diff --git a/.gitignore b/.gitignore
index 3d6c9fd..8d01b58 100644
--- a/.gitignore
+++ b/.gitignore
@@ -25,3 +25,6 @@ dist/
 .pytest_cache/
 .coverage
 htmlcov/
+
+# Environment
+.env
diff --git a/README.md b/README.md
index ee41041..e17fcd8 100644
--- a/README.md
+++ b/README.md
@@ -14,6 +14,7 @@ pip install edgee
 ## Quick Start
 
 ```python
+import os
 from edgee import Edgee
 
 edgee = Edgee("your-api-key")
@@ -52,7 +53,7 @@ The `stream()` method enables real-time streaming responses:
 for chunk in edgee.stream("gpt-4o", "Tell me a story"):
     if chunk.text:
         print(chunk.text, end="", flush=True)
-    
+
     if chunk.finish_reason:
         print(f"\nFinished: {chunk.finish_reason}")
 ```
diff --git a/edgee/__init__.py b/edgee/__init__.py
index 5229fd3..0d135a1 100644
--- a/edgee/__init__.py
+++ b/edgee/__init__.py
@@ -2,10 +2,14 @@
 
 import json
 import os
+from collections.abc import Callable
 from dataclasses import dataclass
+from typing import Any
 from urllib.error import HTTPError
 from urllib.request import Request, urlopen
 
+from pydantic import BaseModel, ValidationError
+
 # API Configuration
 DEFAULT_BASE_URL = "https://api.edgee.ai"
 API_ENDPOINT = "/v1/chat/completions"
@@ -19,7 +23,9 @@ class FunctionDefinition:
 
 
 @dataclass
-class Tool:
+class OpenAITool:
+    """OpenAI tool format for API requests."""
+
     type: str  # "function"
     function: FunctionDefinition
 
@@ -31,6 +37,99 @@ class ToolCall:
     function: dict  # {"name": str, "arguments": str}
 
 
+class Tool:
+    """Tool with schema validation and automatic execution.
+
+    Use this class to define tools that can be automatically executed
+    during the agentic loop when using simple send mode.
+
+    Args:
+        name: The name of the tool (must be unique)
+        schema: A Pydantic BaseModel class defining the tool's parameters
+        handler: A callable that takes the validated parameters and returns a result
+        description: Optional description of what the tool does
+
+    Example:
+        ```python
+        from pydantic import BaseModel
+        from edgee import Tool
+
+        class WeatherParams(BaseModel):
+            location: str
+
+        def get_weather(params: WeatherParams) -> dict:
+            return {"temperature": 22, "location": params.location}
+
+        weather_tool = Tool(
+            name="get_weather",
+            description="Get the current weather for a location",
+            schema=WeatherParams,
+            handler=get_weather,
+        )
+        ```
+    """
+
+    def __init__(
+        self,
+        name: str,
+        schema: type[BaseModel],
+        handler: Callable[..., Any],
+        description: str | None = None,
+    ):
+        self.name = name
+        self.description = description
+        self.schema = schema
+        self.handler = handler
+
+    def to_dict(self) -> dict:
+        """Convert to OpenAI tool format for API requests."""
+        return {
+            "type": "function",
+            "function": {
+                "name": self.name,
+                "description": self.description,
+                "parameters": self.schema.model_json_schema(),
+            },
+        }
+
+    def execute(self, args: dict) -> Any:
+        """Validate arguments with Pydantic and execute the handler.
+
+        Args:
+            args: Dictionary of arguments to validate and pass to the handler
+
+        Returns:
+            The result from the handler function
+
+        Raises:
+            ValidationError: If the arguments don't match the schema
+        """
+        validated = self.schema.model_validate(args)
+        return self.handler(validated)
+
+
+def create_tool(
+    name: str,
+    schema: type[BaseModel],
+    handler: Callable[..., Any],
+    description: str | None = None,
+) -> Tool:
+    """Helper function to create a Tool instance.
+
+    This is an alternative to using the Tool class directly.
+
+    Args:
+        name: The name of the tool
+        schema: A Pydantic BaseModel class defining the tool's parameters
+        handler: A callable that takes the validated parameters and returns a result
+        description: Optional description of what the tool does
+
+    Returns:
+        A configured Tool instance
+    """
+    return Tool(name=name, schema=schema, handler=handler, description=description)
+
+
 @dataclass
 class Message:
     role: str  # "system" | "user" | "assistant" | "tool"
@@ -54,11 +153,23 @@ class Choice:
     finish_reason: str | None
 
 
+@dataclass
+class InputTokenDetails:
+    cached_tokens: int
+
+
+@dataclass
+class OutputTokenDetails:
+    reasoning_tokens: int
+
+
 @dataclass
 class Usage:
     prompt_tokens: int
     completion_tokens: int
     total_tokens: int
+    input_tokens_details: InputTokenDetails
+    output_tokens_details: OutputTokenDetails
 
 
 @dataclass
@@ -95,6 +206,16 @@ def tool_calls(self) -> list | None:
         return None
 
 
+@dataclass
+class StreamToolCallDelta:
+    """Partial tool call in a stream."""
+
+    index: int
+    id: str | None = None
+    type: str | None = None
+    function: dict | None = None  # {"name": str, "arguments": str}
+
+
 @dataclass
 class StreamDelta:
     role: str | None = None
@@ -134,6 +255,51 @@ def finish_reason(self) -> str | None:
             return self.choices[0].finish_reason
         return None
 
