diff --git a/camel/benchmarks/apibank.py b/camel/benchmarks/apibank.py
index 6674503ba6..db3dd20a27 100644
--- a/camel/benchmarks/apibank.py
+++ b/camel/benchmarks/apibank.py
@@ -21,10 +21,6 @@
 from pathlib import Path
 from typing import Any, Dict, List, Literal, Optional
 
-import numpy as np
-from rouge import Rouge
-from tqdm import tqdm
-
 from camel.agents import ChatAgent
 from camel.benchmarks.base import BaseBenchmark
 from camel.messages import BaseMessage
@@ -138,6 +134,8 @@ def load(self, level: str, force_download: bool = False):  # type: ignore[overri
         jsonl_files = [
             f for f in os.listdir(file_path) if f.endswith('.jsonl')
         ]
+        from tqdm import tqdm
+
         for file in tqdm(jsonl_files, desc="Processing files"):
             history = []
             with open(file_path / file, 'r') as f:
@@ -240,6 +238,8 @@ def run(  # type: ignore[override, return]
         total_api_calls, correct_api_calls, rougel_scores = 0, 0, []
 
         with open(self.save_to, "w") as f:
+            from tqdm import tqdm
+
             for test in tqdm(datas, desc="Running"):
                 samples = self._data[test]
                 evaluator = Evaluator(samples)  # type: ignore[arg-type]
@@ -380,6 +380,8 @@ def run(  # type: ignore[override, return]
                 else 0,
             }
         elif dialog_test_enabled:
+            import numpy as np
+
             return {'Dialog_score': np.mean(rougel_scores)}
 
 
@@ -415,6 +417,8 @@ def agent_call(messages: List[Dict], agent: ChatAgent):
 
 def calculate_rouge_l_score(reference, hypothesis):
     r"""Calculate rouge l score between hypothesis and reference."""
+    from rouge import Rouge
+
     rouge = Rouge()
     scores = rouge.get_scores(hypothesis, reference)
     rouge_l_score = scores[0]['rouge-l']['f']
diff --git a/camel/benchmarks/apibench.py b/camel/benchmarks/apibench.py
index 584e2ba285..7dcaf3f68d 100644
--- a/camel/benchmarks/apibench.py
+++ b/camel/benchmarks/apibench.py
@@ -18,10 +18,6 @@
 from pathlib import Path
 from typing import Any, Dict, Literal, Optional
 
-import tree_sitter_python as tspython
-from tqdm import tqdm
-from tree_sitter import Language, Parser
-
 from camel.agents import ChatAgent
 from camel.benchmarks.base import BaseBenchmark
 from camel.utils import download_github_subdirectory
@@ -278,6 +274,8 @@ def run(  # type: ignore[override]
         self._results = []
 
         with open(self.save_to, "w") as f:
+            from tqdm import tqdm
+
             for question in tqdm(datas, desc="Running"):
                 prompt = encode_question(question["text"], dataset_name)
                 try:
@@ -376,6 +374,9 @@ def get_all_sub_trees(root_node):
 
 # Parse the program into AST trees
 def ast_parse(candidate):
+    import tree_sitter_python as tspython
+    from tree_sitter import Language, Parser
+
     PY_LANGUAGE = Language(tspython.language())
     parser = Parser(PY_LANGUAGE)
 
diff --git a/camel/benchmarks/gaia.py b/camel/benchmarks/gaia.py
index ac6bd2ea55..c2d4e36079 100644
--- a/camel/benchmarks/gaia.py
+++ b/camel/benchmarks/gaia.py
@@ -22,8 +22,6 @@
 from pathlib import Path
 from typing import Any, Dict, List, Literal, Optional, Protocol, Union
 
-from tqdm import tqdm
-
 from camel.agents import ChatAgent
 from camel.benchmarks.base import BaseBenchmark
 from camel.messages import BaseMessage
@@ -261,6 +259,8 @@ def run(  # type: ignore[override]
 
