> ## Documentation Index > Fetch the complete documentation index at: https://docs.upsonic.ai/llms.txt > Use this file to discover all available pages before exploring further. # Attributes > Configuration options for the Agent system ## 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 ```python theme={null} 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) ```