diff --git a/.gitignore b/.gitignore
index cfbeb4f..4d08d01 100644
--- a/.gitignore
+++ b/.gitignore
@@ -19,3 +19,66 @@ target/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+env/
+ENV/
+
+# IDE specific files
+.vscode/
+*.swp
+*.swo
+
+# Jupyter Notebook
+.ipynb_checkpoints
+*/.ipynb_checkpoints/*
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytest
+.pytest_cache/
+.coverage
+htmlcov/
+
+# Environment variables
+.env
+.venv
+env/
+venv/
+ENV/
+
+# Distribution / packaging
+.Python
+build/
+*.pyc
+
+.ruff_cache
\ No newline at end of file
diff --git a/.python-version b/.python-version
deleted file mode 100644
index c8cfe39..0000000
--- a/.python-version
+++ /dev/null
@@ -1 +0,0 @@
-3.10
diff --git a/Cargo.lock b/Cargo.lock
index 610f14c..dc1e9eb 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -2,6 +2,12 @@
 # It is not intended for manual editing.
 version = 4
 
+[[package]]
+name = "anyhow"
+version = "1.0.98"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e16d2d3311acee920a9eb8d33b8cbc1787ce4a264e85f964c2404b969bdcd487"
+
 [[package]]
 name = "autocfg"
 version = "1.5.0"
@@ -12,6 +18,7 @@ checksum = "c08606f8c3cbf4ce6ec8e28fb0014a2c086708fe954eaa885384a6165172e7e8"
 name = "fastlap"
 version = "0.1.0"
 dependencies = [
+ "numpy",
  "pyo3",
 ]
 
@@ -33,6 +40,16 @@ version = "0.2.174"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "1171693293099992e19cddea4e8b849964e9846f4acee11b3948bcc337be8776"
 
+[[package]]
+name = "matrixmultiply"
+version = "0.3.10"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a06de3016e9fae57a36fd14dba131fccf49f74b40b7fbdb472f96e361ec71a08"
+dependencies = [
+ "autocfg",
+ "rawpointer",
+]
+
 [[package]]
 name = "memoffset"
 version = "0.9.1"
@@ -42,6 +59,64 @@ dependencies = [
  "autocfg",
 ]
 
+[[package]]
+name = "ndarray"
+version = "0.16.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "882ed72dce9365842bf196bdeedf5055305f11fc8c03dee7bb0194a6cad34841"
+dependencies = [
+ "matrixmultiply",
+ "num-complex",
+ "num-integer",
+ "num-traits",
+ "portable-atomic",
+ "portable-atomic-util",
+ "rawpointer",
+]
+
+[[package]]
+name = "num-complex"
+version = "0.4.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "73f88a1307638156682bada9d7604135552957b7818057dcef22705b4d509495"
+dependencies = [
+ "num-traits",
+]
+
+[[package]]
+name = "num-integer"
+version = "0.1.46"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7969661fd2958a5cb096e56c8e1ad0444ac2bbcd0061bd28660485a44879858f"
+dependencies = [
+ "num-traits",
+]
+
+[[package]]
+name = "num-traits"
+version = "0.2.19"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "071dfc062690e90b734c0b2273ce72ad0ffa95f0c74596bc250dcfd960262841"
+dependencies = [
+ "autocfg",
+]
+
+[[package]]
+name = "numpy"
+version = "0.25.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "29f1dee9aa8d3f6f8e8b9af3803006101bb3653866ef056d530d53ae68587191"
+dependencies = [
+ "libc",
+ "ndarray",
+ "num-complex",
+ "num-integer",
+ "num-traits",
+ "pyo3",
+ "pyo3-build-config",
+ "rustc-hash",
