Agents API

Manage and invoke AI agents via the client API. Agents combine a model, system prompt, tools, guardrails, and RAG modules into a single deployable unit.

List Agents

GET /api/client/v1/agents

Query Parameters

Parameter	Type	Required	Description
status	string	No	Filter by status: active, inactive, draft

Response

{
  "agents": [
    {
      "key": "support-bot",
      "name": "Support Bot",
      "description": "Customer support agent",
      "config": {
        "modelKey": "gpt-4o",
        "temperature": 0.7,
        "topP": 1,
        "maxTokens": 4096
      },
      "status": "active",
      "createdAt": "2026-01-15T10:00:00.000Z"
    }
  ]
}

Get Agent

GET /api/client/v1/agents/:agentKey

Path Parameters

Parameter	Type	Required	Description
agentKey	string	Yes	Unique agent key

Response

{
  "agent": {
    "key": "support-bot",
    "name": "Support Bot",
    "description": "Customer support agent",
    "config": {
      "modelKey": "gpt-4o",
      "temperature": 0.7,
      "topP": 1,
      "maxTokens": 4096
    },
    "status": "active",
    "createdAt": "2026-01-15T10:00:00.000Z"
  }
}

Invoke Agent (Responses API)

Invoke an agent using the OpenAI Responses API format. The agent is identified by the model field.

Two equivalent endpoints are available:

POST /api/client/v1/responses
POST /api/client/v1/agents/responses

Request

{
  "model": "support-bot",
  "input": "How do I reset my password?",
  "previous_response_id": null,
  "version": 1
}

Parameters

Field	Type	Required	Description
model	string	Yes	Agent key (identifies the agent)
input	string | array	Yes	User message — plain string or array of message items
previous_response_id	string	No	ID from a previous response for multi-turn conversations
version	number	No	Specific published version to use (positive integer)

Input Formats

Simple string:

{
  "model": "support-bot",
  "input": "Hello, I need help"
}

Message array:

{
  "model": "support-bot",
  "input": [
    {
      "role": "user",
      "content": "Hello, I need help"
    }
  ]
}

Structured content array:

{
  "model": "support-bot",
  "input": [
    {
      "role": "user",
      "content": [
        { "type": "input_text", "text": "Hello, I need help" }
      ]
    }
  ]
}

Response

{
  "id": "resp_64a1b2c3d4e5f6",
  "object": "response",
  "model": "support-bot",
  "output": [
    {
      "type": "message",
      "role": "assistant",
      "content": [
        {
          "type": "output_text",
          "text": "To reset your password, go to Settings > Security..."
        }
      ]
    }
  ],
  "status": "completed",
  "usage": {
    "input_tokens": 12,
    "output_tokens": 45,
    "total_tokens": 57
  },
  "created_at": 1709500000,
  "previous_response_id": null
}

Multi-Turn Conversations

Use previous_response_id to continue a conversation:

{
  "model": "support-bot",
  "input": "Can you elaborate on the security settings?",
  "previous_response_id": "resp_64a1b2c3d4e5f6"
}

The response ID follows the format resp_{conversationId}. The gateway automatically tracks conversation history and provides context to the agent.

Agent Features

Agents configured in the dashboard can include:

Feature	Description
System Prompt	Base instructions for the agent
Tools	Bound tools from the unified tool system or MCP servers
Guardrails	Input/output validation rules
RAG Modules	Retrieval-augmented generation sources
Temperature / Top-P	Model parameter overrides
Max Tokens	Output length limit
Versioning	Publish and pin specific agent configurations

Errors

Status	Description
400	Missing model or input field, or agent is not active
401	Invalid API token
404	Agent not found, or previous_response_id references invalid conversation
500	Internal error during agent execution