model to get the response as a Pydantic model
`parse_response`	`bool`	`True`	If True, the response is converted into the response_model
`use_json_mode`	`bool`	`False`	If `response_model` is set, sets the response "mode" of the model, i.e. if the model should explicitly respond with a JSON object instead of a Pydantic model
`parser_model`	`Optional[Model]`	`None`	Model to use for parsing the response
`parser_model_prompt`	`Optional[str]`	`None`	Prompt to use for parsing the response
`save_response_to_file`	`Optional[str]`	`None`	Save the response to a file
`stream`	`Optional[bool]`	`None`	Stream the response from the Agent
`stream_intermediate_steps`	`bool`	`False`	Stream the intermediate steps from the Agent
`store_events`	`bool`	`False`	Store the streaming events on the RunResponse
`events_to_skip`	`Optional[List[RunEvent]]`	`None`	Specify which event types to skip when storing events on the RunResponse
`team`	`Optional[List[Agent]]`	`None`	The team of agents that this agent can transfer tasks to
`team_data`	`Optional[Dict[str, Any]]`	`None`	Data shared between team members
`role`	`Optional[str]`	`None`	If this Agent is part of a team, this is the role of the agent
`respond_directly`	`bool`	`False`	If True, member agent responds directly to user
`add_transfer_instructions`	`bool`	`True`	Add instructions for transferring tasks to team members
`team_response_separator`	`str`	`"\n"`	Separator between responses from the team
`debug_mode`	`bool`	`False`	Enable debug logs
`monitoring`	`bool`	`False`	Log Agent information to agno.com for monitoring
`telemetry`	`bool`	`True`	Log minimal telemetry for analytics

Functions

`print_response`

Run the agent and print the response. Parameters:

message (Optional[Union[List, Dict, str, Message]]): The message to send to the agent
session_id (Optional[str]): Session ID to use
user_id (Optional[str]): User ID to use
messages (Optional[List[Union[Dict, Message]]]): List of additional messages to use
audio (Optional[Sequence[Audio]]): Audio files to include
images (Optional[Sequence[Image]]): Image files to include
videos (Optional[Sequence[Video]]): Video files to include
files (Optional[Sequence[File]]): Files to include
stream (Optional[bool]): Whether to stream the response
stream_intermediate_steps (bool): Whether to stream intermediate steps
markdown (bool): Whether to format output as markdown
show_message (bool): Whether to show the message
show_reasoning (bool): Whether to show reasoning
show_full_reasoning (bool): Whether to show full reasoning
console (Optional[Any]): Console to use for output
knowledge_filters (Optional[Dict[str, Any]]): Knowledge filters to apply

`run`

Run the agent. Parameters:

message (Optional[Union[str, List, Dict, Message]]): The message to send to the agent
stream (Optional[bool]): Whether to stream the response
user_id (Optional[str]): User ID to use
session_id (Optional[str]): Session ID to use
audio (Optional[Sequence[Audio]]): Audio files to include
images (Optional[Sequence[Image]]): Image files to include
videos (Optional[Sequence[Video]]): Video files to include
files (Optional[Sequence[File]]): Files to include
messages (Optional[Sequence[Union[Dict, Message]]]): List of additional messages to use
stream_intermediate_steps (Optional[bool]): Whether to stream intermediate steps
retries (Optional[int]): Number of retries to attempt
knowledge_filters (Optional[Dict[str, Any]]): Knowledge filters to apply

Returns:

Union[RunResponse, Iterator[RunResponseEvent]]: Either a RunResponse or an iterator of RunResponseEvents, depending on the stream parameter

`aprint_response`

Run the agent and print the response asynchronously. Parameters:

message (Optional[Union[List, Dict, str, Message]]): The message to send to the agent
session_id (Optional[str]): Session ID to use
user_id (Optional[str]): User ID to use
messages (Optional[List[Union[Dict, Message]]]): List of additional messages to use
audio (Optional[Sequence[Audio]]): Audio files to include
images (Optional[Sequence[Image]]): Image files to include
videos (Optional[Sequence[Video]]): Video files to include
files (Optional[Sequence[File]]): Files to include
stream (Optional[bool]): Whether to stream the response
stream_intermediate_steps (bool): Whether to stream intermediate steps
markdown (bool): Whether to format output as markdown
show_message (bool): Whether to show the message
show_reasoning (bool): Whether to show reasoning
show_full_reasoning (bool): Whether to show full reasoning
console (Optional[Any]): Console to use for output
knowledge_filters (Optional[Dict[str, Any]]): Knowledge filters to apply

