diff --git a/README.md b/README.md
index 4aa1f05..bcb7ec5 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,13 @@
 # Floki: Agentic Workflows Made Simple
 
+[![pypi](https://img.shields.io/pypi/v/floki-ai.svg)](https://pypi.python.org/pypi/floki-ai)
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/floki-ai)](https://pypi.org/project/floki-ai/)
+[![GitHub Repo stars](https://img.shields.io/github/stars/Cyb3rWard0g/floki)](https://github.com/Cyb3rWard0g/floki)
+[![license](https://img.shields.io/github/license/Cyb3rWard0g/floki.svg)](https://github.com/Cyb3rWard0g/floki/blob/main/LICENSE)
+
 ![](docs/logo-workflows.png)
 
-> 🚧 Floki is in the early stages of development. The framework is actively evolving as part of ongoing research, and I am still exploring and learning how to integrate Dapr effectively. Expect breaking changes to APIs and core structures as the framework matures and insights are refined.
+> 🚧 Floki is in active development and evolving with ongoing research. APIs and core structures may change as the framework matures and Dapr integration is refined.
 
 Floki is an open-source framework for researchers and developers to experiment with LLM-based autonomous agents. It provides tools to create, orchestrate, and manage agents while seamlessly connecting to LLM inference APIs. Built on [Dapr](https://docs.dapr.io/), Floki leverages a unified programming model that simplifies microservices and supports both deterministic workflows and event-driven interactions. Using Dapr’s Virtual Actor pattern, Floki enables agents to function as independent, self-contained units that process messages sequentially, eliminating concurrency concerns while seamlessly integrating into larger workflows. It also facilitates agent collaboration through Dapr’s Pub/Sub integration, where agents communicate via a shared message bus, simplifying the design of workflows where tasks are distributed efficiently, and agents work together to achieve shared goals. By bringing together these features, Floki provides a powerful way to explore agentic workflows and the components that enable multi-agent systems to collaborate and scale, all powered by Dapr.
 
diff --git a/docs/home/concepts/messaging.md b/docs/home/concepts/messaging.md
new file mode 100644
index 0000000..9142dde
--- /dev/null
+++ b/docs/home/concepts/messaging.md
@@ -0,0 +1,53 @@
+# Messaging
+
+Messaging is how agents communicate, collaborate, and adapt in workflows. It enables them to share updates, execute tasks, and respond to events seamlessly. Messaging is one of the main components of `event-driven` agentic workflows, ensuring tasks remain scalable, adaptable, and decoupled. Built entirely around the `Pub/Sub (publish/subscribe)` model, messaging leverages a message bus to facilitate communication across agents, services, and workflows.
+
+## Key Role of Messaging in Agentic Workflows
+
+Messaging connects agents in workflows, enabling real-time communication and coordination. It acts as the backbone of event-driven interactions, ensuring that agents work together effectively without requiring direct connections.
+
+Through messaging, agents can:
+
+* **Collaborate Across Tasks**: Agents exchange messages to share updates, broadcast events, or deliver task results.
+* **Orchestrate Workflows**: Tasks are triggered and coordinated through published messages, enabling workflows to adjust dynamically.
+* **Respond to Events**: Agents adapt to real-time changes by subscribing to relevant topics and processing events as they occur.
+
+By using messaging, workflows remain modular and scalable, with agents focusing on their specific roles while seamlessly participating in the broader system.
+
+## How Messaging Works
+
+Messaging relies on the `Pub/Sub` model, which organizes communication into topics. These topics act as channels where agents can publish and subscribe to messages, enabling efficient and decoupled communication.
+
+### Message Bus and Topics
+
+The message bus serves as the central system that manages topics and message delivery. Agents interact with the message bus to send and receive messages:
+
+* **Publishing Messages**: Agents publish messages to a specific topic, making the information available to all subscribed agents.
+* **Subscribing to Topics**: Agents subscribe to topics relevant to their roles, ensuring they only receive the messages they need.
+* **Broadcasting Updates**: Multiple agents can subscribe to the same topic, allowing them to act on shared events or updates.
+
+### Scalability and Adaptability
+
+The message bus ensures that communication scales effortlessly, whether you are adding new agents, expanding workflows, or adapting to changing requirements. Agents remain loosely coupled, allowing workflows to evolve without disruptions.
+
+## Messaging in Event-Driven Workflows
+
+Event-driven workflows depend on messaging to enable dynamic and real-time interactions. Unlike deterministic workflows, which follow a fixed sequence of tasks, event-driven workflows respond to the messages and events flowing through the system.
+
+* **Real-Time Triggers**: Agents can initiate tasks or workflows by publishing specific events.
+* **Asynchronous Execution**: Tasks are coordinated through messages, allowing agents to operate independently and in parallel.
+* **Dynamic Adaptation**: Agents adjust their behavior based on the messages they receive, ensuring workflows remain flexible and resilient.
+
+## Why Pub/Sub Messaging for Agentic Workflows?
+
+Pub/Sub messaging is essential for event-driven agentic workflows because it:
+
+* **Decouples Components**: Agents publish messages without needing to know which agents will receive them, promoting modular and scalable designs.
+* **Enables Real-Time Communication**: Messages are delivered as events occur, allowing agents to react instantly.
+* **Fosters Collaboration**: Multiple agents can subscribe to the same topic, making it easy to share updates or divide responsibilities.
+
+This messaging framework ensures that agents operate efficiently, workflows remain flexible, and systems can scale dynamically.
+
+## Conclusion
+
+Messaging is the backbone of event-driven agentic workflows. By leveraging a robust Pub/Sub model, agents communicate efficiently, adapt dynamically, and collaborate seamlessly. This foundation ensures that workflows scale, evolve, and respond in real time, empowering agents to achieve their goals in a shared, dynamic environment.
\ No newline at end of file
diff --git a/docs/index.md b/docs/index.md
index b0ba4ed..110df94 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,6 +1,8 @@
 # Floki: Agentic Workflows Made Simple
 