+    @property
+    def tool_call_deltas(self) -> list[dict] | None:
+        """Get tool call deltas from the first choice."""
+        if self.choices and self.choices[0].delta.tool_calls:
+            return self.choices[0].delta.tool_calls
+        return None
+
+
+# Stream events for tool-enabled streaming
+@dataclass
+class ChunkEvent:
+    """A chunk of streamed content."""
+
+    type: str = "chunk"
+    chunk: StreamChunk = None
+
+
+@dataclass
+class ToolStartEvent:
+    """Tool execution is starting."""
+
+    type: str = "tool_start"
+    tool_call: dict = None
+
+
+@dataclass
+class ToolResultEvent:
+    """Tool execution completed."""
+
+    type: str = "tool_result"
+    tool_call_id: str = None
+    tool_name: str = None
+    result: Any = None
+
+
+@dataclass
+class IterationCompleteEvent:
+    """One iteration of the tool loop completed."""
+
+    type: str = "iteration_complete"
+    iteration: int = 0
+
+
+StreamEvent = ChunkEvent | ToolStartEvent | ToolResultEvent | IterationCompleteEvent
+
 
 @dataclass
 class EdgeeConfig:
@@ -170,6 +336,8 @@ def send(
         self,
         model: str,
         input: str | InputObject | dict,
+        tools: list[Tool] | None = None,
+        max_tool_iterations: int = 10,
         stream: bool = False,
     ):
         """Send a completion request to the Edgee AI Gateway.
@@ -177,31 +345,161 @@ def send(
         Args:
             model: The model to use for completion
             input: The input (string, dict, or InputObject)
+            tools: Optional list of Tool instances for automatic execution (simple mode only)
+            max_tool_iterations: Maximum number of tool execution iterations (default: 10)
             stream: If True, returns a generator yielding StreamChunk objects.
                    If False, returns a SendResponse object.
 
         Returns:
             SendResponse if stream=False, or a generator yielding StreamChunk objects if stream=True.
-        """
 
+        Note:
+            When using string input with tools, the SDK will automatically:
+            1. Convert tools to OpenAI format
+            2. Execute tools when the model requests them
+            3. Send tool results back to the model
+            4. Loop until the model returns a final response or max iterations reached
+        """
+        # Simple mode: string input with optional tools for automatic execution
         if isinstance(input, str):
-            messages = [{"role": "user", "content": input}]
-            tools = None
-            tool_choice = None
-        elif isinstance(input, InputObject):
+            if tools:
+                # Agentic loop mode: auto-execute tools
+                return self._send_simple(model, input, tools, max_tool_iterations)
+            else:
+                # Simple string without tools
+                messages = [{"role": "user", "content": input}]
+                return self._call_api(model, messages, stream=stream)
+
+        # Advanced mode: full InputObject or dict (manual tool handling)
+        if isinstance(input, InputObject):
             messages = input.messages
-            tools = input.tools
+            api_tools = input.tools
             tool_choice = input.tool_choice
         else:
             messages = input.get("messages", [])
-            tools = input.get("tools")
+            api_tools = input.get("tools")
             tool_choice = input.get("tool_choice")
 
+        return self._call_api(
+            model, messages, api_tools=api_tools, tool_choice=tool_choice, stream=stream
+        )
+
+    def _send_simple(
+        self,
+        model: str,
+        input: str,
+        tools: list[Tool],
+        max_iterations: int,
+    ) -> SendResponse:
+        """Handle simple mode with automatic tool execution (agentic loop).
+
+        Args:
+            model: The model to use
+            input: The user's input string
+            tools: List of Tool instances to use
+            max_iterations: Maximum number of iterations
+
+        Returns:
+            Final SendResponse after all tool executions complete
+        """
+        messages: list[dict] = [{"role": "user", "content": input}]
+        openai_tools = [t.to_dict() for t in tools]
+        tool_map = {t.name: t for t in tools}
+
+        total_usage: Usage | None = None
+
+        for _ in range(max_iterations):
+            response = self._call_api(model, messages, api_tools=openai_tools)
+
+            # Accumulate usage
+            if response.usage:
+                if total_usage is None:
+                    total_usage = Usage(
+                        prompt_tokens=response.usage.prompt_tokens,
+                        completion_tokens=response.usage.completion_tokens,
+                        total_tokens=response.usage.total_tokens,
+                        input_tokens_details=InputTokenDetails(
+                            cached_tokens=response.usage.input_tokens_details.cached_tokens
+                        ),
+                        output_tokens_details=OutputTokenDetails(
+                            reasoning_tokens=response.usage.output_tokens_details.reasoning_tokens
+                        ),
+                    )
+                else:
+                    total_usage.prompt_tokens += response.usage.prompt_tokens
+                    total_usage.completion_tokens += response.usage.completion_tokens
+                    total_usage.total_tokens += response.usage.total_tokens
+                    total_usage.input_tokens_details.cached_tokens += (
+                        response.usage.input_tokens_details.cached_tokens
+                    )
+                    total_usage.output_tokens_details.reasoning_tokens += (
+                        response.usage.output_tokens_details.reasoning_tokens
+                    )
+
+            # No tool calls? We're done - return final response
+            if not response.tool_calls:
+                return SendResponse(choices=response.choices, usage=total_usage)
+
+            # Add assistant's message (with tool_calls) to messages
+            messages.append(response.message)
+
+            # Execute each tool call and add results
+            for tool_call in response.tool_calls:
+                tool_name = tool_call["function"]["name"]
+                tool = tool_map.get(tool_name)
+
+                if tool:
+                    try:
+                        raw_args = json.loads(tool_call["function"]["arguments"])
+                        result = tool.execute(raw_args)
+                    except ValidationError as e:
+                        result = {"error": f"Invalid arguments: {e}"}
+                    except json.JSONDecodeError as e:
+                        result = {"error": f"Failed to parse arguments: {e}"}
+                    except Exception as e:
+                        result = {"error": f"Tool execution failed: {e}"}
+                else:
+                    result = {"error": f"Unknown tool: {tool_name}"}
+
+                # Add tool result to messages
+                messages.append(
+                    {
+                        "role": "tool",
+                        "tool_call_id": tool_call["id"],
+                        "content": result if isinstance(result, str) else json.dumps(result),
+                    }
+                )
+
+            # Loop continues - model will process tool results
+
+        # Max iterations reached
+        raise RuntimeError(f"Max tool iterations ({max_iterations}) reached")
+
+    def _call_api(
+        self,
+        model: str,
+        messages: list[dict],
+        api_tools: list[dict] | None = None,
+        tool_choice: str | dict | None = None,
+        stream: bool = False,
+    ) -> SendResponse:
+        """Make an API call to the Edgee AI Gateway.
+
+        Args:
+            model: The model to use
+            messages: List of message dicts
+            api_tools: Optional list of tools in OpenAI format
+            tool_choice: Optional tool choice configuration
+            stream: Whether to stream the response
+
+        Returns:
+            SendResponse object
+        """
         body: dict = {"model": model, "messages": messages}
         if stream:
             body["stream"] = True
-        if tools:
-            body["tools"] = tools
+        if api_tools:
+            body["tools"] = api_tools
         if tool_choice:
             body["tool_choice"] = tool_choice
 
