pydo.genai.attach_agent_guardrails()

Generated on 8 May 2026 from pydo version v0.34.0

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Usage 

client.genai.attach_agent_guardrails(
    agent_uuid="\"123e4567-e89b-12d3-a456-426614174000\"",
    body={
        "agent_uuid": "\"12345678-1234-1234-1234-123456789012\"",
        "guardrails": [...],
    },
)
Returns JSONRaises HttpResponseError

Description

To attach guardrails to an agent, send a POST request to /v2/gen-ai/agents/{agent_uuid}/guardrails.

Parameters

agent_uuid string required

The UUID of the agent.

agent_uuid string optional

Example: "12345678-1234-1234-1234-123456789012"

The UUID of the agent.

guardrails array of objects optional

The list of guardrails to attach.

Show child properties
guardrail_uuid string optional

Example: 123e4567-e89b-12d3-a456-426614174000

Guardrail uuid

priority integer optional

Example: 123

Priority of the guardrail

Request Sample

Show Request Sample 
import os
from pydo import Client

client = Client(token=os.environ.get("DIGITALOCEAN_TOKEN"))

req = {
  "agent_uuid": "\"12345678-1234-1234-1234-123456789012\"",
  "guardrails": [
    {
      "guardrail_uuid": "123e4567-e89b-12d3-a456-426614174000",
      "priority": 123
    }
  ]
}

resp = client.genai.attach_agent_guardrails(agent_uuid="\"123e4567-e89b-12d3-a456-426614174000\"", body=req)

