diff --git a/tensorflow/contrib/eager/python/BUILD b/tensorflow/contrib/eager/python/BUILD
index 6e9bb87d58d..fb667cd91bd 100644
--- a/tensorflow/contrib/eager/python/BUILD
+++ b/tensorflow/contrib/eager/python/BUILD
@@ -19,6 +19,7 @@ py_library(
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:numerics",
         "//tensorflow/python:resource_variable_ops",
+        "//tensorflow/python:script_ops",
         "//tensorflow/python:util",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python/eager:backprop",
diff --git a/tensorflow/contrib/eager/python/tfe.py b/tensorflow/contrib/eager/python/tfe.py
index 1697c879def..770a7e3e7a0 100644
--- a/tensorflow/contrib/eager/python/tfe.py
+++ b/tensorflow/contrib/eager/python/tfe.py
@@ -23,6 +23,7 @@ To use, at program startup, call `tfe.enable_eager_execution()`.
 @@list_devices
 @@num_gpus
 
+@@py_func
 @@defun
 @@implicit_gradients
 @@implicit_value_and_gradients
@@ -101,8 +102,10 @@ from tensorflow.python.framework.test_util import IsolateTest
 from tensorflow.python.framework.test_util import run_in_graph_and_eager_modes as run_test_in_graph_and_eager_modes
 from tensorflow.python.ops.resource_variable_ops import ResourceVariable as Variable
 from tensorflow.python.ops.variable_scope import EagerVariableStore
+from tensorflow.python.ops import script_ops
 from tensorflow.python.util.all_util import remove_undocumented
 
+py_func = script_ops.eager_py_func
 defun = function.defun
 implicit_gradients = backprop.implicit_grad
 implicit_value_and_gradients = backprop.implicit_val_and_grad
diff --git a/tensorflow/core/api_def/base_api/api_def_EagerPyFunc.pbtxt b/tensorflow/core/api_def/base_api/api_def_EagerPyFunc.pbtxt
new file mode 100644
index 00000000000..9231368e165
--- /dev/null
+++ b/tensorflow/core/api_def/base_api/api_def_EagerPyFunc.pbtxt
@@ -0,0 +1,8 @@
+op {
+  graph_op_name: "EagerPyFunc"
+  summary: "Eagerly executes a python function to compute func(input)->output. The"
+  description: <<END
+semantics of the input, output, and attributes are the same as those for
+PyFunc.
+END
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_EagerPyFunc.pbtxt b/tensorflow/core/api_def/python_api/api_def_EagerPyFunc.pbtxt
new file mode 100644
index 00000000000..ee0f95dacbc
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_EagerPyFunc.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "EagerPyFunc"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/ops/script_ops.cc b/tensorflow/core/ops/script_ops.cc
index 8197327b562..c7c594a999a 100644
--- a/tensorflow/core/ops/script_ops.cc
+++ b/tensorflow/core/ops/script_ops.cc
@@ -51,4 +51,18 @@ REGISTER_OP("PyFuncStateless")
 A stateless version of PyFunc.
 )doc");
 
+REGISTER_OP("EagerPyFunc")
+    .Input("input: Tin")
+    .Output("output: Tout")
+    .Attr("token: string")
+    .Attr("Tin: list(type) >= 0")
+    .Attr("Tout: list(type) >=0")
+    .SetIsStateful()
+    .SetShapeFn(shape_inference::UnknownShape)
+    .Doc(R"doc(
+Eagerly executes a python function to compute func(input)->output. The
+semantics of the input, output, and attributes are the same as those for
+PyFunc.
+)doc");
+
 }  // namespace tensorflow
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 868ffcb4737..e5c4347833b 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -280,10 +280,14 @@ cc_library(
         ":ndarray_tensor_bridge",
         ":numpy_lib",
         ":py_util",
+        ":safe_ptr",
+        "//tensorflow/c:tf_status_helper",
+        "//tensorflow/c/eager:c_api",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:script_ops_op_lib",
+        "//tensorflow/python/eager:pywrap_tfe_lib",
         "//third_party/py/numpy:headers",
         "//util/python:python_headers",
     ],
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index 0660f40300a..f017004e1a9 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -1645,6 +1645,8 @@ cuda_py_test(
         "//tensorflow/python:errors",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:script_ops",
+        "//tensorflow/python/eager:context",
+        "//tensorflow/python/eager:function",
     ],
     tags = ["no_windows"],
 )
diff --git a/tensorflow/python/kernel_tests/py_func_test.py b/tensorflow/python/kernel_tests/py_func_test.py
index 7ed99c1be9b..92fb68820e0 100644
--- a/tensorflow/python/kernel_tests/py_func_test.py
+++ b/tensorflow/python/kernel_tests/py_func_test.py
@@ -23,82 +23,93 @@ from six.moves import queue
 from six.moves import xrange  # pylint: disable=redefined-builtin
 
 from tensorflow.python.client import session as session_lib
+from tensorflow.python.eager import context
+from tensorflow.python.eager import function
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
+from tensorflow.python.framework import test_util
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import script_ops
 from tensorflow.python.platform import test
 
