diff --git a/aixplain/factories/__init__.py b/aixplain/factories/__init__.py
index 70361e77..d540f374 100644
--- a/aixplain/factories/__init__.py
+++ b/aixplain/factories/__init__.py
@@ -21,6 +21,7 @@
 """
 from .asset_factory import AssetFactory
 from .agent_factory import AgentFactory
+from .team_agent_factory import TeamAgentFactory
 from .benchmark_factory import BenchmarkFactory
 from .corpus_factory import CorpusFactory
 from .data_factory import DataFactory
diff --git a/aixplain/factories/agent_factory/__init__.py b/aixplain/factories/agent_factory/__init__.py
index 134b3560..2a16e191 100644
--- a/aixplain/factories/agent_factory/__init__.py
+++ b/aixplain/factories/agent_factory/__init__.py
@@ -34,7 +34,7 @@
 from aixplain.utils import config
 from typing import Dict, List, Optional, Text, Union
 
-from aixplain.factories.agent_factory.utils import build_agent, validate_llm
+from aixplain.factories.agent_factory.utils import build_agent, validate_llm, validate_name
 from aixplain.utils.file_utils import _request_with_retry
 from urllib.parse import urljoin
 
@@ -44,36 +44,31 @@ class AgentFactory:
     def create(
         cls,
         name: Text,
-        llm_id: Text,
+        description: Text,
+        llm_id: Text = "669a63646eb56306647e1091",
         tools: List[Tool] = [],
-        description: Text = "",
         api_key: Text = config.TEAM_API_KEY,
         supplier: Union[Dict, Text, Supplier, int] = "aiXplain",
         version: Optional[Text] = None,
-        use_mentalist_and_inspector: bool = False,
     ) -> Agent:
         """Create a new agent in the platform.
 
         Args:
             name (Text): name of the agent
-            llm_id (Text): aiXplain ID of the large language model to be used as agent.
+            description (Text): description of the agent role.
+            llm_id (Text, optional): aiXplain ID of the large language model to be used as agent. Defaults to "669a63646eb56306647e1091" (GPT-4o mini).
             tools (List[Tool], optional): list of tool for the agent. Defaults to [].
-            description (Text, optional): description of the agent role. Defaults to "".
             api_key (Text, optional): team/user API key. Defaults to config.TEAM_API_KEY.
             supplier (Union[Dict, Text, Supplier, int], optional): owner of the agent. Defaults to "aiXplain".
             version (Optional[Text], optional): version of the agent. Defaults to None.
-            use_mentalist_and_inspector (bool, optional): flag to enable mentalist and inspector agents (which only works when a supervisor is enabled). Defaults to False.
 
         Returns:
             Agent: created Agent
         """
+        validate_name(name)
         # validate LLM ID
         validate_llm(llm_id)
 
-        orchestrator_llm_id, mentalist_and_inspector_llm_id = llm_id, None
-        if use_mentalist_and_inspector is True:
-            mentalist_and_inspector_llm_id = llm_id
-
         try:
             agent = None
             url = urljoin(config.BACKEND_URL, "sdk/agents")
@@ -117,8 +112,6 @@ def create(
                 "supplier": supplier,
                 "version": version,
                 "llmId": llm_id,
-                "supervisorId": orchestrator_llm_id,
-                "plannerId": mentalist_and_inspector_llm_id,
             }
 
             logging.info(f"Start service for POST Create Agent  - {url} - {headers} - {json.dumps(payload)}")
@@ -128,13 +121,13 @@ def create(
                 agent = build_agent(payload=response, api_key=api_key)
             else:
                 error = r.json()
-                error_msg = "Agent Onboarding Error: Please contant the administrators."
+                error_msg = "Agent Onboarding Error: Please contact the administrators."
                 if "message" in error:
                     msg = error["message"]
                     if error["message"] == "err.name_already_exists":
                         msg = "Agent name already exists."
                     elif error["message"] == "err.asset_is_not_available":
-                        msg = "Some the tools are not available."
+                        msg = "Some tools are not available."
                     error_msg = f"Agent Onboarding Error (HTTP {r.status_code}): {msg}"
                 logging.exception(error_msg)
                 raise Exception(error_msg)
@@ -190,7 +183,7 @@ def list(cls) -> Dict:
                     agents.append(build_agent(agent))
                 return {"results": agents, "page_total": page_total, "page_number": 0, "total": total}
             else:
-                error_msg = "Agent Listing Error: Please contant the administrators."
+                error_msg = "Agent Listing Error: Please contact the administrators."
                 if "message" in resp:
                     msg = resp["message"]
                     error_msg = f"Agent Listing Error (HTTP {r.status_code}): {msg}"
@@ -214,7 +207,7 @@ def get(cls, agent_id: Text, api_key: Optional[Text] = None) -> Agent:
         if 200 <= r.status_code < 300:
             return build_agent(resp)
         else:
-            msg = "Please contant the administrators."
+            msg = "Please contact the administrators."
             if "message" in resp:
                 msg = resp["message"]
             error_msg = f"Agent Get Error (HTTP {r.status_code}): {msg}"
diff --git a/aixplain/factories/agent_factory/utils.py b/aixplain/factories/agent_factory/utils.py
index 6aed75ae..d86982ef 100644
--- a/aixplain/factories/agent_factory/utils.py
+++ b/aixplain/factories/agent_factory/utils.py
@@ -7,6 +7,8 @@
 from typing import Dict, Text
 from urllib.parse import urljoin
 
