diff --git a/README.md b/README.md
index 11e101225f262c9a21af3d4d338ce4e3664726bd..3bd8061115e0c073edc0894b8c59be862b4009b0 100644
--- a/README.md
+++ b/README.md
@@ -52,6 +52,7 @@ X2Paddle是飞桨生态下的模型转换工具，致力于帮助其它深度学
 - tensorflow == 1.14 (如需转换TensorFlow模型)
 - onnx >= 1.6.0 (如需转换ONNX模型)
 - torch >= 1.5.0 (如需转换PyTorch模型)
+- paddlelite == 2.9.0 (如需一键转换成Paddle-Lite支持格式)
 
 ### pip安装(推荐）
 
@@ -100,6 +101,12 @@ x2paddle --framework=caffe --prototxt=deploy.prototxt --weight=deploy.caffemodel
 | --model              | 当framework为tensorflow/onnx时，该参数指定tensorflow的pb模型文件或onnx模型路径 |
 | --caffe_proto        | **[可选]** 由caffe.proto编译成caffe_pb2.py文件的存放路径，当存在自定义Layer时使用，默认为None |
 | --define_input_shape | **[可选]** For TensorFlow, 当指定该参数时，强制用户输入每个Placeholder的shape，见[文档Q2](./docs/inference_model_convertor/FAQ.md) |
+| --to_lite            | **[可选]** 是否使用opt工具转成Paddle-Lite支持格式，默认为False |
+| --lite_valid_places  | **[可选]** 指定转换类型，可以同时指定多个backend(以逗号分隔)，opt将会自动选择最佳方式，默认为arm |
+| --lite_model_type    | **[可选]** 指定模型转化类型，目前支持两种类型：protobuf和naive_buffer，默认为naive_buffer |
+
+#### 一键转换Paddle-Lite支持格式
+可参考[使用X2paddle导出Padde-Lite支持格式](docs/inference_model_convertor/convert2lite_api.md)
 
 ### 功能二：PyTorch模型训练迁移
 
diff --git a/docs/inference_model_convertor/convert2lite_api.md b/docs/inference_model_convertor/convert2lite_api.md
new file mode 100644
index 0000000000000000000000000000000000000000..aa96911cfe30b432942092a2352afe132bc4d543
--- /dev/null
+++ b/docs/inference_model_convertor/convert2lite_api.md
@@ -0,0 +1,110 @@
+# X2paddle Convert2Lite API
+
+
+## 目录
+* [x2paddle.convert.tf2paddle](#1)
+* [x2paddle.convert.caffe2paddle](#2)
+* [x2paddle.convert.onnx2paddle](#3)
+* [x2paddle.convert.pytorch2paddle](#4)
+
+
+## 使用X2paddle导出Padde-Lite支持格式
+
+**背景**：如果想用Paddle-Lite运行第三方来源（TensorFlow、Caffe、ONNX、PyTorch）模型，一般需要经过两次转化。即使用X2paddle工具将第三方模型转化为PaddlePaddle格式，再使用opt将PaddlePaddle模型转化为Padde-Lite可支持格式。
+
+**使用方法**：为了简化这一过程，X2Paddle集成了opt工具，提供一键转换功能，包括API以及命令行，以ONNX为例：
+
+***API方式***
+ ```python
+from x2paddle.convert import onnx2paddle
+
+onnx2paddle(model_path, save_dir,
+            convert_to_lite=True,
+            lite_valid_places="arm",
+            lite_model_type="naive_buffer")
+# model_path(str)为ONNX模型路径
+# save_dir(str)为转换后模型保存路径
+# convert_to_lite(bool)表示是否使用opt工具，默认为False
+# lite_valid_places(str)指定转换类型，默认为arm
+# lite_model_type(str)指定模型转化类型，默认为naive_buffer
+```
+
+***命令行方式***
+```shell
+x2paddle --framework=onnx --model=onnx_model.onnx --save_dir=pd_model --to_lite=True --lite_valid_places=arm --lite_model_type=naive_buffer
+```
+
+TensorFlow、Caffe以及Pytorch模型转换参考如下API
+
+
+## <h2 id="1">x2paddle.convert.tf2paddle</h2>
+
+```python
+x2paddle.convert.tf2paddle(model_path, save_dir, define_input_shape=False, convert_to_lite=False, lite_valid_places="arm", lite_model_type="naive_buffer")
+```
+
+> 转换TensorFlow模型。
+
+> **参数**
+>
+> > - **model_path** (str): TensorFlow pb模型路径
+> > - **save_dir** (str): 转换后模型保存路径
+> > - **define_input_shape** (bool): 是否指定输入大小，默认为False
+> > - **convert_to_lite** (bool): 是否使用opt工具转成Paddle-Lite支持格式，默认为False
+> > - **lite_valid_places** (str): 指定转换类型，可以同时指定多个backend(以逗号分隔)，opt将会自动选择最佳方式，默认为arm
