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-4-5",
    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)

GET STARTED

CONCEPTS

STARTING AN AGENT PROJECT

READY TO USE SNIPPETS

DEPLOYMENT

FURTHER READINGS

Attributes

Attributes

Properties

Configuration Example

GET STARTED

CONCEPTS

STARTING AN AGENT PROJECT

READY TO USE SNIPPETS

DEPLOYMENT

FURTHER READINGS

​Attributes

​Properties

​Configuration Example

Attributes

Properties

Configuration Example