--- title: "DynamoAI Guardrails" description: "import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';" type: skill canonical_url: https://claudary.paisolsolutions.com/skills/dynamoai source: "Claudary" difficulty: intermediate author: "Claude Code Knowledge Pack" date: 2026-07-10T11:24:04.115Z license: CC-BY-4.0 attribution: "DynamoAI Guardrails — Claudary (https://claudary.paisolsolutions.com/skills/dynamoai)" --- # DynamoAI Guardrails import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; ## Overview import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; # DynamoAI Guardrails LiteLLM supports DynamoAI guardrails for content moderation and policy enforcement on LLM inputs and outputs. ## Quick Start ### 1. Define Guardrails on your LiteLLM config.yaml Define your guardrails under the `guardrails` section: ```yaml showLineNumbers title="config.yaml" model_list: - model_name: gpt-4 litellm_params: model: openai/gpt-4 api_key: os.environ/OPENAI_API_KEY guardrails: - guardrail_name: "dynamoai-guard" litellm_params: guardrail: dynamoai mode: "pre_call" api_key: os.environ/DYNAMOAI_API_KEY ``` #### Supported values for `mode` - `pre_call` - Run **before** LLM call, on **input** - `post_call` - Run **after** LLM call, on **output** - `during_call` - Run **during** LLM call, on **input**. Same as `pre_call` but runs in parallel as LLM call ### 2. Set Environment Variables ```bash export DYNAMOAI_API_KEY="your-api-key" # Optional: Set policy IDs via environment variable (comma-separated) export DYNAMOAI_POLICY_IDS="policy-id-1,policy-id-2,policy-id-3" ``` ### 3. Start LiteLLM Gateway ```shell litellm --config config.yaml --detailed_debug ``` ### 4. Test Request **[Langchain, OpenAI SDK Usage Examples](../proxy/user_keys#request-format)** ```shell showLineNumbers title="Successful Request" curl -i http://localhost:4000/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer sk-1234" \\ -d '{ "model": "gpt-4", "messages": [ {"role": "user", "content": "What is the capital of France?"} ], "guardrails": ["dynamoai-guard"] }' ``` **Response: HTTP 200 Success** Content passes all policy checks and is allowed through. ```shell showLineNumbers title="Blocked Request" curl -i http://localhost:4000/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer sk-1234" \\ -d '{ "model": "gpt-4", "messages": [ {"role": "user", "content": "Content that violates policy"} ], "guardrails": ["dynamoai-guard"] }' ``` **Expected Response on Block: HTTP 400 Error** ```json showLineNumbers { "error": { "message": "Guardrail failed: 1 violation(s) detected\\n\\n- POLICY NAME:\\n Action: BLOCK\\n Method: TOXICITY\\n Description: Policy description\\n Policy ID: policy-id-123", "type": "None", "param": "None", "code": "400" } } ``` ## Advanced Configuration ### Specify Policy IDs Configure specific DynamoAI policies to apply: ```yaml showLineNumbers title="config.yaml" guardrails: - guardrail_name: "dynamoai-policies" litellm_params: guardrail: dynamoai mode: "pre_call" api_key: os.environ/DYNAMOAI_API_KEY policy_ids: - "policy-id-1" - "policy-id-2" - "policy-id-3" ``` ### Custom API Base Specify a custom DynamoAI API endpoint: ```yaml showLineNumbers title="config.yaml" guardrails: - guardrail_name: "dynamoai-custom" litellm_params: guardrail: dynamoai mode: "pre_call" api_key: os.environ/DYNAMOAI_API_KEY api_base: "https://custom.dynamo.ai" ``` ### Model ID for Tracking Add a model ID for tracking and logging purposes: ```yaml showLineNumbers title="config.yaml" guardrails: - guardrail_name: "dynamoai-tracked" litellm_params: guardrail: dynamoai mode: "pre_call" api_key: os.environ/DYNAMOAI_API_KEY model_id: "gpt-4-production" ``` ### Input and Output Guardrails Configure separate guardrails for input and output: ```yaml showLineNumbers title="config.yaml" guardrails: # Input guardrail - guardrail_name: "dynamoai-input" litellm_params: guardrail: dynamoai mode: "pre_call" api_key: os.environ/DYNAMOAI_API_KEY # Output guardrail - guardrail_name: "dynamoai-output" litellm_params: guardrail: dynamoai mode: "post_call" api_key: os.environ/DYNAMOAI_API_KEY ``` ## Configuration Options | Parameter | Type | Description | Default | |-----------|------|-------------|---------| | `api_key` | string | DynamoAI API key (required) | `DYNAMOAI_API_KEY` env var | | `api_base` | string | DynamoAI API base URL | `https://api.dynamo.ai` | | `policy_ids` | array | List of DynamoAI policy IDs to apply (optional) | `DYNAMOAI_POLICY_IDS` env var (comma-separated) | | `model_id` | string | Model ID for tracking/logging | `DYNAMOAI_MODEL_ID` env var | | `mode` | string | When to run: `pre_call`, `post_call`, or `during_call` | Required | ## Observability DynamoAI guardrail logs include: - **guardrail_status**: `success`, `guardrail_intervened`, or `guardrail_failed_to_respond` - **guardrail_provider**: `dynamoai` - **guardrail_json_response**: Full API response with policy details - **duration**: Time taken for guardrail check - **start_time** and **end_time**: Timestamps These logs are available through your configured LiteLLM logging callbacks. ## Error Handling The guardrail handles errors gracefully: - **API Failures**: Logs error and raises exception with status `guardrail_failed_to_respond` - **Policy Violations**: Raises `ValueError` with detailed violation information - **Invalid Configuration**: Raises `ValueError` on initialization if API key is missing ## Current Limitations - Only the `BLOCK` action is currently supported - `WARN`, `REDACT`, and `SANITIZE` actions are treated as success (pass through) ## Support For more information about DynamoAI: - Website: [https://dynamo.ai](https://dynamo.ai) - Documentation: Contact DynamoAI for API documentation --- Source: [Claudary](https://claudary.paisolsolutions.com/skills/dynamoai) · https://claudary.paisolsolutions.com