         # Process tasks
         with open(self.save_to, "w") as f:
+            from tqdm import tqdm
+
             for task in tqdm(datas, desc="Running"):
                 if not self._prepare_task(task):
                     continue
diff --git a/camel/benchmarks/nexus.py b/camel/benchmarks/nexus.py
index 33e8f79d6f..b12adde29b 100644
--- a/camel/benchmarks/nexus.py
+++ b/camel/benchmarks/nexus.py
@@ -22,12 +22,9 @@
 from pathlib import Path
 from typing import Any, Dict, List, Literal, Optional, Tuple, Union
 
-import pandas as pd
-from datasets import load_dataset
-from tqdm import tqdm
-
 from camel.agents import ChatAgent
 from camel.benchmarks.base import BaseBenchmark
+from camel.utils import dependencies_required
 
 logger = logging.getLogger(__name__)
 
@@ -89,6 +86,7 @@ class NexusBenchmark(BaseBenchmark):
             (default: :obj:`1`)
     """
 
+    @dependencies_required('datasets', 'pandas')
     def __init__(
         self,
         data_dir: str,
@@ -126,6 +124,7 @@ def load(self, dataset_name: str, force_download: bool = False):  # type: ignore
             dataset_name (str): Name of the specific dataset to be loaded.
             force_download (bool): Whether to force download the data.
         """
+        import pandas as pd
 
         def _load_csv_data(dataset_dir: Path) -> List:
             r"""Load datasets from CSV files."""
@@ -306,6 +305,8 @@ def run(  # type: ignore[override, return]
         # Process samples
         tools = construct_tool_descriptions(task)
         with open(self.save_to, "w") as f:
+            from tqdm import tqdm
+
             for sample in tqdm(datas, desc="Running"):
                 prompt = construct_prompt(input=sample.input, tools=tools)
                 ground_truth_call = sample.output
@@ -363,6 +364,8 @@ def run(  # type: ignore[override, return]
 def construct_tool_descriptions(dataset_name: str) -> str:
     r"""Construct tool descriptions from function definitions and
     descriptions."""
+    from datasets import load_dataset
+
     tool_dataset_mapping = {
         "NVDLibrary": "CVECPE",
         "VirusTotal": "VirusTotal",
diff --git a/camel/benchmarks/ragbench.py b/camel/benchmarks/ragbench.py
index 89ca32f89a..3af0e12879 100644
--- a/camel/benchmarks/ragbench.py
+++ b/camel/benchmarks/ragbench.py
@@ -12,15 +12,25 @@
 # limitations under the License.
 # ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========
 
-from typing import Any, Callable, Dict, List, Literal, Optional, Sequence
-
-import numpy as np
-from datasets import Dataset, load_dataset
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    List,
+    Literal,
+    Optional,
+    Sequence,
+)
+
+if TYPE_CHECKING:
+    from datasets import Dataset
 
 from camel.agents import ChatAgent
 from camel.benchmarks import BaseBenchmark
 from camel.logger import get_logger
 from camel.retrievers import AutoRetriever
+from camel.utils import dependencies_required
 
 logger = get_logger(__name__)
 
@@ -34,10 +44,10 @@ class RagasFields:
 
 
 def annotate_dataset(
-    dataset: Dataset,
+    dataset: 'Dataset',
     context_call: Optional[Callable[[Dict[str, Any]], List[str]]],
     answer_call: Optional[Callable[[Dict[str, Any]], str]],
-) -> Dataset:
+) -> 'Dataset':
     r"""Annotate the dataset by adding context and answers using the provided
     functions.
 
