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POST
/
v1
/
workflows
Create Workflow
curl --request POST \
  --url https://api.example.com/v1/workflows \
  --header 'Content-Type: application/json' \
  --header 'x-api-key: <api-key>' \
  --data '
{
  "name": "<string>",
  "description": "",
  "icon": "🚀",
  "avatar": "male-avatar",
  "model_provider": "anthropic",
  "model_name": "claude-sonnet-4-6",
  "llm_reasoning_effort": "medium",
  "llm_api_base": "<string>",
  "llm_credentials_key": "<string>",
  "llm_credentials_key_type": "xpander",
  "llm_credentials": {
    "name": "<string>",
    "value": "<string>",
    "description": "<string>"
  },
  "llm_extra_headers": {},
  "instructions": {
    "role": [],
    "goal": [],
    "general": ""
  },
  "expected_output": "",
  "output_format": "markdown",
  "output_schema": {},
  "orchestration_nodes": [],
  "task_level_strategies": {
    "retry_strategy": {
      "enabled": false,
      "max_retries": 3
    },
    "iterative_strategy": {
      "enabled": false,
      "max_iterations": 3,
      "end_condition": {
        "type": "regex",
        "term": "<string>",
        "group_id": "<string>",
        "path": "<string>",
        "value": null
      }
    },
    "stop_strategy": {
      "enabled": false,
      "stop_on_failure": true,
      "stop_on_condition": {
        "type": "regex",
        "term": "<string>",
        "group_id": "<string>",
        "path": "<string>",
        "value": null
      }
    },
    "max_runs_per_day": 123,
    "agentic_context_enabled": false,
    "duplication_prevention": {
      "selectors": [
        "<string>"
      ],
      "enabled": false,
      "ttl_minutes": 10
    }
  },
  "notification_settings": {
    "on_success": {},
    "on_error": {}
  },
  "source_nodes": [],
  "deployment_type": "serverless",
  "access_scope": "personal",
  "environment_id": "<string>",
  "using_nemo": false,
  "prompts_caching_enabled": false,
  "on_prem_event_streaming": true,
  "use_oidc_pre_auth": false,
  "pre_auth_audiences": [],
  "use_oidc_pre_auth_token_for_llm": false,
  "oidc_pre_auth_token_llm_audience": "<string>",
  "oidc_pre_auth_token_mcp_audience": "<string>"
}
'
{
  "name": "<string>",
  "organization_id": "<string>",
  "webhook_url": "<string>",
  "id": "<string>",
  "unique_name": "<string>",
  "origin_template": "<string>",
  "delegation_end_strategy": "return-to-start",
  "environment_id": "<string>",
  "sub_agents_continuous_thread": true,
  "deployment_type": "serverless",
  "created_by_prompt": "<string>",
  "prompts": [],
  "is_latest": false,
  "has_pending_changes": false,
  "deep_planning": false,
  "enforce_deep_planning": false,
  "connectivity_details": {},
  "framework": "agno",
  "description": "",
  "tools": [],
  "icon": "🚀",
  "avatar": "male-avatar",
  "source_nodes": [],
  "attached_tools": [],
  "access_scope": "personal",
  "instructions": {
    "role": [],
    "goal": [],
    "general": ""
  },
  "oas": {},
  "graph": [],
  "llm_settings": [],
  "status": "ACTIVE",
  "knowledge_bases": [],
  "version": 1,
  "created_by": "<string>",
  "created_at": "2023-11-07T05:31:56Z",
  "type": "manager",
  "delegation_type": "router",
  "delegation_memory_strategy": "summarization",
  "is_ai_employee": false,
  "using_nemo": false,
  "deletable": true,
  "model_provider": "anthropic",
  "model_name": "claude-sonnet-4-6",
  "llm_reasoning_effort": "medium",
  "llm_api_base": "<string>",
  "output_format": "markdown",
  "voice_id": "<string>",
  "output_schema": {},
  "llm_credentials_key": "<string>",
  "llm_credentials_key_type": "xpander",
  "llm_credentials": {
    "name": "<string>",
    "value": "<string>",
    "description": "<string>"
  },
  "llm_extra_headers": {},
  "expected_output": "",
  "agno_settings": {
    "session_storage": true,
    "learning": false,
    "agent_memories": false,
    "agentic_culture": false,
    "user_memories": false,
    "agentic_memory": false,
    "session_summaries": false,
    "num_history_runs": 10,
    "max_tool_calls_from_history": 0,
    "tool_call_limit": 0,
    "coordinate_mode": true,
    "pii_detection_enabled": false,
    "pii_detection_mask": true,
    "prompt_injection_detection_enabled": false,
    "openai_moderation_enabled": false,
    "reasoning_tools_enabled": false,
    "tool_calls_compression": {
      "enabled": false,
      "instructions": "",
      "threshold": 3
    },
    "max_plan_retries": 15,
    "plan_retry_strategy": "tiered",
    "memory_strategy": "disabled"
  },
  "on_prem_event_streaming": true,
  "prompts_caching_enabled": false,
  "is_supervised": false,
  "orchestration_nodes": [],
  "notification_settings": {},
  "task_level_strategies": {
    "retry_strategy": {
      "enabled": false,
      "max_retries": 3
    },
    "iterative_strategy": {
      "enabled": false,
      "max_iterations": 3,
      "end_condition": {
        "type": "regex",
        "term": "<string>",
        "group_id": "<string>",
        "path": "<string>",
        "value": null
      }
    },
    "stop_strategy": {
      "enabled": false,
      "stop_on_failure": true,
      "stop_on_condition": {
        "type": "regex",
        "term": "<string>",
        "group_id": "<string>",
        "path": "<string>",
        "value": null
      }
    },
    "max_runs_per_day": 123,
    "agentic_context_enabled": false,
    "duplication_prevention": {
      "selectors": [
        "<string>"
      ],
      "enabled": false,
      "ttl_minutes": 10
    }
  },
  "use_oidc_pre_auth": false,
  "pre_auth_audiences": [],
  "use_oidc_pre_auth_token_for_llm": false,
  "oidc_pre_auth_token_llm_audience": "<string>",
  "oidc_pre_auth_token_mcp_audience": "<string>"
}
Create a new workflow — a deterministic, multi-step pipeline where AI agents, tools, and logic nodes are wired together on a canvas. Only name is required — all other fields are optional. Data flows left-to-right through the pipeline: from a START trigger, through your processing nodes, to an END output.

