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AiTechWorlds
AiTechWorlds
Visualize Kruskal's MST sorting edges and using union-find to avoid cycles, with synced code and live weight.
def kruskal(edges, n): # edges: (w, u, v)
p = list(range(n))
def find(x):
while p[x] != x: p[x] = p[p[x]]; x = p[x]
return x
total = 0
for w, u, v in sorted(edges):
if find(u) != find(v):
p[find(u)] = find(v); total += w
return totalKruskal's algorithm builds a minimum spanning tree by sorting all edges and adding the next cheapest edge that does not form a cycle, using a union-find structure.
| Time | O(E log E) |
| Space | O(V) |
| Graph | Undirected, weighted |
The Kruskal's Algorithm Visualizer animates how Kruskal's minimum spanning tree (MST) works: it sorts all edges by weight, then adds each next-cheapest edge if it does not create a cycle, using a union-find (disjoint set) structure. Kruskal's runs in O(E log E) time, uses O(V) space, and is ideal for sparse graphs.
Generate a graph
Click New Graph or set the node-count slider to create a random weighted graph.
Play the animation
Press Play to watch edges considered in weight order; cycle-forming edges are skipped.
Step and scrub
Use Step Forward/Back or the timeline to inspect each accept/skip decision.
Read the synced code
The highlighted pseudocode line tracks the union-find logic; switch tabs for C++, Java, Python, or JavaScript.
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Last reviewed on June 14, 2026 by the AiTechWorlds Tools Team. All processing runs locally in your browser.