+> > - **lite_model_type** (str): 指定模型转化类型，目前支持两种类型：protobuf和naive_buffer，默认为naive_buffer
+
+
+## <h2 id="2">x2paddle.convert.caffe2paddle</h2>
+
+```python
+x2paddle.convert.caffe2paddle(proto_file, weight_file, save_dir, caffe_proto, convert_to_lite=False, lite_valid_places="arm", lite_model_type="naive_buffer")
+```
+
+> 转换Caffe模型。
+
+> **参数**
+>
+> > - **proto_file** (str): caffe模型的prototxt文件
+> > - **weight_file** (str): caffe模型的权重文件
+> > - **save_dir** (str): 转换后模型保存路径
+> > - **caffe_proto** (str): 可选：由caffe.proto编译成caffe_pb2.py文件的存放路径，当存在自定义Layer时使用，默认为None
+> > - **convert_to_lite** (bool): 是否使用opt工具转成Paddle-Lite支持格式，默认为False
+> > - **lite_valid_places** (str): 指定转换类型，可以同时指定多个backend(以逗号分隔)，opt将会自动选择最佳方式，默认为arm
+> > - **lite_model_type** (str): 指定模型转化类型，目前支持两种类型：protobuf和naive_buffer，默认为naive_buffer
+
+
+## <h2 id="3">x2paddle.convert.onnx2paddle</h2>
+
+```python
+x2paddle.convert.onnx2paddle(model_path, save_dir, convert_to_lite=False, lite_valid_places="arm", lite_model_type="naive_buffer")
+```
+
+> 转换ONNX模型。
+
+> **参数**
+>
+> > - **model_path** (str): TensorFlow pb模型路径
+> > - **save_dir** (str): 转换后模型保存路径
+> > - **convert_to_lite** (bool): 是否使用opt工具转成Paddle-Lite支持格式，默认为False
+> > - **lite_valid_places** (str): 指定转换类型，可以同时指定多个backend(以逗号分隔)，opt将会自动选择最佳方式，默认为arm
+> > - **lite_model_type** (str): 指定模型转化类型，目前支持两种类型：protobuf和naive_buffer，默认为naive_buffer
+
+
+## <h2 id="4">x2paddle.convert.pytorch2paddle</h2>
+
+```python
+x2paddle.convert.pytorch2paddle(module, save_dir, jit_type="trace", input_examples=None, convert_to_lite=False, lite_valid_places="arm", lite_model_type="naive_buffer")
+```
+
+> 转换Pytorch模型。
+
+> **参数**
+>
+> > - **module** (torch.nn.Module): PyTorch的Module
+> > - **save_dir** (str): 转换后模型保存路径
+> > - **jit_type** (str): 转换方式。目前有两种:trace和script,默认为trace
+> > - **input_examples** (list[torch.tensor]): torch.nn.Module的输入示例，list的长度必须与输入的长度一致。默认为None。
+> > - **convert_to_lite** (bool): 是否使用opt工具转成Paddle-Lite支持格式，默认为False
+> > - **lite_valid_places** (str): 指定转换类型，可以同时指定多个backend(以逗号分隔)，opt将会自动选择最佳方式，默认为arm
+> > - **lite_model_type** (str): 指定模型转化类型，目前支持两种类型：protobuf和naive_buffer，默认为naive_buffer
diff --git a/x2paddle/convert.py b/x2paddle/convert.py
index 24dd0fc837980d5397ef2d7a5d4bbac12b83809f..89268b6ba4e8e2fc7ec1731229eeb45b8947e22e 100644
--- a/x2paddle/convert.py
+++ b/x2paddle/convert.py
@@ -87,11 +87,44 @@ def arg_parser():
         type=_text_type,
         default=None,
         help="pretrain model file of pytorch model")
+    parser.add_argument(
+        "--to_lite", "-tl", default=False, help="convert to Paddle-Lite format")
+    parser.add_argument(
+        "--lite_valid_places",
+        "-vp",
+        type=_text_type,
+        default="arm",
+        help="Specify the executable backend of the model")
+    parser.add_argument(
+        "--lite_model_type",
+        "-mt",
+        type=_text_type,
+        default="naive_buffer",
+        help="The type of lite model")
 
     return parser
 
 
-def tf2paddle(model_path, save_dir, define_input_shape=False):
+def convert2lite(save_dir,
+                 lite_valid_places="arm",
+                 lite_model_type="naive_buffer"):
+    """Convert to Paddle-Lite format."""