​
Request Body

​
name
string
required
Display name for the workflow
​
description
string
Description of the workflow’s purpose
​
icon
string
Emoji icon for the workflow
​
model_provider
string
LLM provider: openai (default), anthropic, etc.
​
model_name
string
Specific model version
​
llm_reasoning_effort
string
Reasoning effort level for the orchestrator LLM
​
instructions
object
System instructions for the orchestrator
​
orchestration_nodes
array
Array of node definitions that make up the workflow canvas. Each node is powered by an AI agent (except Code nodes). Node types include:
  • pointer — Invokes one of your xpander AI agents with its full tool set and memory
  • classifier — An LLM that classifies, labels, or routes data based on natural language instructions
  • parallel — Runs multiple branches simultaneously
  • code — Executes custom code for deterministic logic (the only node that doesn’t use an LLM)
  • guardrail — An AI judge that evaluates a natural language condition and returns Pass/Fail
  • summarizer — An agent that answers specific questions from large payloads
  • wait — Pauses execution until a condition is met (webhook callback or human approval)
  • send_to_end — Skips remaining nodes and routes directly to the END block
​
task_level_strategies
object
Task-level strategies for retry, stop conditions, and iteration
​
notification_settings
object
Notification configuration (Slack, email, webhook) for workflow events
​
output_format
string
Output format: text or json
​
output_schema
object
JSON schema for structured output when output_format is json
​
expected_output
string
Natural-language description of the desired output
​
deployment_type
string
Deployment infrastructure: serverless (default) or container
​
access_scope
string
personal or organizational (default)
​
source_nodes
array
Source node configurations (e.g., Slack, web UI triggers)

​
Response

Returns the created WorkflowResponse object with generated ID and webhook URL.