 
-class PyOpTest(test.TestCase):
+def np_func(x, y):
+  return np.sinh(x) + np.cosh(y)
 
-  def testBasic(self):
 
-    def my_func(x, y):
-      return np.sinh(x) + np.cosh(y)
+def matmul(x, y):
+  return math_ops.matmul(x, y)
 
-    # single type
+
+class PyFuncTest(test.TestCase):
+  """Encapsulates tests for py_func and eager_py_func."""
+
+  # ----- Tests for py_func -----
+  def testSingleType(self):
     with self.test_session():
       x = constant_op.constant(1.0, dtypes.float32)
       y = constant_op.constant(2.0, dtypes.float32)
-      z = script_ops.py_func(my_func, [x, y], dtypes.float32)
-      self.assertEqual(z.eval(), my_func(1.0, 2.0).astype(np.float32))
+      z = self.evaluate(script_ops.py_func(np_func, [x, y], dtypes.float32))
+      self.assertEqual(z, np_func(1.0, 2.0).astype(np.float32))
 
-    # scalar
+  def testScalar(self):
     with self.test_session():
       x = constant_op.constant(1.0, dtypes.float32)
       y = constant_op.constant(2.0, dtypes.float32)
-      z = script_ops.py_func(my_func, [x, y], [dtypes.float32])
-      self.assertEqual(z[0].eval(), my_func(1.0, 2.0).astype(np.float32))
+      z = self.evaluate(
+          script_ops.eager_py_func(np_func, [x, y], [dtypes.float32]))
+      self.assertEqual(z[0], np_func(1.0, 2.0).astype(np.float32))
 
-    # array
+  def testArray(self):
     with self.test_session():
       x = constant_op.constant([1.0, 2.0], dtypes.float64)
       y = constant_op.constant([2.0, 3.0], dtypes.float64)
-      z = script_ops.py_func(my_func, [x, y], [dtypes.float64])
-      self.assertAllEqual(z[0].eval(),
-                          my_func([1.0, 2.0], [2.0, 3.0]).astype(np.float64))
+      z = self.evaluate(script_ops.py_func(np_func, [x, y], [dtypes.float64]))
+      self.assertAllEqual(z[0],
+                          np_func([1.0, 2.0], [2.0, 3.0]).astype(np.float64))
 
-    # a bit exotic type (complex64)
+  def testComplexType(self):
     with self.test_session():
       x = constant_op.constant(1 + 2j, dtypes.complex64)
       y = constant_op.constant(3 + 4j, dtypes.complex64)
-      z, = script_ops.py_func(my_func, [x, y], [dtypes.complex64])
-      self.assertAllClose(z.eval(), my_func(1 + 2j, 3 + 4j))
+      z = self.evaluate(script_ops.py_func(np_func, [x, y], dtypes.complex64))
+      self.assertAllClose(z, np_func(1 + 2j, 3 + 4j))
 
-    # a bit excotic function (rfft)
+  def testRFFT(self):
     with self.test_session():
       x = constant_op.constant([1., 2., 3., 4.], dtypes.float32)
 
       def rfft(x):
         return np.fft.rfft(x).astype(np.complex64)
 
-      y, = script_ops.py_func(rfft, [x], [dtypes.complex64])
-      self.assertAllClose(y.eval(), np.fft.rfft([1., 2., 3., 4.]))
+      y = self.evaluate(script_ops.py_func(rfft, [x], dtypes.complex64))
+      self.assertAllClose(y, np.fft.rfft([1., 2., 3., 4.]))
 
