diff --git a/tools/psi-visualizer/.gitignore b/tools/psi-visualizer/.gitignore
new file mode 100644
index 00000000000..5ceb3864c29
--- /dev/null
+++ b/tools/psi-visualizer/.gitignore
@@ -0,0 +1 @@
+venv
diff --git a/tools/psi-visualizer/Makefile b/tools/psi-visualizer/Makefile
new file mode 100644
index 00000000000..98360625afd
--- /dev/null
+++ b/tools/psi-visualizer/Makefile
@@ -0,0 +1,18 @@
+
+check-requirements:
+	@command -v python3 >/dev/null 2>&1 || { echo >&2 "Python3 is required but it's not installed.  Aborting."; exit 1; }
+	@command -v pip >/dev/null 2>&1 || { echo >&2 "Pip is required but it's not installed.  Aborting."; exit 1; }
+
+prepare-env: check-requirements
+	@if [ ! -d "venv" ]; then \
+		python3 -m venv venv; \
+		. venv/bin/activate; \
+		pip install -r requirements.txt; \
+	fi
+
+# Print help message, how to use activate the environment
+activate-env: prepare-env
+	@echo -e "To activate the environment, run:\n\tsource venv/bin/activate"
+	@echo -e "To run the visualizer, run:\n\tpython3 visualize.py <path-to-psi-file>"
+	@echo -e "To deactivate the environment later, run:\n\tdeactivate"
+
diff --git a/tools/psi-visualizer/README.md b/tools/psi-visualizer/README.md
new file mode 100644
index 00000000000..d4f649647d3
--- /dev/null
+++ b/tools/psi-visualizer/README.md
@@ -0,0 +1,52 @@
+# PSI (Process Stall Information) Visualizer
+
+This tool visualizes PSI (Process Stall Information) data collected by the Linux
+kernel. PSI is a feature introduced in Linux 4.20 that provides information
+about various kinds of stalls that can happen in the kernel.
+For more information about PSI, see the
+[kernel documentation](https://www.kernel.org/doc/Documentation/accounting/psi.rst).
+
+The tool creates an interactive plot that shows the memory pressure statistics
+over time.
+
+It can be used to understand the dynamics of memory pressure in the system and
+to identify the processes that are causing the pressure.
+
+## Grabbing PSI data
+
+To collect PSI data that can be fed to the visualizer, you need to run the
+`psi-collector` tool. The tool is available in the
+[psi-collector](../../pkg/pillar/agentlog/cmd/psi-collector) directory.
+Documentation on how to use the tool is available in the tool's
+[README](../../pkg/pillar/agentlog/cmd/psi-collector/README.md).
+
+## Preparing the environment
+
+To build the PSI visualizer, you need to have the following dependencies
+installed:
+
+* Python 3
+* pip
+
+To install the dependencies, run:
+
+```sh
+make prepare-env
+```
+
+It will create a virtual environment in the `venv` directory and install the
+required dependencies.
+
+Then you have to activate the virtual environment:
+
+```sh
+source venv/bin/activate
+```
+
+## Running the PSI visualizer
+
+To run the PSI visualizer, run:
+
+```sh
+python3 visualize.py <path-to-psi-file>
+```
diff --git a/tools/psi-visualizer/requirements.txt b/tools/psi-visualizer/requirements.txt
new file mode 100644
index 00000000000..71d2d28e6c7
--- /dev/null
+++ b/tools/psi-visualizer/requirements.txt
@@ -0,0 +1,2 @@
+pandas
+plotly
diff --git a/tools/psi-visualizer/visualize.py b/tools/psi-visualizer/visualize.py
new file mode 100644
index 00000000000..8ca0577321f
--- /dev/null
+++ b/tools/psi-visualizer/visualize.py
@@ -0,0 +1,96 @@
+"""
+SPDX-License-Identifier: Apache-2.0
+Copyright (c) 2024 Zededa, Inc.
+
+This script reads the log file generated by the statistics collector and visualizes the memory
+pressure over time. The script uses Plotly to create interactive plots that can be viewed in a
+web browser.
