Agent

Parameters

Parameter	Type	Default	Description
`model`	`Union[str, Model]`	`"openai/gpt-4o"`	Model identifier or Model instance
`name`	`Optional[str]`	`None`	Agent name for identification
`memory`	`Optional[Memory]`	`None`	Memory instance for conversation history
`db`	`Optional[DatabaseBase]`	`None`	Database instance (overrides memory if provided)
`session_id`	`Optional[str]`	`None`	Session identifier for tracking conversations
`user_id`	`Optional[str]`	`None`	User identifier for multi-user scenarios
`debug`	`bool`	`False`	Enable debug logging
`debug_level`	`int`	`1`	Debug level (1 = standard, 2 = detailed). Only used when debug=True
`company_url`	`Optional[str]`	`None`	Company URL for context
`company_objective`	`Optional[str]`	`None`	Company objective for context
`company_description`	`Optional[str]`	`None`	Company description for context
`company_name`	`Optional[str]`	`None`	Company name for context
`system_prompt`	`Optional[str]`	`None`	Custom system prompt
`reflection`	`bool`	`False`	Reflection capabilities (default is False)
`compression_strategy`	`Literal["none", "simple", "llmlingua"]`	`"none"`	The method for context compression (‘none’, ‘simple’, ‘llmlingua’)
`compression_settings`	`Optional[Dict[str, Any]]`	`None`	A dictionary of settings for the chosen strategy. For “simple”: `{"max_length": 2000}`. For “llmlingua”: `{"ratio": 0.5, "model_name": "...", "instruction": "..."}`
`reliability_layer`	`Optional[Any]`	`None`	Reliability layer for robustness
`agent_id_`	`Optional[str]`	`None`	Specific agent ID
`canvas`	`Optional[Canvas]`	`None`	Canvas instance for visual interactions
`retry`	`int`	`1`	Number of retry attempts
`mode`	`RetryMode`	`"raise"`	Retry mode behavior (“raise” or “return_false”)
`role`	`Optional[str]`	`None`	Agent role
`goal`	`Optional[str]`	`None`	Agent goal
`instructions`	`Optional[str]`	`None`	Specific instructions
`education`	`Optional[str]`	`None`	Agent education background
`work_experience`	`Optional[str]`	`None`	Agent work experience
`feed_tool_call_results`	`bool`	`False`	Include tool results in memory
`show_tool_calls`	`bool`	`True`	Display tool calls
`tool_call_limit`	`int`	`5`	Maximum tool calls per execution
`enable_thinking_tool`	`bool`	`False`	Enable orchestrated thinking
`enable_reasoning_tool`	`bool`	`False`	Enable reasoning capabilities
`tools`	`Optional[list]`	`None`	List of tools to register with this agent (can be functions, ToolKits, or other agents)
`user_policy`	`Optional[Union[Policy, List[Policy]]]`	`None`	User input safety policy (single policy or list of policies)
`agent_policy`	`Optional[Union[Policy, List[Policy]]]`	`None`	Agent output safety policy (single policy or list of policies)
`tool_policy_pre`	`Optional[Union[Policy, List[Policy]]]`	`None`	Tool safety policy for pre-execution validation (single policy or list of policies)
`tool_policy_post`	`Optional[Union[Policy, List[Policy]]]`	`None`	Tool safety policy for post-execution validation (single policy or list of policies)
`user_policy_feedback`	`bool`	`False`	Enable feedback loop for user policy violations (returns helpful message instead of blocking)
`agent_policy_feedback`	`bool`	`False`	Enable feedback loop for agent policy violations (re-executes agent with feedback)
`user_policy_feedback_loop`	`int`	`1`	Maximum retry count for user policy feedback (default 1)
`agent_policy_feedback_loop`	`int`	`1`	Maximum retry count for agent policy feedback (default 1)
`settings`	`Optional[ModelSettings]`	`None`	Model-specific settings
`profile`	`Optional[ModelProfile]`	`None`	Model profile configuration
`reflection_config`	`Optional[ReflectionConfig]`	`None`	Configuration for reflection and self-evaluation
`model_selection_criteria`	`Optional[Dict[str, Any]]`	`None`	Default criteria dictionary for recommend_model_for_task() (see SelectionCriteria)
`use_llm_for_selection`	`bool`	`False`	Default flag for whether to use LLM in recommend_model_for_task()
`reasoning_effort`	`Optional[Literal["low", "medium", "high"]]`	`None`	Reasoning effort level for OpenAI models (“low”, “medium”, “high”)
`reasoning_summary`	`Optional[Literal["concise", "detailed"]]`	`None`	Reasoning summary type for OpenAI models (“concise”, “detailed”)
`thinking_enabled`	`Optional[bool]`	`None`	Enable thinking for Anthropic/Google models (True/False)
`thinking_budget`	`Optional[int]`	`None`	Token budget for thinking (Anthropic: budget_tokens, Google: thinking_budget)
`thinking_include_thoughts`	`Optional[bool]`	`None`	Include thoughts in output (Google models)
`reasoning_format`	`Optional[Literal["hidden", "raw", "parsed"]]`	`None`	Reasoning format for Groq models (“hidden”, “raw”, “parsed”)
`culture_manager`	`Optional[CultureManager]`	`None`	CultureManager instance for cultural knowledge operations
`add_culture_to_context`	`bool`	`False`	Add cultural knowledge to system prompt (default False)
`update_cultural_knowledge`	`bool`	`False`	Extract cultural knowledge after runs (default False)
`enable_agentic_culture`	`bool`	`False`	Give agent tools to update culture (default False)
`metadata`	`Optional[Dict[str, Any]]`	`None`	Agent metadata (passed to prompt)