​
Example Request

curl -X POST "https://api.xpander.ai/v1/workflows" \
  -H "Content-Type: application/json" \
  -H "x-api-key: <your-api-key>" \
  -d '{
    "name": "Customer Onboarding Workflow",
    "description": "Orchestrates the customer onboarding process",
    "model_provider": "openai",
    "model_name": "gpt-4o",
    "instructions": {
      "role": ["Workflow orchestrator"],
      "goal": ["Coordinate onboarding steps efficiently"],
      "general": "Route tasks to the appropriate sub-agents"
    }
  }'

​
Notes

  • The workflow is created with type: orchestration automatically
  • Every node (except Code) is powered by an AI agent — you write natural language instructions, not field mappings
  • Nodes auto-connect: data flows from each node’s output to the next node’s input as context
  • Deploy the workflow using Deploy Workflow after configuration
  • See the Workflows user guide for details on the visual canvas and node types

Authorizations

​
x-api-key
string
header
required

API Key for authentication

Body

application/json

Request model for creating a new workflow on the xpander.ai platform.

A workflow is a directed acyclic graph (DAG) of execution nodes. Each node can be an agent invocation, a classifier (LLM-based routing), a code block, a guardrail, a summarizer, a wait/HITL node, or an action (API call).

Only name is required. The workflow's type is automatically set to 'orchestration' — do not use the Agents API to create workflows.

Agent-specific fields (graph, attached_tools, delegation_*, deep_planning, framework, agno_settings, connectivity_details) are not applicable to workflows and are excluded from this model.

​
name
string
required

Human-readable name for the workflow. Examples: 'Customer Onboarding Pipeline', 'Daily Report Generator'.

​
description
string | null
default:""

Description of what this workflow does, its trigger conditions, and expected outcomes.

​
icon
string | null
default:🚀

Emoji icon for the workflow.

​
avatar
string | null
default:male-avatar

Avatar identifier for visual representation.

​
model_provider
enum<string> | null
default:anthropic

Default LLM provider for LLM-powered nodes (classifiers, guardrails, summarizers) in this workflow. Individual nodes can override this in their own settings.

Available options:
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
​
model_name
string | null
default:claude-sonnet-4-6

Default model for LLM-powered nodes. Individual nodes can override.

​
llm_reasoning_effort
enum<string> | null
default:medium

Default reasoning depth for agent nodes in this workflow.

Available options:
low,
medium,
high,
xhigh
​
llm_api_base
string | null

Custom API base URL for the LLM provider.

​
llm_credentials_key
string | null

Reference key to stored LLM credentials.

​
llm_credentials_key_type
enum<string> | null
default:xpander

Credential storage type.

Available options:
xpander,
custom
​
llm_credentials
LLMCredentials · object

Direct LLM credentials (prefer llm_credentials_key).

​
llm_extra_headers
Llm Extra Headers · object

Extra headers for LLM API requests.

​
instructions
AIAgentInstructions · object

High-level instructions for the workflow. These provide context to agent nodes within the workflow about the overall workflow purpose.

​
expected_output
string | null
default:""

Description of the workflow's expected final output.

​
output_format
enum<string> | null
default:markdown

Format for the workflow's final output.

Available options:
text,
markdown,
json,
voice
​
output_schema
Output Schema · object

JSON Schema for structured workflow output (when output_format is 'json').

​
orchestration_nodes
OrchestrationNode · object[] | null

The workflow's execution DAG — an ordered list of nodes that define the execution flow. Nodes execute sequentially following next_node_ids edges. The first non-special node in the list is the entry point.

Node types:

  • action: Invokes a connector operation or xpander built-in tool.

    • definition.asset_id: "{connection_id}_{operation_catalog_id}" (e.g., "da84aaaa-525d-4be4-ae60-091ea8bb3ae8_6822eb5f961d9263189b4017")
    • definition.type: "action"
    • definition.instructions: Instructions for the LLM on how to use this action's output
    • definition.schema_override: Optional fixed values for tool input properties

  • agent: Invokes an AI agent as a step in the workflow.