+]
+
 [[package]]
 name = "once_cell"
 version = "1.21.3"
@@ -54,6 +129,15 @@ version = "1.11.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "f84267b20a16ea918e43c6a88433c2d54fa145c92a811b5b047ccbe153674483"
 
+[[package]]
+name = "portable-atomic-util"
+version = "0.2.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d8a2f0d8d040d7848a709caf78912debcc3f33ee4b3cac47d73d1e1069e83507"
+dependencies = [
+ "portable-atomic",
+]
+
 [[package]]
 name = "proc-macro2"
 version = "1.0.95"
@@ -69,6 +153,7 @@ version = "0.25.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "8970a78afe0628a3e3430376fc5fd76b6b45c4d43360ffd6cdd40bdde72b682a"
 dependencies = [
+ "anyhow",
  "indoc",
  "libc",
  "memoffset",
@@ -134,6 +219,18 @@ dependencies = [
  "proc-macro2",
 ]
 
+[[package]]
+name = "rawpointer"
+version = "0.2.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "60a357793950651c4ed0f3f52338f53b2f809f32d83a07f72909fa13e4c6c1e3"
+
+[[package]]
+name = "rustc-hash"
+version = "2.1.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "357703d41365b4b27c590e3ed91eabb1b663f07c4c084095e60cbed4362dff0d"
+
 [[package]]
 name = "syn"
 version = "2.0.104"
diff --git a/Cargo.toml b/Cargo.toml
index 1a10cdc..25d78cb 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -9,4 +9,5 @@ name = "fastlap"
 crate-type = ["cdylib"]
 
 [dependencies]
-pyo3 = "0.25.0"
\ No newline at end of file
+pyo3 = { version = "0.25.0", features = ["extension-module", "anyhow", "auto-initialize"] }
+numpy = "0.25"
\ No newline at end of file
diff --git a/README.md b/README.md
index 938159f..244faa4 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,46 @@
+
 # fastlap
-Python's LAP library written in Rust.
+
+**Python’s LAP (Linear Assignment Problem) solver — written in Rust for performance.**
+
+`fastlap` provides a blazing-fast implementation of common assignment algorithms such as:
+
+- Jonker-Volgenant (LAPJV)
+- Hungarian (a.k.a. Munkres)
+
+Built with [Rust](https://www.rust-lang.org/) and exposed to Python via [PyO3](https://pyo3.rs), this library offers performance and interoperability in one package.
+
+
+## ✨ Features
+
+- ✅ Fast and memory-safe implementation in Rust
+- ✅ Python bindings via PyO3
+- ✅ Supports both `lapjv` and `hungarian` algorithms
+- ✅ Can be used in native Rust projects or as a Python package
+
+
+## 📖 Algorithms
+
+* **LAPJV** – Efficient dual-based shortest augmenting path algorithm (Jonker & Volgenant, 1987)
+* **Hungarian** – Classic method with row/column reduction and assignment phases
+
+## Roadmap
+
+- [ ] Release first version
+- [ ] Add more algorithms
+- [ ] Add more features
+- [ ] Add more examples
+- [ ] Add more tests
+- [ ] Add more benchmarks
+
+
+## 📚 References
+
+* Jonker, R., & Volgenant, A. (1987). *A shortest augmenting path algorithm for dense and sparse linear assignment problems*. Computing, 38(4), 325–340.
+* Munkres, J. (1957). *Algorithms for the Assignment and Transportation Problems*. Journal of the Society for Industrial and Applied Mathematics.