@@ -75,6 +85,8 @@ def rmse(
     Returns:
         Optional[float]: RMSE value, or None if inputs have different lengths.
     """
+    import numpy as np
+
     if len(input_trues) != len(input_preds):
         logger.warning("Input lengths mismatch in RMSE calculation")
         return None
@@ -104,6 +116,7 @@ def auroc(trues: Sequence[bool], preds: Sequence[float]) -> float:
     Returns:
         float: AUROC score.
     """
+    import numpy as np
     from sklearn.metrics import roc_auc_score  # type: ignore[import-untyped]
 
     eval_idx = ~np.isnan(preds)
@@ -117,7 +130,7 @@ def auroc(trues: Sequence[bool], preds: Sequence[float]) -> float:
 
 
 def ragas_calculate_metrics(
-    dataset: Dataset,
+    dataset: 'Dataset',
     pred_context_relevance_field: Optional[str],
     pred_faithfulness_field: Optional[str],
     metrics_to_evaluate: Optional[List[str]] = None,
@@ -141,6 +154,8 @@ def ragas_calculate_metrics(
     Returns:
         Dict[str, Optional[float]]: Dictionary of calculated metrics.
     """
+    import numpy as np
+
     metrics_to_evaluate = metrics_to_evaluate or [
         "context_relevancy",
         "faithfulness",
@@ -172,11 +187,11 @@ def ragas_calculate_metrics(
 
 
 def ragas_evaluate_dataset(
-    dataset: Dataset,
+    dataset: 'Dataset',
     contexts_field_name: Optional[str],
     answer_field_name: Optional[str],
     metrics_to_evaluate: Optional[List[str]] = None,
-) -> Dataset:
+) -> 'Dataset':
     r"""Evaluate the dataset using RAGAS metrics.
 
     Args:
@@ -188,6 +203,7 @@ def ragas_evaluate_dataset(
     Returns:
         Dataset: Dataset with added evaluation metrics.
     """
+    from datasets import Dataset
     from ragas import evaluate  # type: ignore[import]
     from ragas.metrics import (  # type: ignore[import]
         context_relevancy,
@@ -236,6 +252,7 @@ class RAGBenchBenchmark(BaseBenchmark):
         split (str, optional): Dataset split to use (e.g., "test").
     """
 
+    @dependencies_required('datasets')
     def __init__(
         self,
         processes: int = 1,
@@ -258,10 +275,12 @@ def __init__(
         super().__init__("ragbench", "rag_bench", "", processes)
         self.subset = subset
         self.split = split
-        self.dataset: Optional[Dataset] = None
+        self.dataset: Optional['Dataset'] = None
 
     def download(self):
         r"""Download the RAGBench dataset."""
+        from datasets import load_dataset
+
         try:
             self.dataset = load_dataset(
                 "rungalileo/ragbench", self.subset, split=self.split
diff --git a/camel/datagen/evol_instruct/evol_instruct.py b/camel/datagen/evol_instruct/evol_instruct.py
index 937e1bbbda..b1c70044ce 100644
--- a/camel/datagen/evol_instruct/evol_instruct.py
+++ b/camel/datagen/evol_instruct/evol_instruct.py
@@ -18,8 +18,6 @@
 from math import ceil
 from typing import Any, Dict, List, Optional, Tuple, Type, Union, cast
 
-from tqdm import tqdm
-
 from camel.agents import ChatAgent
 from camel.datagen.evol_instruct.scorer import BaseScorer, GeneralScorer
 from camel.datagen.evol_instruct.templates import EvolInstructTemplates
@@ -413,6 +411,8 @@ def _process_prompt(
             plan_chunk = evolution_plan[chunk_idx : chunk_idx + chunk_size]
 
             with ThreadPoolExecutor(max_workers=num_threads) as executor:
+                from tqdm import tqdm
+
                 chunk_results = list(
                     tqdm(
                         executor.map(_process_prompt, zip(chunk, plan_chunk)),
diff --git a/camel/datagen/self_instruct/filter/filter_function.py b/camel/datagen/self_instruct/filter/filter_function.py
index 50788d5f90..6f91d5f76c 100644
--- a/camel/datagen/self_instruct/filter/filter_function.py
+++ b/camel/datagen/self_instruct/filter/filter_function.py
@@ -16,8 +16,6 @@
 from abc import ABC, abstractmethod
 from typing import List
 
