diff --git a/README.md b/README.md
index 78cc724d..f349402c 100644
--- a/README.md
+++ b/README.md
@@ -875,6 +875,31 @@ print(video_path)
 
 ```
 
+
+### ModelScope
+```python
+from swarms.models import ModelScopeAutoModel
+
+# Initialize the model
+mp = ModelScopeAutoModel(
+    model_name="AI-ModelScope/Mixtral-8x7B-Instruct-v0.1",
+)
+
+mp.run("Generate a 10,000 word blog on health and wellness.")
+```
+
+```python
+
+from swarms import CogAgent
+
+# Initialize CogAgent
+cog_agent = CogAgent()
+
+# Run the model on the tests
+cog_agent.run("Describe this scene", "images/1.jpg")
+
+```
+
 ----
 
 ## Supported Models :heavy_check_mark:
diff --git a/pyproject.toml b/pyproject.toml
index 2d1dfea4..d7b16594 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "3.4.7"
+version = "3.4.8"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]
diff --git a/swarms/structs/__init__.py b/swarms/structs/__init__.py
index c767ae7b..706690c1 100644
--- a/swarms/structs/__init__.py
+++ b/swarms/structs/__init__.py
@@ -29,6 +29,8 @@ from swarms.structs.utils import (
 )
 from swarms.structs.task import Task
 from swarms.structs.block_wrapper import block
+from swarms.structs.graph_workflow import GraphWorkflow
+
 
 __all__ = [
     "Agent",
@@ -59,4 +61,5 @@ __all__ = [
     "detect_markdown",
     "Task",
     "block",
+    "GraphWorkflow",
 ]
diff --git a/swarms/structs/block_graph.py b/swarms/structs/graph_workflow.py
similarity index 93%
rename from swarms/structs/block_graph.py
rename to swarms/structs/graph_workflow.py
index ff948b4e..c463aef1 100644
--- a/swarms/structs/block_graph.py
+++ b/swarms/structs/graph_workflow.py
@@ -17,6 +17,13 @@ class GraphWorkflow(BaseStructure):
         add_edge(from_node, to_node): Adds an edge between two nodes in the graph.
         add_conditional_edges(from_node, condition, edge_dict): Adds conditional edges from a node to multiple nodes based on a condition.
         run(): Runs the workflow and returns the graph.
+
+    Examples:
+    >>> from swarms.structs import GraphWorkflow
+    >>> graph = GraphWorkflow()
+    >>> graph.add("start", "Start")
+    >>> graph.add("end", "End")
+    >>> graph.start("start")
     """
 
     def __init__(self):
diff --git a/swarms/structs/load_balancer.py b/swarms/structs/load_balancer.py
new file mode 100644
index 00000000..8efa9ec3
--- /dev/null
+++ b/swarms/structs/load_balancer.py
@@ -0,0 +1,48 @@
+from typing import Optional, List
+import multiprocessing as mp
+from swarms.structs.base import BaseStructure
+
+
+class LoadBalancer(BaseStructure):
+    def __init__(
+        self,
+        num_workers: int = 1,
+        agents: Optional[List] = None,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        self.num_workers = num_workers
+        self.agents = agents
+        self.tasks = []
+        self.results = []
+        self.workers = []
+        self._init_workers()
+
+    def _init_workers(self):
+        for i in range(self.num_workers):
+            worker = mp.Process(target=self._worker)
+            worker.start()
+            self.workers.append(worker)
+
+    def _worker(self):
+        while True:
+            task = self._get_task()
+            if task is None:
+                break
+            result = self._run_task(task)
+            self._add_result(result)
+
+    def _get_task(self):
+        if len(self.tasks) == 0:
+            return None
+        return self.tasks.pop(0)
+
+    def _run_task(self, task):
+        return task()
+
+    def _add_result(self, result):
+        self.results.append(result)
+