-    # returns a python literal.
+  def testPythonLiteral(self):
     with self.test_session():
 
       def literal(x):
-        return 1.0 if x == 0.0 else 0.0
+        return 1.0 if float(x) == 0.0 else 0.0
 
       x = constant_op.constant(0.0, dtypes.float64)
-      y, = script_ops.py_func(literal, [x], [dtypes.float64])
-      self.assertAllClose(y.eval(), 1.0)
+      y = self.evaluate(script_ops.py_func(literal, [x], dtypes.float64))
+      self.assertAllClose(y, 1.0)
 
-    # returns a list
+  def testList(self):
     with self.test_session():
 
       def list_func(x):
         return [x, x + 1]
 
       x = constant_op.constant(0.0, dtypes.float64)
-      y, z = script_ops.py_func(list_func, [x], [dtypes.float64] * 2)
-      self.assertAllClose(y.eval(), 0.0)
-      self.assertAllClose(z.eval(), 1.0)
+      y = self.evaluate(
+          script_ops.py_func(list_func, [x], [dtypes.float64] * 2))
+      self.assertAllClose(y, [0.0, 1.0])
 
+  def testTuple(self):
     # returns a tuple
     with self.test_session():
 
@@ -106,17 +117,17 @@ class PyOpTest(test.TestCase):
         return x, x + 1
 
       x = constant_op.constant(0.0, dtypes.float64)
-      y, z = script_ops.py_func(tuple_func, [x], [dtypes.float64] * 2)
-      self.assertAllClose(y.eval(), 0.0)
-      self.assertAllClose(z.eval(), 1.0)
+      y = self.evaluate(
+          script_ops.py_func(tuple_func, [x], [dtypes.float64] * 2))
+      self.assertAllClose(y, [0.0, 1.0])
 
     # returns a tuple, Tout and inp a tuple
     with self.test_session():
       x = constant_op.constant(0.0, dtypes.float64)
-      y, z = script_ops.py_func(tuple_func, (x,), (dtypes.float64,
-                                                   dtypes.float64))
-      self.assertAllClose(y.eval(), 0.0)
-      self.assertAllClose(z.eval(), 1.0)
+      y = self.evaluate(
+          script_ops.py_func(tuple_func, (x,),
+                             (dtypes.float64, dtypes.float64)))
+      self.assertAllClose(y, [0.0, 1.0])
 
   def testStrings(self):
 
@@ -128,10 +139,12 @@ class PyOpTest(test.TestCase):
 
     with self.test_session():
       x = constant_op.constant([b"hello", b"hi"], dtypes.string)
-      y, = script_ops.py_func(read_fixed_length_numpy_strings, [],
-                              [dtypes.string])
-      z, = script_ops.py_func(read_and_return_strings, [x, y], [dtypes.string])
-      self.assertListEqual(list(z.eval()), [b"hello there", b"hi there"])
+      y = self.evaluate(
+          script_ops.py_func(read_fixed_length_numpy_strings, [],
+                             dtypes.string))
+      z = self.evaluate(
+          script_ops.py_func(read_and_return_strings, [x, y], dtypes.string))
+      self.assertAllEqual(z, [b"hello there", b"hi there"])
 
   def testStringsAreConvertedToBytes(self):
 
@@ -143,10 +156,12 @@ class PyOpTest(test.TestCase):
 
     with self.test_session():
       x = constant_op.constant(["hello", "hi"], dtypes.string)
-      y, = script_ops.py_func(read_fixed_length_numpy_strings, [],
-                              [dtypes.string])
-      z, = script_ops.py_func(read_and_return_strings, [x, y], [dtypes.string])
-      self.assertListEqual(list(z.eval()), [b"hello there", b"hi there"])
+      y = self.evaluate(
+          script_ops.py_func(read_fixed_length_numpy_strings, [],
+                             dtypes.string))
+      z = self.evaluate(
+          script_ops.py_func(read_and_return_strings, [x, y], dtypes.string))
+      self.assertAllEqual(z, [b"hello there", b"hi there"])
 
   def testObjectArraysAreConvertedToBytes(self):
 
@@ -186,16 +201,8 @@ class PyOpTest(test.TestCase):
 
   def testNoInput(self):
     with self.test_session():
-      x, = script_ops.py_func(lambda: 42.0, [], [dtypes.float64])
-      self.assertAllClose(x.eval(), 42.0)
-
-  def testCleanup(self):
-    for _ in xrange(1000):
-      g = ops.Graph()
-      with g.as_default():
-        c = constant_op.constant([1.], dtypes.float32)
-        _ = script_ops.py_func(lambda x: x + 1, [c], [dtypes.float32])
-    self.assertTrue(script_ops._py_funcs.size() < 100)
+      x = self.evaluate(script_ops.py_func(lambda: 42.0, [], dtypes.float64))
+      self.assertAllClose(x, 42.0)
 
   def testAlias(self):
     with self.test_session():
@@ -242,8 +249,8 @@ class PyOpTest(test.TestCase):
       # Create a numpy array aliasing a tensor and a tensor aliasing this array
       z, = script_ops.py_func(ident, [p], [dtypes.float32])
       z += 0.0  # Makes sure we release the tensor aliasing the numpy array x[0]
-                # above instead of using its memory as the return value of
-                # session.run
+      # above instead of using its memory as the return value of
+      # session.run
       self.assertEqual(0.0, z.eval(feed_dict={p: [0.0]}))
 
   def testStateful(self):
@@ -319,10 +326,10 @@ class PyOpTest(test.TestCase):
       def value(self):
         return self._value
 