-from rouge import Rouge
-
 from camel.models.reward import BaseRewardModel
 
 
@@ -136,6 +134,8 @@ class RougeSimilarityFilter(FilterFunction):
     def __init__(
         self, existing_instructions: List[str], threshold: float = 0.7
     ):
+        from rouge import Rouge
+
         self.existing_instructions = existing_instructions
         self.threshold = threshold
         self.rouge = Rouge()
diff --git a/camel/datagen/source2synth/data_processor.py b/camel/datagen/source2synth/data_processor.py
index 7929c58040..a2ee8e3368 100644
--- a/camel/datagen/source2synth/data_processor.py
+++ b/camel/datagen/source2synth/data_processor.py
@@ -15,8 +15,6 @@
 import random
 from typing import Any, Dict, List, Optional, Sequence
 
-from tqdm import tqdm
-
 from camel.agents.multi_hop_generator_agent import MultiHopGeneratorAgent
 from camel.datagen.source2synth.user_data_processor_config import (
     ProcessorConfig,
@@ -172,6 +170,8 @@ def construct_examples(
         logger.info("Starting to construct training examples...")
         examples = []
 
+        from tqdm import tqdm
+
         for data in tqdm(raw_data, desc="Constructing examples"):
             # 1. Text preprocessing
             processed_text = self._preprocess_text(data.get('text', ''))
diff --git a/camel/datasets/base_generator.py b/camel/datasets/base_generator.py
index 1dc7b7be81..ff062e21c1 100644
--- a/camel/datasets/base_generator.py
+++ b/camel/datasets/base_generator.py
@@ -20,7 +20,14 @@
 from typing import Any, Dict, List, Union
 
 from pydantic import ValidationError
-from torch.utils.data import IterableDataset
+
+try:
+    from torch.utils.data import IterableDataset
+except ImportError:
+
+    class IterableDataset:  # type: ignore[no-redef]
+        r"""Fallback when PyTorch is not installed."""
+        pass
 
 from camel.logger import get_logger
 
diff --git a/camel/datasets/static_dataset.py b/camel/datasets/static_dataset.py
index 11d384fd84..0c4c226d73 100644
--- a/camel/datasets/static_dataset.py
+++ b/camel/datasets/static_dataset.py
@@ -16,6 +16,7 @@
 from collections.abc import Sequence
 from pathlib import Path
 from typing import (
+    TYPE_CHECKING,
     Any,
     Dict,
     List,
@@ -24,9 +25,20 @@
     Union,
 )
 
-from datasets import Dataset as HFDataset
 from pydantic import ValidationError
-from torch.utils.data import Dataset
+
+try:
+    from datasets import Dataset as HFDataset
+except ImportError:
+    HFDataset = None  # type: ignore[assignment,misc]
+
+try:
+    from torch.utils.data import Dataset
+except ImportError:
+
+    class Dataset:  # type: ignore[no-redef]
+        r"""Fallback when PyTorch is not installed."""
+        pass
 
 from camel.logger import get_logger
 
@@ -116,7 +128,7 @@ def _init_data(
             ValueError: If the Path has an unsupported file extension.
         """
 
-        if isinstance(data, HFDataset):
+        if HFDataset is not None and isinstance(data, HFDataset):
             raw_data = self._init_from_hf_dataset(data)
         elif isinstance(data, Dataset):
             raw_data = self._init_from_pytorch_dataset(data)
diff --git a/camel/models/reward/skywork_model.py b/camel/models/reward/skywork_model.py
index c54d6d84dd..0e75a72e60 100644
--- a/camel/models/reward/skywork_model.py
+++ b/camel/models/reward/skywork_model.py
@@ -13,10 +13,9 @@
 # ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========
 from typing import Dict, List, Optional, Union
 