Functions

`do`

Execute a task synchronously. Parameters:

task (Union[str, Task]): Task to execute (can be a Task object or a string description)
model (Optional[Union[str, Model]]): Override model for this execution
debug (bool): Enable debug mode
retry (int): Number of retries
return_output (bool): If True, return full AgentRunOutput. If False (default), return content only.

Returns:

Any: Task content (str, BaseModel, etc.) if return_output=False, Full AgentRunOutput if return_output=True

`do_async`

Execute a task asynchronously using the pipeline architecture. Parameters:

task (Union[str, Task]): Task to execute
model (Optional[Union[str, Model]]): Override model for this execution
debug (bool): Enable debug mode
retry (int): Number of retries
return_output (bool): If True, return full AgentRunOutput. If False (default), return content only.
state (Optional[State]): Graph execution state
graph_execution_id (Optional[str]): Graph execution identifier
_resume_context (Optional[AgentRunContext]): Internal - context for HITL resumption
_resume_step_index (Optional[int]): Internal - step index to resume from

Returns:

Any: Task content (str, BaseModel, etc.) if return_output=False, Full AgentRunOutput if return_output=True

`stream`

Stream task execution synchronously - yields events/text as they arrive. Parameters:

task (Union[str, Task]): Task to execute
model (Optional[Union[str, Model]]): Override model for this execution
debug (bool): Enable debug mode
retry (int): Number of retries
events (bool): If True, yield AgentEvent objects. If False (default), yield text chunks.
state (Optional[State]): Graph execution state
event (Optional[bool]): Deprecated, use ‘events’ instead.

Returns:

Iterator[Union[str, AgentStreamEvent]]: AgentEvent if events=True, str if events=False

`astream`

Stream task execution asynchronously - yields events or text as they arrive. Note: HITL (Human-in-the-Loop) features are not supported in streaming mode. Use do_async() for HITL functionality. Parameters:

task (Union[str, Task]): Task to execute
model (Optional[Union[str, Model]]): Override model for this execution
debug (bool): Enable debug mode
retry (int): Number of retries
events (bool): If True, yield AgentEvent objects. If False (default), yield text chunks.
state (Optional[State]): Graph execution state
event (Optional[bool]): Deprecated, use ‘events’ instead.

Returns:

AsyncIterator[Union[str, AgentStreamEvent]]: AgentEvent if events=True, str if events=False

`print_do`

Execute a task synchronously and print the result. Parameters:

task (Task): Task to execute
model (Optional[Union[str, Model]]): Override model for this execution
debug (bool): Enable debug mode
retry (int): Number of retries

Returns:

Any: The result object (with output printed to console)

`print_do_async`

Execute a task asynchronously and print the result. Parameters:

task (Task): Task to execute
model (Optional[Union[str, Model]]): Override model for this execution
debug (bool): Enable debug mode
retry (int): Number of retries

Returns:

Any: The result object (with output printed to console)

`continue_run`

Continue a paused agent run (synchronous wrapper). Automatically detects if the original run was streaming and continues in the same mode, or you can override with the streaming parameter. Supports all HITL continuation scenarios:

External tool execution: Pass task object with external results filled
Durable execution (error recovery): Pass run_id to load from storage
Cancel run resumption: Pass run_id to load from storage

Parameters:

task (Optional[Task]): Task object (for external tool execution with results)
run_id (Optional[str]): Run ID to load from storage (for durable/cancel)
model (Optional[Union[str, Model]]): Override model
debug (bool): Enable debug mode
retry (int): Number of retries
return_output (bool): If True, return full AgentRunOutput. If False (default), return content only.
streaming (Optional[bool]): If True, return list of events/text. If False, return result. If None (default), auto-detect from original run.
event (bool): If True (with streaming), return list of AgentEvent objects. If False (with streaming), return list of text chunks.
external_tool_executor (Optional[Callable[[RunRequirement], str]]): Optional function that executes external tools. When provided, if the agent pauses again with NEW external tool requirements, the executor is called automatically for each requirement.