 [![pypi](https://img.shields.io/pypi/v/floki-ai.svg)](https://pypi.python.org/pypi/floki-ai)
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/floki-ai)](https://pypi.org/project/floki-ai/)
+[![GitHub Repo stars](https://img.shields.io/github/stars/Cyb3rWard0g/floki)](https://github.com/Cyb3rWard0g/floki)
 [![license](https://img.shields.io/github/license/Cyb3rWard0g/floki.svg)](https://github.com/Cyb3rWard0g/floki/blob/main/LICENSE)
 
 ![](logo-workflows.png)
diff --git a/docs/logo-workflows.png b/docs/logo-workflows.png
index d6ded0b..aa1ff10 100644
Binary files a/docs/logo-workflows.png and b/docs/logo-workflows.png differ
diff --git a/mkdocs.yml b/mkdocs.yml
index 515d5e3..cd2cbd8 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -139,4 +139,5 @@ nav:
     - Concepts:
       - Enter Floki: home/concepts/floki.md
       - Core Principles: home/concepts/principles.md
-      - Agents: home/concepts/agents.md
\ No newline at end of file
+      - Agents: home/concepts/agents.md
+      - Messaging: home/concepts/messaging.md
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
index 44d549a..85bde7d 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -13,87 +13,87 @@ files = [
 
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 [package.dependencies]
@@ -136,24 +136,24 @@ files = [
 