@@ -240,10 +538,19 @@ def _handle_non_streaming_response(self, request: Request) -> SendResponse:
 
         usage = None
         if "usage" in data:
+            usage_data = data["usage"]
             usage = Usage(
-                prompt_tokens=data["usage"]["prompt_tokens"],
-                completion_tokens=data["usage"]["completion_tokens"],
-                total_tokens=data["usage"]["total_tokens"],
+                prompt_tokens=usage_data["prompt_tokens"],
+                completion_tokens=usage_data["completion_tokens"],
+                total_tokens=usage_data["total_tokens"],
+                input_tokens_details=InputTokenDetails(
+                    cached_tokens=usage_data.get("input_tokens_details", {}).get("cached_tokens", 0)
+                ),
+                output_tokens_details=OutputTokenDetails(
+                    reasoning_tokens=usage_data.get("output_tokens_details", {}).get(
+                        "reasoning_tokens", 0
+                    )
+                ),
             )
 
         return SendResponse(choices=choices, usage=usage)
@@ -298,10 +605,172 @@ def stream(
         self,
         model: str,
         input: str | InputObject | dict,
+        tools: list[Tool] | None = None,
+        max_tool_iterations: int = 10,
     ):
         """Stream a completion request from the Edgee AI Gateway.
 
-        Convenience method that calls send(stream=True).
-        Yields StreamChunk objects as they arrive from the API.
+        Args:
+            model: The model to use for completion
+            input: The input (string, dict, or InputObject)
+            tools: Optional list of Tool instances for automatic execution (simple mode only)
+            max_tool_iterations: Maximum number of tool execution iterations (default: 10)
+
+        Yields:
+            StreamChunk objects if no tools provided.
+            StreamEvent objects (ChunkEvent, ToolStartEvent, ToolResultEvent, IterationCompleteEvent)
+            if tools are provided.
+
+        Example without tools:
+            ```python
+            for chunk in edgee.stream("gpt-4o", "Hello!"):
+                print(chunk.text, end="")
+            ```
+
+        Example with tools:
+            ```python
+            for event in edgee.stream("gpt-4o", "What's the weather?", tools=[weather_tool]):
+                if event.type == "chunk":
+                    print(event.chunk.text, end="")
+                elif event.type == "tool_result":
+                    print(f"Tool result: {event.result}")
+            ```
+        """
+        # Simple mode with tools: use agentic streaming loop
+        if isinstance(input, str) and tools:
+            return self._stream_simple(model, input, tools, max_tool_iterations)
+
+        # Simple mode without tools or advanced mode: regular streaming
+        if isinstance(input, str):
+            messages = [{"role": "user", "content": input}]
+            return self._call_api(model, messages, stream=True)
+
+        # Advanced mode: full InputObject or dict
+        if isinstance(input, InputObject):
+            messages = input.messages
+            api_tools = input.tools
+            tool_choice = input.tool_choice
+        else:
+            messages = input.get("messages", [])
+            api_tools = input.get("tools")
+            tool_choice = input.get("tool_choice")
+
+        return self._call_api(
+            model, messages, api_tools=api_tools, tool_choice=tool_choice, stream=True
+        )
+
+    def _stream_simple(
+        self,
+        model: str,
+        input: str,
+        tools: list[Tool],
+        max_iterations: int,
+    ):
+        """Handle simple mode streaming with automatic tool execution.
+
+        Yields StreamEvent objects for chunks, tool starts, tool results, and iteration completion.
         """
-        return self.send(model=model, input=input, stream=True)
+        messages: list[dict] = [{"role": "user", "content": input}]
+        openai_tools = [t.to_dict() for t in tools]
+        tool_map = {t.name: t for t in tools}
+
+        for iteration in range(1, max_iterations + 1):
+            # Accumulate the full response from stream
+            role: str | None = None
+            content = ""
+            tool_calls_accumulator: dict[int, dict] = {}
+
+            # Stream the response
+            for chunk in self._call_api(model, messages, api_tools=openai_tools, stream=True):
+                # Yield the chunk as an event
+                yield ChunkEvent(chunk=chunk)
+
+                # Accumulate role
+                if chunk.role:
+                    role = chunk.role
+
+                # Accumulate content
+                if chunk.text:
+                    content += chunk.text
+
+                # Accumulate tool calls from deltas
+                tool_call_deltas = chunk.tool_call_deltas
+                if tool_call_deltas:
+                    for delta in tool_call_deltas:
+                        idx = delta.get("index", 0)
+                        if idx in tool_calls_accumulator:
+                            # Append to existing tool call
+                            existing = tool_calls_accumulator[idx]
+                            if delta.get("function", {}).get("arguments"):
