Attributes

The Agent system is configured through the Agent class, which provides the following attributes:

Attribute	Type	Default	Description
`model`	str \| Model	`"openai/gpt-4o"`	Model identifier or Model instance
`model_name`	str	(from `model`)	Original model identifier string (set from `model` parameter)
`name`	str \| None	`None`	Agent name for identification
`memory`	Memory \| None	`None`	Memory instance for conversation history
`db`	DatabaseBase \| None	`None`	Database instance (overrides memory if provided)
`session_id`	str \| None	`None`	Session identifier for conversation tracking
`user_id`	str \| None	`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`	str \| None	`None`	Company URL for context
`company_objective`	str \| None	`None`	Company objective for context
`company_description`	str \| None	`None`	Company description for context
`company_name`	str \| None	`None`	Company name for context
`system_prompt`	str \| None	`None`	Custom system prompt
`reflection`	bool	`False`	Enable reflection capabilities
`compression_strategy`	Literal[“none”, “simple”, “llmlingua”]	`"none"`	Context compression method: ‘none’, ‘simple’, ‘llmlingua’
`compression_settings`	Dict[str, Any] \| None	`None`	Settings for compression strategy
`reliability_layer`	Any \| None	`None`	Reliability layer for robustness
`agent_id_`	str \| None	`None`	Unique identifier for the agent instance
`canvas`	Canvas \| None	`None`	Canvas instance for visual interactions
`retry`	int	`1`	Number of retry attempts
`mode`	RetryMode	`"raise"`	Retry mode behavior: ‘raise’ or ‘return_false’
`role`	str \| None	`None`	Agent role
`goal`	str \| None	`None`	Agent goal
`instructions`	str \| None	`None`	Specific instructions
`education`	str \| None	`None`	Agent education background
`work_experience`	str \| None	`None`	Agent work experience
`feed_tool_call_results`	bool \| None	`None`	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`	List[Any] \| None	`None`	Agent-level tools (can also be added via `add_tools()`)
`user_policy`	Policy \| List[Policy] \| None	`None`	User input safety policy
`agent_policy`	Policy \| List[Policy] \| None	`None`	Agent output safety policy
`tool_policy_pre`	Policy \| List[Policy] \| None	`None`	Tool safety policy for pre-execution validation
`tool_policy_post`	Policy \| List[Policy] \| None	`None`	Tool safety policy for post-execution validation
`user_policy_feedback`	bool	`False`	Enable feedback loop for user policy violations
`agent_policy_feedback`	bool	`False`	Enable feedback loop for agent policy violations
`user_policy_feedback_loop`	int	`1`	Maximum retry count for user policy feedback
`agent_policy_feedback_loop`	int	`1`	Maximum retry count for agent policy feedback
`settings`	ModelSettings \| None	`None`	Model-specific settings
`profile`	ModelProfile \| None	`None`	Model profile configuration
`reflection_config`	ReflectionConfig \| None	`None`	Configuration for reflection
`model_selection_criteria`	Dict[str, Any] \| None	`None`	Default criteria for recommend_model_for_task()
`use_llm_for_selection`	bool	`False`	Use LLM in recommend_model_for_task()
`reasoning_effort`	Literal[“low”, “medium”, “high”] \| None	`None`	Reasoning effort: ‘low’, ‘medium’, ‘high’ (OpenAI)
`reasoning_summary`	Literal[“concise”, “detailed”] \| None	`None`	Reasoning summary: ‘concise’, ‘detailed’ (OpenAI)
`thinking_enabled`	bool \| None	`None`	Enable thinking (Anthropic/Google)
`thinking_budget`	int \| None	`None`	Token budget for thinking
`thinking_include_thoughts`	bool \| None	`None`	Include thoughts in output (Google)
`reasoning_format`	Literal[“hidden”, “raw”, “parsed”] \| None	`None`	Reasoning format: ‘hidden’, ‘raw’, ‘parsed’ (Groq)
`culture_manager`	CultureManager \| None	`None`	Culture manager instance for cultural knowledge (experimental)
`add_culture_to_context`	bool	`False`	Add cultural knowledge to context (experimental)
`update_cultural_knowledge`	bool	`False`	Update cultural knowledge based on interactions (experimental)
`enable_agentic_culture`	bool	`False`	Enable agentic culture capabilities (experimental)
`metadata`	Dict[str, Any] \| None	`None`	Agent metadata (passed to prompt)

Properties

The Agent class provides the following read-only properties:

Property	Type	Description
`agent_id`	str	Unique agent identifier (auto-generated if not provided via `agent_id_`)
`session_id`	str \| None	Session identifier (from override, memory, or db)
`user_id`	str \| None	User identifier (from override, memory, or db)

Configuration Example

from upsonic import Agent, Task
from upsonic.storage.providers.sqlite import SqliteStorage
from upsonic.storage import Memory

# Create storage and memory
storage = SqliteStorage(
    db_file="agent_memory.db",
    agent_sessions_table_name="sessions"
)

memory = Memory(
    storage=storage,
    session_id="session_001",
    user_id="user_001",
    full_session_memory=True,
    summary_memory=True,
    model="openai/gpt-4o-mini"
)

# Create agent with configuration
agent = Agent(
    model="anthropic/claude-sonnet-4-6",
    name="Assistant",
    memory=memory,
    debug=True,
    role="AI Assistant",
    goal="Help users with their questions",
    show_tool_calls=True,
    tool_call_limit=5
)

# Execute a task
task = Task("Hello! What can you help me with?")
result = agent.do(task)
print(result)

​Attributes

​Properties

​Configuration Example

Attributes

Properties

Configuration Example