+GPT_4o_ID = "6646261c6eb563165658bbb1"
+
 
 def build_agent(payload: Dict, api_key: Text = config.TEAM_API_KEY) -> Agent:
     """Instantiate a new agent in the platform."""
@@ -41,7 +43,7 @@ def build_agent(payload: Dict, api_key: Text = config.TEAM_API_KEY) -> Agent:
         supplier=payload["teamId"] if "teamId" in payload else None,
         version=payload["version"] if "version" in payload else None,
         cost=payload["cost"] if "cost" in payload else None,
-        llm_id=payload["llmId"] if "llmId" in payload else "6646261c6eb563165658bbb1",
+        llm_id=payload["llmId"] if "llmId" in payload else GPT_4o_ID,
         api_key=api_key,
         status=AssetStatus(payload["status"]),
     )
@@ -57,3 +59,11 @@ def validate_llm(model_id: Text) -> None:
         assert llm.function == Function.TEXT_GENERATION, "Large Language Model must be a text generation model."
     except Exception:
         raise Exception(f"Large Language Model with ID '{model_id}' not found.")
+
+
+def validate_name(name: Text) -> None:
+    import re
+
+    assert (
+        re.match("^[a-zA-Z0-9 ]*$", name) is not None
+    ), "Agent Creation Error: Agent name must not contain special characters."
diff --git a/aixplain/factories/pipeline_factory/__init__.py b/aixplain/factories/pipeline_factory/__init__.py
index 051c63fb..cb4336fe 100644
--- a/aixplain/factories/pipeline_factory/__init__.py
+++ b/aixplain/factories/pipeline_factory/__init__.py
@@ -290,7 +290,7 @@ def create(
                     pipeline = json.load(f)
 
             for i, node in enumerate(pipeline["nodes"]):
-                if "functionType" in node and node["functionType"] == "AI":
+                if "functionType" in node:
                     pipeline["nodes"][i]["functionType"] = pipeline["nodes"][i]["functionType"].lower()
             # prepare payload
             payload = {
diff --git a/aixplain/factories/pipeline_factory/utils.py b/aixplain/factories/pipeline_factory/utils.py
index 465e5e7f..9584863f 100644
--- a/aixplain/factories/pipeline_factory/utils.py
+++ b/aixplain/factories/pipeline_factory/utils.py
@@ -7,7 +7,8 @@
 from aixplain.modules.pipeline.designer import (
     Input,
     Output,
-    AssetNode,
+    BareAsset,
+    BareMetric,
     Decision,
     Router,
     Route,
@@ -36,14 +37,16 @@ def build_from_response(response: Dict, load_architecture: bool = False) -> Pipe
         try:
             # instantiating nodes
             for node_json in response["nodes"]:
-                print(node_json)
                 if node_json["type"].lower() == "input":
                     node = Input(
                         data=node_json["data"] if "data" in node_json else None,
                         data_types=[DataType(dt) for dt in node_json["dataType"]],
                     )
                 elif node_json["type"].lower() == "asset":
-                    node = AssetNode(asset_id=node_json["assetId"])
+                    if node_json["functionType"] == "metric":
+                        node = BareMetric(asset_id=node_json["assetId"])
+                    else:
+                        node = BareAsset(asset_id=node_json["assetId"])
                 elif node_json["type"].lower() == "segmentor":
                     raise NotImplementedError()
                 elif node_json["type"].lower() == "reconstructor":
@@ -53,7 +56,7 @@ def build_from_response(response: Dict, load_architecture: bool = False) -> Pipe
                 elif node_json["type"].lower() == "router":
                     node = Router(routes=[Route(**route) for route in node_json["routes"]])
                 elif node_json["type"].lower() == "script":
-                    node = Script(fileId=node_json["fileId"])
+                    node = Script(fileId=node_json["fileId"], fileMetadata=node_json["fileMetadata"])
                 elif node_json["type"].lower() == "output":
                     node = Output()
 
diff --git a/aixplain/factories/script_factory.py b/aixplain/factories/script_factory.py
index 35789561..14835752 100644
--- a/aixplain/factories/script_factory.py
+++ b/aixplain/factories/script_factory.py
@@ -8,15 +8,12 @@
 
 
 class ScriptFactory:
-
     @classmethod
     def upload_script(cls, script_path: str) -> Tuple[str, str]:
         try:
             url = f"{config.BACKEND_URL}/sdk/pipelines/script"
             headers = {"Authorization": f"Token {config.TEAM_API_KEY}"}
-            r = requests.post(
-                url, headers=headers, files={"file": open(script_path, "rb")}
-            )
+            r = requests.post(url, headers=headers, files={"file": open(script_path, "rb")})
             if 200 <= r.status_code < 300:
                 response = r.json()
             else:
@@ -26,6 +23,6 @@ def upload_script(cls, script_path: str) -> Tuple[str, str]:
 