-    with self.test_session() as sess:
+    with self.test_session():
       s = State()
       op = s.increment(constant_op.constant(2, dtypes.int64))
-      ret = sess.run(op)
+      ret = self.evaluate(op)
       self.assertIsNone(ret)
       self.assertAllEqual([3], s.value)
 
@@ -336,15 +343,24 @@ class PyOpTest(test.TestCase):
     with self.test_session() as sess:
       self.assertEqual(sess.run(f), [])
 
-  def _testExceptionHandling(self, py_exp, tf_exp):
+  def _testExceptionHandling(self, py_exp, tf_exp, eager=False):
 
     def raise_exception():
       raise py_exp("blah")  # pylint: disable=not-callable
 
-    f = script_ops.py_func(raise_exception, [], [])
-    with self.test_session() as sess:
+    if eager:
+      if context.in_eager_mode():
+        with self.assertRaisesRegexp(tf_exp, "blah"):
+          f = script_ops.eager_py_func(raise_exception, [], [])
+        return
+      else:
+        f = script_ops.eager_py_func(raise_exception, [], [])
+    else:
+      f = script_ops.py_func(raise_exception, [], [])
+
+    with self.test_session():
       with self.assertRaisesRegexp(tf_exp, "blah"):
-        sess.run(f)
+        self.evaluate(f)
 
   def testExceptionHandling(self):
     self._testExceptionHandling(ValueError, errors.InvalidArgumentError)
@@ -358,6 +374,89 @@ class PyOpTest(test.TestCase):
 
     self._testExceptionHandling(WeirdError, errors.UnknownError)
 
+  # ----- Tests shared by py_func and eager_py_func -----
+  def testCleanup(self):
+    for _ in xrange(1000):
+      g = ops.Graph()
+      with g.as_default():
+        c = constant_op.constant([1.], dtypes.float32)
+        _ = script_ops.py_func(lambda x: x + 1, [c], [dtypes.float32])
+        _ = script_ops.eager_py_func(lambda x: x + 1, [c], [dtypes.float32])
+    self.assertTrue(script_ops._py_funcs.size() < 100)
+
+  # ----- Tests for eager_py_func -----
+  @test_util.run_in_graph_and_eager_modes()
+  def testEagerSingleOutputInt32(self):
+    a = array_ops.ones((3, 3), dtype=dtypes.int32)
+    x = array_ops.ones((3, 1), dtype=dtypes.int32)
+    output = script_ops.eager_py_func(matmul, inp=[a, x], Tout=dtypes.int32)
+    with self.test_session():
+      ret = self.evaluate(output)
+      self.assertAllEqual(ret, [[3], [3], [3]])
+
+  @test_util.run_in_graph_and_eager_modes()
+  def testEagerSingleOutputFloat32(self):
+    a = array_ops.ones((3, 3), dtype=dtypes.float32)
+    x = array_ops.ones((3, 1), dtype=dtypes.float32)
+    output = script_ops.eager_py_func(matmul, inp=[a, x], Tout=dtypes.float32)
+    with self.test_session():
+      ret = self.evaluate(output)
+      self.assertAllClose(ret, [[3.0], [3.0], [3.0]])
+
+  @test_util.run_in_graph_and_eager_modes()
+  def testEagerArrayOutput(self):
+    a = array_ops.ones((3, 3), dtype=dtypes.int32)
+    x = array_ops.ones((3, 1), dtype=dtypes.int32)
+    output = script_ops.eager_py_func(
+        lambda a, x: [matmul(a, x)], inp=[a, x], Tout=[dtypes.int32])
+
+    with self.test_session():
+      ret = self.evaluate(output)
+      self.assertAllEqual(ret, [[[3], [3], [3]]])
+
+  @test_util.run_in_graph_and_eager_modes()
+  def testEagerReturnNone(self):
+
+    def no_return_value():
+      return
+
+    output = script_ops.eager_py_func(no_return_value, inp=[], Tout=[])
+    ret = self.evaluate(output)
+    if context.in_eager_mode():
+      self.assertEquals(len(ret), 0)
+    else:
+      self.assertIsNone(ret)
+
+  @test_util.run_in_graph_and_eager_modes()
+  def testEagerPyFuncInDefun(self):
+
+    def wrapper():
+      a = array_ops.ones((3, 3), dtype=dtypes.int32)
+      x = array_ops.ones((3, 1), dtype=dtypes.int32)
+      return script_ops.eager_py_func(matmul, inp=[a, x], Tout=dtypes.int32)
+
+    wrapped = function.defun(wrapper)
+    ret = self.evaluate(wrapped())
+    self.assertAllEqual(ret, [[3], [3], [3]])
+
+  @test_util.run_in_graph_and_eager_modes()
+  def testEagerExceptionHandling(self):
+    self._testExceptionHandling(
+        ValueError, errors.InvalidArgumentError, eager=True)
+    self._testExceptionHandling(
+        TypeError, errors.InvalidArgumentError, eager=True)
+    self._testExceptionHandling(
+        StopIteration, errors.OutOfRangeError, eager=True)
+    self._testExceptionHandling(
+        MemoryError, errors.ResourceExhaustedError, eager=True)
+    self._testExceptionHandling(
+        NotImplementedError, errors.UnimplementedError, eager=True)
+
+    class WeirdError(Exception):
+      pass
+
+    self._testExceptionHandling(WeirdError, errors.UnknownError, eager=True)
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/lib/core/py_func.cc b/tensorflow/python/lib/core/py_func.cc
index a42282b0553..eae1c2eea69 100644
--- a/tensorflow/python/lib/core/py_func.cc
+++ b/tensorflow/python/lib/core/py_func.cc
@@ -18,6 +18,8 @@ limitations under the License.
 #include <array>
 
