diff --git a/src/crewai/__init__.py b/src/crewai/__init__.py
index 0833afd58..ce2bec75a 100644
--- a/src/crewai/__init__.py
+++ b/src/crewai/__init__.py
@@ -4,6 +4,7 @@ from crewai.agent import Agent
 from crewai.crew import Crew
 from crewai.flow.flow import Flow
 from crewai.knowledge.knowledge import Knowledge
+from crewai.lite_agent import LiteAgent
 from crewai.llm import LLM
 from crewai.process import Process
 from crewai.task import Task
@@ -23,4 +24,5 @@ __all__ = [
     "LLM",
     "Flow",
     "Knowledge",
+    "LiteAgent",
 ]
diff --git a/src/crewai/lite_agent.py b/src/crewai/lite_agent.py
new file mode 100644
index 000000000..a48f4b6c6
--- /dev/null
+++ b/src/crewai/lite_agent.py
@@ -0,0 +1,258 @@
+import os
+from typing import Any, Dict, List, Optional, Union
+
+from pydantic import Field, InstanceOf, PrivateAttr, model_validator
+
+from crewai.agents import CacheHandler
+from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.agents.crew_agent_executor import CrewAgentExecutor
+from crewai.llm import LLM
+from crewai.task import Task
+from crewai.tools import BaseTool
+from crewai.tools.base_tool import Tool
+from crewai.utilities import Converter, Prompts
+from crewai.utilities.token_counter_callback import TokenCalcHandler
+
+
+class LiteAgent(BaseAgent):
+    """Represents a lightweight agent in a system.
+
+    Each agent has a role, a goal, a backstory, and an optional language model (llm).
+    The agent can execute tasks but with fewer features compared to the full Agent class.
+
+    This is a simplified version of the Agent class with less dependencies and overhead.
+
+    Attributes:
+        agent_executor: An instance of the CrewAgentExecutor class.
+        role: The role of the agent.
+        goal: The objective of the agent.
+        backstory: The backstory of the agent.
+        llm: The language model that will run the agent.
+        max_iter: Maximum number of iterations for an agent to execute a task.
+        verbose: Whether the agent execution should be in verbose mode.
+        tools: Tools at agent's disposal
+    """
+
+    _times_executed: int = PrivateAttr(default=0)
+    max_execution_time: Optional[int] = Field(
+        default=None,
+        description="Maximum execution time for an agent to execute a task",
+    )
+    cache_handler: InstanceOf[CacheHandler] = Field(
+        default=None, description="An instance of the CacheHandler class."
+    )
+    llm: Union[str, InstanceOf[LLM], Any] = Field(
+        description="Language model that will run the agent.", default=None
+    )
+    max_iter: int = Field(
+        default=20,
+        description="Maximum number of iterations for an agent to execute a task before giving it's best answer",
+    )
+    max_retry_limit: int = Field(
+        default=2,
+        description="Maximum number of retries for an agent to execute a task when an error occurs.",
+    )
+    tools_results: Optional[List[Any]] = Field(
+        default=[], description="Results of the tools used by the agent."
+    )
+
+    @model_validator(mode="after")
+    def post_init_setup(self):
+        if isinstance(self.llm, str):
+            self.llm = LLM(model=self.llm)
+        elif isinstance(self.llm, LLM):
+            pass
+        elif self.llm is None:
+            model_name = (
+                os.environ.get("OPENAI_MODEL_NAME")
+                or os.environ.get("MODEL")
+                or "gpt-4o-mini"
+            )
+            llm_params = {"model": model_name}
+
+            api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get(
+                "OPENAI_BASE_URL"
+            )
+            if api_base:
+                llm_params["base_url"] = api_base
+
+            api_key = os.environ.get("OPENAI_API_KEY")
+            if api_key:
+                llm_params["api_key"] = api_key
+
+            self.llm = LLM(**llm_params)
+        else:
+            llm_params = {
+                "model": getattr(self.llm, "model_name", None)
+                or getattr(self.llm, "deployment_name", None)
+                or str(self.llm),
+                "temperature": getattr(self.llm, "temperature", None),
+                "max_tokens": getattr(self.llm, "max_tokens", None),
+                "api_key": getattr(self.llm, "api_key", None),
+                "base_url": getattr(self.llm, "base_url", None),
+                "organization": getattr(self.llm, "organization", None),
+            }
+            llm_params = {k: v for k, v in llm_params.items() if v is not None}
+            self.llm = LLM(**llm_params)
+
+        if not self.agent_executor:
+            self._setup_agent_executor()
+
+        return self
+
+    def _setup_agent_executor(self):
+        if not self.cache_handler:
+            self.cache_handler = CacheHandler()
+        self.set_cache_handler(self.cache_handler)
+
+    def execute_task(
+        self,
+        task: Task,
+        context: Optional[str] = None,
+        tools: Optional[List[BaseTool]] = None,
+    ) -> str:
+        """Execute a task with the agent.