+
+
+## 📃 License
+
+MIT License © 2025
+
diff --git a/pyproject.toml b/pyproject.toml
index b2eb46f..596f024 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,5 +11,12 @@ classifiers = [
     "Programming Language :: Python :: Implementation :: PyPy",
 ]
 dynamic = ["version"]
+dependencies = [
+    "lap>=0.4.0",
+    "maturin>=1.8.7",
+    "numpy>=1.20,<2.0",
+    "pytest>=7.0",
+    "scipy>=1.8",
+]
 [tool.maturin]
 features = ["pyo3/extension-module"]
diff --git a/python/fastlaps.py b/python/fastlaps.py
new file mode 100644
index 0000000..a684179
--- /dev/null
+++ b/python/fastlaps.py
@@ -0,0 +1,52 @@
+import time
+import fastlap
+import numpy as np
+from scipy.optimize import linear_sum_assignment
+import lap
+
+
+if __name__ == "__main__":
+    # Quick example for a 3x3 matrix
+    matrix = np.random.rand(4, 5)
+    for algo in ["lapjv", "hungarian"]:
+        print(f"\nAlgorithm: {algo}")
+        start = time.time()
+        fastlap_cost, fastlap_rows, fastlap_cols = fastlap.solve_lap(matrix, algo)
+        fastlap_end = time.time()
+        print(f"fastlap.{algo}: Time={fastlap_end - start:.8f}s")
+        print(
+            f"fastlap.{algo}: Cost={fastlap_cost}, Rows={fastlap_rows}, Cols={fastlap_cols}"
+        )
+        if algo == "hungarian":
+            start = time.time()
+            scipy_rows, scipy_cols = linear_sum_assignment(matrix)
+            scipy_cost = matrix[scipy_rows, scipy_cols].sum()
+            scipy_end = time.time()
+            print(
+                f"scipy.optimize.linear_sum_assignment: Time={scipy_end - start:.8f}s"
+            )
+            print(
+                f"scipy.optimize.linear_sum_assignment: Cost={scipy_cost}, Rows={list(scipy_rows)}, Cols={list(scipy_cols)}"
+            )
+        if algo == "lapjv":
+            start = time.time()
+            lap_cost, lap_rows, lap_cols = lap.lapjv(matrix, extend_cost=True)
+            lap_end = time.time()
+            print(f"lap.lapjv: Time={lap_end - start:.8f}s")
+            print(f"lap.lapjv: Cost={lap_cost}, Rows={lap_rows}, Cols={lap_cols}")
+
+"""
+First release:
+
+Algorithm: lapjv
+fastlap.lapjv: Time=0.00017548s
+fastlap.lapjv: Cost=inf, Rows=[2, 0, 1, 4, 3], Cols=[1, 2, 0, 4, 3]
+lap.lapjv: Time=0.00013208s
+lap.lapjv: Cost=0.6229432588732741, Rows=[2 0 1 4], Cols=[ 1  2  0 -1  3]
+
+Algorithm: hungarian
+fastlap.hungarian: Time=0.00000453s
+fastlap.hungarian: Cost=0.7465856501551806, Rows=[2, 0, 1, 3], Cols=[1, 2, 0, 3, 18446744073709551615]
+scipy.optimize.linear_sum_assignment: Time=0.00001287s
+scipy.optimize.linear_sum_assignment: Cost=0.6229432588732741, Rows=[0, 1, 2, 3], Cols=[2, 0, 1, 4]
+"""
diff --git a/python/init_test.py b/python/init_test.py
deleted file mode 100644
index d18c5eb..0000000
--- a/python/init_test.py
+++ /dev/null
@@ -1,4 +0,0 @@
-import fastlap
-
-if __name__ == "__main__":
-    print(fastlap.sum_as_string(1, 2))
\ No newline at end of file
diff --git a/python/lapjv.py b/python/lapjv.py
new file mode 100644
index 0000000..fab44f3
--- /dev/null
+++ b/python/lapjv.py
@@ -0,0 +1,12 @@
+import lap
+import numpy as np
+import time
+
+
+if __name__ == "__main__":
+    array = np.random.rand(4, 5)
+    start = time.time()
+    result = lap.lapjv(array, extend_cost=True)
+    end = time.time()
+    print(f"lapjv: Time={end - start:.8f}s")
+    print(result)
diff --git a/src/lap.rs b/src/lap.rs
new file mode 100644
index 0000000..cabbb0f
--- /dev/null
+++ b/src/lap.rs
@@ -0,0 +1,230 @@
+pub fn lapjv(matrix: Vec<Vec<f64>>) -> (f64, Vec<usize>, Vec<usize>) {
+    let n = matrix.len();
+    if n == 0 {
+        return (0.0, vec![], vec![]);
+    }
+    let m = matrix[0].len();
+    let matrix = if n != m {
+        // Handle non-square matrices by padding with high costs
+        let max_cost = matrix
+            .iter()
+            .flatten()
+            .fold(f64::INFINITY, |a, &b| a.max(b))
+            + 1.0;
+        let mut padded_matrix = vec![vec![max_cost; n.max(m)]; n.max(m)];