 #include "numpy/arrayobject.h"
+#include "tensorflow/c/eager/c_api.h"
+#include "tensorflow/c/tf_status_helper.h"
 #include "tensorflow/core/framework/allocation_description.pb.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/lib/core/errors.h"
@@ -25,8 +27,10 @@ limitations under the License.
 #include "tensorflow/core/platform/macros.h"
 #include "tensorflow/core/platform/mutex.h"
 #include "tensorflow/core/platform/types.h"
+#include "tensorflow/python/eager/pywrap_tfe.h"
 #include "tensorflow/python/lib/core/ndarray_tensor_bridge.h"
 #include "tensorflow/python/lib/core/py_util.h"
+#include "tensorflow/python/lib/core/safe_ptr.h"
 #include <Python.h>
 
 namespace tensorflow {
@@ -48,6 +52,9 @@ struct PyCall {
   // with this "token".
   string token;
 
+  // True if the call is associated with an EagerPyFunc.
+  bool eager;
+
   // Inputs and outputs of this function invocation.
   std::vector<Tensor> ins;
   std::vector<Tensor> out;
@@ -55,19 +62,26 @@ struct PyCall {
 
 // Givens the 'call', prepares the token and inputs as a python tuple
 // that is appropriate for calling the trampoline.
-Status MakeArgTuple(PyCall* call, PyObject** tuple) {
+Status MakeArgTuple(const PyCall* call, PyObject** tuple) {
   int64 n = call->ins.size();
   PyObject* lst = PyList_New(n);
   CHECK(lst);
   for (int64 i = 0; i < n; ++i) {
+    PyObject* arg = nullptr;
     const Tensor& t = call->ins[i];
-    PyObject* a = nullptr;
-    Status s = ConvertTensorToNdarray(t, &a);
-    if (!s.ok()) {
-      Py_DECREF(lst);
-      return s;
+    if (call->eager) {
+      arg = EagerTensorFromHandle(TFE_NewTensorHandle(t));
+      if (arg == nullptr) {
+        return errors::Internal("Unable to procure EagerTensor from Tensor.");
+      }
+    } else {
+      Status s = ConvertTensorToNdarray(t, &arg);
+      if (!s.ok()) {
+        Py_DECREF(lst);
+        return s;
+      }
     }
-    PyList_SetItem(lst, i, a);
+    PyList_SetItem(lst, i, arg);
   }
   *tuple = Py_BuildValue("(sN)", call->token.c_str(), lst);
   CHECK(*tuple);
@@ -133,6 +147,18 @@ bool IsSingleNone(PyObject* obj) {
   return item == Py_None;
 }
 
