curl --request POST \
--url https://api.orq.ai/v2/agents/stream-run \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"key": "<string>",
"model": "<string>",
"role": "<string>",
"instructions": "<string>",
"message": {
"role": "user",
"parts": [
{
"kind": "text",
"text": "<string>"
}
],
"messageId": "<string>"
},
"path": "Default",
"settings": {
"tools": [],
"tool_approval_required": "none",
"max_iterations": 100,
"max_execution_time": 600,
"evaluators": [
{
"id": "<string>",
"execute_on": "input",
"sample_rate": 50
}
],
"guardrails": [
{
"id": "<string>",
"execute_on": "input",
"sample_rate": 50
}
]
},
"task_id": "<string>",
"fallback_models": [
"<string>"
],
"variables": {},
"contact": {
"id": "contact_01ARZ3NDEKTSV4RRFFQ69G5FAV",
"display_name": "Jane Doe",
"email": "[email protected]",
"metadata": [
{
"department": "Engineering",
"role": "Senior Developer"
}
],
"logo_url": "https://example.com/avatars/jane-doe.jpg",
"tags": [
"hr",
"engineering"
]
},
"thread": {
"id": "thread_01ARZ3NDEKTSV4RRFFQ69G5FAV",
"tags": [
"customer-support",
"priority-high"
]
},
"memory": {
"entity_id": "<string>"
},
"description": "<string>",
"system_prompt": "<string>",
"memory_stores": [],
"knowledge_bases": [],
"team_of_agents": [],
"metadata": {},
"stream_timeout_seconds": 1800.5
}
'{
"data": {
"type": "agents.execution_started",
"timestamp": "<string>",
"data": {
"agent_task_id": "<string>",
"workspace_id": "<string>",
"trace_id": "<string>"
}
}
}Dynamically configures and executes an agent while streaming the interaction in real-time via Server-Sent Events (SSE). Intelligently manages agent versioning by reusing existing agents with matching configurations or creating new versions when configurations differ. Combines the flexibility of inline configuration with real-time streaming, making it ideal for dynamic agent interactions with live feedback. The stream provides continuous updates including message chunks, tool executions, and status changes until completion or timeout.
curl --request POST \
--url https://api.orq.ai/v2/agents/stream-run \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"key": "<string>",
"model": "<string>",
"role": "<string>",
"instructions": "<string>",
"message": {
"role": "user",
"parts": [
{
"kind": "text",
"text": "<string>"
}
],
"messageId": "<string>"
},
"path": "Default",
"settings": {
"tools": [],
"tool_approval_required": "none",
"max_iterations": 100,
"max_execution_time": 600,
"evaluators": [
{
"id": "<string>",
"execute_on": "input",
"sample_rate": 50
}
],
"guardrails": [
{
"id": "<string>",
"execute_on": "input",
"sample_rate": 50
}
]
},
"task_id": "<string>",
"fallback_models": [
"<string>"
],
"variables": {},
"contact": {
"id": "contact_01ARZ3NDEKTSV4RRFFQ69G5FAV",
"display_name": "Jane Doe",
"email": "[email protected]",
"metadata": [
{
"department": "Engineering",
"role": "Senior Developer"
}
],
"logo_url": "https://example.com/avatars/jane-doe.jpg",
"tags": [
"hr",
"engineering"
]
},
"thread": {
"id": "thread_01ARZ3NDEKTSV4RRFFQ69G5FAV",
"tags": [
"customer-support",
"priority-high"
]
},
"memory": {
"entity_id": "<string>"
},
"description": "<string>",
"system_prompt": "<string>",
"memory_stores": [],
"knowledge_bases": [],
"team_of_agents": [],
"metadata": {},
"stream_timeout_seconds": 1800.5
}
'{
"data": {
"type": "agents.execution_started",
"timestamp": "<string>",
"data": {
"agent_task_id": "<string>",
"workspace_id": "<string>",
"trace_id": "<string>"
}
}
}Bearer authentication header of the form Bearer <token>, where <token> is your auth token.
A unique identifier for the agent. This key must be unique within the same workspace and cannot be reused. When executing the agent, this key determines if the agent already exists. If the agent version differs, a new version is created at the end of the execution, except for the task. All agent parameters are evaluated to decide if a new version is needed.
1A model ID string (e.g., openai/gpt-4o or anthropic/claude-haiku-4-5-20251001). The agent can be run with a wide range of models with different capabilities, performance characteristics, and price points. Only models that support tool calling (function_calling) can be used to run agents. See (supported models)[/docs/proxy/supported-models] documentation for the complete list of available models.
Specifies the agent's function and area of expertise.
1Provides context and purpose for the agent. Combined with the system prompt template to generate the agent's instructions.
1The A2A format message containing the task for the agent to perform.
Show child attributes
Entity storage path in the format: project/folder/subfolder/...
The first element identifies the project, followed by nested folders (auto-created as needed).
With project-based API keys, the first element is treated as a folder name, as the project is predetermined by the API key.
"Default"
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Optional task ID to continue an existing agent execution. When provided, the agent will continue the conversation from the existing task state. The task must be in an inactive state to continue.
Optional array of fallback models used when the primary model fails. Fallbacks are attempted in order. All models must support tool calling.
A fallback model ID string (e.g., openai/gpt-4o-mini). Will be used if the primary model request fails. Must support tool calling.
Optional variables for template replacement in system prompt, instructions, and messages
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Information about the contact making the request. If the contact does not exist, it will be created automatically.
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Thread information to group related requests
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Memory configuration for the agent execution. Used to associate memory stores with specific entities like users or sessions.
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A brief summary of the agent's purpose.
1A custom system prompt template for the agent. If omitted, the default template is used.
1Array of memory store identifiers that are accessible to the agent. Accepts both memory store IDs and keys.
Knowledge base configurations for the agent to access
Show child attributes
The agents that are accessible to this orchestrator. The main agent can hand off to these agents to perform tasks.
Show child attributes
Optional metadata for the agent run as key-value pairs that will be included in traces
Show child attributes
Stream timeout in seconds (1-3600). Default: 1800 (30 minutes)
1 <= x <= 3600Server-Sent Event stream successfully established. Delivers real-time agent execution events including message fragments, tool invocations, intermediate results, and completion status. Stream terminates with [DONE] sentinel upon completion.
Initial event emitted when an agent stream begins. Contains the task ID for tracking, workspace context, and trace ID for observability.
Show child attributes
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