+        for i in 0..n {
+            for j in 0..m {
+                padded_matrix[i][j] = matrix[i][j];
+            }
+        }
+        padded_matrix
+    } else {
+        matrix
+    };
+
+    let n = matrix.len();
+    let mut u = vec![0.0; n]; // Dual variables for rows
+    let mut v = vec![0.0; n]; // Dual variables for columns
+    let mut row_assign = vec![usize::MAX; n];
+    let mut col_assign = vec![usize::MAX; n];
+
+    // Greedy initialization
+    for i in 0..n {
+        if let Some((j_min, &min_val)) = matrix[i]
+            .iter()
+            .enumerate()
+            .filter(|(j, _)| col_assign[*j] == usize::MAX)
+            .min_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
+        {
+            row_assign[i] = j_min;
+            col_assign[j_min] = i;
+            u[i] = min_val;
+        }
+    }
+
+    // Augmenting path loop
+    for i in 0..n {
+        if row_assign[i] != usize::MAX {
+            continue;
+        }
+        let mut min_slack = vec![f64::INFINITY; n];
+        let mut prev = vec![usize::MAX; n];
+        let mut visited = vec![false; n];
+        let mut marked_row = i;
+
+        #[allow(unused_assignments)]
+        let mut marked_col = usize::MAX;
+
+        loop {
+            visited[marked_row] = true;
+            for j in 0..n {
+                if !visited[j] && col_assign[j] != usize::MAX {
+                    let slack = matrix[marked_row][j] - u[marked_row] - v[j];
+                    if slack < min_slack[j] {
+                        min_slack[j] = slack;
+                        prev[j] = marked_row;
+                    }
+                }
+            }
+
+            let (j, &delta) = min_slack
+                .iter()
+                .enumerate()
+                .filter(|(j, _)| !visited[*j] && col_assign[*j] != usize::MAX)
+                .min_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
+                .unwrap_or((0, &f64::INFINITY));
+
+            if delta == f64::INFINITY {
+                // Find unassigned column
+                let unassigned_j = (0..n).find(|&j| col_assign[j] == usize::MAX).unwrap();
+                marked_col = unassigned_j;
+                break;
+            }
+
+            for k in 0..n {
+                if visited[k] {
+                    u[k] += delta;
+                    v[k] -= delta;
+                } else {
+                    min_slack[k] -= delta;
+                }
+            }
+
+            marked_row = col_assign[j];
+        }
+
+        // Augment path
+        while marked_col != usize::MAX {
+            let i_prev = prev[marked_col];
+            let j_prev = row_assign[i_prev];
+            row_assign[i_prev] = marked_col;
+            col_assign[marked_col] = i_prev;
+            marked_col = j_prev;
+        }
+    }
+
+    let total_cost: f64 = row_assign
+        .iter()
+        .enumerate()
+        .filter(|(_, &j)| j != usize::MAX)
+        .map(|(i, &j)| matrix[i][j])
+        .sum();
+
+    (total_cost, row_assign, col_assign)
+}
+
+pub fn hungarian(matrix: Vec<Vec<f64>>) -> (f64, Vec<usize>, Vec<usize>) {
+    let n = matrix.len();
+    if n == 0 {
+        return (0.0, vec![], vec![]);
+    }
+    let m = matrix[0].len();
+    let mut cost = matrix.clone();
+
+    // Row reduction
+    for i in 0..n {
+        let min_val = cost[i].iter().cloned().fold(f64::INFINITY, f64::min);
+        for j in 0..m {
+            cost[i][j] -= min_val;
+        }
+    }
+
+    // Column reduction
+    for j in 0..m {
+        let min_val = (0..n).map(|i| cost[i][j]).fold(f64::INFINITY, f64::min);
+        for i in 0..n {
+            cost[i][j] -= min_val;
+        }
+    }
+
+    // Cover zeros
+    let mut row_covered = vec![false; n];
+    let mut col_covered = vec![false; m];
+    let mut row_assign = vec![usize::MAX; n];
+    let mut col_assign = vec![usize::MAX; m];
+
+    // Initial assignment
+    for i in 0..n {