+// Retrieves a Tensor from `eager_tensor` and stores it in `output_tensor`.
+Status ExtractTensorFromEagerTensor(const PyObject* eager_tensor,
+                                    Tensor* output_tensor,
+                                    TF_Status* tf_status) {
+  // TODO(akshayka): Lift the restriction requiring output tensors to
+  // lie in host memory; EagerPyFunc should be able to dispatch ops on GPU
+  // tensors, so we should eventually implement a GPU kernel for EagerPyFunc.
+  *output_tensor = *TFE_TensorHandleUnderlyingTensorInHostMemory(
+      EagerTensor_Handle(eager_tensor), tf_status);
+  return StatusFromTF_Status(tf_status);
+}
+
 // Calls the registered py function through the trampoline.
 Status DoCallPyFunc(PyCall* call, bool* out_log_on_error) {
   *out_log_on_error = true;
@@ -172,21 +198,37 @@ Status DoCallPyFunc(PyCall* call, bool* out_log_on_error) {
     }
   }
 
-  // Process the return values and converts them to tf Tensors.
+  // Process the return values and convert them to TF Tensors.
   Status s;
   if (PyList_Check(result)) {
-    // 'result' is a list.
     call->out.clear();
     for (int i = 0; i < PyList_Size(result); ++i) {
       Tensor t;
-      s = ConvertNdarrayToTensor(PyList_GetItem(result, i), &t);
+      if (call->eager) {
+        auto tf_status = tensorflow::make_safe(TF_NewStatus());
+        s = ExtractTensorFromEagerTensor(PyList_GetItem(result, i), &t,
+                                         tf_status.get());
+      } else {
+        s = ConvertNdarrayToTensor(PyList_GetItem(result, i), &t);
+      }
+
       if (!s.ok()) {
         break;
       }
       call->out.push_back(t);
     }
+  } else if (EagerTensor_CheckExact(result) || result == Py_None) {
+    DCHECK(call->eager);
+    Tensor t;
+    if (result != Py_None) {
+      auto tf_status = tensorflow::make_safe(TF_NewStatus());
+      s = ExtractTensorFromEagerTensor(result, &t, tf_status.get());
+      if (s.ok()) {
+        call->out.push_back(t);
+      }
+    }
   } else if (PyArray_Check(result)) {
-    // 'result' is a single ndarray.
+    DCHECK(!call->eager);
     if (!IsSingleNone(result)) {
       Tensor t;
       s = ConvertNdarrayToTensor(result, &t);
@@ -375,11 +417,13 @@ class PyFuncOp : public OpKernel {
  public:
   explicit PyFuncOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
     OP_REQUIRES_OK(ctx, ctx->GetAttr("token", &token_));
+    eager_ = type_string() == "EagerPyFunc";
   }
 
   void Compute(OpKernelContext* ctx) override {
     PyCall call;
     call.token = token_;
+    call.eager = eager_;
     for (int i = 0; i < ctx->num_inputs(); ++i) {
       call.ins.push_back(ctx->input(i));
     }
@@ -418,9 +462,15 @@ class PyFuncOp : public OpKernel {
  private:
   string token_;
 
+  // True if and only if this op should execute the python function eagerly,
+  // i.e., if and only if the eager attribute is set.
+  bool eager_;
+
   TF_DISALLOW_COPY_AND_ASSIGN(PyFuncOp);
 };
+
 REGISTER_KERNEL_BUILDER(Name("PyFunc").Device(DEVICE_CPU), PyFuncOp);
 REGISTER_KERNEL_BUILDER(Name("PyFuncStateless").Device(DEVICE_CPU), PyFuncOp);
+REGISTER_KERNEL_BUILDER(Name("EagerPyFunc").Device(DEVICE_CPU), PyFuncOp);
 
 }  // end namespace tensorflow
diff --git a/tensorflow/python/lib/core/py_func.h b/tensorflow/python/lib/core/py_func.h
index 5a451d5f432..3197a7ddfa0 100644
--- a/tensorflow/python/lib/core/py_func.h
+++ b/tensorflow/python/lib/core/py_func.h
@@ -24,21 +24,27 @@ limitations under the License.
 
