A* Pathfinding in React JS: Animate Shortest Path in 2026

Introduction

Welcome to the world of pathfinding algorithms. In this article, I walk you through an implementation of the _A\ algorithm_ — one of the most widely used pathfinding algorithms in software engineering — built with React and hooks. A combines aspects of Dijkstra's algorithm and heuristic search to efficiently find the shortest path between nodes. As one of the common pathfinding algorithms, A* is favored for its balance between speed and accuracy in calculating distances.

This article is for React developers and frontend engineers who want to understand how A works under the hood and see a concrete React implementation with visual animation. You will learn the core math (g(n), h(n), f(n)), see React hooks managing the algorithm state, and understand the tradeoffs of using React for algorithmic visualization versus tools like Canvas or C++. For deeper reading on A and Dijkstra's algorithm, check out:

If you want to jump straight to the code, the full source is at github.com/MobileReality/astar and a live demo runs at mobilereality.github.io/astar.

About Pathfinding Algorithms

Pathfinding is a fundamental concept in computer science. We encounter it every day — whether navigating with Google Maps or intuitively determining a path between two points. Our ability to find an optimal path seems effortless, but mathematically it involves complex considerations: edges, cost paths, priority queues, and neighboring nodes.

In software development, various algorithms support graph traversal and help identify possible paths. Examples include Dijkstra's algorithm, breadth-first search, and greedy best-first search. One of the most popular is A, which efficiently balances heuristics and admissible heuristics. The algorithm has a long history and is widely regarded for its practical effectiveness. To illustrate its popularity, I ran a quick check using Google Trends — A clearly outperforms other pathfinding algorithms in search interest.

The applications of A* extend far beyond simple navigation. The algorithm powers robotics, games development, and even parts of NLP routing. Pathfinding challenges often involve abstract obstacles beyond physical barriers, making a solid understanding of nodes, trees, and steps essential. While machine learning and AI continue to advance, traditional pathfinding techniques often remain more effective for specific graph traversal tasks.

Fundamentals of the A* Algorithm

Let us explore the fundamentals of A. According to Wikipedia, A is an informed search algorithm or a best-first search, meaning it is formulated in terms of weighted graphs. Starting from a specific starting node of a graph, it aims to find a path to the goal node with the smallest cost (least distance, shortest time, etc.).

The key elements are the starting node, the goal node, and the node with the smallest cost at each step. These nodes are connected through a tree structure, where each decision leads to possible optimal paths. To find the best route, A* calculates costs using a heuristic function to estimate the remaining distance to the goal. The calculation follows this formula:

This formula consists of two primary components: g(n) and h(n).

g(n) calculates the cost from the starting node to the current node, plus the cost of moving to the nearest neighbor.
h(n) is the heuristic function, estimating the cost from the current node to the goal node.

The heuristic function plays a critical role in guiding the search. A good heuristic makes A* an efficient pathfinder. In my example, g(n) uses Pythagorean distance (square root calculation) to measure distance between square tiles. For h(n), I used the same Pythagorean calculation. This approach is not optimal but effectively demonstrates the concept. More advanced heuristics include:

Manhattan Distance
Diagonal Distance
Euclidean Distance
Euclidean Distance (Squared)

The choice of heuristic depends on your project and the type of pathfinding you are performing. Selecting an admissible heuristic ensures the search algorithm remains efficient and accurate. Here is a step-by-step animation of A* in action:

Legend:

The black square represents the player
The pink square represents the goal
Green squares are the neighbors currently being considered
Yellow squares form the chosen path
Gray squares represent nodes excluded during the current selection
The numbers inside the squares represent the F(n) value, calculated using g(n) and h(n)

The algorithm always selects the node with the lowest F(n) value, dynamically updating the cost as neighbors are evaluated. This ensures the most efficient path is chosen.

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Algorithm Pseudocode

A* can run faster or slower depending on the number of neighbours. In the example above, we can make it faster by excluding previously evaluated neighbours — though this brute-force variant may be faster in choosing the next neighbour without necessarily finding a shorter overall road. The tradeoff is between per-step speed and global path quality.

For this article, I created the animation above using Remotion — a useful tool for programmatic video generation in React.

