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SDK Reference

This reference documents the functions and classes available with import agentops. The AgentOps SDK is designed for easy integration with your agent applications, offering both simple auto-instrumentation and more detailed manual tracing capabilities.

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Core Functions

These are the primary functions you’ll use to initialize and configure AgentOps in your application.

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init()

Initializes the AgentOps SDK and automatically starts tracking your application.

Parameters:

  • api_key (str, optional): API Key for AgentOps services. If not provided, the key will be read from the AGENTOPS_API_KEY environment variable.
  • endpoint (str, optional): The endpoint for the AgentOps service. If not provided, will be read from the AGENTOPS_API_ENDPOINT environment variable. Defaults to ‘https://api.agentops.ai’.
  • app_url (str, optional): The dashboard URL for the AgentOps app. If not provided, will be read from the AGENTOPS_APP_URL environment variable. Defaults to ‘https://app.agentops.ai’.
  • max_wait_time (int, optional): The maximum time to wait in milliseconds before flushing the queue. Defaults to 5,000 (5 seconds).
  • max_queue_size (int, optional): The maximum size of the event queue. Defaults to 512.
  • default_tags (List[str], optional): Default tags for the sessions that can be used for grouping or sorting later (e.g. [“GPT-4”]).
  • tags (List[str], optional): [Deprecated] Use default_tags instead.
  • instrument_llm_calls (bool, optional): Whether to instrument LLM calls automatically. Defaults to True.
  • auto_start_session (bool, optional): Whether to start a session automatically when the client is created. Defaults to True.
  • auto_init (bool, optional): Whether to automatically initialize the client on import. Defaults to True.
  • skip_auto_end_session (bool, optional): Don’t automatically end session based on your framework’s decision-making. Defaults to False.
  • env_data_opt_out (bool, optional): Whether to opt out of collecting environment data. Defaults to False.
  • log_level (str, int, optional): The log level to use for the client. Defaults to ‘INFO’.
  • fail_safe (bool, optional): Whether to suppress errors and continue execution when possible. Defaults to False.
  • exporter_endpoint (str, optional): Endpoint for the exporter. If not provided, will be read from the AGENTOPS_EXPORTER_ENDPOINT environment variable. Defaults to ‘https://otlp.agentops.ai/v1/traces’.
  • export_flush_interval (int, optional): Time interval in milliseconds between automatic exports of telemetry data. Defaults to 1000.

Returns:

  • If auto_start_session=True, returns the created Session object. Otherwise, returns None.

Example:

import agentops

# Basic initialization with automatic session creation
agentops.init("your-api-key")

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configure()

Updates client configuration after initialization. Supports the same parameters as init().

Parameters:

  • api_key (str, optional): API Key for AgentOps services.
  • endpoint (str, optional): The endpoint for the AgentOps service.
  • app_url (str, optional): The dashboard URL for the AgentOps app.
  • max_wait_time (int, optional): Maximum time to wait in milliseconds before flushing the queue.
  • max_queue_size (int, optional): Maximum size of the event queue.
  • default_tags (List[str], optional): Default tags for the sessions.
  • instrument_llm_calls (bool, optional): Whether to instrument LLM calls.
  • auto_start_session (bool, optional): Whether to start a session automatically.
  • auto_init (bool, optional): Whether to automatically initialize the client on import.
  • skip_auto_end_session (bool, optional): Don’t automatically end session.
  • env_data_opt_out (bool, optional): Whether to opt out of collecting environment data.
  • log_level (str, int, optional): The log level to use for the client.
  • fail_safe (bool, optional): Whether to suppress errors and continue execution.
  • exporter (object, optional): Custom span exporter for OpenTelemetry trace data.
  • processor (object, optional): Custom span processor for OpenTelemetry trace data.
  • exporter_endpoint (str, optional): Endpoint for the exporter.
  • export_flush_interval (int, optional): Time interval in milliseconds between automatic exports of telemetry data.

Example:

import agentops

# Initialize first
agentops.init()

# Later, update configuration
agentops.configure(
    max_wait_time=10000,
    max_queue_size=200,
    default_tags=["production", "gpt-4"]
)

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get_client()

Gets the singleton client instance. Most users won’t need to use this function directly.

Returns:

  • The AgentOps client instance.

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Session Management

These functions help you manage the lifecycle of tracking sessions.

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start_session()

Starts a new AgentOps session manually. This is useful when you’ve disabled automatic session creation or need multiple separate sessions.

Parameters:

  • tags (Union[Dict[str, Any], List[str]], optional): Optional tags to attach to the session, useful for filtering in the dashboard. Can be a list of strings or a dict of key-value pairs.

Example:

import agentops

# Initialize without auto-starting a session
agentops.init("your-api-key", auto_start_session=False)

# Later, manually start a session
session = agentops.start_session(tags=["customer-query"])

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Decorators for Detailed Instrumentation

For more granular control, AgentOps provides decorators that explicitly track different components of your application. These decorators are imported from agentops.sdk.decorators.

import agentops
from agentops.sdk.decorators import session, agent, operation, workflow, task

# Initialize without automatic session creation
agentops.init("your-api-key", auto_start_session=False)

# Create and run a session using the decorator
@session
def my_workflow():
    # Your workflow code here
    pass

# Run the workflow, which creates and manages the session
my_workflow()

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Available Decorators

  • @session: Creates a session span, which serves as the root for all other spans
  • @agent: Creates an agent span for tracking agent operations
  • @operation / @task: Creates operation/task spans for tracking specific operations (these are aliases)
  • @workflow: Creates workflow spans for organizing related operations

See Decorators for more detailed documentation on using these decorators.

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Legacy Functions

The following functions are maintained for backward compatibility with older versions of the SDK and integrations. New code should use the functions and decorators described above instead.

  • record(event): Legacy function to record an event. Replaced by decorator-based tracing.
  • track_agent(): Legacy decorator for marking agents. Replaced by the @agent decorator.
  • track_tool(): Legacy decorator for marking tools. Replaced by the @tool decorator.
  • ToolEvent(), ErrorEvent(), ActionEvent(), LLMEvent(): Legacy event types. Replaced by automatic instrumentation and decorators.