 namespace tensorflow {
 
-// Called by py code on initialization.
+// Called by python code on initialization.
 //
 // "trampoline" must represent a python function which has the
 // following signature:
-//   (string, list(ndarray)) -> ndarray | list(ndarray) | python scalar
+//   (string, list(ndarray)) | (string, list(EagerTensor)) ->
+//     ndarray | list(ndarray) | python scalar |
+//     EagerTensor | list(EagerTensor) | None
 //
 // The trampoline takes two arguments, the first is a string token
 // used by the python frontend's dispatching logic; the second is a
-// list of numpy ndarrays.
+// list of numpy ndarrays or EagerTensor objects. It can return a
+// single numpy ndarray, a list of numpy ndarrays, a python scalar, an
+// EagerTensor, a list of EagerTensors, or None.
 //
-// The trampoline can return a single numpy ndarray, a list of numpy
-// ndarrays, or a simply python scalar. The C++ runtime converts them,
-// if supported, back to Tensor objects.
+// PyFunc requires inputs and outputs to be ndarrays. EagerPyFunc requires
+// inputs to be a list of EagerTensors and outputs to be an EagerTensor, a list
+// of EagerTensors, or None.
 //
-// This is called by script_ops.py during its module initialization.
+// The C++ runtime converts outputs back to Tensor objects.
+//
+// This function is called by script_ops.py during its module initialization.
 //
 // TODO(zhifengc): Support distributed runtime.
 void InitializePyTrampoline(PyObject* trampoline);
diff --git a/tensorflow/python/ops/hidden_ops.txt b/tensorflow/python/ops/hidden_ops.txt
index af014a7e393..b3f7c26695f 100644
--- a/tensorflow/python/ops/hidden_ops.txt
+++ b/tensorflow/python/ops/hidden_ops.txt
@@ -341,6 +341,7 @@ TruncatedNormal
 # script_ops
 PyFunc
 PyFuncStateless
+EagerPyFunc
 
 # sdca_ops
 
diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py
index 2c3667dffed..c0c1ade4954 100644
--- a/tensorflow/python/ops/script_ops.py
+++ b/tensorflow/python/ops/script_ops.py
@@ -29,11 +29,41 @@ import numpy as np
 import six
 
 from tensorflow.python import pywrap_tensorflow
+from tensorflow.python.eager import context
 from tensorflow.python.framework import function
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import gen_script_ops
 
 
+class EagerFunc(object):
+  """A wrapper for a function owned by an EagerPyFunc."""
+
+  def __init__(self, func, Tout):
+    """Constructs an EagerFunc.
+
+    Args:
+      func: The function to wrap.
+      Tout: A list of datatypes for the output; an empty list if the output is
+            None.
+    """
+    self._func = func
+    self._out_dtypes = Tout
+
+  def __call__(self, *args, **kwargs):
+    """Passes args, kwargs to `self._func`, which is executed eagerly."""
+    with context.eager_mode():
+      ret = self._func(*args, **kwargs)
+      if isinstance(ret, (tuple, list)):
+        return [
+            ops.convert_to_tensor(x, dtype=dtype)
+            for (x, dtype) in zip(ret, self._out_dtypes)
+        ]
+      elif ret is None:
+        return ret
+      else:
+        return ops.convert_to_tensor(ret, dtype=self._out_dtypes[0])
+
+
 class FuncRegistry(object):
   """A helper class to keep track of registered py functions.
 
@@ -91,16 +121,20 @@ class FuncRegistry(object):
     if func is None:
       raise ValueError("callback %s is not found" % token)
     ret = func(*args)
-    # Strings seem to lead to a memory leak here if they're not wrapped in a
-    # list.
-    if isinstance(ret, six.binary_type):
-      ret = [ret]
-    # Ensures that we return either a single numpy array or a list of numpy
-    # arrays.
-    if isinstance(ret, (tuple, list)):
-      return [self._convert(x) for x in ret]
+
+    if isinstance(func, EagerFunc):
+      return ret
     else:
-      return self._convert(ret)
+      # Strings seem to lead to a memory leak here if they're not wrapped in a
+      # list.
+      if isinstance(ret, six.binary_type):
+        ret = [ret]
+      # Ensures that we return either a single numpy array or a list of numpy
+      # arrays.
+      if isinstance(ret, (tuple, list)):
+        return [self._convert(x) for x in ret]
+      else:
+        return self._convert(ret)
 
   def size(self):
     """Returns how many functions are currently registered."""
@@ -129,6 +163,86 @@ class CleanupFunc(object):
     _py_funcs.remove(self._token)
 
