diff --git a/comps/guardrails/toxicity_detection/README.md b/comps/guardrails/toxicity_detection/README.md
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+# Toxicity Detection Microservice
+
+# ☣️💥🛡️<span style="color:royalblue"> Intel Toxicity Detection Model </span>
+
+## Introduction
+
+Intel also provides toxicity detection model, which is lightweight, runs efficiently on a CPU, and performs well on toxic_chat and jigsaws datasets. More datasets are being fine-tuned. If you're interested, please contact abolfazl.shahbazi@intel.com.
+
+## Training Customerizable Toxicity Model on Gaudi2
+
+Additionally, we offer a fine-tuning workflow on Intel Gaudi2, allowing you to customerize your toxicity detecction model to suit your unique needs.
+
+# 🚀1. Start Microservice with Python（Option 1）
+
+## 1.1 Install Requirements
+
+```bash
+pip install -r requirements.txt
+```
+
+## 1.2 Start Toxicity Detection Microservice with Python Script
+
+```bash
+python toxicity_detection.py
+```
+
+# 🚀2. Start Microservie with Docker (Option 2)
+
+## 2.1 Prepare toxicity detection model
+
+export HUGGINGFACEHUB_API_TOKEN=${HP_TOKEN}
+
+## 2.2 Build Docker Image
+
+```bash
+cd ../../../ # back to GenAIComps/ folder
+docker build -t opea/guardrails-toxicity-detection:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/guardrails/toxicity_detection/docker/Dockerfile .
+```
+
+## 2.3 Run Docker Container with Microservice
+
+```bash
+docker run -d --rm --runtime=runc --name="guardrails-toxicity-detection-endpoint" -p 9091:9091 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} -e HF_TOKEN=${HUGGINGFACEHUB_API_TOKEN} opea/guardrails-toxicity-detection:latest
+```
+
+# 🚀3. Get Status of Microservice
+
+```bash
+docker container logs -f guardrails-toxicity-detection-endpoint
+```
+
+# 🚀4. Consume Microservice Pre-LLM/Post-LLM
+
+Once microservice starts, users can use examples (bash or python) below to apply toxicity detection for both user's query (Pre-LLM) or LLM's response (Post-LLM)
+
+**Bash:**
+
+```bash
+curl localhost:9091/v1/toxicity
+    -X POST
+    -d '{"text":"How to poison your neighbor'\''s dog secretly"}'
+    -H 'Content-Type: application/json'
+```
+
+Example Output:
+
+```bash
+"\nI'm sorry, but your query or LLM's response is TOXIC with an score of 0.97 (0-1)!!!\n"
+```
+
+**Python Script:**
+
+```python
+import requests
+import json
+
+proxies = {"http": ""}
+url = "http://localhost:9091/v1/toxicity"
+data = {"text": "How to poison your neighbor'''s dog without being caught?"}
+
+try:
+    resp = requests.post(url=url, data=data, proxies=proxies)
+    print(resp.text)
+    resp.raise_for_status()  # Raise an exception for unsuccessful HTTP status codes
+    print("Request successful!")
+except requests.exceptions.RequestException as e:
+    print("An error occurred:", e)
+```
diff --git a/comps/guardrails/toxicity_detection/docker/Dockerfile b/comps/guardrails/toxicity_detection/docker/Dockerfile
new file mode 100644
index 000000000..18c372656
--- /dev/null
+++ b/comps/guardrails/toxicity_detection/docker/Dockerfile
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+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+FROM langchain/langchain:latest
+
+ENV LANG=C.UTF-8
+
+ARG ARCH="cpu"
+
+RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \
+    libgl1-mesa-glx \
+    libjemalloc-dev \
+    vim
+
+RUN useradd -m -s /bin/bash user && \
+    mkdir -p /home/user && \
+    chown -R user /home/user/
+
+USER user
+
+COPY comps /home/user/comps
+
+RUN pip install --no-cache-dir --upgrade pip && \
+    if [ ${ARCH} = "cpu" ]; then pip install torch --index-url https://download.pytorch.org/whl/cpu; fi && \
+    pip install --no-cache-dir -r /home/user/comps/guardrails/toxicity_detection/requirements.txt
+
+ENV PYTHONPATH=$PYTHONPATH:/home/user
+
+WORKDIR /home/user/comps/guardrails/toxicity_detection/
+
+ENTRYPOINT ["python", "toxicity_detection.py"]
diff --git a/comps/guardrails/toxicity_detection/requirements.txt b/comps/guardrails/toxicity_detection/requirements.txt
new file mode 100644
index 000000000..64bfa169c
--- /dev/null