+
+    from paddlelite.lite import Opt
+    opt = Opt()
+    opt.set_model_dir(save_dir + "/inference_model")
+    opt.set_valid_places(lite_valid_places)
+    opt.set_model_type(lite_model_type)
+    opt.set_optimize_out(save_dir + "/opt")
+    opt.run()
+
+
+def tf2paddle(model_path,
+              save_dir,
+              define_input_shape=False,
+              convert_to_lite=False,
+              lite_valid_places="arm",
+              lite_model_type="naive_buffer"):
     # check tensorflow installation and version
     try:
         import os
@@ -120,20 +153,28 @@ def tf2paddle(model_path, save_dir, define_input_shape=False):
     graph_opt = GraphOptimizer(source_frame="tf")
     graph_opt.optimize(mapper.paddle_graph)
     mapper.paddle_graph.gen_model(save_dir)
+    if convert_to_lite:
+        convert2lite(save_dir, lite_valid_places, lite_model_type)
 
 
-def caffe2paddle(proto, weight, save_dir, caffe_proto):
+def caffe2paddle(proto_file,
+                 weight_file,
+                 save_dir,
+                 caffe_proto,
+                 convert_to_lite=False,
+                 lite_valid_places="arm",
+                 lite_model_type="naive_buffer"):
     from x2paddle.decoder.caffe_decoder import CaffeDecoder
     from x2paddle.op_mapper.caffe2paddle.caffe_op_mapper import CaffeOpMapper
     import google.protobuf as gpb
     ver_part = gpb.__version__.split('.')
     version_satisfy = False
     if (int(ver_part[0]) == 3 and int(ver_part[1]) >= 6) \
-        or (int(ver_part[0]) > 3):
+            or (int(ver_part[0]) > 3):
         version_satisfy = True
     assert version_satisfy, '[ERROR] google.protobuf >= 3.6.0 is required'
     print("Now translating model from caffe to paddle.")
-    model = CaffeDecoder(proto, weight, caffe_proto)
+    model = CaffeDecoder(proto_file, weight_file, caffe_proto)
     mapper = CaffeOpMapper(model)
     mapper.paddle_graph.build()
     print("Model optimizing ...")
@@ -142,9 +183,15 @@ def caffe2paddle(proto, weight, save_dir, caffe_proto):
     graph_opt.optimize(mapper.paddle_graph)
     print("Model optimized.")
     mapper.paddle_graph.gen_model(save_dir)
+    if convert_to_lite:
+        convert2lite(save_dir, lite_valid_places, lite_model_type)
 
 
-def onnx2paddle(model_path, save_dir):
+def onnx2paddle(model_path,
+                save_dir,
+                convert_to_lite=False,
+                lite_valid_places="arm",
+                lite_model_type="naive_buffer"):
     # check onnx installation and version
     try:
         import onnx
@@ -165,9 +212,17 @@ def onnx2paddle(model_path, save_dir):
     mapper = ONNXOpMapper(model)
     mapper.paddle_graph.build()
     mapper.paddle_graph.gen_model(save_dir)
+    if convert_to_lite:
+        convert2lite(save_dir, lite_valid_places, lite_model_type)
 
 
-def pytorch2paddle(module, save_dir, jit_type="trace", input_examples=None):
+def pytorch2paddle(module,
+                   save_dir,
+                   jit_type="trace",
+                   input_examples=None,
+                   convert_to_lite=False,
+                   lite_valid_places="arm",
+                   lite_model_type="naive_buffer"):
     # check pytorch installation and version
     try:
         import torch
@@ -199,6 +254,8 @@ def pytorch2paddle(module, save_dir, jit_type="trace", input_examples=None):
     graph_opt.optimize(mapper.paddle_graph)
     print("Model optimized.")
     mapper.paddle_graph.gen_model(save_dir, jit_type=jit_type)
+    if convert_to_lite:
+        convert2lite(save_dir, lite_valid_places, lite_model_type)
 
 
 def main():
@@ -250,15 +307,32 @@ def main():
             define_input_shape = False
             if args.define_input_shape:
                 define_input_shape = True
-            tf2paddle(args.model, args.save_dir, define_input_shape)
+            tf2paddle(
+                args.model,
+                args.save_dir,
+                define_input_shape,
+                convert_to_lite=args.to_lite,
+                lite_valid_places=args.lite_valid_places,
+                lite_model_type=args.lite_model_type)
 
         elif args.framework == "caffe":
             assert args.prototxt is not None and args.weight is not None, "--prototxt and --weight should be defined while translating caffe model"
-            caffe2paddle(args.prototxt, args.weight, args.save_dir,
-                         args.caffe_proto)
+            caffe2paddle(
+                args.prototxt,
+                args.weight,
+                args.save_dir,
+                args.caffe_proto,
+                convert_to_lite=args.to_lite,
+                lite_valid_places=args.lite_valid_places,
+                lite_model_type=args.lite_model_type)
         elif args.framework == "onnx":
             assert args.model is not None, "--model should be defined while translating onnx model"
-            onnx2paddle(args.model, args.save_dir)
+            onnx2paddle(
+                args.model,
+                args.save_dir,
+                convert_to_lite=args.to_lite,
+                lite_valid_places=args.lite_valid_places,
+                lite_model_type=args.lite_model_type)
         elif args.framework == "paddle2onnx":
             print(
                 "Paddle to ONNX tool has been migrated to the new github: https://github.com/PaddlePaddle/paddle2onnx"