         # get metadata info
         fname = os.path.splitext(os.path.basename(script_path))[0]
-        file_size_kb = int(os.path.getsize(script_path) / 1024)
-        metadata = json.dumps({"name": fname, "size": file_size_kb})
+        file_size = int(os.path.getsize(script_path))
+        metadata = json.dumps({"name": fname, "size": file_size})
         return response["fileId"], metadata
diff --git a/aixplain/factories/team_agent_factory/__init__.py b/aixplain/factories/team_agent_factory/__init__.py
new file mode 100644
index 00000000..72d47c03
--- /dev/null
+++ b/aixplain/factories/team_agent_factory/__init__.py
@@ -0,0 +1,166 @@
+__author__ = "lucaspavanelli"
+
+"""
+Copyright 2024 The aiXplain SDK authors
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+Author: Thiago Castro Ferreira and Lucas Pavanelli
+Date: August 15th 2024
+Description:
+    TeamAgent Factory Class
+"""
+
+import json
+import logging
+
+from aixplain.enums.supplier import Supplier
+from aixplain.factories.agent_factory import AgentFactory
+from aixplain.factories.agent_factory.utils import validate_llm, validate_name
+from aixplain.modules.agent import Agent
+from aixplain.modules.team_agent import TeamAgent
+from aixplain.utils import config
+from aixplain.factories.team_agent_factory.utils import build_team_agent
+from aixplain.utils.file_utils import _request_with_retry
+from typing import Dict, List, Optional, Text, Union
+from urllib.parse import urljoin
+
+
+class TeamAgentFactory:
+    @classmethod
+    def create(
+        cls,
+        name: Text,
+        agents: List[Union[Text, Agent]],
+        llm_id: Text = "669a63646eb56306647e1091",
+        description: Text = "",
+        api_key: Text = config.TEAM_API_KEY,
+        supplier: Union[Dict, Text, Supplier, int] = "aiXplain",
+        version: Optional[Text] = None,
+        use_mentalist_and_inspector: bool = True,
+    ) -> TeamAgent:
+        """Create a new team agent in the platform."""
+        validate_name(name)
+        # validate LLM ID
+        validate_llm(llm_id)
+        assert len(agents) > 0, "TeamAgent Onboarding Error: At least one agent must be provided."
+        for agent in agents:
+            if isinstance(agent, Text) is True:
+                try:
+                    agent = AgentFactory.get(agent)
+                except Exception:
+                    raise Exception(f"TeamAgent Onboarding Error: Agent {agent} does not exist.")
+            else:
+                assert isinstance(agent, Agent), "TeamAgent Onboarding Error: Agents must be instances of Agent class"
+
+        mentalist_and_inspector_llm_id = None
+        if use_mentalist_and_inspector is True:
+            mentalist_and_inspector_llm_id = llm_id
+        try:
+            team_agent = None
+            url = urljoin(config.BACKEND_URL, "sdk/agent-communities")
+            headers = {"x-api-key": api_key}
+
+            if isinstance(supplier, dict):
+                supplier = supplier["code"]
+            elif isinstance(supplier, Supplier):
+                supplier = supplier.value["code"]
+
+            agent_list = []
+            for idx, agent in enumerate(agents):
+                agent_list.append({"assetId": agent.id, "number": idx, "type": "AGENT", "label": "AGENT"})
+
+            payload = {
+                "name": name,
+                "agents": agent_list,
+                "links": [],
+                "description": description,
+                "llmId": llm_id,
+                "supervisorId": llm_id,
+                "plannerId": mentalist_and_inspector_llm_id,
+                "supplier": supplier,
+                "version": version,
+            }
+
+            logging.info(f"Start service for POST Create TeamAgent  - {url} - {headers} - {json.dumps(payload)}")
+            r = _request_with_retry("post", url, headers=headers, json=payload)
+            if 200 <= r.status_code < 300:
+                response = r.json()
+                team_agent = build_team_agent(payload=response, api_key=api_key)
+            else:
+                error = r.json()
+                error_msg = "TeamAgent Onboarding Error: Please contact the administrators."
+                if "message" in error:
+                    msg = error["message"]
+                    if error["message"] == "err.name_already_exists":
+                        msg = "TeamAgent name already exists."
+                    elif error["message"] == "err.asset_is_not_available":
+                        msg = "Some tools are not available."
+                    error_msg = f"TeamAgent Onboarding Error (HTTP {r.status_code}): {msg}"
+                logging.exception(error_msg)
+                raise Exception(error_msg)
+        except Exception as e:
+            raise Exception(e)
+        return team_agent
+
+    @classmethod
+    def list(cls) -> Dict:
+        """List all agents available in the platform."""
+        url = urljoin(config.BACKEND_URL, "sdk/agent-communities")
+        headers = {"x-api-key": config.TEAM_API_KEY, "Content-Type": "application/json"}
+
+        payload = {}
+        logging.info(f"Start service for GET List Agents - {url} - {headers} - {json.dumps(payload)}")
+        try:
+            r = _request_with_retry("get", url, headers=headers)
+            resp = r.json()
+
+            if 200 <= r.status_code < 300:
+                agents, page_total, total = [], 0, 0
+                results = resp
+                page_total = len(results)
+                total = len(results)
+                logging.info(f"Response for GET List Agents - Page Total: {page_total} / Total: {total}")
+                for agent in results:
+                    agents.append(build_team_agent(agent))
+                return {"results": agents, "page_total": page_total, "page_number": 0, "total": total}
+            else:
+                error_msg = "Agent Listing Error: Please contact the administrators."
+                if "message" in resp:
+                    msg = resp["message"]
+                    error_msg = f"Agent Listing Error (HTTP {r.status_code}): {msg}"
+                logging.exception(error_msg)
+                raise Exception(error_msg)
+        except Exception as e:
+            raise Exception(e)
+
+    @classmethod
+    def get(cls, agent_id: Text, api_key: Optional[Text] = None) -> Agent:
+        """Get agent by id."""
+        url = urljoin(config.BACKEND_URL, f"sdk/agent-communities/{agent_id}")
+        if config.AIXPLAIN_API_KEY != "":
+            headers = {"x-aixplain-key": f"{config.AIXPLAIN_API_KEY}", "Content-Type": "application/json"}
+        else:
+            api_key = api_key if api_key is not None else config.TEAM_API_KEY
+            headers = {"x-api-key": api_key, "Content-Type": "application/json"}
+        logging.info(f"Start service for GET Agent  - {url} - {headers}")
+        r = _request_with_retry("get", url, headers=headers)
+        resp = r.json()
+        if 200 <= r.status_code < 300:
+            return build_team_agent(resp)
+        else:
+            msg = "Please contact the administrators."
+            if "message" in resp:
+                msg = resp["message"]
+            error_msg = f"Agent Get Error (HTTP {r.status_code}): {msg}"
+            raise Exception(error_msg)
diff --git a/aixplain/factories/team_agent_factory/utils.py b/aixplain/factories/team_agent_factory/utils.py
new file mode 100644
index 00000000..42fa5f6c
--- /dev/null
+++ b/aixplain/factories/team_agent_factory/utils.py
@@ -0,0 +1,34 @@
+__author__ = "lucaspavanelli"
+
+import aixplain.utils.config as config
+from aixplain.enums.asset_status import AssetStatus
+from aixplain.modules.team_agent import TeamAgent
+from aixplain.factories.agent_factory import AgentFactory
+from typing import Dict, Text
+from urllib.parse import urljoin
+
+GPT_4o_ID = "6646261c6eb563165658bbb1"
+
+
+def build_team_agent(payload: Dict, api_key: Text = config.TEAM_API_KEY) -> TeamAgent:
+    """Instantiate a new team agent in the platform."""
+    agents = payload["agents"]
+    for i, agent in enumerate(agents):
+        agent = AgentFactory.get(agent["assetId"])
+        agents[i] = agent
+
+    team_agent = TeamAgent(
+        id=payload["id"],
+        name=payload["name"] if "name" in payload else "",
+        agents=agents,
+        description=payload["description"] if "description" in payload else "",
+        supplier=payload["teamId"] if "teamId" in payload else None,
+        version=payload["version"] if "version" in payload else None,
+        cost=payload["cost"] if "cost" in payload else None,
+        llm_id=payload["llmId"] if "llmId" in payload else GPT_4o_ID,
+        use_mentalist_and_inspector=True if "plannerId" in payload and payload["plannerId"] is not None else False,
+        api_key=api_key,
+        status=AssetStatus(payload["status"]),
+    )
+    team_agent.url = urljoin(config.BACKEND_URL, f"sdk/agent-communities/{team_agent.id}/run")
+    return team_agent
diff --git a/aixplain/modules/__init__.py b/aixplain/modules/__init__.py
index c7246dac..bad0c225 100644
--- a/aixplain/modules/__init__.py
+++ b/aixplain/modules/__init__.py
@@ -35,3 +35,4 @@
 from .benchmark_job import BenchmarkJob
 from .agent import Agent
 from .agent.tool import Tool
+from .team_agent import TeamAgent
diff --git a/aixplain/modules/agent/__init__.py b/aixplain/modules/agent/__init__.py
index c0604f6a..546ea4d8 100644
--- a/aixplain/modules/agent/__init__.py
+++ b/aixplain/modules/agent/__init__.py
@@ -47,7 +47,7 @@ class Agent(Model):
         name (Text): Name of the Agent
         tools (List[Tool]): List of tools that the Agent uses.
         description (Text, optional): description of the Agent. Defaults to "".
-        llm_id (Text, optional): large language model. Defaults to GPT-4o (6646261c6eb563165658bbb1).
+        llm_id (Text): large language model. Defaults to GPT-4o (6646261c6eb563165658bbb1).
         supplier (Text): Supplier of the Agent.
         version (Text): Version of the Agent.
         backend_url (str): URL of the backend.
@@ -59,8 +59,8 @@ def __init__(
         self,
         id: Text,
         name: Text,
+        description: Text,
         tools: List[Tool] = [],
-        description: Text = "",
         llm_id: Text = "6646261c6eb563165658bbb1",
         api_key: Optional[Text] = config.TEAM_API_KEY,
         supplier: Union[Dict, Text, Supplier, int] = "aiXplain",
@@ -69,13 +69,13 @@ def __init__(
         status: AssetStatus = AssetStatus.ONBOARDING,
         **additional_info,
     ) -> None:
-        """Create a FineTune with the necessary information.
+        """Create an Agent with the necessary information.
 