+
+        Args:
+            task: Task to execute.
+            context: Context to execute the task in.
+            tools: Tools to use for the task.
+
+        Returns:
+            Output of the agent
+        """
+        if self.tools_handler:
+            self.tools_handler.last_used_tool = {}
+
+        task_prompt = task.prompt()
+
+        if task.output_json or task.output_pydantic:
+            if task.output_json:
+                schema = Converter.generate_model_description(task.output_json)
+            elif task.output_pydantic:
+                schema = Converter.generate_model_description(task.output_pydantic)
+
+            task_prompt += "\n" + self.i18n.slice("formatted_task_instructions").format(
+                output_format=schema
+            )
+
+        if context:
+            task_prompt = self.i18n.slice("task_with_context").format(
+                task=task_prompt, context=context
+            )
+
+        tools = tools or self.tools or []
+        self.create_agent_executor(tools=tools, task=task)
+
+        try:
+            result = self.agent_executor.invoke(
+                {
+                    "input": task_prompt,
+                    "tool_names": self.agent_executor.tools_names,
+                    "tools": self.agent_executor.tools_description,
+                    "ask_for_human_input": task.human_input,
+                }
+            )["output"]
+        except Exception as e:
+            self._times_executed += 1
+            if self._times_executed > self.max_retry_limit:
+                raise e
+            result = self.execute_task(task, context, tools)
+
+        if self.max_rpm and self._rpm_controller:
+            self._rpm_controller.stop_rpm_counter()
+
+        for tool_result in self.tools_results:
+            if tool_result.get("result_as_answer", False):
+                result = tool_result["result"]
+
+        return result
+
+    def create_agent_executor(
+        self, tools: Optional[List[BaseTool]] = None, task=None
+    ) -> None:
+        """Create an agent executor for the agent.
+
+        Returns:
+            An instance of the CrewAgentExecutor class.
+        """
+        tools = tools or self.tools or []
+        parsed_tools = self._parse_tools(tools)
+
+        prompt = Prompts(
+            agent=self,
+            tools=tools,
+            i18n=self.i18n,
+        ).task_execution()
+
+        stop_words = [self.i18n.slice("observation")]
+
+        self.agent_executor = CrewAgentExecutor(
+            llm=self.llm,
+            task=task,
+            agent=self,
+            crew=self.crew,
+            tools=parsed_tools,
+            prompt=prompt,
+            original_tools=tools,
+            stop_words=stop_words,
+            max_iter=self.max_iter,
+            tools_handler=self.tools_handler,
+            tools_names=self.__tools_names(parsed_tools),
+            tools_description=self._render_text_description_and_args(parsed_tools),
+            respect_context_window=True,
+            request_within_rpm_limit=(
+                self._rpm_controller.check_or_wait if self._rpm_controller else None
+            ),
+            callbacks=[TokenCalcHandler(self._token_process)],
+        )
+
+    def get_delegation_tools(self, agents: List[BaseAgent]):
+        """Stub implementation - LiteAgent doesn't support delegation."""
+        return []
+
+    def get_multimodal_tools(self) -> List[Tool]:
+        """Stub implementation - LiteAgent doesn't support multimodal tools."""
+        return []
+
+    def get_code_execution_tools(self):
+        """Stub implementation - LiteAgent doesn't support code execution."""
+        return []
+
+    def get_output_converter(self, llm, text, model, instructions):
+        """Get the output converter for the agent."""
+        return Converter(llm=llm, text=text, model=model, instructions=instructions)
+
+    def _parse_tools(self, tools: List[Any]) -> List[Any]:
+        """Parse tools to be used for the task."""
+        tools_list = []
+        try:
+            from crewai.tools import BaseTool as CrewAITool
+
+            for tool in tools:
+                if isinstance(tool, CrewAITool):
+                    tools_list.append(tool.to_structured_tool())
+                else:
+                    tools_list.append(tool)
+        except ModuleNotFoundError:
+            tools_list = []
+            for tool in tools:
+                tools_list.append(tool)
+
+        return tools_list
+
+    def _render_text_description_and_args(self, tools: List[BaseTool]) -> str:
+        """Render the tool name, description, and args in plain text."""