+                                existing["function"]["arguments"] += delta["function"]["arguments"]
+                        else:
+                            # Start new tool call
+                            tool_calls_accumulator[idx] = {
+                                "id": delta.get("id", ""),
+                                "type": "function",
+                                "function": {
+                                    "name": delta.get("function", {}).get("name", ""),
+                                    "arguments": delta.get("function", {}).get("arguments", ""),
+                                },
+                            }
+
+            # Convert accumulated tool calls to list
+            tool_calls = list(tool_calls_accumulator.values())
+
+            # No tool calls? We're done
+            if not tool_calls:
+                return
+
+            # Add assistant's message (with tool_calls) to messages
+            assistant_message: dict = {
+                "role": role or "assistant",
+                "tool_calls": tool_calls,
+            }
+            if content:
+                assistant_message["content"] = content
+            messages.append(assistant_message)
+
+            # Execute each tool call and add results
+            for tool_call in tool_calls:
+                tool_name = tool_call["function"]["name"]
+                tool = tool_map.get(tool_name)
+
+                # Yield tool_start event
+                yield ToolStartEvent(tool_call=tool_call)
+
+                if tool:
+                    try:
+                        raw_args = json.loads(tool_call["function"]["arguments"])
+                        result = tool.execute(raw_args)
+                    except ValidationError as e:
+                        result = {"error": f"Invalid arguments: {e}"}
+                    except json.JSONDecodeError as e:
+                        result = {"error": f"Failed to parse arguments: {e}"}
+                    except Exception as e:
+                        result = {"error": f"Tool execution failed: {e}"}
+                else:
+                    result = {"error": f"Unknown tool: {tool_name}"}
+
+                # Yield tool_result event
+                yield ToolResultEvent(
+                    tool_call_id=tool_call["id"],
+                    tool_name=tool_name,
+                    result=result,
+                )
+
+                # Add tool result to messages
+                messages.append(
+                    {
+                        "role": "tool",
+                        "tool_call_id": tool_call["id"],
+                        "content": result if isinstance(result, str) else json.dumps(result),
+                    }
+                )
+
+            # Yield iteration complete event
+            yield IterationCompleteEvent(iteration=iteration)
+
+            # Loop continues - model will process tool results
+
+        # Max iterations reached
+        raise RuntimeError(f"Max tool iterations ({max_iterations}) reached")
diff --git a/example/test.py b/example/test.py
deleted file mode 100644
index 2351002..0000000
--- a/example/test.py
+++ /dev/null
@@ -1,71 +0,0 @@
-"""Example usage of Edgee Gateway SDK"""
-
-import os
-import sys
-
-# Add parent directory to path for local testing
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
-from edgee import Edgee
-
-edgee = Edgee(os.environ.get("EDGEE_API_KEY", "test-key"))
-
-# Test 1: Simple string input
-print("Test 1: Simple string input")
-response1 = edgee.send(
-    model="mistral/mistral-small-latest",
-    input="What is the capital of France?",
-)
-print(f"Content: {response1.text}")
-print(f"Usage: {response1.usage}")
-print()
-
-# Test 2: Full input object with messages
-print("Test 2: Full input object with messages")
-response2 = edgee.send(
-    model="mistral/mistral-small-latest",
-    input={
-        "messages": [
-            {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": "Say hello!"},
-        ],
-    },
-)
-print(f"Content: {response2.text}")
-print()
-
-# Test 3: With tools
-print("Test 3: With tools")
-response3 = edgee.send(
-    model="gpt-4o",
-    input={
-        "messages": [{"role": "user", "content": "What is the weather in Paris?"}],
-        "tools": [
-            {
-                "type": "function",
-                "function": {
-                    "name": "get_weather",
-                    "description": "Get the current weather for a location",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "location": {"type": "string", "description": "City name"},
-                        },
-                        "required": ["location"],
-                    },
-                },
-            },
-        ],
-        "tool_choice": "auto",
-    },
-)
-print(f"Content: {response3.text}")
-print(f"Tool calls: {response3.tool_calls}")
-print()
-
-# Test 4: Streaming
-print("Test 4: Streaming")
-for chunk in edgee.stream(model="mistral/mistral-small-latest", input="What is Python?"):
-    if chunk.text:
-        print(chunk.text, end="", flush=True)
-print("\n")
diff --git a/examples/.env.example b/examples/.env.example
new file mode 100644
index 0000000..c7878ee