Response Example

Show Response Example 
{
  "agent": {
    "anthropic_api_key": {
      "created_at": "2023-01-01T00:00:00Z",
      "created_by": "12345",
      "deleted_at": "2023-01-01T00:00:00Z",
      "name": "example name",
      "updated_at": "2023-01-01T00:00:00Z",
      "uuid": "123e4567-e89b-12d3-a456-426614174000"
    },
    "api_key_infos": [],
    "api_keys": [],
    "chatbot": {
      "allowed_domains": [
        "example string"
      ],
      "button_background_color": "example string",
      "logo": "example string",
      "name": "example name",
      "primary_color": "example string",
      "secondary_color": "example string",
      "starting_message": "example string"
    },
    "chatbot_identifiers": [],
    "child_agents": [],
    "conversation_logs_enabled": true,
    "created_at": "2023-01-01T00:00:00Z",
    "deployment": {
      "created_at": "2023-01-01T00:00:00Z",
      "name": "example name",
      "status": "STATUS_UNKNOWN",
      "updated_at": "2023-01-01T00:00:00Z",
      "url": "example string",
      "uuid": "123e4567-e89b-12d3-a456-426614174000",
      "visibility": "VISIBILITY_UNKNOWN"
    },
    "description": "example string",
    "functions": [],
    "guardrails": [],
    "if_case": "example string",
    "instruction": "example string",
    "k": 123,
    "knowledge_bases": [],
    "logging_config": {
      "galileo_project_id": "123e4567-e89b-12d3-a456-426614174000",
      "galileo_project_name": "example name",
      "insights_enabled": true,
      "insights_enabled_at": "2023-01-01T00:00:00Z",
      "log_stream_id": "123e4567-e89b-12d3-a456-426614174000",
      "log_stream_name": "example name"
    },
    "max_tokens": 123,
    "mcp_servers": [],
    "model": {
      "capabilities": [
        "example string"
      ],
      "context_window": "12345",
      "created_at": "2023-01-01T00:00:00Z",
      "endpoints": [],
      "inference_name": "example name",
      "inference_version": "example string",
      "is_foundational": true,
      "kb_default_chunk_size": 123,
      "kb_max_chunk_size": 123,
      "kb_min_chunk_size": 123,
      "lifecycle_status": "example string",
      "name": "example name",
      "parameter_count": 123,
      "parent_uuid": "123e4567-e89b-12d3-a456-426614174000",
      "provider": "MODEL_PROVIDER_DIGITALOCEAN",
      "reasoning_efforts": [
        "example string"
      ],
      "settings": [],
      "thinking": true,
      "type": "example string",
      "updated_at": "2023-01-01T00:00:00Z",
      "upload_complete": true,
      "url": "example string",
      "usecases": [
        "MODEL_USECASE_AGENT",
        "MODEL_USECASE_GUARDRAIL"
      ],
      "uuid": "123e4567-e89b-12d3-a456-426614174000"
    },
    "model_provider_key": {
      "api_key_uuid": "123e4567-e89b-12d3-a456-426614174000",
      "created_at": "2023-01-01T00:00:00Z",
      "created_by": "12345",
      "deleted_at": "2023-01-01T00:00:00Z",
      "models": [],
      "name": "example name",
      "provider": "MODEL_PROVIDER_DIGITALOCEAN",
      "updated_at": "2023-01-01T00:00:00Z"
    },
    "model_router": {
      "created_at": "2023-01-01T00:00:00Z",
      "description": "example string",
      "name": "example name",
      "regions": [
        "example string"
      ],
      "updated_at": "2023-01-01T00:00:00Z",
      "uuid": "123e4567-e89b-12d3-a456-426614174000"
    },
    "name": "example name",
    "openai_api_key": {
      "created_at": "2023-01-01T00:00:00Z",
      "created_by": "12345",
      "deleted_at": "2023-01-01T00:00:00Z",
      "models": [],
      "name": "example name",
      "updated_at": "2023-01-01T00:00:00Z",
      "uuid": "123e4567-e89b-12d3-a456-426614174000"
    },
    "parent_agents": [],
    "project_id": "123e4567-e89b-12d3-a456-426614174000",
    "provide_citations": true,
    "reasoning_effort": "example string",
    "region": "example string",
    "retrieval_method": "RETRIEVAL_METHOD_UNKNOWN",
    "route_created_at": "2023-01-01T00:00:00Z",
    "route_created_by": "12345",
    "route_name": "example name",
    "route_uuid": "123e4567-e89b-12d3-a456-426614174000",
    "tags": [
      "example string"
    ],
    "temperature": 123,
    "template": {
      "created_at": "2023-01-01T00:00:00Z",
      "description": "example string",
      "guardrails": [],
      "instruction": "example string",
      "k": 123,
      "knowledge_bases": [],
      "long_description": "\"Enhance your customer service with an AI agent designed to provide consistent, helpful, and accurate support across multiple channels. This template creates an agent that can answer product questions, troubleshoot common issues, process simple requests, and maintain a friendly, on-brand voice throughout customer interactions. Reduce response times, handle routine inquiries efficiently, and ensure your customers feel heard and helped.\"",
      "max_tokens": 123,
      "name": "example name",
      "short_description": "\"This template has been designed with question-answer and conversational use cases in mind. It comes with validated agent instructions, fine-tuned model settings, and preconfigured guardrails defined for customer support-related use cases.\"",
      "summary": "example string",
      "tags": [
        "example string"
      ],
      "temperature": 123,
      "template_type": "AGENT_TEMPLATE_TYPE_STANDARD",
      "top_p": 123,
      "updated_at": "2023-01-01T00:00:00Z",
      "uuid": "123e4567-e89b-12d3-a456-426614174000"
    },
    "thinking_token_budget": 123,
    "top_p": 123,
    "updated_at": "2023-01-01T00:00:00Z",
    "url": "example string",
    "user_id": "12345",
    "uuid": "123e4567-e89b-12d3-a456-426614174000",
    "version_hash": "example string",
    "vpc_egress_ips": [
      "example string"
    ],
    "vpc_uuid": "\"12345678-1234-1234-1234-123456789012\"",
    "workspace": {
      "agents": [],
      "created_at": "2023-01-01T00:00:00Z",
      "created_by": "12345",
      "created_by_email": "[email protected]",
      "deleted_at": "2023-01-01T00:00:00Z",
      "description": "example string",
      "evaluation_test_cases": [],
      "name": "example name",
      "updated_at": "2023-01-01T00:00:00Z",
      "uuid": "123e4567-e89b-12d3-a456-426614174000"
    }
  }
}

More Information

See /v2/gen-ai/agents/{agent_uuid}/guardrails in the API reference for additional detail on responses, headers, parameters, and more.

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