+        for j in 0..m {
+            if cost[i][j] == 0.0 && !row_covered[i] && !col_covered[j] {
+                row_assign[i] = j;
+                col_assign[j] = i;
+                row_covered[i] = true;
+                col_covered[j] = true;
+                break;
+            }
+        }
+    }
+
+    // Iterative augmentation
+    while row_covered.iter().any(|&x| !x) {
+        let mut zeros = vec![];
+        for i in 0..n {
+            if !row_covered[i] {
+                for j in 0..m {
+                    if cost[i][j] == 0.0 && !col_covered[j] {
+                        zeros.push((i, j));
+                    }
+                }
+            }
+        }
+
+        if zeros.is_empty() {
+            // Find minimum uncovered value
+            let mut min_uncovered = f64::INFINITY;
+            for i in 0..n {
+                if !row_covered[i] {
+                    for j in 0..m {
+                        if !col_covered[j] {
+                            min_uncovered = min_uncovered.min(cost[i][j]);
+                        }
+                    }
+                }
+            }
+
+            // Adjust matrix
+            for i in 0..n {
+                for j in 0..m {
+                    if row_covered[i] && col_covered[j] {
+                        cost[i][j] += min_uncovered;
+                    } else if !row_covered[i] && !col_covered[j] {
+                        cost[i][j] -= min_uncovered;
+                    }
+                }
+            }
+
+            // Retry assignment
+            for i in 0..n {
+                if !row_covered[i] {
+                    for j in 0..m {
+                        if cost[i][j] == 0.0 && !col_covered[j] {
+                            row_assign[i] = j;
+                            col_assign[j] = i;
+                            row_covered[i] = true;
+                            col_covered[j] = true;
+                            break;
+                        }
+                    }
+                }
+            }
+        } else {
+            // Augment path (simplified)
+            for &(i, j) in &zeros {
+                row_assign[i] = j;
+                col_assign[j] = i;
+                row_covered[i] = true;
+                col_covered[j] = true;
+                break;
+            }
+        }
+    }
+
+    let total_cost: f64 = row_assign
+        .iter()
+        .enumerate()
+        .filter(|(_, &j)| j != usize::MAX)
+        .map(|(i, &j)| matrix[i][j])
+        .sum();
+
+    (total_cost, row_assign, col_assign)
+}
diff --git a/src/lib.rs b/src/lib.rs
index 4dc9464..3b36895 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -1,14 +1,57 @@
+use numpy::{PyArray2, PyArrayMethods};
 use pyo3::prelude::*;
 
-/// Formats the sum of two numbers as string.
+pub mod lap;
+use crate::lap::*;
+
 #[pyfunction]
-fn sum_as_string(a: usize, b: usize) -> PyResult<String> {
-    Ok((a + b).to_string())
+fn solve_lap<'py>(
+    _py: Python<'py>,
+    cost_matrix: &Bound<'py, PyArray2<f64>>, // Changed to use Bound
+    algorithm: &str,
+) -> PyResult<(f64, Vec<usize>, Vec<usize>)> {
+    // Convert NumPy array to dense matrix
+    let matrix: Vec<Vec<f64>> = cost_matrix
+        .readonly()
+        .as_array()
+        .rows()
+        .into_iter()
+        .map(|row| row.iter().copied().collect::<Vec<f64>>())
+        .collect();
+
+    // Validate dense matrix
+    if matrix.is_empty() || matrix.iter().any(|row| row.len() != matrix[0].len()) {
+        return Err(PyErr::new::<pyo3::exceptions::PyValueError, _>(
+            "Matrix must be non-empty and rectangular",
+        ));
+    }
+
+    match algorithm {
+        "lapjv" => Ok(lapjv(matrix)),
+        "hungarian" => Ok(hungarian(matrix)),
+        _ => Err(PyErr::new::<pyo3::exceptions::PyValueError, _>(
+            "Unknown algorithm. Supported algorithms: 'lapjv', 'hungarian'",
+        )),
+    }
 }
 
-/// A Python module implemented in Rust.