--- /dev/null
+++ b/examples/.env.example
@@ -0,0 +1,2 @@
+EDGEE_API_KEY=your_api_key_here
+EDGEE_BASE_URL=https://api.edgee.ai
diff --git a/examples/messages.py b/examples/messages.py
new file mode 100644
index 0000000..97247e8
--- /dev/null
+++ b/examples/messages.py
@@ -0,0 +1,26 @@
+"""
+Full input object with messages
+
+Use the messages array for multi-turn conversations
+and system prompts.
+"""
+
+from dotenv import load_dotenv
+
+from edgee import Edgee
+
+load_dotenv()
+
+edgee = Edgee()
+
+response = edgee.send(
+    model="devstral2",
+    input={
+        "messages": [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Say hello!"},
+        ],
+    },
+)
+
+print(f"Content: {response.text}")
diff --git a/examples/simple.py b/examples/simple.py
new file mode 100644
index 0000000..7c3b4c5
--- /dev/null
+++ b/examples/simple.py
@@ -0,0 +1,21 @@
+"""
+Simple string input example
+
+The most basic way to use the SDK - just pass a string prompt.
+"""
+
+from dotenv import load_dotenv
+
+from edgee import Edgee
+
+load_dotenv()
+
+edgee = Edgee()
+
+response = edgee.send(
+    model="devstral2",
+    input="What is the capital of France?",
+)
+
+print(f"Content: {response.text}")
+print(f"Usage: {response.usage}")
diff --git a/examples/stream.py b/examples/stream.py
new file mode 100644
index 0000000..427621e
--- /dev/null
+++ b/examples/stream.py
@@ -0,0 +1,21 @@
+"""
+Simple streaming without tools
+
+Stream responses token by token for real-time output.
+"""
+
+from dotenv import load_dotenv
+
+from edgee import Edgee
+
+load_dotenv()
+
+edgee = Edgee()
+
+print("Response: ", end="", flush=True)
+
+for chunk in edgee.stream(model="devstral2", input="Say hello in 10 words!"):
+    if chunk.text:
+        print(chunk.text, end="", flush=True)
+
+print()
diff --git a/examples/stream_tools.py b/examples/stream_tools.py
new file mode 100644
index 0000000..dabbf08
--- /dev/null
+++ b/examples/stream_tools.py
@@ -0,0 +1,105 @@
+"""
+Streaming with automatic tool execution
+
+Combine streaming with auto tool execution.
+You receive events for chunks, tool starts, and tool results.
+"""
+
+import json
+
+from dotenv import load_dotenv
+from pydantic import BaseModel
+
+from edgee import Edgee, Tool
+
+load_dotenv()
+
+edgee = Edgee()
+
+
+# Weather tool
+class WeatherParams(BaseModel):
+    location: str
+
+
+def get_weather(params: WeatherParams) -> dict:
+    weather_data = {
+        "Paris": {"temperature": 18, "condition": "partly cloudy"},
+        "London": {"temperature": 12, "condition": "rainy"},
+        "New York": {"temperature": 22, "condition": "sunny"},
+    }
+    data = weather_data.get(params.location, {"temperature": 20, "condition": "unknown"})
+    return {
+        "location": params.location,
+        "temperature": data["temperature"],
+        "condition": data["condition"],
+    }
+
+
+weather_tool = Tool(
+    name="get_weather",
+    description="Get the current weather for a location",
+    schema=WeatherParams,
+    handler=get_weather,
+)
+
+
+# Calculator tool
+class CalculatorParams(BaseModel):
+    operation: str
+    a: float
+    b: float
+
+
+def calculate(params: CalculatorParams) -> dict:
+    ops = {
+        "add": lambda a, b: a + b,
+        "subtract": lambda a, b: a - b,
+        "multiply": lambda a, b: a * b,
+        "divide": lambda a, b: a / b if b != 0 else "Error: division by zero",
+    }
+    op = ops.get(params.operation)
+    if op:
+        return {
+            "operation": params.operation,
+            "a": params.a,
+            "b": params.b,
+            "result": op(params.a, params.b),
+        }
+    return {"error": f"Unknown operation: {params.operation}"}
+
+
+calculator_tool = Tool(
+    name="calculate",
+    description="Perform basic arithmetic operations",
+    schema=CalculatorParams,
+    handler=calculate,
+)
+
+print("Streaming with tools...\n")
+print("Response: ", end="", flush=True)
+
+for event in edgee.stream(
+    model="devstral2",
+    input="What's 15 multiplied by 7, and what's the weather in Paris?",
+    tools=[weather_tool, calculator_tool],
+):
+    if event.type == "chunk":
+        # Stream content as it arrives
+        if event.chunk.text:
+            print(event.chunk.text, end="", flush=True)
+
+    elif event.type == "tool_start":
+        # Tool is about to be executed
+        print(f"\n  [Tool starting: {event.tool_call['function']['name']}]")
+
+    elif event.type == "tool_result":
+        # Tool finished executing
+        print(f"  [Tool result: {event.tool_name} -> {json.dumps(event.result)}]")
+        print("Response: ", end="", flush=True)
+
+    elif event.type == "iteration_complete":
+        # One iteration of the tool loop completed
+        print(f"  [Iteration {event.iteration} complete, continuing...]")
+
+print()
diff --git a/examples/tools_auto.py b/examples/tools_auto.py
new file mode 100644
index 0000000..f52cf32
--- /dev/null
+++ b/examples/tools_auto.py
@@ -0,0 +1,56 @@
+"""
+Automatic tool execution (Simple mode)
+
+Define tools with Pydantic schemas and handlers.
+The SDK automatically executes tools and loops
+until the model produces a final response.
+"""
+
+from dotenv import load_dotenv
+from pydantic import BaseModel
+
+from edgee import Edgee, Tool