Here is the high-level pseudocode for A*:

A_Star(start, goal, currentPos) {
    gScore = func which calculates g score
    hScore = func which calculates h score
    fScore = hScore(start) + gScore(start)

    playerPosition = start
    open = []

    getNeighbours = (position) => {
        for each neighbour of position {
            gScore = gScore(neighbour) + currentPos.gScore
            hScore = hScore(neighbour)
            fScore = gScore + hScore
        }
        return neighbours
    }

    while open is not empty {
        if player == goal
            break

        neighbours = getNeighbours(currentPos)
        open = [...open, ...neighbours]  // update list of available tiles to check

        tileToMove = getMinFScore(open)  // get min fScore from available tiles
        open.remove(tileToMove)          // remove chosen tile from available tiles

        player = tileToMove              // move player
    }

    return false
}

Now let me show how I implemented this in React. We need a Map on which our Player can move and reach the ending point from the starting point with proper obstacle avoidance. We start with hooks.

React Implementation: Player Hook

Here is the player hook that manages position and movement:

import { useState, useEffect } from 'react';
import { letCalculateLowerPosition, letCalculateHigherPosition } from '../utils/evaluateCalculations';

export const usePlayer = (startingPoint) => {
  const extendUserData = {
    gCost: 0,
    parent: null,
  };

  const [player, setPosition] = useState({
    ...startingPoint,
    ...extendUserData,
  });

  useEffect(() => {
    setPosition((prevValue) => ({
      ...prevValue,
      x: startingPoint.x,
      y: startingPoint.y,
    }));
  }, [startingPoint.x, startingPoint.y]);

  const move = (position) => {
    setPosition(position);
  };

  return {
    player,
    setPosition,
    move,
    extendUserData,
  };
};

The hook sets up the player's initial position and exposes a move utility. Other data is passed along for easier state management downstream.

Blockers Hook

Now the blockers — obstacles the player must avoid:

import { useState } from 'react';
import { maps } from '../constants/maps';

export const useBlockers = ({ dimension }) => {
  const [blockers, setBlockers] = useState([]);

  const calculateBlockers = () => {
    const calculate = () => {
      const coordinate = Math.round(Math.random() * dimension);
      if (coordinate !== 0) return coordinate - 1;
      return coordinate;
    };

    return new Array(dimension * 8).fill(0).map(() => ({
      x: calculate(),
      y: calculate(),
    }))
      .filter(({ x, y }) => (x !== 0 && y !== 0))
      .filter(({ x, y }) => (x !== dimension - 1 && y !== dimension - 1));
  };

  const setBlockersBasedOnGeneratedMap = (mapName) => {
    const blockersInMap = [];
    maps[mapName].reverse().forEach((row, yIndex) => {
      row.forEach((tile, xIndex) => {
        if (tile === '-') return;
        blockersInMap.push({ x: yIndex, y: xIndex });
      });
    });
    setBlockers(blockersInMap);
  };

  const setBlockersOnMap = () => {
    setBlockers(calculateBlockers());
  };

  const setTileAsBlocker = (tile) => {
    setBlockers((prevState) => prevState.concat(tile));
  };

  return {
    setBlockersOnMap,
    blockers,
    setTileAsBlocker,
    setBlockersBasedOnGeneratedMap,
  };
};

The main responsibility here is placing blockers on the map — either randomly or from predefined patterns.

Start and Goal Hook

A straightforward hook for managing start and goal points:

import { useState } from 'react';
import { START, GOAL } from '../constants';

export const useGoalAndStart = () => {
  const [start, setStart] = useState(START);
  const [isStartSetting, setIsStartSetting] = useState(false);
  const [goal, setGoal] = useState(GOAL);
  const [isGoalSetting, setIsGoalSetting] = useState(false);

  return {
    start,
    setStart,
    goal,
    setGoal,
    isStartSetting,
    isGoalSetting,
    setIsStartSetting,
    setIsGoalSetting,
  };
};

Self-explanatory — it holds start and goal coordinates plus flags for UI interaction.

