Andrii Bidochko

• Updated: March 21, 2026
• 2 min read

Adding Real‑Time Sentiment Analysis to Your OpenClaw Customer Support Agent

Adding Real‑Time Sentiment Analysis to Your OpenClaw Customer Support Agent

Understanding how customers feel in real time can dramatically improve the quality of support you provide. In this guide we walk you through selecting a sentiment‑analysis model, wiring it into the OpenClaw pipeline, handling streaming results, and visualising sentiment scores on your dashboards.

1. Selecting a Sentiment Model

OpenClaw works with any model that exposes a simple predict(text) API. For most developers the Hugging Face distilbert-base-uncased-finetuned-sst-2-english model offers a good balance of speed and accuracy. If you need higher throughput you can switch to a lightweight distilled model or even a custom TensorFlow Lite model.

2. Integrating the Model into the OpenClaw Pipeline

OpenClaw processes incoming messages through a series of agents. Add a new SentimentAgent that receives the raw user message, forwards it to the model, and attaches the sentiment label and confidence score to the message context.

class SentimentAgent(Agent):
    def __init__(self, model):
        self.model = model
    async def handle(self, msg):
        sentiment, score = self.model.predict(msg.text)
        msg.context["sentiment"] = {"label": sentiment, "score": score}
        return msg

Register the agent in pipeline.yaml right after the IntentRecognitionAgent so that downstream agents can use the sentiment data.

3. Handling Streaming Results

When you enable OpenClaw’s streaming mode, each partial token is emitted as a separate event. To keep sentiment up‑to‑date, run the model on the accumulated text each time a new token arrives. The SentimentAgent can be configured to debounce calls (e.g., every 300 ms) to avoid excessive API usage.

4. Visualising Sentiment Scores

Push the sentiment data to your observability stack (Grafana, Datadog, etc.) using OpenClaw’s MetricsAgent. Example Grafana dashboard panels:

• Time series of average sentiment score per hour.
• Bar chart of sentiment distribution (positive, neutral, negative) per support channel.

For a quick start, you can use the built‑in /metrics endpoint and point Grafana at http://your‑openclaw:8000/metrics.

5. Publishing the Article

Make sure to include a contextual internal link to help readers explore related content:

How to host OpenClaw on UBOS

With the steps above, you now have a fully‑functional real‑time sentiment analysis layer in your OpenClaw‑powered support agent.

Andrii Bidochko

CTO UBOS

Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.