         Args:
             id (Text): ID of the Agent
             name (Text): Name of the Agent
+            description (Text): description of the Agent.
             tools (List[Tool]): List of tools that the Agent uses.
-            description (Text, optional): description of the Agent. Defaults to "".
             llm_id (Text, optional): large language model. Defaults to GPT-4o (6646261c6eb563165658bbb1).
             supplier (Text): Supplier of the Agent.
             version (Text): Version of the Agent.
@@ -83,7 +83,6 @@ def __init__(
             api_key (str): The TEAM API key used for authentication.
             cost (Dict, optional): model price. Defaults to None.
         """
-        # assert len(tools) > 0, "At least one tool must be provided."
         super().__init__(id, name, description, api_key, supplier, version, cost=cost)
         self.additional_info = additional_info
         self.tools = tools
@@ -144,7 +143,7 @@ def run(
             return response
         except Exception as e:
             msg = f"Error in request for {name} - {traceback.format_exc()}"
-            logging.error(f"Model Run: Error in running for {name}: {e}")
+            logging.error(f"Agent Run: Error in running for {name}: {e}")
             end = time.time()
             return {"status": "FAILED", "error": msg, "elapsed_time": end - start}
 
@@ -210,7 +209,7 @@ def run_async(
         payload = json.dumps(payload)
 
         r = _request_with_retry("post", self.url, headers=headers, data=payload)
-        logging.info(f"Model Run Async: Start service for {name} - {self.url} - {payload} - {headers}")
+        logging.info(f"Agent Run Async: Start service for {name} - {self.url} - {payload} - {headers}")
 
         resp = None
         try:
@@ -222,13 +221,13 @@ def run_async(
         except Exception:
             response = {"status": "FAILED"}
             msg = f"Error in request for {name} - {traceback.format_exc()}"
-            logging.error(f"Model Run Async: Error in running for {name}: {resp}")
+            logging.error(f"Agent Run Async: Error in running for {name}: {resp}")
             if resp is not None:
                 response["error"] = msg
         return response
 
     def delete(self) -> None:
-        """Delete Corpus service"""
+        """Delete Agent service"""
         try:
             url = urljoin(config.BACKEND_URL, f"sdk/agents/{self.id}")
             headers = {"x-api-key": config.TEAM_API_KEY, "Content-Type": "application/json"}
diff --git a/aixplain/modules/agent/tool/__init__.py b/aixplain/modules/agent/tool/__init__.py
index 2a22511a..9c7a7a09 100644
--- a/aixplain/modules/agent/tool/__init__.py
+++ b/aixplain/modules/agent/tool/__init__.py
@@ -29,7 +29,7 @@ class Tool(ABC):
 
     Attributes:
         name (Text): name of the tool
-        description (Text): descriptiion of the tool
+        description (Text): description of the tool
         version (Text): version of the tool
     """
 
diff --git a/aixplain/modules/agent/tool/model_tool.py b/aixplain/modules/agent/tool/model_tool.py
index c88f1ee0..3a84c45b 100644
--- a/aixplain/modules/agent/tool/model_tool.py
+++ b/aixplain/modules/agent/tool/model_tool.py
@@ -20,7 +20,7 @@
 Description:
     Agentification Class
 """
-from typing import Optional, Union, Text
+from typing import Optional, Union, Text, Dict
 
 from aixplain.enums.function import Function
 from aixplain.enums.supplier import Supplier
@@ -32,23 +32,24 @@ class ModelTool(Tool):
     """Specialized software or resource designed to assist the AI in executing specific tasks or functions based on user commands.
 
     Attributes:
-        function (Function): task that the tool performs
-        supplier (Optional[Union[Dict, Text, Supplier, int]], optional): Preferred supplier to perform the task. Defaults to None.
+        function (Optional[Union[Function, Text]]): task that the tool performs.
+        supplier (Optional[Union[Dict, Supplier]]): Preferred supplier to perform the task.
+        model (Optional[Union[Text, Model]]): Model function.
     """
 
     def __init__(
         self,
-        function: Optional[Function] = None,
-        supplier: Optional[Supplier] = None,
+        function: Optional[Union[Function, Text]] = None,
+        supplier: Optional[Union[Dict, Supplier]] = None,
         model: Optional[Union[Text, Model]] = None,
         **additional_info,
     ) -> None:
         """Specialized software or resource designed to assist the AI in executing specific tasks or functions based on user commands.
 