Map Components: Tile, Row, Map

Before the calculation logic, here are the Map rendering components:

Tile.js

import React, { useMemo } from 'react';
import './Tile.css';

export const MemoTile = ({
  item, isBlocker, isOpen, isRoad, isGoal, isPath, isUserPosition,
  setTileAsBlocker, isSetting, isGoalSetting, isStartSetting,
  onSetStart, onSetGoal,
}) => {
  const classes = isBlocker ? 'block_tile' : 'tile';
  const isOpenClass = isOpen ? 'is_open' : '';
  const isRoadClass = isRoad ? 'is_road' : '';
  const isGoalClass = isGoal ? 'is_goal' : '';
  const isUserPositionClass = isUserPosition ? 'is_user' : '';
  const isPathClass = isPath ? 'is_path' : '';

  const memoIsRoadClass = useMemo(() => isRoadClass, [isRoadClass]);
  const memoIsGoalClass = useMemo(() => isGoalClass, [isGoalClass]);
  const memoIsOpenClass = useMemo(() => isOpenClass, [isOpenClass]);

  const resolveClickBehaviour = () => {
    if (isStartSetting) onSetStart({ x: item.x, y: item.y });
    if (isGoalSetting) onSetGoal({ x: item.x, y: item.y });
    if (isSetting) setTileAsBlocker({ x: item.x, y: item.y });
    return false;
  };

  return (
    <div
      onClick={resolveClickBehaviour}
      className={`size ${classes} ${memoIsOpenClass} ${memoIsRoadClass} ${memoIsGoalClass} ${isUserPositionClass} ${isPathClass}`}
    />
  );
};

export const Tile = React.memo(MemoTile);

The useMemo and React.memo choices are intentional. React is not inherently designed for motion planning or game engines. A Tile is the smallest element of the map, and during graph search operations each calculation can trigger the Tile to rerender. If a large number of elements continuously rerender, performance degrades quickly. React.memo and useMemo optimize rendering so only the necessary components update.

Class legend:

isOpenClass — open tile (a neighbour currently available for selection)
isGoalClass — the goal
isRoadClass — the road travelled so far, shown while the goal is not yet reached
isPathClass — the final reconstructed path after reaching the goal
isUserPositionClass — the player's current position

Row.js

import React from 'react';
import { Tile } from './Tile';
import './Row.css';

const MemoRow = ({
  x, columns, blockers, open, road, goal, path, userPosition,
  setTileAsBlocker, isSetting, isGoalSetting, isStartSetting,
  onSetGoal, onSetStart,
}) => {
  const columnsToRender = new Array(columns).fill(x);

  const isOpen = (y) => open.length > 0 && open.find((openTile) => openTile.y === y);
  const isBlocker = (y) => blockers.length > 0 && blockers.find((blocker) => blocker.y === y);
  const isRoad = (y) => road.length > 0 && road.find((roadTile) => roadTile.y === y);
  const isGoal = (y) => goal && goal.y === y;
  const isPath = (y) => path.length > 0 && path.find((pathTile) => pathTile.y === y);
  const isUserPosition = (ax, y) => userPosition.x === ax && userPosition.y === y;

  return (
    <div className="row">
      {columnsToRender
        .map((item, index) => ({ x: item, y: index, ...item }))
        .map((item, index) => (
          <Tile
            key={`${item.x}-${item.y}`}
            item={item}
            isBlocker={isBlocker(item.y)}
            isOpen={isOpen(item.y)}
            isRoad={isRoad(item.y)}
            isGoal={isGoal(item.y)}
            isPath={isPath(item.y)}
            isUserPosition={isUserPosition(item.x, item.y)}
            setTileAsBlocker={setTileAsBlocker}
            isSetting={isSetting}
            isGoalSetting={isGoalSetting}
            isStartSetting={isStartSetting}
            onSetStart={onSetStart}
            onSetGoal={onSetGoal}
          />
        ))}
    </div>
  );
};

export const Row = React.memo(MemoRow);

All interaction logic lives in Tile. I considered using React Context to avoid prop drilling, but this is the only place where heavy prop passing occurs, so explicit props stay for clarity.

