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Teaching Mistral Agents to Say No: Content Moderation from Prompt to Response
In this tutorial, we’ll implement content moderation guardrails for Mistral agents to ensure safe and policy-compliant interactions. By using Mistral’s moderation APIs, we’ll validate both the user input and the agent’s response against categories like financial advice, self-harm, PII, and more. This helps prevent harmful or inappropriate content from being generated or processed — a […] The post Teaching Mistral Agents to Say No: Content Moderation from Prompt to Response appeared first on MarkTechPost.
In this tutorial, we’ll implement content moderation guardrails for Mistral agents to ensure safe and policy-compliant interactions. By using Mistral’s moderation APIs, we’ll validate both the user input and the agent’s response against categories like financial advice, self-harm, PII, and more. This helps prevent harmful or inappropriate content from being generated or processed — a key step toward building responsible and production-ready AI systems.
The categories are mentioned in the table below:
Setting up dependencies
Install the Mistral library
pip install mistralaiLoading the Mistral API Key
You can get an API key from https://console.mistral.ai/api-keys
from getpass import getpass
MISTRAL_API_KEY = getpass('Enter Mistral API Key: ')Creating the Mistral client and Agent
We’ll begin by initializing the Mistral client and creating a simple Math Agent using the Mistral Agents API. This agent will be capable of solving math problems and evaluating expressions.
from mistralai import Mistral
client = Mistral(api_key=MISTRAL_API_KEY)
math_agent = client.beta.agents.create(
model="mistral-medium-2505",
description="An agent that solves math problems and evaluates expressions.",
name="Math Helper",
instructions="You are a helpful math assistant. You can explain concepts, solve equations, and evaluate math expressions using the code interpreter.",
tools=[{"type": "code_interpreter"}],
completion_args={
"temperature": 0.2,
"top_p": 0.9
}
)Creating Safeguards
Getting the Agent response
Since our agent utilizes the code_interpreter tool to execute Python code, we’ll combine both the general response and the final output from the code execution into a single, unified reply.
def get_agent_response(response) -> str:
general_response = response.outputs[0].content if len(response.outputs) > 0 else ""
code_output = response.outputs[2].content if len(response.outputs) > 2 else ""
if code_output:
return f"{general_response}\n\n
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