diff --git a/recipes/tinyarray/meta.yaml b/recipes/tinyarray/meta.yaml
new file mode 100755
index 0000000000000..7df931e513126
--- /dev/null
+++ b/recipes/tinyarray/meta.yaml
@@ -0,0 +1,53 @@
+{% set name = "tinyarray" %}
+{% set version = "1.2.0a1" %}
+
+package:
+  name: {{ name|lower }}
+  version: {{ version }}
+
+source:
+  fn: v{{ version }}.tar.gz
+  url: https://gitlab.kwant-project.org/kwant/tinyarray/repository/archive.tar.gz?ref=v{{ version }}
+  sha256: e155a1c373c90d81877f953810f108cff72cf22a50c44b9cf86a803035198d04
+  
+build:
+  number: 0
+  script: python setup.py install --single-version-externally-managed --record record.txt
+
+requirements:
+  build:
+    - python
+    - setuptools
+    - toolchain
+    - nose
+  run:
+    - python
+
+test:
+  requires:
+    - numpy
+#    - nose
+#  source_files:
+#    - test_tinyarray.py
+#  commands:
+#    - python test_tinyarray.py
+  imports:
+    - tinyarray
+
+about:
+  home: http://git.kwant-project.org/tinyarray/about/
+  license: BSD 2-Clause
+  license_file: LICENSE.rst
+  summary: 'Arrays of numbers for Python, optimized for small sizes'
+  description: |
+    Tinyarrays are similar to NumPy arrays, but optimized for small sizes. 
+    Common operations on very small arrays are to 3-7 times faster than 
+    with NumPy (with NumPy 1.6 it used to be up to 35 times), and 3 times 
+    less memory is used to store them. Tinyarrays are useful if you need 
+    many small arrays of numbers, and cannot combine them into a few 
+    large ones.
+  doc_url: https://gitlab.kwant-project.org/kwant/tinyarray
+
+extra:
+  recipe-maintainers:
+    - basnijholt