Map.js

import React from 'react';
import { Row } from './Row';
import './Map.css';

export const Map = ({
  columns, rows, blockers, open, road, goal, path, userPosition,
  setTileAsBlocker, isSetting, isGoalSetting, isStartSetting,
  onSetGoal, onSetStart,
}) => {
  const rowsToRender = new Array(rows).fill(0);

  return (
    <div className="map">
      {rowsToRender
        .map((_, index) => (
          <Row
            key={index}
            x={index}
            columns={columns}
            blockers={blockers}
            open={open}
            road={road}
            goal={goal}
            path={path}
            userPosition={userPosition}
            setTileAsBlocker={setTileAsBlocker}
            isSetting={isSetting}
            isGoalSetting={isGoalSetting}
            isStartSetting={isStartSetting}
            onSetGoal={onSetGoal}
            onSetStart={onSetStart}
          />
        ))
        .reverse()}
    </div>
  );
};

Map renders rows — straightforward composition.

Core Calculations: g, h, and Cost

With Map, User, Blockers, Start, and Goal in place, we move to the core — cost calculations:

const gCost = (tilePosition, playerPosition) => {
  const width = tilePosition.x - playerPosition.x;
  const height = tilePosition.y - playerPosition.y;
  return Math.sqrt(width * width + height * height);
};

const hCost = (tilePosition, goal) => {
  const width = goal.x - tilePosition.x;
  const height = goal.y - tilePosition.y;
  return Math.sqrt(width * width + height * height);
};

export const addCosts = (item, goal, player) => {
  if (!item) return undefined;
  const g_cost = gCost(item, player) + player.gCost;
  const h_cost = hCost(item, goal);
  const cost = g_cost + h_cost;
  return {
    x: item.x,
    y: item.y,
    gCost: g_cost,
    hCost: h_cost,
    cost: cost,
    parent: player,
  };
};

Key design notes on the React-centric approach:

All structures are arrays because they simplify React data flow through props
Most results propagate via .map, .concat, or .filter
Every update returns a new value instead of mutating existing state

addCosts is the critical function — it assigns costs to a tile relative to the current player and goal.

Tile Cost Assignment: doCalculations

Next we need a function that distributes costs to all eight neighbours (4-directional + diagonal):

export const doCalculations = (player, open, goal) => {
  const check = (tile) => checkIfAlreadyAddedToOpen(tile, open);

  const leftTile = addCosts(
    checkIfCanReturn({ x: letCalculateLowerPosition(player.x), y: player.y }),
    goal, player,
  );
  const rightTile = addCosts(
    checkIfCanReturn({ x: letCalculateHigherPosition(player.x), y: player.y }),
    goal, player,
  );
  const topTile = addCosts(
    checkIfCanReturn({ x: player.x, y: letCalculateHigherPosition(player.y) }),
    goal, player,
  );
  const bottomTile = addCosts(
    checkIfCanReturn({ x: player.x, y: letCalculateLowerPosition(player.y) }),
    goal, player,
  );
  const topLeftTile = addCosts(
    checkIfCanReturn({ x: letCalculateLowerPosition(player.x), y: letCalculateHigherPosition(player.y) }),
    goal, player,
  );
  const topRightTile = addCosts(
    checkIfCanReturn({ x: letCalculateHigherPosition(player.x), y: letCalculateHigherPosition(player.y) }),
    goal, player,
  );
  const bottomLeftTile = addCosts(
    checkIfCanReturn({ x: letCalculateLowerPosition(player.x), y: letCalculateLowerPosition(player.y) }),
    goal, player,
  );
  const bottomRightTile = addCosts(
    checkIfCanReturn({ x: letCalculateHigherPosition(player.x), y: letCalculateLowerPosition(player.y) }),
    goal, player,
  );

  return {
    leftTile: leftTile && check(leftTile),
    rightTile: rightTile && check(rightTile),
    topTile: topTile && check(topTile),
    bottomTile: bottomTile && check(bottomTile),
    topLeftTile: topLeftTile && check(topLeftTile),
    topRightTile: topRightTile && check(topRightTile),
    bottomLeftTile: bottomLeftTile && check(bottomLeftTile),
    bottomRightTile: bottomRightTile && check(bottomRightTile),
    neighbours: {
      leftTile, rightTile, topTile, bottomTile,
      topLeftTile, topRightTile, bottomLeftTile, bottomRightTile,
    },
  };
};

The function takes the player's position and assigns costs to all eight neighbours. addCosts returns undefined when a neighbour:

falls outside the map, or
is a blocker

This undefined return is the trigger for downstream filtering.