         Args:
-            function (Optional[Function], optional): task that the tool performs. Defaults to None.
-            supplier (Optional[Supplier], optional): Preferred supplier to perform the task. Defaults to None.. Defaults to None.
-            model (Optional[Union[Text, Model]], optional): Model function. Defaults to None.
+            function (Optional[Union[Function, Text]]): task that the tool performs. Defaults to None.
+            supplier (Optional[Union[Dict, Supplier]]): Preferred supplier to perform the task. Defaults to None. Defaults to None.
+            model (Optional[Union[Text, Model]]): Model function. Defaults to None.
         """
         assert (
             function is not None or model is not None
diff --git a/aixplain/modules/agent/tool/pipeline_tool.py b/aixplain/modules/agent/tool/pipeline_tool.py
index 5ad2915a..fa8394ea 100644
--- a/aixplain/modules/agent/tool/pipeline_tool.py
+++ b/aixplain/modules/agent/tool/pipeline_tool.py
@@ -30,7 +30,7 @@ class PipelineTool(Tool):
     """Specialized software or resource designed to assist the AI in executing specific tasks or functions based on user commands.
 
     Attributes:
-        description (Text): descriptiion of the tool
+        description (Text): description of the tool
         pipeline (Union[Text, Pipeline]): pipeline
     """
 
diff --git a/aixplain/modules/pipeline/asset.py b/aixplain/modules/pipeline/asset.py
index 860a08a5..0e9ed56e 100644
--- a/aixplain/modules/pipeline/asset.py
+++ b/aixplain/modules/pipeline/asset.py
@@ -262,10 +262,11 @@ def __prepare_payload(
                         dasset = DatasetFactory.get(str(data_asset[node_label]))
                         asset_payload["dataAsset"]["dataset_id"] = dasset.id
 
-                        if (
-                            len([dfield for dfield in dasset.source_data if dasset.source_data[dfield].id == data[node_label]])
-                            > 0
-                        ):
+                        source_data_list = [
+                            dfield for dfield in dasset.source_data if dasset.source_data[dfield].id == data[node_label]
+                        ]
+
+                        if len(source_data_list) > 0:
                             data_found = True
                         else:
                             for target in dasset.target_data:
@@ -341,9 +342,11 @@ def run_async(
                     error = "Validation-related error: Please ensure all required fields are provided and correctly formatted."
                 else:
                     status_code = str(r.status_code)
-                    error = f"Status {status_code}: Unspecified error: An unspecified error occurred while processing your request."
+                    error = (
+                        f"Status {status_code}: Unspecified error: An unspecified error occurred while processing your request."
+                    )
                 response = {"status": "FAILED", "error_message": error}
-                logging.error(f"Error in request for {name} - {r.status_code}: {error}")               
+                logging.error(f"Error in request for {name} - {r.status_code}: {error}")
         except Exception:
             response = {"status": "FAILED"}
             if resp is not None:
@@ -355,6 +358,7 @@ def update(
         pipeline: Union[Text, Dict],
         save_as_asset: bool = False,
         api_key: Optional[Text] = None,
+        name: Optional[Text] = None,
     ):
         """Update Pipeline
 
@@ -376,12 +380,14 @@ def update(
                     pipeline = json.load(f)
 
             for i, node in enumerate(pipeline["nodes"]):
-                if "functionType" in node and node["functionType"] == "AI":
+                if "functionType" in node:
                     pipeline["nodes"][i]["functionType"] = pipeline["nodes"][i]["functionType"].lower()
             # prepare payload
             status = "draft"
             if save_as_asset is True:
                 status = "onboarded"
+            if name:
+                self.name = name
             payload = {
                 "name": self.name,
                 "status": status,
@@ -431,7 +437,7 @@ def save(self, save_as_asset: bool = False, api_key: Optional[Text] = None):
             pipeline = self.to_dict()
 
             for i, node in enumerate(pipeline["nodes"]):
-                if "functionType" in node and node["functionType"] == "AI":
+                if "functionType" in node:
                     pipeline["nodes"][i]["functionType"] = pipeline["nodes"][i]["functionType"].lower()
             # prepare payload
             status = "draft"
diff --git a/aixplain/modules/pipeline/designer/__init__.py b/aixplain/modules/pipeline/designer/__init__.py
index 0bb56542..81571f21 100644
--- a/aixplain/modules/pipeline/designer/__init__.py
+++ b/aixplain/modules/pipeline/designer/__init__.py
@@ -8,6 +8,9 @@
     Router,
     BaseReconstructor,
     BaseSegmentor,
+    BaseMetric,
+    BareAsset,
+    BareMetric
 )
 from .pipeline import DesignerPipeline
 from .base import (
@@ -36,6 +39,7 @@
 __all__ = [
     "DesignerPipeline",
     "AssetNode",
+    "BareAsset",
     "Decision",
     "Script",
     "Input",
@@ -63,4 +67,6 @@
     "ParamProxy",
     "TI",
     "TO",
+    "BaseMetric",
+    "BareMetric"
 ]
diff --git a/aixplain/modules/pipeline/designer/base.py b/aixplain/modules/pipeline/designer/base.py
index 8bea73d6..76e6196d 100644
--- a/aixplain/modules/pipeline/designer/base.py
+++ b/aixplain/modules/pipeline/designer/base.py
@@ -188,8 +188,8 @@ def validate(self):
         if from_param.data_type and to_param.data_type:
             if from_param.data_type != to_param.data_type:
                 raise ValueError(
-                    f"Data type mismatch between {from_param.data_type} and {to_param.data_type}"
-                )  # noqa
+                    f"Data type mismatch between {from_param.data_type} and {to_param.data_type}"  # noqa
+                )
 
