Get Workflow
Retrieve detailed information about a specific workflow by its unique identifier
orchestration_nodes array represents the canvas layout — each node is a step in the pipeline (Agent, Action, Classifier, Guardrail, etc.).
Path Parameters
Response
Returns the fullWorkflowResponse object with all configuration details including the node graph, trigger settings, notification configuration, and task-level strategies.
Example Request
Notes
- Returns
404if the ID belongs to a regular agent (not a workflow) - The
orchestration_nodesarray contains the full canvas graph — each node includes its type, instructions, and connections to other nodes - Node types include:
pointer(Agent),classifier,parallel,code,guardrail,summarizer,wait,send_to_end
Authorizations
API Key for authentication
Path Parameters
Response
Successful Response
Response model for workflow endpoints.
Inherits from AIAgent but excludes agent-specific fields that are not relevant to workflows (orchestrations). This provides a clean API surface for workflow consumers without exposing confusing agent-only concepts.
The workflow's execution logic is defined in orchestration_nodes — a DAG
of typed nodes. Agent-specific fields like graph, attached_tools,
delegation_*, framework, and agno_settings are hidden.
serverless, container - AIAgentConnectivityDetailsA2A
- AIAgentConnectivityDetailsCurl
- Connectivity Details
personal, organizational Enumeration of possible agent statuses.
Attributes: DRAFT: Agent is in a draft state. ACTIVE: Agent is active and operational. INACTIVE: Agent is inactive and not operational.
DRAFT, ACTIVE, INACTIVE Enumeration of the agent types.
Attributes: Manager: marks the agent as a Managing agent. Regular: marks the agent as a regular agent. A2A: marks the agent as an external agent used via A2A protocol. Curl: marks the agent as an external agent used via a CURL. Orchestration: marks the agent as an Orchestration object.
manager, regular, a2a, curl, orchestration openai, nim, amazon_bedrock, azure_ai_foundary, huggingFace, friendlyAI, anthropic, gemini, fireworks, google_ai_studio, helicone, bytedance, tzafon_lightcone, open_router, nebius, cloudflare_ai_gw low, medium, high, xhigh text, markdown, json, voice xpander, custom Configuration for event-based notifications.
Attributes: on_success: Notifications to send when an operation succeeds. Maps notification types to a list of notification configurations. on_error: Notifications to send when an operation fails. Maps notification types to a list of notification configurations.
Configuration object for task-level execution strategies.
This model groups optional strategy configurations that control how a task is executed and managed over time, including retries, iterative execution, stopping conditions, and daily run limits.
Attributes: retry_strategy: Optional retry policy configuration that defines how the task should behave when execution fails (e.g., max attempts, backoff rules).
iterative_strategy:
Optional iterative execution configuration for tasks that may run in
repeated cycles/steps until completion or a stop condition is met.
stop_strategy:
Optional stopping policy configuration that defines when the task
should stop running (e.g., timeout, max iterations, success criteria).
max_runs_per_day:
Optional limit on how many times the task is allowed to run within a
24-hour period. If not set, no explicit daily limit is enforced.
agentic_context_enabled:
if agentic memory is enabled and accesible to the executor.