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 description = "High level compatibility layer for multiple asynchronous event loop implementations"
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 python-versions = ">=3.9"
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-version = "0.28.0"
+version = "0.28.1"
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-dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.20,<0.21)", "urllib3 (<2.0.0)"]
-dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "libcst", "librosa", "nltk (<=3.8.1)", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.20,<0.21)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=1.0.11)", "tokenizers (>=0.21,<0.22)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.21,<0.22)", "urllib3 (<2.0.0)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "libcst", "librosa", "nltk (<=3.8.1)", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=1.0.11)", "tokenizers (>=0.21,<0.22)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
 flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 ftfy = ["ftfy"]
@@ -4479,12 +4382,12 @@ tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<
 tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 tiktoken = ["blobfile", "tiktoken"]
-timm = ["timm (<=0.9.16)"]
-tokenizers = ["tokenizers (>=0.20,<0.21)"]
+timm = ["timm (<=1.0.11)"]
+tokenizers = ["tokenizers (>=0.21,<0.22)"]
 torch = ["accelerate (>=0.26.0)", "torch"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.20,<0.21)", "torch", "tqdm (>=4.27)"]
+torchhub = ["filelock", "huggingface-hub (>=0.24.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.21,<0.22)", "torch", "tqdm (>=4.27)"]
 video = ["av (==9.2.0)"]
 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
diff --git a/pyproject.toml b/pyproject.toml
index b7fbaf4..e0d5901 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "floki-ai"
-version = "0.4.0"
+version = "0.5.0"
 description = "Agentic Workflows Made Simple"
 readme = "README.md"
 authors = [{ name = "Roberto Rodriguez" }]
@@ -39,7 +39,7 @@ homepage = "https://github.com/Cyb3rWard0g/floki"
 
 [tool.poetry]
 name = "floki"
-version = "0.4.0"
+version = "0.5.0"
 description = "Agentic Workflows Made Simple"
 authors = ["Roberto Rodriguez"]
 license = "MIT"
diff --git a/setup.py b/setup.py
index e2d8658..20ee6ed 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setup(
     name="floki-ai",
-    version="0.4.0",
+    version="0.5.0",
     author="Roberto Rodriguez",
     description="Agentic Workflows Made Simple",
     long_description=long_description,
diff --git a/src/floki/agent/services/base.py b/src/floki/agent/services/base.py
index ff68a50..0ec794f 100644
--- a/src/floki/agent/services/base.py
+++ b/src/floki/agent/services/base.py
@@ -304,21 +304,15 @@ def add_subscription_rule(subscription: dict, match_condition: str, route: str)
                 subscription["routes"]["rules"].append(rule)
                 logger.info(f"Added match condition: {match_condition}")
                 logger.debug(f"Rule: {rule}")
-
+        
         def create_dependency_injector(model):
             """Factory to create a dependency injector for a specific message model."""
             async def dependency_injector(request: Request):
                 if not model:
                     raise ValueError("No message model provided for dependency injection.")
                 logger.info(f"Using model '{model.__name__}' for this request.")
-                message, metadata, message_type = await parse_cloudevent(request, model)
-                dependencies = {
-                    "message": message,
-                    "source": metadata.get("source"),
-                    "type": message_type,
-                    "headers": metadata.get("headers", {}),
-                }
-                return dependencies
+                message, metadata = await parse_cloudevent(request, model)
+                return {"message": message, "metadata": metadata}
             return dependency_injector
         
         # Define the handler wrapper within a factory function to capture the method correctly
diff --git a/src/floki/agent/services/default.py b/src/floki/agent/services/default.py
index cb5c6b0..d8fa0dd 100644
--- a/src/floki/agent/services/default.py
+++ b/src/floki/agent/services/default.py
@@ -1,7 +1,7 @@
 from floki.agent.services.base import AgentServiceBase
 from floki.agent.services.messaging import message_router
 from floki.types.agent import AgentActorMessage
-from floki.types.message import BaseMessage
+from floki.types.message import BaseMessage, EventMessageMetadata
 from fastapi import Response, status
 from pydantic import BaseModel
 from typing import Optional
@@ -18,15 +18,15 @@ class AgentService(AgentServiceBase):
     """
     
     @message_router
-    async def process_trigger_action(self, message: TriggerActionMessage, source: str, type: str, headers: dict,) -> Response:
+    async def process_trigger_action(self, message: TriggerActionMessage, metadata: EventMessageMetadata) -> Response:
         """
         Processes TriggerAction messages sent directly to the agent's topic.
         """
         try:
-            logger.info(f"{self.agent.name} received {type} message from {source}.")
+            logger.info(f"{self.agent.name} received {metadata.type} from {metadata.source}.")
 