     def attach_to(self, pipeline: "DesignerPipeline"):
         """
@@ -344,6 +344,9 @@ def __init__(
         if pipeline:
             self.attach_to(pipeline)
 
+    def build_label(self):
+        return f"{self.type.value}(ID={self.number})"
+
     def attach_to(self, pipeline: "DesignerPipeline"):
         """
         Attach the node to the pipeline.
@@ -359,7 +362,7 @@ def attach_to(self, pipeline: "DesignerPipeline"):
         if self.number is None:
             self.number = len(pipeline.nodes)
         if self.label is None:
-            self.label = f"{self.type.value}(ID={self.number})"
+            self.label = self.build_label()
 
         assert not pipeline.get_node(self.number), "Node number already exists"
         pipeline.nodes.append(self)
diff --git a/aixplain/modules/pipeline/designer/enums.py b/aixplain/modules/pipeline/designer/enums.py
index 4c044dba..fe4cbfed 100644
--- a/aixplain/modules/pipeline/designer/enums.py
+++ b/aixplain/modules/pipeline/designer/enums.py
@@ -22,8 +22,6 @@ class NodeType(str, Enum):
     INPUT = "INPUT"
     OUTPUT = "OUTPUT"
     SCRIPT = "SCRIPT"
-    SEGMENTOR = "SEGMENT"
-    RECONSTRUCTOR = "RECONSTRUCT"
     ROUTER = "ROUTER"
     DECISION = "DECISION"
 
@@ -33,9 +31,11 @@ class AssetType(str, Enum):
 
 
 class FunctionType(str, Enum):
-    AI = "AI"
-    SEGMENTOR = "SEGMENTOR"
-    RECONSTRUCTOR = "RECONSTRUCTOR"
+    AI = "ai"
+    SEGMENTOR = "segmentor"
+    RECONSTRUCTOR = "reconstructor"
+    UTILITY = "utility"
+    METRIC = "metric"
 
 
 class ParamType:
diff --git a/aixplain/modules/pipeline/designer/nodes.py b/aixplain/modules/pipeline/designer/nodes.py
index 22152239..a6879e04 100644
--- a/aixplain/modules/pipeline/designer/nodes.py
+++ b/aixplain/modules/pipeline/designer/nodes.py
@@ -55,8 +55,9 @@ def __init__(
         supplier: str = None,
         version: str = None,
         pipeline: "DesignerPipeline" = None,
+        **kwargs,
     ):
-        super().__init__(pipeline=pipeline)
+        super().__init__(pipeline=pipeline, **kwargs)
         self.asset_id = asset_id
         self.supplier = supplier
         self.version = version
@@ -84,9 +85,7 @@ def populate_asset(self):
 
         if self.function:
             if self.asset.function.value != self.function:
-                raise ValueError(
-                    f"Function {self.function} is not supported by asset {self.asset_id}"
-                )  # noqa
+                raise ValueError(f"Function {self.function} is not supported by asset {self.asset_id}")  # noqa
         else:
             self.function = self.asset.function.value
             self._auto_populate_params()
@@ -129,6 +128,18 @@ def serialize(self) -> dict:
         return obj
 
 
+class BareAssetInputs(Inputs):
+    pass
+
+
+class BareAssetOutputs(Outputs):
+    pass
+
+
+class BareAsset(AssetNode[BareAssetInputs, BareAssetOutputs]):
+    pass
+
+
 class InputInputs(Inputs):
     pass
 
@@ -163,10 +174,11 @@ def __init__(
         data: Optional[str] = None,
         data_types: Optional[List[DataType]] = None,
         pipeline: "DesignerPipeline" = None,
+        **kwargs,
     ):
         from aixplain.factories.file_factory import FileFactory
 
-        super().__init__(pipeline=pipeline)
+        super().__init__(pipeline=pipeline, **kwargs)
         self.data_types = data_types or []
         self.data = data
 
@@ -205,12 +217,8 @@ class Output(Node[OutputInputs, OutputOutputs]):
     inputs_class: Type[TI] = OutputInputs
     outputs_class: Type[TO] = OutputOutputs
 