+
+load_dotenv()
+
+edgee = Edgee()
+
+
+# Define the tool schema using Pydantic
+class WeatherParams(BaseModel):
+    location: str
+
+
+# Define the tool handler
+def get_weather(params: WeatherParams) -> dict:
+    """Simulated weather API call."""
+    weather_data = {
+        "Paris": {"temperature": 18, "condition": "partly cloudy"},
+        "London": {"temperature": 12, "condition": "rainy"},
+        "New York": {"temperature": 22, "condition": "sunny"},
+    }
+    data = weather_data.get(params.location, {"temperature": 20, "condition": "unknown"})
+    return {
+        "location": params.location,
+        "temperature": data["temperature"],
+        "condition": data["condition"],
+    }
+
+
+# Create the tool
+weather_tool = Tool(
+    name="get_weather",
+    description="Get the current weather for a location",
+    schema=WeatherParams,
+    handler=get_weather,
+)
+
+# Send request with automatic tool execution
+response = edgee.send(
+    model="devstral2",
+    input="What's the weather like in Paris?",
+    tools=[weather_tool],
+)
+
+print(f"Content: {response.text}")
+print(f"Total usage: {response.usage}")
diff --git a/examples/tools_manual.py b/examples/tools_manual.py
new file mode 100644
index 0000000..7ef57e9
--- /dev/null
+++ b/examples/tools_manual.py
@@ -0,0 +1,46 @@
+"""
+Manual tool handling (Advanced mode)
+
+Define tools using raw OpenAI format and handle
+tool calls manually in your code.
+"""
+
+from dotenv import load_dotenv
+
+from edgee import Edgee
+
+load_dotenv()
+
+edgee = Edgee()
+
+response = edgee.send(
+    model="devstral2",
+    input={
+        "messages": [{"role": "user", "content": "What is the weather in Paris?"}],
+        "tools": [
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "description": "Get the current weather for a location",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {"type": "string", "description": "City name"},
+                        },
+                        "required": ["location"],
+                    },
+                },
+            },
+        ],
+        "tool_choice": "auto",
+    },
+)
+
+print(f"Content: {response.text}")
+print(f"Tool calls: {response.tool_calls}")
+
+# In manual mode, you would:
+# 1. Check if response.tool_calls exists
+# 2. Execute the tool yourself
+# 3. Send another request with the tool result
diff --git a/examples/tools_multiple.py b/examples/tools_multiple.py
new file mode 100644
index 0000000..219813e
--- /dev/null
+++ b/examples/tools_multiple.py
@@ -0,0 +1,81 @@
+"""
+Multiple tools with automatic execution
+
+Pass multiple tools and the model will decide
+which ones to call based on the user's request.
+"""
+
+from dotenv import load_dotenv
+from pydantic import BaseModel
+
+from edgee import Edgee, Tool
+
+load_dotenv()
+
+edgee = Edgee()
+
+
+# Weather tool
+class WeatherParams(BaseModel):
+    location: str
+
+
+def get_weather(params: WeatherParams) -> dict:
+    weather_data = {
+        "Paris": {"temperature": 18, "condition": "partly cloudy"},
+        "London": {"temperature": 12, "condition": "rainy"},
+        "New York": {"temperature": 22, "condition": "sunny"},
+    }
+    data = weather_data.get(params.location, {"temperature": 20, "condition": "unknown"})
+    return {
+        "location": params.location,
+        "temperature": data["temperature"],
+        "condition": data["condition"],
+    }
+
+
+weather_tool = Tool(
+    name="get_weather",
+    description="Get the current weather for a location",
+    schema=WeatherParams,
+    handler=get_weather,
+)
+
+
+# Calculator tool
+class CalculatorParams(BaseModel):
+    operation: str  # "add", "subtract", "multiply", "divide"
+    a: float
+    b: float
+
+
+def calculate(params: CalculatorParams) -> dict:
+    operations = {
+        "add": lambda a, b: a + b,
+        "subtract": lambda a, b: a - b,
+        "multiply": lambda a, b: a * b,
+        "divide": lambda a, b: a / b if b != 0 else "Error: division by zero",
+    }
+    op = operations.get(params.operation)
+    if op:
+        result = op(params.a, params.b)
+        return {"operation": params.operation, "a": params.a, "b": params.b, "result": result}
+    return {"error": f"Unknown operation: {params.operation}"}
+
+
+calculator_tool = Tool(
+    name="calculate",
+    description="Perform basic arithmetic operations (add, subtract, multiply, divide)",
+    schema=CalculatorParams,
+    handler=calculate,
+)
+
+# Use both tools
+response = edgee.send(
+    model="devstral2",
+    input="What's 25 multiplied by 4, and then what's the weather in London?",
+    tools=[weather_tool, calculator_tool],
+)
+
+print(f"Content: {response.text}")
+print(f"Total usage: {response.usage}")
diff --git a/pyproject.toml b/pyproject.toml
index f526841..10d6adb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,7 @@ description = "Lightweight Python SDK for Edgee AI Gateway"
 readme = "README.md"
 license = "Apache-2.0"
 requires-python = ">=3.10"
-dependencies = []
+dependencies = ["pydantic>=2.0.0"]
 authors = [
     { name = "Edgee", email = "support@edgee.cloud" }
 ]
@@ -26,7 +26,7 @@ Homepage = "https://github.com/edgee-cloud/python-sdk"
 Repository = "https://github.com/edgee-cloud/python-sdk"
 