             # Extract workflow_instance_id if available
-            workflow_instance_id = headers.get("workflow_instance_id")
+            workflow_instance_id = metadata.headers.get("workflow_instance_id")
             logger.debug(f"Workflow instance ID: {workflow_instance_id}")
 
             # Execute the task or fallback to memory
@@ -56,20 +56,20 @@ async def process_trigger_action(self, message: TriggerActionMessage, source: st
             return Response(content=f"Error processing message: {str(e)}", status_code=status.HTTP_500_INTERNAL_SERVER_ERROR)
 
     @message_router(broadcast=True)
-    async def process_broadcast_message(self, message: BaseMessage, source: str, type: str) -> Response:
+    async def process_broadcast_message(self, message: BaseMessage, metadata: EventMessageMetadata) -> Response:
         """
         Processes a message from the broadcast topic.
         """
         try:
-            logger.info(f"{self.agent.name} received broadcast message of type '{type}' from '{source}'.")
+            logger.info(f"{self.agent.name} received broadcast message of type '{metadata.type}' from '{metadata.source}'.")
 
             # Ignore messages sent by this agent
-            if source == self.agent.name:
-                logger.info(f"{self.agent.name} ignored its own broadcast message of type '{type}'.")
+            if metadata.source == self.agent.name:
+                logger.info(f"{self.agent.name} ignored its own broadcast message of type '{metadata.type}'.")
                 return Response(status_code=status.HTTP_204_NO_CONTENT)
 
             # Log and process the valid broadcast message
-            logger.info(f"{self.agent.name} is processing broadcast message of type '{type}' from '{source}'.")
+            logger.info(f"{self.agent.name} is processing broadcast message of type '{metadata.type}' from '{metadata.source}'.")
             logger.debug(f"Message content: {message.content}")
 
             # Add the message to the agent's memory
diff --git a/src/floki/agent/services/messaging.py b/src/floki/agent/services/messaging.py
index e01e3eb..d42a25c 100644
--- a/src/floki/agent/services/messaging.py
+++ b/src/floki/agent/services/messaging.py
@@ -1,8 +1,10 @@
-from typing import Optional, Any, Callable, get_type_hints, Tuple, Type
-from fastapi import HTTPException, Request
-from cloudevents.http import from_http
-from cloudevents.http.event import CloudEvent
+from typing import Optional, Any, Callable, get_type_hints, Tuple, Type, Dict
+from floki.types.message import EventMessageMetadata
 from pydantic import BaseModel, ValidationError
+from inspect import signature, Parameter
+from cloudevents.http.event import CloudEvent
+from cloudevents.http import from_http
+from fastapi import HTTPException, Request
 from copy import deepcopy
 import logging
 
@@ -23,23 +25,28 @@ async def parse_cloudevent(request: Request, model: Optional[Type[BaseModel]] =
         raise HTTPException(status_code=400, detail=f"Invalid CloudEvent: {str(e)}")
 
     # Extract metadata
-    cloud_event_metadata = {
-        "id": event.get("id"),
-        "source": event.get("source"),
-        "type": event.get("type"),
-        "topic": event.get("topic"),
-        "pubsubname": event.get("pubsubname"),
-        "time": event.get("time"),
-        "headers": dict(headers),
-    }
-    logger.debug(f"Extracted CloudEvent metadata: {cloud_event_metadata}")
+    metadata = EventMessageMetadata(
+        id=event.get("id"),
+        datacontenttype=event.get("datacontenttype"),
+        pubsubname=event.get("pubsubname"),
+        source=event.get("source"),
+        specversion=event.get("specversion"),
+        time=event.get("time"),
+        topic=event.get("topic"),
+        traceid=event.get("traceid"),
+        traceparent=event.get("traceparent"),
+        type=event.get("type"),
+        tracestate=event.get("tracestate"),
+        headers=dict(headers),
+    )
+
+    logger.debug(f"Extracted CloudEvent metadata: {metadata}")
 
     # Validate and parse message payload
     if model:
         try:
             logger.debug(f"Validating payload with model '{model.__name__}'...")
             message = model(**event.data)
-            message_type = model.__name__
         except ValidationError as ve:
             logger.error(f"Message validation failed for model '{model.__name__}': {ve}")
             raise HTTPException(status_code=422, detail=f"Message validation failed: {ve}")
@@ -47,9 +54,34 @@ async def parse_cloudevent(request: Request, model: Optional[Type[BaseModel]] =
         logger.error("No Pydantic model provided for message validation.")
         raise HTTPException(status_code=500, detail="Message validation failed: No Pydantic model provided.")
 
