diff --git a/examples/custom_tools/pid.png b/examples/custom_tools/pid.png
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diff --git a/examples/custom_tools/pid_template.png b/examples/custom_tools/pid_template.png
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diff --git a/examples/custom_tools/run_custom_tool.py b/examples/custom_tools/run_custom_tool.py
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+++ b/examples/custom_tools/run_custom_tool.py
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+import vision_agent as va
+from vision_agent.image_utils import get_image_size, normalize_bbox
+from vision_agent.tools import Tool, register_tool
+
+from template_match import template_matching_with_rotation
+
+
+@register_tool
+class TemplateMatch(Tool):
+    name = "template_match_"
+    description = "'template_match_' takes a template image and finds all locations where that template appears in the input image."
+    usage = {
+        "required_parameters": [
+            {"name": "target_image", "type": "str"},
+            {"name": "template_image", "type": "str"},
+        ],
+        "examples": [
+            {
+                "scenario": "Can you detect the location of the template in the target image? Image name: target.png Reference image: template.png",
+                "parameters": {
+                    "target_image": "target.png",
+                    "template_image": "template.png",
+                },
+            },
+        ],
+    }
+
+    def __call__(self, target_image: str, template_image: str) -> dict:
+        image_size = get_image_size(target_image)
+        matches = template_matching_with_rotation(target_image, template_image)
+        matches["bboxes"] = [
+            normalize_bbox(box, image_size) for box in matches["bboxes"]
+        ]
+        return matches
+
+
+if __name__ == "__main__":
+    agent = va.agent.VisionAgent(verbose=True)
+    resp, tools = agent.chat_with_workflow(
+        [
+            {
+                "role": "user",
+                "content": "Can you find the locations of the pid_template.png in pid.png and tell me if any are nearby 'NOTE 5'?",
+            }
+        ],
+        image="pid.png",
+        reference_data={"image": "pid_template.png"},
+        visualize_output=True,
+    )
diff --git a/examples/custom_tools/template_match.py b/examples/custom_tools/template_match.py
new file mode 100644
index 00000000..1dd9fbe0
--- /dev/null
+++ b/examples/custom_tools/template_match.py
@@ -0,0 +1,96 @@
+import cv2
+import numpy as np
+import torch
+from torchvision.ops import nms
+
+
+def rotate_image(mat, angle):
+    """
+    Rotates an image (angle in degrees) and expands image to avoid cropping
+    """
+
+    height, width = mat.shape[:2]  # image shape has 3 dimensions
+    image_center = (
+        width / 2,
+        height / 2,
+    )  # getRotationMatrix2D needs coordinates in reverse order (width, height) compared to shape
+
+    rotation_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)
+
+    # rotation calculates the cos and sin, taking absolutes of those.
+    abs_cos = abs(rotation_mat[0, 0])
+    abs_sin = abs(rotation_mat[0, 1])
+
+    # find the new width and height bounds
+    bound_w = int(height * abs_sin + width * abs_cos)
+    bound_h = int(height * abs_cos + width * abs_sin)
+
+    # subtract old image center (bringing image back to origo) and adding the new image center coordinates
+    rotation_mat[0, 2] += bound_w / 2 - image_center[0]
+    rotation_mat[1, 2] += bound_h / 2 - image_center[1]
+
+    # rotate image with the new bounds and translated rotation matrix
+    rotated_mat = cv2.warpAffine(mat, rotation_mat, (bound_w, bound_h))
+    return rotated_mat
+
+
+def template_matching_with_rotation(
+    main_image_path: str,
+    template_path: str,
+    max_rotation: int = 360,
+    step: int = 90,
+    threshold: float = 0.75,
+    visualize: bool = False,
+) -> dict:
+    main_image = cv2.imread(main_image_path)
+    template = cv2.imread(template_path)
+    template_height, template_width = template.shape[:2]
+
+    # Convert images to grayscale
+    main_image_gray = cv2.cvtColor(main_image, cv2.COLOR_BGR2GRAY)
+    template_gray = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
+
+    boxes = []
+    scores = []
+
+    for angle in range(0, max_rotation, step):
+        # Rotate the template
+        rotated_template = rotate_image(template_gray, angle)
+
+        # Perform template matching
+        result = cv2.matchTemplate(
+            main_image_gray,
+            rotated_template,
+            cv2.TM_CCOEFF_NORMED,
+        )
+
+        y_coords, x_coords = np.where(result >= threshold)
+        for x, y in zip(x_coords, y_coords):
+            boxes.append(
+                (x, y, x + rotated_template.shape[1], y + rotated_template.shape[0])
+            )
+            scores.append(result[y, x])
+
+    indices = (
+        nms(
+            torch.tensor(boxes).float(),
+            torch.tensor(scores).float(),
+            0.2,
+        )
+        .numpy()
+        .tolist()
+    )
+    boxes = [boxes[i] for i in indices]
+    scores = [scores[i] for i in indices]
+
+    if visualize:
+        # Draw a rectangle around the best match
+        for box in boxes:
+            cv2.rectangle(main_image, (box[0], box[1]), (box[2], box[3]), 255, 2)
+
+        # Display the result
+        cv2.imshow("Best Match", main_image)
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+
+    return {"bboxes": boxes, "scores": scores}