diff --git a/dsms/knowledge/data_format.py b/dsms/knowledge/data_format.py
new file mode 100644
index 0000000..6f1960a
--- /dev/null
+++ b/dsms/knowledge/data_format.py
@@ -0,0 +1,11 @@
+"""Data Formats"""
+
+from enum import Enum
+
+
+class DataFormat(Enum):
+    """Data formats"""
+
+    JSON = "json"
+    YAML = "yaml"
+    HDF5 = "hdf5"
diff --git a/dsms/knowledge/kitem.py b/dsms/knowledge/kitem.py
index abf4193..50567ff 100644
--- a/dsms/knowledge/kitem.py
+++ b/dsms/knowledge/kitem.py
@@ -1,5 +1,6 @@
 """Knowledge Item implementation of the DSMS"""
 
+import json
 import logging
 import warnings
 from datetime import datetime
@@ -9,6 +10,7 @@
 from uuid import UUID, uuid4
 
 import pandas as pd
+import yaml
 from rdflib import Graph
 
 from pydantic import (  # isort:skip
@@ -48,6 +50,8 @@
     UserGroupsProperty,
 )
 
+from dsms.knowledge.data_format import DataFormat  # isort:skip
+
 from dsms.knowledge.ktype import KType  # isort:skip
 
 from dsms.knowledge.utils import (  # isort:skip
@@ -655,3 +659,41 @@ def is_a(self, to_be_compared: KType) -> bool:
     def refresh(self) -> None:
         """Refresh the KItem"""
         _refresh_kitem(self)
+
+    def export(self, data_format: DataFormat) -> Any:
+        """Export kitems to different formats"""
+
+        if data_format == DataFormat.HDF5:
+            from dsms.knowledge.knowledge_wrapper import (  # isort:skip
+                data_to_dict,
+                dict_to_hdf5,
+            )
+
+            return dict_to_hdf5(data_to_dict(self))
+
+        if data_format == DataFormat.JSON:
+            from dsms.knowledge.knowledge_wrapper import data_to_dict
+
+            return json.dumps(data_to_dict(self))
+
+        if data_format == DataFormat.YAML:
+            from dsms.knowledge.knowledge_wrapper import data_to_dict
+
+            return yaml.dump(data_to_dict(self), default_flow_style=False)
+
+        raise ValueError(f"Unsupported data format: {data_format}")
+
+    def import_kitem(data, data_format: DataFormat) -> Any:
+        """Import objects in different formats to KItem"""
+
+        if data_format == DataFormat.HDF5:
+            from dsms.knowledge.knowledge_wrapper import hdf5_to_dict
+
+            return hdf5_to_dict(data)
+
+        if data_format == DataFormat.JSON:
+            return json.load(data)
+        if data_format == DataFormat.YAML:
+            return yaml.safe_load(data)
+
+        raise ValueError(f"Unsupported data format: {data_format}")
diff --git a/dsms/knowledge/knowledge_wrapper.py b/dsms/knowledge/knowledge_wrapper.py
new file mode 100644
index 0000000..f782ce8
--- /dev/null
+++ b/dsms/knowledge/knowledge_wrapper.py
@@ -0,0 +1,189 @@
+"""Wrapper for data conversion to and from different data formats"""
+
+import base64
+import io
+from typing import Any
+
+import h5py
+import numpy as np
+from pydantic import BaseModel
+
+
+def data_to_dict(data) -> Any:
+    """Convert data to python dictionary"""
+
+    data_dict = {}
+
+    def handle_value(key, value):
+        """Handles the values under different scenarios"""
+
+        result = None  # Default value for result
+
+        # Handle special cases based on 'key' and 'value'
+        if not isinstance(
+            value, (int, float, str, bytes, bool, type(None))
+        ) and hasattr(value, "__dict__"):
+            result = data_to_dict(value)
+
+        elif key == "id":
+            result = str(value)
+
+        elif key == "summary":
+            summary = getattr(data, "summary", None)
+            result = summary.text if summary else None
+
+        elif key == "dataframe":
+            dataframe = getattr(data, "dataframe", None)
+            if dataframe:
+                result = dataframe.to_df().to_json()
+
+        elif key == "file":
+            avatar = getattr(data, "avatar", None)
+            if avatar:
+                image = avatar.download()
+                image_bytes = io.BytesIO()
+                image.save(image_bytes, format="PNG")
+                image_bytes.seek(0)
+                result = base64.b64encode(image_bytes.getvalue()).decode(
+                    "utf-8"
+                )
+
+        elif key == "subgraph" and value is not None:
+            result = value.serialize()
+
+        elif key == "content":
+            content = data.download().encode("utf-8")
+            bytes_io = io.BytesIO(content) if content else None
+            result = base64.b64encode(bytes_io.getvalue()).decode("utf-8")
+
+        # Process the value for other cases (lists, dicts, models, etc.)
+        if result is None:
+            if isinstance(value, (int, float, str, bytes, bool, type(None))):
+                result = str(value)
+            elif isinstance(value, list):
+                result = [handle_value(key, v) for v in value]
+            elif isinstance(value, dict):
+                result = {k: handle_value(k, v) for k, v in value.items()}