 
+def _internal_py_func(func, inp, Tout, stateful=None, eager=False, name=None):
+  """See documentation for py_func and eager_py_func."""
+
+  is_list_or_tuple = False
+  if isinstance(Tout, (list, tuple)):
+    is_list_or_tuple = True
+  else:
+    Tout = [Tout]
+
+  if eager:
+    func = EagerFunc(func, Tout)
+
+  token = _py_funcs.insert(func)
+  # We tie the registered function's lifetime with the current default graph,
+  # i.e., when the current graph is destroyed, we remove its py funcs.
+  graph = ops.get_default_graph()
+
+  # pylint: disable=protected-access
+  while isinstance(graph, function._FuncGraph):
+    # If the py_func was declared inside a _FuncGraph, its lifetime should be
+    # bound to that of the outer graph instead.
+    graph = graph._outer_graph
+
+  cleanup = CleanupFunc(token)
+
+  # TODO(zhifengc): Consider adding a Graph method to collect
+  # `cleanup` objects in one of its member.
+  if not hasattr(graph, "_cleanup_py_funcs_used_in_graph"):
+    graph._cleanup_py_funcs_used_in_graph = []
+
+  # When `graph` is destroyed, elements in _cleanup_py_funcs_used_in_graph
+  # will be destroyed and their __del__ will remove the 'token' from
+  # the funcs registry.
+  graph._cleanup_py_funcs_used_in_graph.append(cleanup)
+  # pylint: enable=protected-access
+
+  # pylint: disable=protected-access
+  if eager:
+    result = gen_script_ops._eager_py_func(
+        input=inp, token=token, Tout=Tout, name=name)
+  else:
+    if stateful:
+      result = gen_script_ops._py_func(
+          input=inp, token=token, Tout=Tout, name=name)
+    else:
+      result = gen_script_ops._py_func_stateless(
+          input=inp, token=token, Tout=Tout, name=name)
+  # pylint: enable=protected-access
+  return result if is_list_or_tuple else result[0]
+
+
+def eager_py_func(func, inp, Tout, name=None):
+  """Wraps a python function into a TensorFlow op.
+
+  When the returned op is executed, `func` is invoked with eager execution
+  enabled. Inputs are Tensor objects and func must return None or objects
+  that may be converted to Tensor objects.
+
+  This function has the same limitations as `py_func` with respect to
+  serialization and distribution.
+
+  Args:
+    func: A Python function which accepts a list of `Tensor` objects
+      having element types that match the corresponding `tf.Tensor` objects
+      in `inp` and returns a list of `Tensor` objects (or a single
+      `Tensor`, or `None`) having element types that match the
+      corresponding values in `Tout`.
+    inp: A list of `Tensor` objects.
+    Tout: A list or tuple of tensorflow data types or a single tensorflow data
+      type if there is only one, indicating what `func` returns; an empty list
+      if no value is returned (i.e., if the return value is `None`).
+    name: A name for the operation (optional).
+
+  Returns:
+    A list of `Tensor` or a single `Tensor` which `func` computes; an empty list
+    if `func` returns None.
+  """
+  return _internal_py_func(func=func, inp=inp, Tout=Tout, eager=True, name=name)
+
+
 def py_func(func, inp, Tout, stateful=True, name=None):
   """Wraps a python function and uses it as a TensorFlow op.
 
@@ -182,46 +296,12 @@ def py_func(func, inp, Tout, stateful=True, name=None):
   Returns:
     A list of `Tensor` or a single `Tensor` which `func` computes.
   """
-  token = _py_funcs.insert(func)
-  # We tie the registered function's life-time with the current
-  # default graph. I.e., when the current graph is destroyed, we
-  # should remove its py funcs.
-  g = ops.get_default_graph()
-
-  # pylint: disable=protected-access
-  while isinstance(g, function._FuncGraph):
-    # If the py_func was declared inside a _FuncGraph, its lifetime should be
-    # bound to that of the outer graph instead.
-    g = g._outer_graph
-
-  cleanup = CleanupFunc(token)
-
-  # TODO(zhifengc): Consider adding a Graph method to collect
-  # `cleanup` objects in one of its member.
-  if not hasattr(g, "_cleanup_py_funcs_used_in_graph"):
-    g._cleanup_py_funcs_used_in_graph = []
-
-  # When g is destroyed, elements in _cleanup_py_funcs_used_in_graph
-  # will be destroyed and their __del__ will remove the 'token' from
-  # the funcs registry.
-  g._cleanup_py_funcs_used_in_graph.append(cleanup)
-  # pylint: enable=protected-access
-
-  if isinstance(Tout, (list, tuple)):
-    is_list_or_tuple = True
-  else:
-    Tout = [Tout]
-    is_list_or_tuple = False
-  # pylint: disable=protected-access
-  if stateful:
-    result = gen_script_ops._py_func(
-        input=inp, token=token, Tout=Tout, name=name)
-  else:
-    result = gen_script_ops._py_func_stateless(
-        input=inp, token=token, Tout=Tout, name=name)
-  # pylint: enable=protected-access
-  return result if is_list_or_tuple else result[0]
+  return _internal_py_func(
+      func=func, inp=inp, Tout=Tout, stateful=stateful, eager=False, name=name)
 
 
+# TODO(akshayka): PyFuncs where the 'eager' attribute is set to True should be
+# differentiable, i.e., the gradient of PyFunc should propagate Nones if the
+# eager attribute is not set, and otherwise, it should return the gradient.
 ops.NotDifferentiable("PyFunc")
 ops.NotDifferentiable("PyFuncStateless")