+        tool_strings = []
+        for tool in tools:
+            tool_strings.append(tool.description)
+
+        return "\n".join(tool_strings)
+
+    @staticmethod
+    def __tools_names(tools) -> str:
+        """Get the names of the tools as a comma-separated string."""
+        return ", ".join([t.name for t in tools])
+
+    def __repr__(self):
+        return f"LiteAgent(role={self.role}, goal={self.goal}, backstory={self.backstory})"
diff --git a/tests/lite_agent_test.py b/tests/lite_agent_test.py
new file mode 100644
index 000000000..6bf3da18e
--- /dev/null
+++ b/tests/lite_agent_test.py
@@ -0,0 +1,125 @@
+"""Test LiteAgent creation and execution basic functionality."""
+
+import os
+from unittest.mock import patch, MagicMock
+
+import pytest
+
+from crewai import LiteAgent, Task
+from crewai.llm import LLM
+from crewai.tools import tool
+
+
+def test_lite_agent_creation():
+    """Test creating a LiteAgent with basic properties."""
+    agent = LiteAgent(role="test role", goal="test goal", backstory="test backstory")
+
+    assert agent.role == "test role"
+    assert agent.goal == "test goal"
+    assert agent.backstory == "test backstory"
+    assert agent.tools == []
+
+
+def test_lite_agent_default_values():
+    """Test default values for LiteAgent."""
+    agent = LiteAgent(role="test role", goal="test goal", backstory="test backstory")
+    assert agent.llm.model == "gpt-4o-mini"
+    assert agent.max_iter == 20
+    assert agent.max_retry_limit == 2
+
+
+def test_custom_llm():
+    """Test creating a LiteAgent with a custom LLM string."""
+    agent = LiteAgent(
+        role="test role", goal="test goal", backstory="test backstory", llm="gpt-4"
+    )
+    assert agent.llm.model == "gpt-4"
+
+
+def test_custom_llm_with_langchain():
+    """Test creating a LiteAgent with a langchain LLM."""
+    mock_langchain_llm = MagicMock()
+    mock_langchain_llm.model_name = "gpt-4"
+    
+    agent = LiteAgent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=mock_langchain_llm,
+    )
+
+    assert agent.llm.model == "gpt-4"
+
+
+@patch("crewai.agents.crew_agent_executor.CrewAgentExecutor.invoke")
+def test_lite_agent_execute_task(mock_invoke):
+    """Test executing a task with a LiteAgent."""
+    mock_invoke.return_value = {"output": "The area of a circle with radius 5 cm is 78.54 square centimeters."}
+    
+    agent = LiteAgent(
+        role="Math Tutor",
+        goal="Solve math problems accurately",
+        backstory="You are an experienced math tutor with a knack for explaining complex concepts simply.",
+    )
+
+    task = Task(
+        description="Calculate the area of a circle with radius 5 cm.",
+        expected_output="The calculated area of the circle in square centimeters.",
+        agent=agent,
+    )
+
+    result = agent.execute_task(task)
+
+    assert result is not None
+    assert "square centimeters" in result.lower()
+    mock_invoke.assert_called_once()
+
+
+@patch("crewai.agents.crew_agent_executor.CrewAgentExecutor.invoke")
+def test_lite_agent_execution(mock_invoke):
+    """Test executing a simple task."""
+    mock_invoke.return_value = {"output": "1 + 1 = 2"}
+    
+    agent = LiteAgent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+    )
+
+    task = Task(
+        description="How much is 1 + 1?",
+        agent=agent,
+        expected_output="the result of the math operation.",
+    )
+
+    output = agent.execute_task(task)
+    assert "2" in output
+    mock_invoke.assert_called_once()
+
+
+@patch("crewai.agents.crew_agent_executor.CrewAgentExecutor.invoke")
+def test_lite_agent_execution_with_tools(mock_invoke):
+    """Test executing a task with tools."""
+    mock_invoke.return_value = {"output": "3 times 4 is 12"}
+    
+    @tool
+    def multiplier(first_number: int, second_number: int) -> float:
+        """Useful for when you need to multiply two numbers together."""
+        return first_number * second_number
+
+    agent = LiteAgent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        tools=[multiplier],
+    )
+
+    task = Task(
+        description="What is 3 times 4?",
+        agent=agent,
+        expected_output="The result of the multiplication.",
+    )
+    
+    output = agent.execute_task(task)
+    assert "12" in output
+    mock_invoke.assert_called_once()