• mojo 驅動：提供 shell 用于 read-eval-print-loop 或 REPL 的 shell，支持構建和運行 Mojo 程序、打包 Mojo 模塊（包括對 擴展的支持）、生成文檔和格式化代碼?

• VS Code 擴展：支持多項生產力功能，例如語法高亮顯示、自動補全代碼等 ?

• Jupyter kernel：支持構建和運行 Mojo notebooks，包括 Python 代碼??

• 調試工具（即將推出）：進入并檢查正在運行的 Mojo 程序，甚至包括混合 C++ 和 Mojo 代碼的框架

$ mojo
Welcome to Mojo! 
Expressions are delimited by a blank line.
Type `:mojo help` for further assistance.
1> %%python
2. import numpy as np
3. n = 10000000
4. anp = np.random.rand(n)
5. bnp = np.random.rand(n)

6> from tensor import Tensor
7. let n: Int = 10000000
8. var a = Tensor[DType.float64](n)
9. var b = Tensor[DType.float64](n)
10. for i in range(n):
11.    a[i] = anp[i].to_float64()
12.    b[i] = bnp[i].to_float64()

13> from math import sqrt
14. def mojo_naive_dist(a: Tensor[DType.float64], b: Tensor[DType.float64]) -> Float64:
15.    var s: Float64 = 0.0
16.    n = a.num_elements()
17.    for i in range(n):
18.       dist = a[i] - b[i]
19.       s += dist*dist
20.    return sqrt(s)

23> fn mojo_fn_dist(a: Tensor[DType.float64], b: Tensor[DType.float64]) -> Float64:
24.    var s: Float64 = 0.0
25.    let n = a.num_elements()
26.    for i in range(n):
27.       let dist = a[i] - b[i]
28.       s += dist*dist
29.    return sqrt(s)
30.
31> let naive_dist = mojo_naive_dist(a, b)
32. let fn_dist = mojo_fn_dist(a, b)
33. print(fn_dist)
34. 
1290.8521425092235
35. print(naive_dist)
36. 
1290.8521425092235

$ mojo build hello.

$ ./hello
Hello Mojo !
9
6
3

$ ls -lGtranh hello*
-rw-r--r-- 1 0   817 Sep  3 23:59 hello.