Bringing It All Together: useRoad Hook

The final step aggregates the logic in a custom hook, useRoad.js. Here is the core:

import { useState, useEffect } from 'react';
// ...imports

export const useRoad = (goal, player, blockers, count, move, withSkipping, withNeighbourEvaluation) => {
  const [road, setRoad] = useState([player]);
  const [path, setPath] = useState([]);

  // Initialization — initial tiles
  const {
    leftTile, rightTile, topTile, bottomTile,
    topLeftTile, topRightTile, bottomLeftTile, bottomRightTile,
  } = doCalculations(player, [], goal);

  const uniques = removeUndefined([
    leftTile, rightTile, topTile, bottomTile,
    topLeftTile, topRightTile, bottomLeftTile, bottomRightTile,
  ]);

  const [neighbours, setCurrentNeighbours] = useState(evaluateTilesFromOpen(uniques, road));
  const [open, setOpen] = useState(evaluateTilesFromOpen(uniques, road));

  const isGoalReached = (position) => position && position.x === goal.x && position.y === goal.y;

  useEffect(() => {
    const {
      leftTile, rightTile, topTile, bottomTile,
      topLeftTile, topRightTile, bottomLeftTile, bottomRightTile,
      neighbours,
    } = doCalculations(player, open, goal);

    const newUniques = removeUndefined([
      leftTile, rightTile, topTile, bottomTile,
      topLeftTile, topRightTile, bottomLeftTile, bottomRightTile,
    ]);

    const newNeighbours = removeUndefined([
      neighbours.leftTile, neighbours.rightTile,
      neighbours.bottomTile, neighbours.topTile,
      neighbours.topLeftTile, neighbours.topRightTile,
      neighbours.bottomLeftTile, neighbours.bottomRightTile,
    ]);

    const parseData = (uniques, prevState = []) => {
      const uniquesWithoutRoadTiles = evaluateTilesFromOpen(uniques, road.concat(player));
      const withoutBlocker = removeBlockerTilesFromOpen(uniquesWithoutRoadTiles, blockers);
      const withoutCurrentPlace = removeCurrentPositionFromOpen(prevState.concat(withoutBlocker), player);
      return withoutCurrentPlace;
    };

    setCurrentNeighbours(parseData(newNeighbours));
    setOpen((prevState) => parseData(newUniques, prevState));
  }, [player.x, player.y]);

  // ...decision-making logic below

  return {
    open,
    road,
    path,
    setFinalPath,
    isGoalReached,
    clearAll,
  };
};

Everything happens inside useEffect, keyed on player.x and player.y. Working inside a useEffect scope was not ideal but necessary to keep movement, neighbor updates, and cost recalculation in sync.

Quick breakdown:

doCalculations generates all tiles with their associated costs
removeUndefined filters out invalid (off-map or blocked) tiles
parseData excludes the player, the road already travelled, and blockers

Due to how mazes work, the duplication between newUniques and newNeighbours is intentional. In downstream functions, only the current neighbours are evaluated for the lowest-cost brute-force approach, while uniques (all open tiles) feeds the standard f(n)-based decision.

Decision Logic: Finding the Lowest Cost Tile

The decision-making sits in useRoad.js:

const findLowestCostTile = () => {
  if (withNeighbourEvaluation) {
    // evaluating only neighbour tiles
    const neighboursCosts = getMinCostTiles(neighbours);

    if (neighboursCosts.min < min) {
      if (neighboursCosts.minArray.length > 1) {
        return getMinHCostTile(neighboursCosts.minArray);
      }
      return getMinCostTile(neighbours, neighboursCosts.min);
    }
  }

  // evaluating all open tiles
  const { minArray, min } = getMinCostTiles(open);
  if (minArray.length > 1) {
    return getMinHCostTile(minArray);
  }
  return getMinCostTile(open, min);
};

useEffect(() => {
  if (count > 0 && !isGoalReached(road[road.length - 1])) {
    const nextTile = findLowestCostTile();
    move(nextTile);
    setRoad((prevState) => prevState.concat(nextTile));
  }
}, [count]);

findLowestCostTile returns the tile with the lowest f(n). The withNeighbourEvaluation boolean toggles between the brute-force variant (neighbours only) and the full open-set evaluation. move executes in a separate useEffect keyed on count — our frame counter.