+    def add_task(self, task):
+        self.tasks.append(task)
diff --git a/tests/models/test_cogagent.py b/tests/models/test_cogagent.py
new file mode 100644
index 00000000..ac7fec56
--- /dev/null
+++ b/tests/models/test_cogagent.py
@@ -0,0 +1,71 @@
+import pytest
+from swarms.models.cog_agent import CogAgent
+from unittest.mock import MagicMock
+from PIL import Image
+
+
+@pytest.fixture
+def cogagent_params():
+    return {
+        "model_name": "ZhipuAI/cogagent-chat",
+        "tokenizer_name": "I-ModelScope/vicuna-7b-v1.5",
+        "dtype": "torch.bfloat16",
+        "low_cpu_mem_usage": True,
+        "load_in_4bit": True,
+        "trust_remote_code": True,
+        "device": "cuda",
+    }
+
+
+@pytest.fixture
+def cogagent(cogagent_params):
+    return CogAgent(**cogagent_params)
+
+
+def test_init(mocker, cogagent_params, cogagent):
+    mock_model = mocker.patch(
+        "swarms.models.cog_agent.AutoModelForCausalLM.from_pretrained"
+    )
+    mock_tokenizer = mocker.patch(
+        "swarms.models.cog_agent.AutoTokenizer.from_pretrained"
+    )
+
+    for param, value in cogagent_params.items():
+        assert getattr(cogagent, param) == value
+
+    mock_tokenizer.assert_called_once_with(
+        cogagent_params["tokenizer_name"]
+    )
+    mock_model.assert_called_once_with(
+        cogagent_params["model_name"],
+        torch_dtype=cogagent_params["dtype"],
+        low_cpu_mem_usage=cogagent_params["low_cpu_mem_usage"],
+        load_in_4bit=cogagent_params["load_in_4bit"],
+        trust_remote_code=cogagent_params["trust_remote_code"],
+    )
+
+
+def test_run(mocker, cogagent):
+    task = "How are you?"
+    img = "images/1.jpg"
+    mock_image = mocker.patch(
+        "PIL.Image.open", return_value=MagicMock(spec=Image.Image)
+    )
+    cogagent.model.build_conversation_input_ids = MagicMock(
+        return_value={
+            "input_ids": MagicMock(),
+            "token_type_ids": MagicMock(),
+            "attention_mask": MagicMock(),
+            "images": [MagicMock()],
+        }
+    )
+    cogagent.model.__call__ = MagicMock(return_value="Mocked output")
+    cogagent.decode = MagicMock(return_value="Mocked response")
+
+    output = cogagent.run(task, img)
+
+    assert output is not None
+    mock_image.assert_called_once_with(img)
+    cogagent.model.build_conversation_input_ids.assert_called_once()
+    cogagent.model.__call__.assert_called_once()
+    cogagent.decode.assert_called_once()
diff --git a/tests/models/test_modeelscope_pipeline.py b/tests/models/test_modeelscope_pipeline.py
new file mode 100644
index 00000000..16da0155
--- /dev/null
+++ b/tests/models/test_modeelscope_pipeline.py
@@ -0,0 +1,39 @@
+import pytest
+from swarms.models.modelscope_pipeline import ModelScopePipeline
+from unittest.mock import MagicMock
+
+
+@pytest.fixture
+def pipeline_params():
+    return {
+        "type_task": "text-generation",
+        "model_name": "gpt2",
+    }
+
+
+@pytest.fixture
+def pipeline_model(pipeline_params):
+    return ModelScopePipeline(**pipeline_params)
+
+
+def test_init(mocker, pipeline_params, pipeline_model):
+    mock_pipeline = mocker.patch(
+        "swarms.models.modelscope_pipeline.pipeline"
+    )
+
+    for param, value in pipeline_params.items():
+        assert getattr(pipeline_model, param) == value
+
+    mock_pipeline.assert_called_once_with(
+        pipeline_params["type_task"],
+        model=pipeline_params["model_name"],
+    )
+
+
+def test_run(mocker, pipeline_model):
+    task = "Generate a 10,000 word blog on health and wellness."
