> ## Documentation Index > Fetch the complete documentation index at: https://docs.xpander.ai/llms.txt > Use this file to discover all available pages before exploring further. # Agent class > The Agent object (attributes, computed properties, and class methods). `Agent` is the rich object returned by `Agents.aget()` and `AgentsListItem.aload()`. It carries the agent's full stored configuration, plus methods for creating tasks, invoking tools, and managing sessions. ```python theme={"dark"} from xpander_sdk import Agents agent = await Agents().aget("agent-123") print(agent.name) print(agent.model_provider, agent.model_name) print(agent.deployment_type.value) # "serverless" or "container" print(len(agent.tools.list)) ``` ## Class methods ### `Agent.aload(agent_id, configuration=None, version=None) -> Agent` Load an agent by ID. This is what `Agents.aget()` calls internally. Use the module-level `agents.aget(...)` in normal code; `Agent.aload(...)` is for places where you have a `Configuration` but no `Agents` instance. ```python theme={"dark"} from xpander_sdk import Agent, Configuration config = Configuration(api_key="...", organization_id="...") agent = await Agent.aload(agent_id="agent-123", configuration=config) ``` | Parameter | Type | Required | Default | Description | | --------------- | --------------- | -------- | ------- | ------------------------------------- | | `agent_id` | `str` | Yes | – | Agent to load. | | `configuration` | `Configuration` | No | `None` | SDK config. Uses defaults if omitted. | | `version` | `int` | No | `None` | Specific version. Latest if omitted. | The sync sibling is `Agent.load(...)`. ## Attributes | Attribute | Type | Description | | ----------------------- | ------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | `id` | `str` | Agent identifier. | | `organization_id` | `str` | Owning organization. | | `name` | `str` | Display name. | | `description` | `str \| None` | Long description. | | `unique_name` | `str` | URL-safe slug. | | `icon` | `str` | Emoji or icon. Defaults to `"🚀"`. | | `status` | `AgentStatus` | `DRAFT`, `ACTIVE`, or `INACTIVE`. | | `version` | `int` | Loaded version. | | `created_by` | `str \| None` | Creator user id. | | `created_at` | `datetime \| None` | Creation timestamp. | | `access_scope` | `AgentAccessScope` | `Personal` or `Organizational`. | | `type` | `AgentType \| None` | `Manager`, `Regular`, `A2A`, `Curl`, `Orchestration`. | | `framework` | `Framework` | Always `Agno` today. Other values reserved. | | `deployment_type` | `AgentDeploymentType` | `Serverless` or `Container`. | | `instructions` | `AgentInstructions` | `role: list[str]`, `goal: list[str]`, `general: str`. | | `graph` | `AgentGraph` | Execution graph (source nodes, tools, sub-agents, MCP). | | `tools` | `ToolsRepository` | Repository of attached tools. See [Tools Repository](/developers/sdk-reference/tools/overview). | | `knowledge_bases` | `list[AgentKnowledgeBase]` | Linked knowledge bases (id pointers). | | `model_provider` | `str` | e.g. `"openai"`, `"anthropic"`, `"bedrock"`. | | `model_name` | `str` | e.g. `"gpt-4o"`, `"claude-sonnet-4"`. | | `llm_reasoning_effort` | `LLMReasoningEffort` | `Low`, `Medium`, or `High`. | | `llm_api_base` | `str \| None` | Override for the LLM endpoint (Bedrock / Azure / Ollama). | | `llm_extra_headers` | `dict[str, str]` | Extra headers for LLM calls. | | `output_format` | `OutputFormat` | Default output format. | | `output_schema` | `dict \| None` | Default JSON schema (used when `output_format == Json`). | | `expected_output` | `str` | Description of the expected output. | | `agno_settings` | `AgnoSettings` | Memory, tool-call limits, guardrails, reasoning settings. | | `webhook_url` | `str \| None` | Configured webhook for completed tasks. | | `connectivity_details` | `AIAgentConnectivityDetailsA2A \| AIAgentConnectivityDetailsCurl \| dict` | Wire details for `A2A` and `Curl` agent types. | | `voice_id` | `str \| None` | Voice ID for `OutputFormat.Voice`. | | `task_level_strategies` | `TaskLevelStrategies \| None` | Retry / iterative / stop strategies and per-day caps. | | `orchestration_nodes` | `list[OrchestrationNode]` | Sub-nodes for orchestration agents. | | `notification_settings` | `NotificationSettings` | Alerting config. | | `use_oidc_pre_auth` | `bool` | Whether the agent uses OIDC pre-auth tokens. | | `pre_auth_audiences` | `list[str]` | OIDC audiences for pre-auth. | | `using_nemo` | `bool` | Whether NeMo is in use. | | `deep_planning` | `bool` | Whether deep planning is enabled. | | `enforce_deep_planning` | `bool` | Strict plan enforcement. | | `llm_credentials` | `LLMCredentials \| None` | Per-agent LLM credentials, if overridden. | ## Computed properties | Property | Type | Description | | ------------------------- | ------------------------ | ------------------------------------------------------------------------------- | | `mcp_servers` | `list[MCPServerDetails]` | MCP servers configured in the graph. | | `output` | `AgentOutput` | Resolved output configuration (Pydantic schema, markdown flag, JSON-mode flag). | | `search_knowledge` | `bool` | `True` when the agent has at least one linked knowledge base. | | `is_a_team` | `bool` | `True` when the agent has sub-agents (Manager / Team mode). | | `is_active` | `bool` | `agent.status == AgentStatus.ACTIVE`. | | `sanitized_name` | `str` | `name` munged to a valid Python identifier. | | `strands_tools` | `list` | Strands-formatted tool wrappers. | | `openai_agents_sdk_tools` | `list` | OpenAI Agents SDK-formatted tool wrappers. | ## Instance methods | Method | Reference | | ---------------------------------------------- | --------------------------------------------------------------------------------------------- | | `acreate_task` / `create_task` | [Create a task](/developers/sdk-reference/agents/create-task) | | `ainvoke_tool` / `invoke_tool` | [Invoke a tool](/developers/sdk-reference/agents/invoke-tool) | | `aget_knowledge_bases` / `get_knowledge_bases` | [Knowledge bases](/developers/sdk-reference/agents/knowledge-bases) | | `attach_knowledge_base` | [Knowledge bases](/developers/sdk-reference/agents/knowledge-bases#attach_knowledge_base) | | `knowledge_bases_retriever` | [Knowledge bases](/developers/sdk-reference/agents/knowledge-bases#knowledge_bases_retriever) | | `aget_db` / `get_db` | [Sessions](/developers/sdk-reference/agents/sessions) | | `aget_user_sessions` / `get_user_sessions` | [Sessions](/developers/sdk-reference/agents/sessions) | | `aget_session` / `get_session` | [Sessions](/developers/sdk-reference/agents/sessions) | | `adelete_session` / `delete_session` | [Sessions](/developers/sdk-reference/agents/sessions) | ### Streaming spec `agent.aget_streaming_spec() -> StreamingSpecResponse`: returns the deployed agent's streaming URL and an API key for the container's `/invoke` endpoint. Useful when you want to bypass the SSE channel and POST directly to a deployed worker. ```python theme={"dark"} spec = await agent.aget_streaming_spec() print(spec.url) # https://.containers.xpander.ai/invoke print(spec.api_key) # auth token for the /invoke endpoint ``` `StreamingSpecResponse` has two fields: `url` (str | None) and `api_key` (str | None). Both can be `None` if the agent isn't deployed yet. ### Connection string `agent.aget_connection_string() -> DatabaseConnectionString`: returns DB connection details for agents using session storage. The result is cached on the instance after the first call. ```python theme={"dark"} conn = await agent.aget_connection_string() print(conn.connection_uri.uri) ``` `DatabaseConnectionString` has `id`, `name`, `organization_id`, and `connection_uri.uri` (a Postgres-compatible URI). ### sync\_local\_tools `await agent.sync_local_tools(tools=[...])`: pushes locally-decorated `@register_tool(add_to_graph=True)` tools to the platform's graph. Called automatically when `Agent.aload` detects unsynced tools, so you rarely need to invoke it directly.