-    # Return the validated message, metadata, and message_type
+    # Return the validated message, and metadata
     logger.debug(f"Message successfully parsed and validated: {message}")
-    return message, cloud_event_metadata, message_type
+    return message, metadata
+
+def filter_parameters(func: Callable[..., Any]) -> Dict[str, Parameter]:
+    """
+    Filter parameters of a callable, excluding instance (`self`) or class (`cls`) references
+    when the callable is in a class context.
+
+    Args:
+        func (Callable): The callable to inspect.
+
+    Returns:
+        Dict[str, Parameter]: Filtered parameters.
+    """
+    sig = signature(func)
+    parameters = sig.parameters
+
+    if hasattr(func, "__self__") or getattr(func, "__qualname__", "").split(".")[0] != func.__name__:
+        # Class context: skip the first parameter (assumed to be 'self' or 'cls')
+        return {
+            name: param
+            for idx, (name, param) in enumerate(parameters.items())
+            if idx != 0
+        }
+    else:
+        # Regular functions: include all parameters
+        return parameters
 
 def message_router(
     func: Optional[Callable[..., Any]] = None,
@@ -63,31 +95,49 @@ def message_router(
     """
     Decorator for dynamically registering message handlers for pub/sub topics.
 
+    This decorator validates the signature of a handler function to ensure it conforms
+    to the expected parameters (`message` and optionally `metadata`). It also attaches
+    routing metadata to the handler for pub/sub integration.
+
     Args:
-        func (Optional[Callable]): The function to decorate.
-        pubsub (Optional[str]): The pubsub name.
-        topic (Optional[str]): The topic name.
-        route (Optional[str]): The custom route.
-        dead_letter_topic (Optional[str]): Dead letter topic name.
-        broadcast (bool): Whether the message is broadcast.
+        func (Optional[Callable]): The function or method to decorate.
+        pubsub (Optional[str]): The name of the pub/sub component to use.
+        topic (Optional[str]): The topic name for the handler.
+        route (Optional[str]): The custom route for this handler (optional).
+        dead_letter_topic (Optional[str]): Name of the dead letter topic for failed messages (optional).
+        broadcast (bool): Indicates if the message should be broadcast to multiple subscribers.
 
     Returns:
-        Callable: The decorated function.
+        Callable: The decorated handler with additional metadata.
+
+    Raises:
+        ValueError: If the handler's signature does not meet the requirements.
     """
     def decorator(f: Callable[..., Any]) -> Callable[..., Any]:
-        # Extract type hints to identify the Pydantic model
+        # Filter parameters of the function/method
+        filtered_params = filter_parameters(f)
+
+        # Extract type hints
         type_hints = get_type_hints(f)
-        message_model = None
-        for param_name, param_type in type_hints.items():
-            if isinstance(param_type, type) and issubclass(param_type, BaseModel):
-                message_model = param_type
-                logger.debug(f"Parameter '{param_name}' is identified as the message model of type '{message_model.__name__}'.")
-                break
 
-        if not message_model:
-            raise ValueError(f"No Pydantic model found in parameters for handler '{f.__name__}'.")
+        # Ensure the 'message' parameter is defined and is a Pydantic BaseModel
+        if "message" not in filtered_params:
+            raise ValueError(f"The handler '{f.__name__}' must have a 'message' parameter of type BaseModel.")
+        if not isinstance(type_hints.get("message"), type) or not issubclass(type_hints["message"], BaseModel):
+            raise ValueError(f"The 'message' parameter in handler '{f.__name__}' must be a Pydantic BaseModel.")
+
+        # Validate the optional 'metadata' parameter
+        if "metadata" in filtered_params:
+            if type_hints.get("metadata") != EventMessageMetadata:
+                raise ValueError(f"If 'metadata' is defined in handler '{f.__name__}', it must be of type EventMessageMetadata.")
+
+        # Check for unsupported parameters
+        allowed_params = {"message", "metadata"}
+        extra_params = set(filtered_params) - allowed_params
+        if extra_params:
+            raise ValueError(f"The handler '{f.__name__}' has unsupported parameters: {extra_params}.")
 