 #[pymodule]
-fn fastlap(m: &Bound<'_, PyModule>) -> PyResult<()> {
-    m.add_function(wrap_pyfunction!(sum_as_string, m)?)?;
+fn fastlap(_py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> {
+    m.add_function(wrap_pyfunction!(solve_lap, m)?)?;
     Ok(())
 }
+
+pub fn fastlap_rust(_py: Python, matrix: &Vec<Vec<f64>>, algo: &str) -> PyResult<(f64, Vec<usize>, Vec<usize>)> {
+    if matrix.is_empty() || matrix.iter().any(|row| row.len() != matrix[0].len()) {
+        return Err(pyo3::exceptions::PyValueError::new_err("Matrix must be square and non-empty"));
+    }
+
+    let result = match algo {
+        "lapjv" => lapjv(matrix.clone()),
+        "hungarian" => hungarian(matrix.clone()),
+        _ => return Err(pyo3::exceptions::PyValueError::new_err("Unsupported algorithm")),
+    };
+
+    Ok(result)
+}
+
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..6095eba
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,32 @@
+import numpy as np
+from scipy.optimize import linear_sum_assignment
+import lap
+import fastlap
+
+# Set random seed for reproducibility
+np.random.seed(42)
+
+
+def generate_test_matrix(size, max_val=100.0):
+    """Generate a random square matrix of given size with float64 values."""
+    return np.random.uniform(0, max_val, (size, size)).astype(np.float64)
+
+
+def fastlap_execute(
+    matrix: np.ndarray, algo: str
+) -> tuple[float, list[int], list[int]]:
+    """Execute fastlap with the given algorithm."""
+    return fastlap.solve_lap(matrix, algo)
+
+
+def scipy_execute(matrix: np.ndarray) -> tuple[float, list[int], list[int]]:
+    """Execute scipy with the given algorithm."""
+    rows, cols = linear_sum_assignment(matrix)
+    cost = matrix[rows, cols].sum()
+    return cost, rows.tolist(), cols.tolist()
+
+
+def lap_execute(matrix: np.ndarray) -> tuple[float, list[int], list[int]]:
+    """Execute lap with the given algorithm."""
+    cost, rows, cols = lap.lapjv(matrix, extend_cost=True)
+    return cost, rows.tolist(), cols.tolist()
diff --git a/tests/test_correctness.py b/tests/test_correctness.py
new file mode 100644
index 0000000..e0a66bc
--- /dev/null
+++ b/tests/test_correctness.py
@@ -0,0 +1,63 @@
+import pytest
+
+from conftest import generate_test_matrix, fastlap_execute, scipy_execute, lap_execute
+
+
+def compare_assignments(
+    row_ind1, col_ind1, row_ind2, col_ind2, cost1, cost2, algo1, algo2, tol=1e-8
+):
+    """Compare assignments and costs between two algorithms."""
+    assert abs(cost1 - cost2) < tol, (
+        f"Cost mismatch: {algo1} ({cost1}) vs {algo2} ({cost2})"
+    )
+    mapping1 = dict(zip(row_ind1, col_ind1))
+    mapping2 = dict(zip(row_ind2, col_ind2))
+    assert mapping1 == mapping2, f"Assignment mismatch: {algo1} vs {algo2}"
+
+
+@pytest.mark.parametrize("size", [2, 3, 4, 5])
+def test_correctness_hungarian(size):
+    """Test fastlap correctness against SciPy for various matrix sizes."""
+    matrix = generate_test_matrix(size)
+
+    # fastlap
+    fastlap_cost, fastlap_rows, fastlap_cols = fastlap_execute(matrix, "hungarian")
+
+    # SciPy
+    scipy_cost, scipy_rows, scipy_cols = scipy_execute(matrix)
+
+    # Compare
+    compare_assignments(
+        fastlap_rows,
+        fastlap_cols,
+        scipy_rows,
+        scipy_cols,
+        fastlap_cost,
+        scipy_cost,
+        f"fastlap.hungarian",
+        "scipy",
+    )
+
+
+@pytest.mark.parametrize("size", [2, 3, 4, 5])
+def test_correctness_lapjv(size):
+    """Test fastlap correctness against SciPy for various matrix sizes."""
