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POST
/
v2
/
agents
/
run
Run an agent with configuration
curl --request POST \
  --url https://api.orq.ai/v2/agents/run \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "key": "<string>",
  "task_id": "<string>",
  "model": "<string>",
  "fallback_models": [
    "<string>"
  ],
  "role": "<string>",
  "instructions": "<string>",
  "message": {
    "messageId": "<string>",
    "role": "user",
    "parts": [
      {
        "kind": "text",
        "text": "<string>"
      }
    ]
  },
  "variables": {},
  "contact": {
    "id": "contact_01ARZ3NDEKTSV4RRFFQ69G5FAV",
    "display_name": "Jane Doe",
    "email": "jane.doe@example.com",
    "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>"
  },
  "path": "Default",
  "description": "<string>",
  "system_prompt": "<string>",
  "memory_stores": [],
  "knowledge_bases": [],
  "team_of_agents": [],
  "settings": {
    "tools": [],
    "tool_approval_required": "none",
    "max_iterations": 100,
    "max_execution_time": 300,
    "evaluators": [
      {
        "id": "<string>",
        "sample_rate": 50,
        "execute_on": "input"
      }
    ],
    "guardrails": [
      {
        "id": "<string>",
        "sample_rate": 50,
        "execute_on": "input"
      }
    ]
  },
  "metadata": {}
}'
{
  "id": "<string>",
  "contextId": "<string>",
  "kind": "task",
  "status": {
    "state": "submitted",
    "timestamp": "<string>",
    "message": {
      "kind": "message",
      "messageId": "<string>",
      "role": "user",
      "parts": [
        {
          "kind": "text",
          "text": "<string>"
        }
      ]
    }
  },
  "metadata": {}
}

Authorizations

​
Authorization
string
header
required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Body

application/json
​
key
string
required

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.

Minimum length: 1
​
model
required

Model configuration for this execution. Can override the agent manifest defaults if the agent already exists.

A 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.

​
role
string
required

Specifies the agent's function and area of expertise.

Minimum length: 1
​
instructions
string
required

Provides context and purpose for the agent. Combined with the system prompt template to generate the agent's instructions.

Minimum length: 1
​
message
object
required

The A2A format message containing the task for the agent to perform.

​
path
string
required

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.

Example:

"Default"

​
settings
object
required
​
task_id
string

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.

​
fallback_models
(string | Fallback Model Configuration · object)[]

Optional array of fallback models used when the primary model fails. Fallbacks are attempted in order. All models must support tool calling.

​
variables
object

Optional variables for template replacement in system prompt, instructions, and messages

​
contact
object

Information about the contact making the request. If the contact does not exist, it will be created automatically.

​
thread
object

Thread information to group related requests

​
memory
object

Memory configuration for the agent execution. Used to associate memory stores with specific entities like users or sessions.

​
description
string

A brief summary of the agent's purpose.

Minimum length: 1
​
system_prompt
string

A custom system prompt template for the agent. If omitted, the default template is used.

Minimum length: 1
​
memory_stores
string[]

Array of memory store identifiers that are accessible to the agent. Accepts both memory store IDs and keys.

​
knowledge_bases
object[]

Knowledge base configurations for the agent to access

​
team_of_agents
object[]

The agents that are accessible to this orchestrator. The main agent can hand off to these agents to perform tasks.

​
metadata
object

Optional metadata for the agent run as key-value pairs that will be included in traces

Response

200 - application/json

Agent execution initiated successfully. Returns task details including the task ID, execution status, and agent response. The task ID can be used to track execution progress and retrieve results.

Response format following the Agent-to-Agent (A2A) protocol. Returned when starting or continuing an agent task execution.

​
id
string
required

The unique ID of the created agent execution task

​
contextId
string
required

The correlation ID for this execution (used for tracking)

​
kind
enum<string>
required

A2A entity type identifier

Available options:
task
​
status
object
required

Current task status information

​
metadata
object

Task metadata containing workspace_id and trace_id for feedback and tracking