-        # Attach metadata to the function for registration
+        # Attach routing metadata to the function for registration
         f._is_message_handler = True
         f._message_router_data = deepcopy({
             "pubsub": pubsub,
@@ -95,9 +145,10 @@ def decorator(f: Callable[..., Any]) -> Callable[..., Any]:
             "route": route,
             "dead_letter_topic": dead_letter_topic,
             "is_broadcast": broadcast,
-            "message_model": message_model,
+            "message_model": type_hints["message"],
         })
 
         return f
 
+    # If the decorator is applied directly, return the decorated function
     return decorator(func) if func else decorator
\ No newline at end of file
diff --git a/src/floki/types/message.py b/src/floki/types/message.py
index 840d6da..6db57db 100644
--- a/src/floki/types/message.py
+++ b/src/floki/types/message.py
@@ -1,5 +1,5 @@
 from pydantic import BaseModel,field_validator, ValidationError, model_validator, ConfigDict
-from typing import List, Optional
+from typing import List, Optional, Dict
 import json
 
 class BaseMessage(BaseModel):
@@ -266,4 +266,53 @@ class MessagePlaceHolder(BaseModel):
     model_config = ConfigDict(frozen=True)
     
     def __repr__(self):
-        return f"MessagePlaceHolder(variable_name={self.variable_name})"
\ No newline at end of file
+        return f"MessagePlaceHolder(variable_name={self.variable_name})"
+
+class EventMessageMetadata(BaseModel):
+    """
+    Represents CloudEvent metadata for describing event context and attributes.
+
+    This class encapsulates core attributes as defined by the CloudEvents specification.
+    Each field corresponds to a CloudEvent context attribute, providing additional metadata
+    about the event.
+
+    Attributes:
+        id (Optional[str]): 
+            Identifies the event. Producers MUST ensure that source + id is unique for each 
+            distinct event. Required and must be a non-empty string.
+        datacontenttype (Optional[str]): 
+            Content type of the event data value, e.g., 'application/json'. 
+            Optional and must adhere to RFC 2046.
+        pubsubname (Optional[str]): 
+            Name of the Pub/Sub system delivering the event. Optional and specific to implementation.
+        source (Optional[str]): 
+            Identifies the context in which an event happened. Required and must be a non-empty URI-reference.
+        specversion (Optional[str]): 
+            The version of the CloudEvents specification used by this event. Required and must be non-empty.
+        time (Optional[str]): 
+            The timestamp of when the occurrence happened in RFC 3339 format. Optional.
+        topic (Optional[str]): 
+            The topic name that categorizes the event within the Pub/Sub system. Optional and specific to implementation.
+        traceid (Optional[str]): 
+            The identifier for tracing systems to correlate events. Optional.
+        traceparent (Optional[str]): 
+            Parent identifier in the tracing system. Optional and adheres to the W3C Trace Context standard.
+        type (Optional[str]): 
+            Describes the type of event related to the originating occurrence. Required and must be a non-empty string.
+        tracestate (Optional[str]): 
+            Vendor-specific tracing information. Optional and adheres to the W3C Trace Context standard.
+        headers (Optional[Dict[str, str]]): 
+            HTTP headers or transport metadata. Optional and contains key-value pairs.
+    """
+    id: Optional[str]
+    datacontenttype: Optional[str]
+    pubsubname: Optional[str]
+    source: Optional[str]
+    specversion: Optional[str]
+    time: Optional[str]
+    topic: Optional[str]
+    traceid: Optional[str]
+    traceparent: Optional[str]
+    type: Optional[str]
+    tracestate: Optional[str]
+    headers: Optional[Dict[str, str]]
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