+    pipeline_model.model = MagicMock(return_value="Mocked output")
+
+    output = pipeline_model.run(task)
+
+    assert output is not None
diff --git a/tests/models/test_modelscope_llm.py b/tests/models/test_modelscope_llm.py
new file mode 100644
index 00000000..7e9310b2
--- /dev/null
+++ b/tests/models/test_modelscope_llm.py
@@ -0,0 +1,58 @@
+import pytest
+from swarms.models.modelscope_llm import ModelScopeAutoModel
+from unittest.mock import MagicMock
+
+
+@pytest.fixture
+def model_params():
+    return {
+        "model_name": "gpt2",
+        "tokenizer_name": None,
+        "device": "cuda",
+        "device_map": "auto",
+        "max_new_tokens": 500,
+        "skip_special_tokens": True,
+    }
+
+
+@pytest.fixture
+def modelscope(model_params):
+    return ModelScopeAutoModel(**model_params)
+
+
+def test_init(mocker, model_params, modelscope):
+    mock_model = mocker.patch(
+        "swarms.models.modelscope_llm.AutoModelForCausalLM.from_pretrained"
+    )
+    mock_tokenizer = mocker.patch(
+        "swarms.models.modelscope_llm.AutoTokenizer.from_pretrained"
+    )
+
+    for param, value in model_params.items():
+        assert getattr(modelscope, param) == value
+
+    mock_tokenizer.assert_called_once_with(
+        model_params["tokenizer_name"]
+    )
+    mock_model.assert_called_once_with(
+        model_params["model_name"],
+        device_map=model_params["device_map"],
+    )
+
+
+def test_run(mocker, modelscope):
+    task = "Generate a 10,000 word blog on health and wellness."
+    mocker.patch(
+        "swarms.models.modelscope_llm.AutoTokenizer.decode",
+        return_value="Mocked output",
+    )
+    modelscope.model.generate = MagicMock(
+        return_value=["Mocked token"]
+    )
+    modelscope.tokenizer = MagicMock(
+        return_value={"input_ids": "Mocked input_ids"}
+    )
+
+    output = modelscope.run(task)
+
+    assert output is not None
diff --git a/tests/structs/tests_graph_workflow.py b/tests/structs/tests_graph_workflow.py
new file mode 100644
index 00000000..bacdd681
--- /dev/null
+++ b/tests/structs/tests_graph_workflow.py
@@ -0,0 +1,56 @@
+import pytest
+from swarms.structs.graph_workflow import GraphWorkflow
+
+@pytest.fixture
+def graph_workflow():
+    return GraphWorkflow()
+
+def test_init(graph_workflow):
+    assert graph_workflow.graph == {}
+    assert graph_workflow.entry_point is None
+
+def test_add(graph_workflow):
+    graph_workflow.add("node1", "value1")
+    assert "node1" in graph_workflow.graph
+    assert graph_workflow.graph["node1"]["value"] == "value1"
+    assert graph_workflow.graph["node1"]["edges"] == {}
+
+def test_set_entry_point(graph_workflow):
+    graph_workflow.add("node1", "value1")
+    graph_workflow.set_entry_point("node1")
+    assert graph_workflow.entry_point == "node1"
+
+def test_set_entry_point_nonexistent_node(graph_workflow):
+    with pytest.raises(ValueError, match="Node does not exist in graph"):
+        graph_workflow.set_entry_point("nonexistent")
+
+def test_add_edge(graph_workflow):
+    graph_workflow.add("node1", "value1")
+    graph_workflow.add("node2", "value2")
+    graph_workflow.add_edge("node1", "node2")
+    assert "node2" in graph_workflow.graph["node1"]["edges"]
+
+def test_add_edge_nonexistent_node(graph_workflow):
+    graph_workflow.add("node1", "value1")
+    with pytest.raises(ValueError, match="Node does not exist in graph"):
+        graph_workflow.add_edge("node1", "nonexistent")
+
+def test_add_conditional_edges(graph_workflow):
+    graph_workflow.add("node1", "value1")
+    graph_workflow.add("node2", "value2")
+    graph_workflow.add_conditional_edges("node1", "condition1", {"condition_value1": "node2"})
+    assert "node2" in graph_workflow.graph["node1"]["edges"]
+
+def test_add_conditional_edges_nonexistent_node(graph_workflow):
+    graph_workflow.add("node1", "value1")
+    with pytest.raises(ValueError, match="Node does not exist in graph"):
+        graph_workflow.add_conditional_edges("node1", "condition1", {"condition_value1": "nonexistent"})
+
+def test_run(graph_workflow):
+    graph_workflow.add("node1", "value1")
+    graph_workflow.set_entry_point("node1")
+    assert graph_workflow.run() == graph_workflow.graph
+
+def test_run_no_entry_point(graph_workflow):
+    with pytest.raises(ValueError, match="Entry point not set"):
+        graph_workflow.run()
\ No newline at end of file