    • definition.asset_id: the agent UUID
    • definition.type: "agent"
    • definition.instructions: Task instructions for the agent
    • definition.persist_thread_id: True to maintain conversation context across runs

  • classifier: LLM-based routing — classifies input into groups and routes to different downstream nodes.

    • definition.groups: list of {id, name, evaluation_criteria} for each classification category
    • Downstream nodes use condition.group_id to match classifier output

  • guardrail: LLM-based pass/fail check with "pass" and "fail" groups.

    • Routes to pass/fail downstream nodes based on evaluation

  • summarizer: LLM-based text processing — summarizes, extracts, or transforms the previous node's output.

    • definition.instructions: What to summarize/extract
    • The special "end-summarizer" (id="end-summarizer") runs at the end of the workflow to produce the final output

  • code: Executes arbitrary Python code.

    • definition.code: Python source code string

  • wait: Pauses execution for an external event (webhook or human-in-the-loop approval).

    • definition.type: "webhook" or "hitl"

  • send_to_end: Routes execution to end nodes (end-summarizer, end-classifier).

  • parallel: Executes multiple child nodes simultaneously.

    • definition.nodes: list of child OrchestrationNode objects

Flow control:

  • next_node_ids: list of node IDs to execute after this node completes
  • condition: optional condition on a node for conditional routing (used with classifier/guardrail)
  • The workflow ends when a node has empty next_node_ids, or routes to end-summarizer

Strategies (per-node):

  • retry_strategy: {enabled, max_retries} — retry on failure
  • iterative_strategy: {enabled, max_iterations, end_condition} — loop until condition met
  • stop_strategy: {enabled, stop_on_failure, stop_on_condition} — halt workflow

Example (Extract → Email → Summarize): [ {"type": "action", "id": "step-1", "name": "Extract Content", "next_node_ids": ["step-2"], "definition": {"asset_id": "<connection_id><operation_id>", "type": "action", "instructions": "Extract content from the provided URL"}}, {"type": "action", "id": "step-2", "name": "Send Email", "next_node_ids": [], "definition": {"asset_id": "<connection_id><operation_id>", "type": "action", "instructions": "Send summary to recipient"}}, {"type": "summarizer", "id": "end-summarizer", "name": "Output Summarizer", "next_node_ids": [], "definition": {"instructions": "Summarize the workflow execution results", "settings": {"model_provider": "anthropic", "model_name": "claude-sonnet-4-6"}}} ]

​
task_level_strategies
TaskLevelStrategies · object

Execution strategies: retry (max retries on failure), iterative (repeated execution with stop conditions), stop (halt conditions), max_runs_per_day (daily execution limit), and agentic_context (persistent memory across workflow runs).

​
notification_settings
NotificationSettings · object

Notifications on workflow completion — email, Slack, or webhook on success/error.

​
source_nodes
AIAgentSourceNode · object[] | null

How this workflow can be triggered: SDK, scheduled task, webhook, assistant UI, MCP, A2A, Telegram, Slack.

​
deployment_type
enum<string> | null
default:serverless

Where the workflow runs. 'serverless' (recommended) or 'container'.

Available options:
serverless,
container
​
access_scope
enum<string> | null
default:personal

Who can access this workflow. 'personal' or 'organizational'.

Available options:
personal,
organizational
​
environment_id
string | null

Target deployment environment.

​
using_nemo
boolean | null
default:false

Enable NeMo guardrails.

​
prompts_caching_enabled
boolean | null
default:false

Enable prompt caching for LLM-powered nodes.

​
on_prem_event_streaming
boolean | null
default:true

Enable real-time event streaming.

​
use_oidc_pre_auth
boolean | null
default:false

Require OIDC pre-authentication before workflow execution.

​
pre_auth_audiences
string[] | null

Allowed OIDC token audiences.

​
use_oidc_pre_auth_token_for_llm
boolean | null
default:false

Forward OIDC token to LLM provider.

​
oidc_pre_auth_token_llm_audience
string | null

OIDC audience for LLM access.

​
oidc_pre_auth_token_mcp_audience
string | null

OIDC audience for MCP server access.

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.