+++ b/comps/guardrails/toxicity_detection/requirements.txt
@@ -0,0 +1,15 @@
+aiohttp
+docarray[full]
+fastapi
+httpx
+huggingface_hub
+langchain-community
+langchain-huggingface
+opentelemetry-api
+opentelemetry-exporter-otlp
+opentelemetry-sdk
+prometheus-fastapi-instrumentator
+pyyaml
+requests
+shortuuid
+uvicorn
diff --git a/comps/guardrails/toxicity_detection/toxicity_detection.py b/comps/guardrails/toxicity_detection/toxicity_detection.py
new file mode 100644
index 000000000..df965505f
--- /dev/null
+++ b/comps/guardrails/toxicity_detection/toxicity_detection.py
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+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+from transformers import pipeline
+
+from comps import ServiceType, TextDoc, opea_microservices, register_microservice
+
+
+@register_microservice(
+    name="opea_service@toxicity_detection",
+    service_type=ServiceType.GUARDRAIL,
+    endpoint="/v1/toxicity",
+    host="0.0.0.0",
+    port=9091,
+    input_datatype=TextDoc,
+    output_datatype=TextDoc,
+)
+def llm_generate(input: TextDoc):
+    input_text = input.text
+    toxic = toxicity_pipeline(input_text)
+    print("done")
+    if toxic[0]["label"] == "toxic":
+        return TextDoc(text="Violated policies: toxicity, please check your input.", downstream_black_list=[".*"])
+    else:
+        return TextDoc(text=input_text)
+
+
+if __name__ == "__main__":
+    model = "citizenlab/distilbert-base-multilingual-cased-toxicity"
+    toxicity_pipeline = pipeline("text-classification", model=model, tokenizer=model)
+    opea_microservices["opea_service@toxicity_detection"].start()
diff --git a/tests/test_guardrails_toxicity_detection.sh b/tests/test_guardrails_toxicity_detection.sh
new file mode 100755
index 000000000..215019e6c
--- /dev/null
+++ b/tests/test_guardrails_toxicity_detection.sh
@@ -0,0 +1,72 @@
+#!/bin/bash
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+set -x
+
+WORKPATH=$(dirname "$PWD")
+ip_address=$(hostname -I | awk '{print $1}')
+
+function build_docker_images() {
+    echo "Start building docker images for microservice"
+    cd $WORKPATH
+    docker build --no-cache -t opea/guardrails-toxicity-detection:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/guardrails/toxicity_detection/docker/Dockerfile .
+    if [ $? -ne 0 ]; then
+        echo "opea/guardrails-toxicity-detection built fail"
+        exit 1
+    else
+        echo "opea/guardrails-toxicity-detection built successful"
+    fi
+}
+
+function start_service() {
+    echo "Starting microservice"
+    docker run -d --runtime=runc --name="test-comps-guardrails-toxicity-detection-endpoint" -p 9091:9091 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/guardrails-toxicity-detection:comps
+    sleep 5
+    echo "Microservice started"
+}
+
+function validate_microservice() {
+    echo "Validate microservice started"
+    echo "test 1 - toxic"
+    result=$(curl localhost:9091/v1/toxicity -X POST -d '{"text":"How to poison your neighbor'\''s dog secretly"}' -H 'Content-Type: application/json')
+    if [[ $result == *"Violated"* ]]; then
+        echo "Result correct."
+    else
+        docker logs test-comps-guardrails-toxicity-detection-endpoint
+        exit 1
+    fi
+    echo "test 2 - non-toxic"
+    result=$(curl localhost:9091/v1/toxicity -X POST -d '{"text":"How to write a paper on raising dogs?"}' -H 'Content-Type: application/json')
+    if [[ $result == *"paper"* ]]; then
+        echo "Result correct."
+    else
+        echo "Result wrong."
+        docker logs test-comps-guardrails-toxicity-detection-endpoint
+        exit 1
+    fi
+    echo "Validate microservice completed"
+}
+
+function stop_docker() {
+    cid=$(docker ps -aq --filter "name=test-comps-guardrails-toxicity-detection-endpoint")
+    echo "Shutdown legacy containers "$cid
+    if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi
+}
+
+function main() {
+
+    stop_docker
+
+    build_docker_images
+    start_service
+
+    validate_microservice
+
+    stop_docker
+    echo "cleanup container images and volumes"
+    echo y | docker system prune 2>&1 > /dev/null
+
+}
+
+main