+"""
+
+import sys
+import os
+
+import pandas as pd
+import plotly.graph_objects as go
+
+EXPECTED_HEADER = 'date time someAvg10 someAvg60 someAvg300 someTotal ' \
+                  'fullAvg10 fullAvg60 fullAvg300 fullTotal'
+
+
+def visualize_memory_pressure(log_file):
+    """
+    Visualizes the memory pressure over time using an interactive plot.
+    :param log_file: Path to the log file generated by the statistics collector.
+    :return: None
+    """
+    # Read the log file into a DataFrame
+    dataframe = pd.read_csv(log_file, sep=r'\s+')
+
+    # Combine 'date' and 'time' columns into a single 'Timestamp' column
+    dataframe['Timestamp'] = pd.to_datetime(dataframe['date'] + ' ' + dataframe['time'],
+                                            format='%Y-%m-%d %H:%M:%S')
+
+    # Drop the now redundant 'date' and 'time' columns
+    dataframe.drop(columns=['date', 'time'], inplace=True)
+
+    # Create interactive plots using Plotly
+    fig = go.Figure()
+
+    # Adding traces for 'some' values
+    fig.add_trace(go.Scatter(x=dataframe['Timestamp'], y=dataframe['someAvg10'], mode='lines',
+                             name='someAvg10', yaxis="y1"))
+    fig.add_trace(go.Scatter(x=dataframe['Timestamp'], y=dataframe['someAvg60'], mode='lines',
+                             name='someAvg60', yaxis="y1"))
+    fig.add_trace(go.Scatter(x=dataframe['Timestamp'], y=dataframe['someAvg300'], mode='lines',
+                             name='someAvg300', yaxis="y1"))
+
+    # Adding traces for 'full' values
+    fig.add_trace(go.Scatter(x=dataframe['Timestamp'], y=dataframe['fullAvg10'], mode='lines',
+                             name='fullAvg10', yaxis="y1"))
+    fig.add_trace(go.Scatter(x=dataframe['Timestamp'], y=dataframe['fullAvg60'], mode='lines',
+                             name='fullAvg60', yaxis="y1"))
+    fig.add_trace(go.Scatter(x=dataframe['Timestamp'], y=dataframe['fullAvg300'], mode='lines',
+                             name='fullAvg300', yaxis="y1"))
+
+    # Adding cumulative area plots for total values
+    fig.add_trace(go.Scatter(x=dataframe['Timestamp'], y=dataframe['someTotal'], mode='lines',
+                             name='someTotal', line={"width": 0.5, "color": 'rgb(131, 90, 241)'},
+                             stackgroup='one', yaxis="y2"))  # for area plot
+    fig.add_trace(go.Scatter(x=dataframe['Timestamp'], y=dataframe['fullTotal'], mode='lines',
+                             name='fullTotal', line={"width": 0.5, "color": 'rgb(255, 50, 50)'},
+                             stackgroup='two', yaxis="y2"))  # for area plot
+
+    # Update layout for better readability
+    fig.update_layout(
+        title="Memory Pressure Over Time",
+        xaxis_title="Timestamp",
+        yaxis_title="Values",
+        yaxis={"range": [0, 100], "title": "Pressure Averages"},
+        yaxis2={"title": "Total Values", "overlaying": "y", "side": "right"},
+        legend_title="Metrics",
+        hovermode="x unified"
+    )
+
+    # Show the interactive plot
+    fig.show()
+
+
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        print("Usage: python visualize.py <log_file>")
+        sys.exit(1)
+
+    # Check if the log file exists
+    if not os.path.exists(sys.argv[1]):
+        print(f"Error: Log file '{sys.argv[1]}' not found!")
+        sys.exit(1)
+
+    # Check the header of the log file
+    with open(sys.argv[1], encoding='utf-8') as f:
+        header = f.readline().strip()
+        if header != EXPECTED_HEADER:
+            print(f"Error: Invalid log file '{sys.argv[1]}'!")
+            sys.exit(1)
+
+    LOG_FILE_ARG = sys.argv[1]
+    visualize_memory_pressure(LOG_FILE_ARG)