+            elif isinstance(value, BaseModel):
+                result = {
+                    k: handle_value(k, v)
+                    for k, v in value.model_dump().items()
+                }
+            elif isinstance(value, io.BytesIO):
+                result = base64.b64encode(value.getvalue()).decode("utf-8")
+
+        return result
+
+    for k, v in data.model_dump().items():
+        if k == "attachments":
+            for attachment in getattr(data, "attachments"):
+                data_dict.setdefault("attachments", []).append(
+                    handle_value(k, attachment)
+                )
+            continue
+        if k == "linked_kitems":
+            for linked_kitem in getattr(data, "linked_kitems"):
+                item = {}
+                for key in ["id", "name", "slug", "ktype_id"]:
+                    value = getattr(linked_kitem, key)
+                    item[key] = str(value)
+                data_dict.setdefault("linked_kitems", []).append(item)
+            continue
+        data_dict[k] = handle_value(k, v)
+
+    return data_dict
+
+
+def dict_to_hdf5(dict_data):
+    """Converts data from a dictionary to HDF5"""
+    byte_data = io.BytesIO()
+
+    # Create an HDF5 file in memory
+    with h5py.File(byte_data, "w") as f:
+        # Recursively add dictionary contents
+        def add_to_hdf5(data, group):
+            for key, value in data.items():
+                if isinstance(value, dict):
+                    # Handle nested dictionaries recursively
+                    subgroup = group.create_group(key)
+                    add_to_hdf5(value, subgroup)
+                elif isinstance(value, list):
+                    # Handle lists, check if the list contains dictionaries
+                    subgroup = group.create_group(key)
+                    for idx, item in enumerate(value):
+                        if isinstance(item, dict):
+                            item_group = subgroup.create_group(f"item_{idx}")
+                            add_to_hdf5(item, item_group)
+                        else:
+                            subgroup.create_dataset(f"item_{idx}", data=item)
+                elif value is not None:
+                    group.create_dataset(key, data=value)
+                else:
+                    group.create_dataset(key, data="")
+
+        # Add data to the root group
+        add_to_hdf5(dict_data, f)
+
+    # Get the bytes data from the memory buffer
+    byte_data.seek(0)
+    return byte_data.read()
+
+
+def hdf5_to_dict(hdf5_file: io.BytesIO) -> dict:
+    """Convert an HDF5 file into a Python dictionary."""
+
+    def decode_if_bytes(value):
+        """Decode bytes to string if needed."""
+        if isinstance(value, bytes):
+            return value.decode("utf-8")
+        if isinstance(value, np.ndarray) and value.dtype.type is np.bytes_:
+            return [elem.decode("utf-8") for elem in value.tolist()]
+        return value
+
+    def convert_numpy(obj):
+        """Convert numpy data types to native Python types."""
+        if isinstance(obj, np.generic):
+            return obj.item()
+        if isinstance(obj, dict):
+            return {key: convert_numpy(value) for key, value in obj.items()}
+        if isinstance(obj, list):
+            return [convert_numpy(item) for item in obj]
+        return obj
+
+    def read_group(group):
+        """Recursively read HDF5 groups, grouping 'item_X' keys into lists efficiently."""
+        data_dict = {}
+        grouped_items = []
+
+        for key, value in group.attrs.items():
+            data_dict[key] = decode_if_bytes(value)
+
+        for key, dataset in group.items():
+            if isinstance(dataset, h5py.Dataset):
+                data = dataset[()]
+                if isinstance(data, np.ndarray) and data.dtype == np.uint8:
+                    try:
+                        value = data.tobytes().decode()
+                    except UnicodeDecodeError:
+                        value = data.tobytes()
+                elif isinstance(data, np.ndarray):
+                    value = decode_if_bytes(data.tolist())
+                else:
+                    value = decode_if_bytes(data)
+
+            elif isinstance(dataset, h5py.Group):
+                value = read_group(dataset)
+
+            if key.startswith("item_") and key[5:].isdigit():
+                grouped_items.append(value)
+            else:
+                data_dict[key] = value
+
+        # If there are grouped items, store them correctly
+        if grouped_items:
+            return grouped_items
+
+        return convert_numpy(data_dict)
+
+    with h5py.File(hdf5_file, "r") as hdf:
+        return read_group(hdf)
diff --git a/dsms/knowledge/ktype.py b/dsms/knowledge/ktype.py
index c4e2f3c..c8dbb46 100644
--- a/dsms/knowledge/ktype.py
+++ b/dsms/knowledge/ktype.py
@@ -1,13 +1,16 @@
 """KItem types"""
 
