diff --git a/fastNLP/core/callback.py b/fastNLP/core/callback.py
index ed1b0697..cd97b64b 100644
--- a/fastNLP/core/callback.py
+++ b/fastNLP/core/callback.py
@@ -29,7 +29,7 @@ callback模块实现了 fastNLP 中的许多 callback 类，用于增强 :class:
                 callback.on_valid_end()  # 可以进行在其它数据集上进行验证
         callback.on_epoch_end()  # epoch结束调用
     callback.on_train_end() # 训练结束
-    callback.on_exception() # 这是一个特殊的步骤，在训练过程中遭遇exception会跳转到这里
+    callback.on_exception() # 这是一个特殊的步骤，在训练过程中遭遇exception会跳转到这里。
 
 如下面的例子所示，我们可以使用内置的 callback 类，或者继承 :class:`~fastNLP.core.callback.Callback`
 定义自己的 callback 类::
@@ -64,7 +64,7 @@ __all__ = [
 import os
 
 import torch
-
+from copy import deepcopy
 try:
     from tensorboardX import SummaryWriter
     
@@ -73,7 +73,13 @@ except:
     tensorboardX_flag = False
 
 from ..io.model_io import ModelSaver, ModelLoader
+from .dataset import DataSet
+from .tester import Tester
 
+try:
+    import fitlog
+except:
+    pass
 
 class Callback(object):
     """
@@ -425,6 +431,90 @@ class EarlyStopCallback(Callback):
         else:
             raise exception  # 抛出陌生Error
 
+class FitlogCallback(Callback):
+    """
+    别名: :class:`fastNLP.FitlogCallback` :class:`fastNLP.core.callback.FitlogCallback`
+
+    该callback将loss和progress自动写入到fitlog中; 如果Trainer有dev的数据，将自动把dev的结果写入到log中; 同时还支持传入
+        一个(或多个)test数据集进行测试(只有在trainer具有dev时才能使用)，每次在dev上evaluate之后会在这些数据集上验证一下。
+        并将验证结果写入到fitlog中。这些数据集的结果是根据dev上最好的结果报道的，即如果dev在第3个epoch取得了最佳，则
+        fitlog中记录的关于这些数据集的结果就是来自第三个epoch的结果。
+
+    :param DataSet,dict(DataSet) data: 传入DataSet对象，会使用多个Trainer中的metric对数据进行验证。如果需要传入多个
+        DataSet请通过dict的方式传入，dict的key将作为对应dataset的name传递给fitlog。若tester不为None时，data需要通过
+        dict的方式传入。如果仅传入DataSet, 则被命名为test
+    :param Tester tester: Tester对象，将在on_valid_end时调用。tester中的会被命名为test
+    :param int verbose: 是否在终端打印内容，0不打印
+    :param bool log_exception: fitlog是否记录发生的exception信息
+    """
+
+    def __init__(self, data=None, tester=None, verbose=0, log_exception=False):
+        super().__init__()
+        self.datasets = {}
+        self.testers = {}
+        self._log_exception = log_exception
+        if tester is not None:
+            assert isinstance(tester, Tester), "Only fastNLP.Tester allowed."
+            assert isinstance(data, dict) or data is None, "If tester is not None, only dict[DataSet] allowed for data."
+            if data is not None:
+                assert 'test' not in data, "Cannot use `test` as DataSet key, when tester is passed."
+            setattr(tester, 'verbose', 0)
+            self.testers['test'] = tester
+
+        if isinstance(data, dict):
+            for key, value in data.items():
+                assert isinstance(value, DataSet), f"Only DataSet object is allowed, not {type(value)}."
+            for key, value in data.items():
+                self.datasets[key] = value
+        elif isinstance(data, DataSet):
+            self.datasets['test'] = data
+        else:
+            raise TypeError("data receives dict[DataSet] or DataSet object.")
+
+        self.verbose = verbose
+
+    def on_train_begin(self):
+        if (len(self.datasets)>0 or len(self.testers)>0 ) and self.trainer.dev_data is None:
+            raise RuntimeError("Trainer has no dev data, you cannot pass extra data to do evaluation.")
+
+        if len(self.datasets)>0:
+            for key, data in self.datasets.items():
+                tester = Tester(data=data, model=self.model, batch_size=self.batch_size, metrics=self.trainer.metrics,
+                                verbose=0)
+                self.testers[key] = tester
+        fitlog.add_progress(total_steps=self.n_steps)
+
+    def on_backward_begin(self, loss):
+        fitlog.add_loss(loss.item(), name='loss', step=self.step, epoch=self.epoch)
+
+    def on_valid_end(self, eval_result, metric_key, optimizer, better_result):
+        if better_result:
+            eval_result = deepcopy(eval_result)
+            eval_result['step'] = self.step
+            eval_result['epoch'] = self.epoch
+            fitlog.add_best_metric(eval_result)
+        fitlog.add_metric(eval_result, step=self.step, epoch=self.epoch)
+        if len(self.testers)>0:
+            for key, tester in self.testers.items():
+                try:
+                    eval_result = tester.test()
+                    if self.verbose!=0:
+                        self.pbar.write("Evaluation on DataSet {}:".format(key))
+                        self.pbar.write(tester._format_eval_results(eval_result))
+                    fitlog.add_metric(eval_result, name=key, step=self.step, epoch=self.epoch)
+                    if better_result:
+                        fitlog.add_best_metric(eval_result, name=key)
+                except Exception:
+                    self.pbar.write("Exception happens when evaluate on DataSet named `{}`.".format(key))
+
+    def on_train_end(self):
+        fitlog.finish()
+
+    def on_exception(self, exception):
+        fitlog.finish(status=1)
+        if self._log_exception:
+            fitlog.add_other(str(exception), name='except_info')
+
 
 class LRScheduler(Callback):
     """