​
name
string
required
​
organization_id
string
required
​
webhook_url
string
required
​
id
string | null
​
unique_name
string | null
​
origin_template
string | null
​
delegation_end_strategy
enum<string> | null
default:return-to-start

Enumeration of the agent delegation end strategies.

Attributes: ReturnToStart: when last agent is finished and about to announce "finish" it will summarize and return to the first agent. FinishWithLast: finish at the last agent.

Available options:
return-to-start,
finish-with-last
​
environment_id
string | null
​
sub_agents_continuous_thread
boolean | null
default:true
​
deployment_type
enum<string> | null
default:serverless
Available options:
serverless,
container
​
created_by_prompt
string | null
​
prompts
string[] | null
​
is_latest
boolean | null
default:false
​
has_pending_changes
boolean | null
default:false
​
deep_planning
boolean | null
default:false
​
enforce_deep_planning
boolean | null
default:false
​
connectivity_details
AIAgentConnectivityDetailsA2A · object
​
framework
string | null
default:agno
​
description
string | null
default:""
​
tools
any[] | null
​
icon
string | null
default:🚀
​
avatar
string | null
default:male-avatar
​
source_nodes
AIAgentSourceNode · object[] | null
​
attached_tools
Connector · object[] | null
​
access_scope
enum<string> | null
default:personal
Available options:
personal,
organizational
​
instructions
AIAgentInstructions · object
​
oas
Oas · object
​
graph
AIAgentGraphItem · object[] | null
​
llm_settings
AIAgentGraphItemLLMSettings · object[] | null
​
status
enum<string> | null
default:ACTIVE

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.

Available options:
DRAFT,
ACTIVE,
INACTIVE
​
knowledge_bases
AgentKnowledgeBase · object[] | null
​
version
integer | null
default:1
​
created_by
string | null
​
created_at
string<date-time> | null
​
type
enum<string> | null

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.

Available options:
manager,
regular,
a2a,
curl,
orchestration
​
delegation_type
enum<string> | null
default:router

Enumeration of the agent delegation types.

Attributes: Router: Marks the agent as a router agent - xpanderAI's LLM will decide which sub-agent to trigger. Sequence: Marks the agent as a sequence agent - sub-agents will delegate to other sub-agents.

Available options:
router,
sequence
​
delegation_memory_strategy
enum<string> | null
default:summarization

Enumeration of the agent delegation memory strategies.

Attributes: Full: The memory object will be passed completely between agents. Summarization: Between each sub-agent delegation, a summarization will occur, and a new thread will be created for each agent. OriginalInput: the sub agent will get the initial task with a fresh memory thread

Available options:
full,
summarization,
original-input
​
is_ai_employee
boolean | null
default:false
​
using_nemo
boolean | null
default:false
​
deletable
boolean | null
default:true
​
model_provider
enum<string> | null
default:anthropic
Available options:
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
​
model_name
string | null
default:claude-sonnet-4-6
​
llm_reasoning_effort
enum<string> | null
default:medium
Available options:
low,
medium,
high,
xhigh
​
llm_api_base
string | null
​
output_format
enum<string> | null
default:markdown
Available options:
text,
markdown,
json,
voice
​
voice_id
string | null
​
output_schema
Output Schema · object
​
llm_credentials_key
string | null
​
llm_credentials_key_type
enum<string> | null
default:xpander
Available options:
xpander,
custom
​
llm_credentials
LLMCredentials · object
​
llm_extra_headers
Llm Extra Headers · object
​
expected_output
string | null
default:""
​
agno_settings
AgnoSettings · object
​
on_prem_event_streaming
boolean | null
default:true
​
prompts_caching_enabled
boolean | null
default:false
​
is_supervised
boolean | null
default:false
​
orchestration_nodes
OrchestrationNode · object[] | null
​
notification_settings
NotificationSettings · object

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.

​
task_level_strategies
TaskLevelStrategies · object

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.
​
use_oidc_pre_auth
boolean | null
default:false
​
pre_auth_audiences
string[] | null
​
use_oidc_pre_auth_token_for_llm
boolean | null
default:false
​
oidc_pre_auth_token_llm_audience
string | null
​
oidc_pre_auth_token_mcp_audience
string | null