 [project.optional-dependencies]
-dev = ["pytest>=8.0.0", "ruff>=0.8.0"]
+dev = ["pytest>=8.0.0", "ruff>=0.8.0", "python-dotenv>=1.0.0"]
 
 [build-system]
 requires = ["hatchling"]
@@ -61,4 +61,5 @@ indent-style = "space"
 dev = [
     "pytest>=8.0.0",
     "ruff>=0.8.0",
+    "python-dotenv>=1.0.0",
 ]
diff --git a/tests/test_edgee.py b/tests/test_edgee.py
index 048a6b7..964fd4e 100644
--- a/tests/test_edgee.py
+++ b/tests/test_edgee.py
@@ -5,8 +5,9 @@
 from unittest.mock import MagicMock, patch
 
 import pytest
+from pydantic import BaseModel, ValidationError
 
-from edgee import Edgee, EdgeeConfig
+from edgee import Edgee, EdgeeConfig, Tool, create_tool
 
 
 class TestEdgeeConstructor:
@@ -306,3 +307,320 @@ def test_config_base_url_overrides_env(self, mock_urlopen):
 
         call_args = mock_urlopen.call_args[0][0]
         assert call_args.full_url == f"{config_base_url}/v1/chat/completions"
+
+
+class TestToolClass:
+    """Test Tool class"""
+
+    def test_tool_creation(self):
+        """Should create a Tool with all properties"""
+
+        class TestParams(BaseModel):
+            name: str
+            count: int = 1
+
+        def handler(params: TestParams) -> dict:
+            return {"name": params.name, "count": params.count}
+
+        tool = Tool(
+            name="test_tool",
+            description="A test tool",
+            schema=TestParams,
+            handler=handler,
+        )
+
+        assert tool.name == "test_tool"
+        assert tool.description == "A test tool"
+        assert tool.schema == TestParams
+        assert tool.handler == handler
+
+    def test_tool_to_dict(self):
+        """Should convert Tool to OpenAI format"""
+
+        class TestParams(BaseModel):
+            location: str
+
+        tool = Tool(
+            name="get_weather",
+            description="Get weather for a location",
+            schema=TestParams,
+            handler=lambda p: {"temp": 20},
+        )
+
+        result = tool.to_dict()
+
+        assert result["type"] == "function"
+        assert result["function"]["name"] == "get_weather"
+        assert result["function"]["description"] == "Get weather for a location"
+        assert "properties" in result["function"]["parameters"]
+        assert "location" in result["function"]["parameters"]["properties"]
+
+    def test_tool_execute(self):
+        """Should validate args and execute handler"""
+
+        class CalcParams(BaseModel):
+            a: int
+            b: int
+
+        def add(params: CalcParams) -> int:
+            return params.a + params.b
+
+        tool = Tool(name="add", schema=CalcParams, handler=add)
+        result = tool.execute({"a": 5, "b": 3})
+
+        assert result == 8
+
+    def test_tool_execute_validation_error(self):
+        """Should raise ValidationError for invalid args"""
+
+        class StrictParams(BaseModel):
+            value: int
+
+        tool = Tool(
+            name="test",
+            schema=StrictParams,
+            handler=lambda p: p.value,
+        )
+
+        with pytest.raises(ValidationError):
+            tool.execute({"value": "not_an_int"})
+
+    def test_create_tool_helper(self):
+        """Should create Tool using create_tool helper"""
+
+        class Params(BaseModel):
+            x: int
+
+        tool = create_tool(
+            name="helper_test",
+            description="Test helper",
+            schema=Params,
+            handler=lambda p: p.x * 2,
+        )
+
+        assert isinstance(tool, Tool)
+        assert tool.name == "helper_test"
+        assert tool.execute({"x": 5}) == 10
+
+
+class TestAgenticLoop:
+    """Test automatic tool execution (agentic loop)"""
+
+    def setup_method(self):
+        os.environ.pop("EDGEE_API_KEY", None)
+        os.environ.pop("EDGEE_BASE_URL", None)
+
+    def _mock_response(self, data: dict):
+        """Create a mock response"""
+        mock = MagicMock()
+        mock.read.return_value = json.dumps(data).encode("utf-8")
+        mock.__enter__ = MagicMock(return_value=mock)
+        mock.__exit__ = MagicMock(return_value=False)
+        return mock
+
+    @patch("edgee.urlopen")
+    def test_simple_send_with_tool_execution(self, mock_urlopen):
+        """Should execute tools automatically and return final response"""
+
+        # First call: model requests tool call
+        tool_call_response = {
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {
+                        "role": "assistant",
+                        "content": None,
+                        "tool_calls": [
+                            {
+                                "id": "call_123",
+                                "type": "function",
+                                "function": {
+                                    "name": "get_weather",
+                                    "arguments": '{"location": "Paris"}',
+                                },
+                            }
+                        ],
+                    },
+                    "finish_reason": "tool_calls",
+                }
+            ],
+            "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
+        }
+
+        # Second call: model returns final response
+        final_response = {
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {
+                        "role": "assistant",
+                        "content": "The weather in Paris is sunny with 22°C.",
+                    },
+                    "finish_reason": "stop",
+                }
+            ],
+            "usage": {"prompt_tokens": 20, "completion_tokens": 10, "total_tokens": 30},
+        }
+
+        mock_urlopen.side_effect = [
+            self._mock_response(tool_call_response),
+            self._mock_response(final_response),
+        ]
+
+        class WeatherParams(BaseModel):
+            location: str
+
+        def get_weather(params: WeatherParams) -> dict:
+            return {"temperature": 22, "condition": "sunny", "location": params.location}