Returns:

For direct mode: Task content if return_output=False, AgentRunOutput if return_output=True
For streaming mode: List of events (if event=True) or text chunks (if event=False)

`continue_run_async`

Continue a paused agent run using StepResult-based intelligent resumption. Note: HITL continuation is only supported in direct call mode (streaming=False). Supports all HITL continuation scenarios:

External tool execution: Resume from MessageBuildStep with tool results
Durable execution (error recovery): Resume from exact failed step
Cancel run resumption: Resume from exact cancelled step

Parameters:

task (Optional[Task]): Task object with external results (for external tool continuation)
run_id (Optional[str]): Run ID to load from storage (for durable/cancel continuation)
model (Optional[Union[str, Model]]): Override model
debug (bool): Enable debug mode
retry (int): Number of retries
return_output (bool): If True, return full AgentRunOutput. If False, return content only.
state (Optional[State]): Graph execution state
streaming (bool): Must be False. Streaming mode not supported for HITL continuation.
event (bool): Ignored (streaming not supported)
external_tool_executor (Optional[Callable[[RunRequirement], str]]): Optional function that executes external tools. When provided, if the agent pauses again with NEW external tool requirements, the executor is called automatically for each requirement.
graph_execution_id (Optional[str]): Graph execution identifier

Returns:

Any: Task content or AgentRunOutput (if return_output=True)

Raises:

ValueError: If streaming=True is passed

`recommend_model_for_task`

Get a model recommendation for a specific task (synchronous version of recommend_model_for_task_async). Parameters:

task (Union[Task, str]): Task object or task description string
criteria (Optional[Dict[str, Any]]): Optional criteria dictionary for model selection
use_llm (Optional[bool]): Optional flag to use LLM for selection

Returns:

ModelRecommendation: Object containing recommendation details

`recommend_model_for_task_async`

Get a model recommendation for a specific task. This method analyzes the task and returns a recommendation for the best model to use. The user can then decide whether to use the recommended model or stick with the default. Parameters:

task (Union[Task, str]): Task object or task description string
criteria (Optional[Dict[str, Any]]): Optional criteria dictionary for model selection (overrides agent’s default)
use_llm (Optional[bool]): Optional flag to use LLM for selection (overrides agent’s default)

Returns:

ModelRecommendation: Object containing:
- model_name: Recommended model identifier
- reason: Explanation for the recommendation
- confidence_score: Confidence level (0.0 to 1.0)
- selection_method: “rule_based” or “llm_based”
- estimated_cost_tier: Cost estimate (1-10)
- estimated_speed_tier: Speed estimate (1-10)
- alternative_models: List of alternative model names

`get_agent_id`

Get display-friendly agent ID. Returns:

str: Agent name or formatted agent ID

`get_cache_stats`

Get cache statistics for this agent’s session. Returns:

Dict[str, Any]: Cache statistics

`clear_cache`

Clear the agent’s session cache.

`get_run_output`

Get the AgentRunOutput from the last execution. Returns:

Optional[AgentRunOutput]: The complete run output, or None if no run has been executed

`get_run_id`

Get the current run ID. Returns:

Optional[str]: The current run ID, or None if no run is active.

`cancel_run`

Cancel a run by its ID. If no run_id is provided, cancels the current run. Parameters:

run_id (Optional[str]): The ID of the run to cancel. If None, cancels the current run.

Returns:

bool: True if the run was found and cancelled, False otherwise.

`get_last_model_recommendation`

Get the last model recommendation made by the agent. Returns:

Optional[ModelRecommendation]: ModelRecommendation object or None if no recommendation was made

`add_tools`

Dynamically add tools to the agent and register them. Parameters:

tools (Union[Any, List[Any]]): A single tool or list of tools to add

Raises:

DisallowedOperation: If any tool is blocked by the safety policy

`remove_tools`

Remove tools from the agent. Supports removing:

Tool names (strings)
Function objects
Agent objects
MCP handlers (and all their tools)
Class instances (ToolKit or regular classes, and all their tools)
Builtin tools (AbstractBuiltinTool instances)

Parameters:

tools (Union[str, List[str], Any, List[Any]]): Single tool or list of tools to remove (any type)

`get_tool_defs`

Get the tool definitions for all currently registered tools. Returns:

List[ToolDefinition]: List of tool definitions from the ToolManager

Task

Team

Memory

​Parameters

​Functions

​do

​do_async

​stream

​astream

​print_do

​print_do_async

​continue_run

​continue_run_async

​recommend_model_for_task

​recommend_model_for_task_async

​get_agent_id

​get_cache_stats

​clear_cache

​get_run_output

​get_run_id

​cancel_run

​get_last_model_recommendation

​add_tools

​remove_tools

​get_tool_defs

Parameters

Functions

`do`

`do_async`

`stream`

`astream`

`print_do`

`print_do_async`

`continue_run`

`continue_run_async`

`recommend_model_for_task`

`recommend_model_for_task_async`

`get_agent_id`

`get_cache_stats`

`clear_cache`

`get_run_output`

`get_run_id`

`cancel_run`

`get_last_model_recommendation`

`add_tools`

`remove_tools`

`get_tool_defs`