-import torch
-
 from camel.models.reward import BaseRewardModel
 from camel.types import ModelType
+from camel.utils import dependencies_required
 
 
 class SkyworkRewardModel(BaseRewardModel):
@@ -36,6 +35,7 @@ class SkyworkRewardModel(BaseRewardModel):
             (default: :obj:`offload`)
     """
 
+    @dependencies_required('torch', 'transformers')
     def __init__(
         self,
         model_type: Union[ModelType, str],
@@ -45,6 +45,7 @@ def __init__(
         attn_implementation: Optional[str] = "flash_attention_2",
         offload_folder: Optional[str] = "offload",
     ) -> None:
+        import torch
         from transformers import (
             AutoModelForSequenceClassification,
             AutoTokenizer,
@@ -70,6 +71,8 @@ def evaluate(self, messages: List[Dict[str, str]]) -> Dict[str, float]:
         Returns:
             ChatCompletion: A ChatCompletion object with the scores.
         """
+        import torch
+
         inputs = self._tokenizer.apply_chat_template(
             messages,
             tokenize=True,
diff --git a/camel/personas/persona_hub.py b/camel/personas/persona_hub.py
index cdf85c529d..a0a29dda80 100644
--- a/camel/personas/persona_hub.py
+++ b/camel/personas/persona_hub.py
@@ -17,7 +17,6 @@
 from functools import lru_cache
 from typing import Dict, List, Literal, Optional, Union
 
-import numpy as np
 from pydantic import BaseModel, Field
 
 from camel.agents import ChatAgent
@@ -235,13 +234,15 @@ def _get_embedding(
         return embedding_model.embed(description)
 
     @staticmethod
-    def _cosine_similarity(vec1: np.ndarray, vec2: np.ndarray) -> float:
+    def _cosine_similarity(vec1: 'np.ndarray', vec2: 'np.ndarray') -> float:
         r"""Copmute the cosine similarity of two vectors.
 
         Args:
             vec1 (np.ndarray): Vector 1
             vec2 (np.ndarray): Vector 2
         """
+        import numpy as np
+
         return np.dot(vec1, vec2) / (
             np.linalg.norm(vec1) * np.linalg.norm(vec2)
         )
@@ -276,6 +277,8 @@ def _is_similar(
             embedding_model, persona2_description
         )
 
+        import numpy as np
+
         similarity = self._cosine_similarity(
             np.array(persona1_embeddings), np.array(persona2_embeddings)
         )
diff --git a/camel/runtimes/docker_runtime.py b/camel/runtimes/docker_runtime.py
index 2e8d650cbc..c119c8526d 100644
--- a/camel/runtimes/docker_runtime.py
+++ b/camel/runtimes/docker_runtime.py
@@ -24,7 +24,6 @@
 
 import requests
 from pydantic import BaseModel
-from tqdm import tqdm
 
 from camel.runtimes import BaseRuntime, TaskConfig
 from camel.toolkits import FunctionTool
@@ -235,6 +234,8 @@ def build(self, time_out: int = 15) -> "DockerRuntime":
                 )
 
         if self.tasks:
+            from tqdm import tqdm
+
             for task in tqdm(self.tasks, desc="Running tasks"):
                 self.exec_run(task)
 
diff --git a/test/datasets/test_base_dataset.py b/test/datasets/test_base_dataset.py
index b914188a41..07a7c7ec7f 100644
--- a/test/datasets/test_base_dataset.py
+++ b/test/datasets/test_base_dataset.py
@@ -20,7 +20,13 @@
 import pytest
 from datasets import Dataset as HFDataset
 from pydantic import ValidationError
-from torch.utils.data import Dataset
+
+try:
+    from torch.utils.data import Dataset
+except ImportError:
+
+    class Dataset:  # type: ignore[no-redef]
+        pass
 
 from camel.datasets import (
     DataPoint,