+
+        weather_tool = Tool(
+            name="get_weather",
+            description="Get weather",
+            schema=WeatherParams,
+            handler=get_weather,
+        )
+
+        client = Edgee("test-api-key")
+        result = client.send(
+            model="gpt-4",
+            input="What's the weather in Paris?",
+            tools=[weather_tool],
+        )
+
+        # Should return final response
+        assert result.text == "The weather in Paris is sunny with 22°C."
+
+        # Should accumulate usage
+        assert result.usage.total_tokens == 45  # 15 + 30
+
+        # Should have made 2 API calls
+        assert mock_urlopen.call_count == 2
+
+    @patch("edgee.urlopen")
+    def test_simple_send_no_tool_calls(self, mock_urlopen):
+        """Should return immediately if model doesn't request tools"""
+        response = {
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {"role": "assistant", "content": "I don't need any tools."},
+                    "finish_reason": "stop",
+                }
+            ],
+            "usage": {"prompt_tokens": 5, "completion_tokens": 5, "total_tokens": 10},
+        }
+        mock_urlopen.return_value = self._mock_response(response)
+
+        class Params(BaseModel):
+            x: int
+
+        tool = Tool(name="unused", schema=Params, handler=lambda p: p.x)
+
+        client = Edgee("test-api-key")
+        result = client.send(model="gpt-4", input="Hello", tools=[tool])
+
+        assert result.text == "I don't need any tools."
+        assert mock_urlopen.call_count == 1
+
+    @patch("edgee.urlopen")
+    def test_simple_send_max_iterations(self, mock_urlopen):
+        """Should raise error when max iterations reached"""
+        # Always return tool calls
+        tool_call_response = {
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {
+                        "role": "assistant",
+                        "content": None,
+                        "tool_calls": [
+                            {
+                                "id": "call_123",
+                                "type": "function",
+                                "function": {"name": "loop_tool", "arguments": "{}"},
+                            }
+                        ],
+                    },
+                    "finish_reason": "tool_calls",
+                }
+            ],
+        }
+        mock_urlopen.return_value = self._mock_response(tool_call_response)
+
+        class EmptyParams(BaseModel):
+            pass
+
+        tool = Tool(name="loop_tool", schema=EmptyParams, handler=lambda p: "result")
+
+        client = Edgee("test-api-key")
+
+        with pytest.raises(RuntimeError, match="Max tool iterations"):
+            client.send(
+                model="gpt-4",
+                input="Loop forever",
+                tools=[tool],
+                max_tool_iterations=3,
+            )
+
+        # Should have made exactly 3 calls
+        assert mock_urlopen.call_count == 3
+
+    @patch("edgee.urlopen")
+    def test_simple_send_unknown_tool(self, mock_urlopen):
+        """Should handle unknown tool gracefully"""
+        # First call: model requests unknown tool
+        tool_call_response = {
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {
+                        "role": "assistant",
+                        "content": None,
+                        "tool_calls": [
+                            {
+                                "id": "call_123",
+                                "type": "function",
+                                "function": {
+                                    "name": "unknown_tool",
+                                    "arguments": "{}",
+                                },
+                            }
+                        ],
+                    },
+                    "finish_reason": "tool_calls",
+                }
+            ],
+        }
+
+        # Second call: model handles error
+        final_response = {
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {"role": "assistant", "content": "Tool not found."},
+                    "finish_reason": "stop",
+                }
+            ],
+        }
+
+        mock_urlopen.side_effect = [
+            self._mock_response(tool_call_response),
+            self._mock_response(final_response),
+        ]
+
+        class Params(BaseModel):
+            x: int
+
+        tool = Tool(name="known_tool", schema=Params, handler=lambda p: p.x)
+
+        client = Edgee("test-api-key")
+        result = client.send(model="gpt-4", input="Test", tools=[tool])
+
+        # Should send error message for unknown tool and continue
+        assert result.text == "Tool not found."
+
+        # Verify the tool result message was sent with error
+        second_call = mock_urlopen.call_args_list[1][0][0]
+        body = json.loads(second_call.data.decode("utf-8"))
+        tool_message = next(m for m in body["messages"] if m["role"] == "tool")
+        assert "Unknown tool" in tool_message["content"]
diff --git a/uv.lock b/uv.lock
index ccc443e..66afd0e 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,43 +1,58 @@
 version = 1
-revision = 3
 requires-python = ">=3.10"
 
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
+]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697, upload-time = "2022-10-25T02:36:22.414Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335 },
 ]
 
 [[package]]
 name = "edgee"
-version = "0.1.1"
+version = "1.0.0"
 source = { editable = "." }
+dependencies = [
+    { name = "pydantic" },
+]
 
 [package.optional-dependencies]
 dev = [
     { name = "pytest" },
+    { name = "python-dotenv" },
     { name = "ruff" },
 ]
 
 [package.dev-dependencies]
 dev = [
     { name = "pytest" },
+    { name = "python-dotenv" },
     { name = "ruff" },
 ]
 
 [package.metadata]
 requires-dist = [
+    { name = "pydantic", specifier = ">=2.0.0" },
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