+    matrix = generate_test_matrix(size)
+
+    # fastlap
+    fastlap_cost, fastlap_rows, fastlap_cols = fastlap_execute(matrix, "lapjv")
+
+    # lap
+    lap_cost, lap_rows, lap_cols = lap_execute(matrix)
+
+    # Compare
+    compare_assignments(
+        fastlap_rows,
+        fastlap_cols,
+        lap_rows,
+        lap_cols,
+        fastlap_cost,
+        lap_cost,
+        f"fastlap.lapjv",
+        "lap",
+    )
diff --git a/tests/test_performance.py b/tests/test_performance.py
new file mode 100644
index 0000000..5a5ac29
--- /dev/null
+++ b/tests/test_performance.py
@@ -0,0 +1,42 @@
+import time
+import pytest
+import numpy as np
+from scipy.optimize import linear_sum_assignment
+import fastlap
+from conftest import generate_test_matrix, fastlap_execute, scipy_execute, lap_execute
+
+
+@pytest.mark.parametrize("size", [2, 3, 4, 5])
+def test_performance(size):
+    """Measure execution time and report for fastlap and SciPy."""
+    matrix = generate_test_matrix(size)
+    iterations = 1000  # Number of runs for stable timing
+
+    print(f"\nMatrix size: {size}x{size}")
+
+    for algo in ["lapjv", "hungarian"]:
+        total_time = 0.0
+        for _ in range(iterations):
+            start_time = time.time()
+            fastlap_execute(matrix, algo)
+            total_time += time.time() - start_time
+        avg_time = total_time / iterations
+        print(f"fastlap.{algo}: Avg Time={avg_time:.8f}s")
+
+    # SciPy
+    total_time = 0.0
+    for _ in range(iterations):
+        start_time = time.time()
+        scipy_execute(matrix)
+        total_time += time.time() - start_time
+    avg_time = total_time / iterations
+    print(f"scipy: Avg Time={avg_time:.8f}s")
+
+    # lap
+    total_time = 0.0
+    for _ in range(iterations):
+        start_time = time.time()
+        lap_execute(matrix)
+        total_time += time.time() - start_time
+    avg_time = total_time / iterations
+    print(f"lap: Avg Time={avg_time:.8f}s")
diff --git a/uv.lock b/uv.lock
index 31bba50..6f45250 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,16 +1,114 @@
 version = 1
-requires-python = ">=3.10"
+requires-python = ">=3.8"
+
+[[package]]
+name = "colorama"
+version = "0.4.6"
+source = { registry = "https://pypi.org/simple" }
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+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335 },
+]
+
+[[package]]
+name = "exceptiongroup"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/0b/9f/a65090624ecf468cdca03533906e7c69ed7588582240cfe7cc9e770b50eb/exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88", size = 29749 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/36/f4/c6e662dade71f56cd2f3735141b265c3c79293c109549c1e6933b0651ffc/exceptiongroup-1.3.0-py3-none-any.whl", hash = "sha256:4d111e6e0c13d0644cad6ddaa7ed0261a0b36971f6d23e7ec9b4b9097da78a10", size = 16674 },
+]
 
 [[package]]
 name = "fastlap"
 version = "0.1.0"
-source = { virtual = "." }
+source = { editable = "." }
 dependencies = [
+    { name = "lap" },
     { name = "maturin" },
+    { name = "numpy" },
+    { name = "pytest" },
+    { name = "scipy" },
 ]
 
 [package.metadata]
-requires-dist = [{ name = "maturin", specifier = ">=1.8.7" }]
+requires-dist = [
+    { name = "lap", specifier = ">=0.4.0" },
+    { name = "maturin", specifier = ">=1.8.7" },
+    { name = "numpy", specifier = ">=1.20,<2.0" },
+    { name = "pytest", specifier = ">=7.0" },
+    { name = "scipy", specifier = ">=1.8" },
+]
+
+[[package]]
+name = "iniconfig"
+version = "2.1.0"
+source = { registry = "https://pypi.org/simple" }
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+wheels = [
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+]
+
+[[package]]
+name = "lap"
+version = "0.5.12"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
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