Frame Loop: Driving the Animation

The frame counter lives in App.js:

const [count, setCount] = useState(0); // frames

const positionRef = useRef(player);

// update ref each frame
useEffect(() => {
  positionRef.current = player;
  // ...
}, [count]);

const moveByOneTile = () => setCount((prevState) => prevState + 1);

Incrementing count forces React to rerender, updating the player's position — which in turn triggers the cost recalculation in useRoad. To animate the full path, we wrap this in setInterval:

const moveToLowestCost = () => {
  const handler = setInterval(() => {
    if (isGoalReached(positionRef.current)) {
      clearInterval(handler);
      return;
    }
    moveByOneTile();
  }, 5);
};

And that is the full A* pathfinding pipeline in React — from theory to running animation.

Code and live demo:

GitHub: github.com/MobileReality/astar
Demo: mobilereality.github.io/astar

Conclusion

Implementing A* pathfinding in React taught me a few things about where React fits and where it does not. React is a rendering library for component trees — not a game loop or a graph solver. For simple visualizations like this one it works, but when you push it toward high-frequency state updates tied to algorithmic computation, you spend more time fighting render cycles than solving the actual problem.

Key takeaways:

React is fine for demos and educational visualizations — component composition makes the grid easy to express and hooks handle state cleanly
React is the wrong layer for performance-critical pathfinding — for production use cases (game engines, large-scale robotics), Canvas/WebGL, vanilla JS, or a language like C++ gives you better control over the render loop
Hooks-based state can hide re-render costs — React.memo and useMemo help, but the underlying issue is that algorithmic state changes per step, and React wants to reconcile every change
The math is the easy part — g(n), h(n), and f(n) are simple; the hard part is wiring state updates to the frame loop without fighting the framework
Heuristic choice matters more than framework choice — switching from Euclidean to Manhattan distance changes search behaviour far more than switching from React to vanilla JS

If you build React applications and need help with complex frontend logic, check our React development services. For broader JavaScript engineering, see our JavaScript development services.

Other sources:

Frequently Asked Questions

What are g(n), h(n), and f(n) in the A* algorithm?

g(n) is the exact cost from the starting node to the current node, h(n) is the heuristic estimate of the remaining cost from the current node to the goal, and f(n) = g(n) + h(n) is the total estimated cost of the cheapest path through that node. The algorithm always selects the node with the lowest f(n) value to expand next, guaranteeing the shortest path when the heuristic is admissible.

How does A* differ from Dijkstra's algorithm?

Dijkstra's algorithm is a special case of A* where the heuristic h(n) is always zero, so it explores uniformly outward without any guidance toward the goal. A* adds a heuristic that directs the search, typically reducing the number of nodes examined while still guaranteeing optimality if the heuristic is admissible and consistent.

Why did the React implementation wrap Tile components in React.memo and useMemo?

Every step of A* updates many tile costs, triggering re-renders across the entire grid. Wrapping each Tile in React.memo plus selective useMemo ensures only tiles whose visual state actually changed are re-rendered, keeping the animation smooth and preventing performance collapse as the grid grows.

What makes a heuristic admissible, and why does it matter?

An admissible heuristic never overestimates the true remaining cost to the goal (h(n) ≤ actual cost). This property guarantees that A* will find the shortest path first; over-estimating can cause the algorithm to overlook cheaper routes and return a sub-optimal path.

When should I choose React over Canvas or native code for pathfinding visualization?

React plus SVG/CSS is fine for educational demos, small grids, or when you already need a component-based UI. Switch to Canvas, WebGL, or a native language like C++ when you require large grids, tight frame budgets, or thousands of state updates per second—React's reconciliation overhead becomes the bottleneck long before the A* math does.

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Web DevelopmentA* Pathfinding in React: Building Visual Pathfinding with Hooks