`arun`

Run the agent asynchronously. Parameters:

message (Optional[Union[str, List, Dict, Message]]): The message to send to the agent
stream (Optional[bool]): Whether to stream the response
user_id (Optional[str]): User ID to use
session_id (Optional[str]): Session ID to use
audio (Optional[Sequence[Audio]]): Audio files to include
images (Optional[Sequence[Image]]): Image files to include
videos (Optional[Sequence[Video]]): Video files to include
files (Optional[Sequence[File]]): Files to include
messages (Optional[Sequence[Union[Dict, Message]]]): List of additional messages to use
stream_intermediate_steps (Optional[bool]): Whether to stream intermediate steps
retries (Optional[int]): Number of retries to attempt
knowledge_filters (Optional[Dict[str, Any]]): Knowledge filters to apply

Returns:

Union[RunResponse, AsyncIterator[RunResponseEvent]]: Either a RunResponse or an iterator of RunResponseEvents, depending on the stream parameter

`continue_run`

Continue a run. Parameters:

run_response (Optional[RunResponse]): The run response to continue
run_id (Optional[str]): The run ID to continue
updated_tools (Optional[List[ToolExecution]]): Updated tools to use, required if the run is resumed using run_id
stream (Optional[bool]): Whether to stream the response
stream_intermediate_steps (Optional[bool]): Whether to stream intermediate steps
user_id (Optional[str]): User ID to use
session_id (Optional[str]): Session ID to use
retries (Optional[int]): Number of retries to attempt
knowledge_filters (Optional[Dict[str, Any]]): Knowledge filters to apply

Returns:

Union[RunResponse, Iterator[RunResponseEvent]]: Either a RunResponse or an iterator of RunResponseEvents, depending on the stream parameter

`acontinue_run`

Continue a run asynchronously. Parameters:

run_response (Optional[RunResponse]): The run response to continue
run_id (Optional[str]): The run ID to continue
updated_tools (Optional[List[ToolExecution]]): Updated tools to use, required if the run is resumed using run_id
stream (Optional[bool]): Whether to stream the response
stream_intermediate_steps (Optional[bool]): Whether to stream intermediate steps
user_id (Optional[str]): User ID to use
session_id (Optional[str]): Session ID to use
retries (Optional[int]): Number of retries to attempt
knowledge_filters (Optional[Dict[str, Any]]): Knowledge filters to apply

Returns:

Union[RunResponse, AsyncIterator[RunResponseEvent]]: Either a RunResponse or an iterator of RunResponseEvents, depending on the stream parameter

get_session_summary

Get the session summary for the given session ID and user ID. Parameters:

session_id (Optional[str]): Session ID to use (if not provided, the current session is used)
user_id (Optional[str]): User ID to use (if not provided, the current user is used)

Returns:

Optional[SessionSummary]: The session summary

get_user_memories

Get the user memories for the given user ID. Parameters:

user_id (Optional[str]): User ID to use (if not provided, the current user is used)

Returns:

Optional[List[UserMemory]]: The user memories

add_tool

Add a tool to the agent. Parameters:

tool (Union[Toolkit, Callable, Function, Dict]): The tool to add

set_tools

Replace the tools of the agent. Parameters:

tools (List[Union[Toolkit, Callable, Function, Dict]]): The tools to set

​Parameters

​Functions

​print_response

​run

​aprint_response

​arun

​continue_run

​acontinue_run

​get_session_summary

​get_user_memories

​add_tool

​set_tools

Parameters

Functions

`print_response`

`run`

`aprint_response`

`arun`

`continue_run`

`acontinue_run`

get_session_summary

get_user_memories

add_tool

set_tools