+import json
 import logging
 from datetime import datetime
 from typing import TYPE_CHECKING, Any, Optional, Union
 from uuid import UUID
 
+import yaml
 from pydantic import BaseModel, Field, model_serializer
 
 from dsms.core.logging import handler
+from dsms.knowledge.data_format import DataFormat
 from dsms.knowledge.utils import _ktype_exists, _refresh_ktype, print_ktype
 from dsms.knowledge.webform import Webform
 
@@ -137,3 +140,42 @@ def serialize(self):
             )
             for key, value in self.__dict__.items()
         }
+
+    def export(self, data_format: DataFormat) -> Any:
+        """Export ktypes to different formats"""
+
+        if data_format == DataFormat.HDF5:
+            from dsms.knowledge.knowledge_wrapper import (  # isort:skip
+                data_to_dict,
+                dict_to_hdf5,
+            )
+
+            return dict_to_hdf5(data_to_dict(self))
+
+        if data_format == DataFormat.JSON:
+            from dsms.knowledge.knowledge_wrapper import data_to_dict
+
+            return json.dumps(data_to_dict(self))
+
+        if data_format == DataFormat.YAML:
+            from dsms.knowledge.knowledge_wrapper import data_to_dict
+
+            return yaml.dump(data_to_dict(self), default_flow_style=False)
+
+        raise ValueError(f"Unsupported data format: {data_format}")
+
+    def import_ktype(data, data_format: DataFormat) -> Any:
+        """Import objects in different formats to KType"""
+
+        if data_format == DataFormat.HDF5:
+            from dsms.knowledge.knowledge_wrapper import hdf5_to_dict
+
+            return hdf5_to_dict(data)
+
+        if data_format == DataFormat.JSON:
+            return json.load(data)
+
+        if data_format == DataFormat.YAML:
+            return yaml.safe_load(data)
+
+        raise ValueError(f"Unsupported data format: {data_format}")
diff --git a/setup.cfg b/setup.cfg
index 7a3ef24..2e7f80b 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -21,6 +21,7 @@ packages = find:
 install_requires =
     PyYAML>=6,<7
     click>=8,<9
+    h5py>=3,<4
     html5lib>=1,<2
     lru-cache<1
     oyaml==1