-    def __init__(
-        self,
-        data_types: Optional[List[DataType]] = None,
-        pipeline: "DesignerPipeline" = None,
-    ):
-        super().__init__(pipeline=pipeline)
+    def __init__(self, data_types: Optional[List[DataType]] = None, pipeline: "DesignerPipeline" = None, **kwargs):
+        super().__init__(pipeline=pipeline, **kwargs)
         self.data_types = data_types or []
 
     def serialize(self) -> dict:
@@ -237,21 +245,25 @@ def __init__(
         pipeline: "DesignerPipeline" = None,
         script_path: Optional[str] = None,
         fileId: Optional[str] = None,
+        fileMetadata: Optional[str] = None,
+        **kwargs,
     ):
         from aixplain.factories.script_factory import ScriptFactory
 
-        super().__init__(pipeline=pipeline)
+        super().__init__(pipeline=pipeline, **kwargs)
 
         assert script_path or fileId, "script_path or fileId is required"
 
         if not fileId:
-            self.fileId = ScriptFactory.upload_script(script_path)
+            self.fileId, self.fileMetadata = ScriptFactory.upload_script(script_path)
         else:
             self.fileId = fileId
+            self.fileMetadata = fileMetadata
 
     def serialize(self) -> dict:
         obj = super().serialize()
         obj["fileId"] = self.fileId
+        obj["fileMetadata"] = self.fileMetadata
         return obj
 
 
@@ -266,13 +278,7 @@ class Route(Serializable):
     operation: Operation
     type: RouteType
 
-    def __init__(
-        self,
-        value: DataType,
-        path: List[Union[Node, int]],
-        operation: Operation,
-        type: RouteType,
-    ):
+    def __init__(self, value: DataType, path: List[Union[Node, int]], operation: Operation, type: RouteType, **kwargs):
         """
         Post init method to convert the nodes to node numbers if they are
         nodes.
@@ -286,10 +292,7 @@ def __init__(
             raise ValueError("Path is not valid, should be a list of nodes")
 
         # convert nodes to node numbers if they are nodes
-        self.path = [
-            node.number if isinstance(node, Node) else node
-            for node in self.path
-        ]
+        self.path = [node.number if isinstance(node, Node) else node for node in self.path]
 
     def serialize(self) -> dict:
         return {
@@ -327,10 +330,8 @@ class Router(Node[RouterInputs, RouterOutputs], LinkableMixin):
     inputs_class: Type[TI] = RouterInputs
     outputs_class: Type[TO] = RouterOutputs
 
-    def __init__(
-        self, routes: List[Route], pipeline: "DesignerPipeline" = None
-    ):
-        super().__init__(pipeline=pipeline)
+    def __init__(self, routes: List[Route], pipeline: "DesignerPipeline" = None, **kwargs):
+        super().__init__(pipeline=pipeline, **kwargs)
         self.routes = routes
 
     def serialize(self) -> dict:
@@ -368,10 +369,8 @@ class Decision(Node[DecisionInputs, DecisionOutputs], LinkableMixin):
     inputs_class: Type[TI] = DecisionInputs
     outputs_class: Type[TO] = DecisionOutputs
 
-    def __init__(
-        self, routes: List[Route], pipeline: "DesignerPipeline" = None
-    ):
-        super().__init__(pipeline=pipeline)
+    def __init__(self, routes: List[Route], pipeline: "DesignerPipeline" = None, **kwargs):
+        super().__init__(pipeline=pipeline, **kwargs)
         self.routes = routes
 
     def link(
@@ -396,7 +395,7 @@ class BaseSegmentor(AssetNode[TI, TO]):
     into smaller fragments for much easier and efficient processing.
     """
 
-    type: NodeType = NodeType.SEGMENTOR
+    type: NodeType = NodeType.ASSET
     functionType: FunctionType = FunctionType.SEGMENTOR
 
 
@@ -418,7 +417,7 @@ class BareSegmentor(BaseSegmentor[SegmentorInputs, SegmentorOutputs]):
     into smaller fragments for much easier and efficient processing.
     """
 
-    type: NodeType = NodeType.SEGMENTOR
+    type: NodeType = NodeType.ASSET
     functionType: FunctionType = FunctionType.SEGMENTOR
     inputs_class: Type[TI] = SegmentorInputs
     outputs_class: Type[TO] = SegmentorOutputs
@@ -430,7 +429,7 @@ class BaseReconstructor(AssetNode[TI, TO]):
     output of the segmented lines of execution.
     """
 
-    type: NodeType = NodeType.RECONSTRUCTOR
+    type: NodeType = NodeType.ASSET
     functionType: FunctionType = FunctionType.RECONSTRUCTOR
 
 
@@ -450,15 +449,46 @@ def __init__(self, node: Node):
         self.data = self.create_param("data")
 
 
-class BareReconstructor(
-    BaseReconstructor[ReconstructorInputs, ReconstructorOutputs]
-):
+class BareReconstructor(BaseReconstructor[ReconstructorInputs, ReconstructorOutputs]):
     """
     Reconstructor node class, this node will be used to reconstruct the
     output of the segmented lines of execution.
     """
 
-    type: NodeType = NodeType.RECONSTRUCTOR
+    type: NodeType = NodeType.ASSET
     functionType: FunctionType = FunctionType.RECONSTRUCTOR
     inputs_class: Type[TI] = ReconstructorInputs
     outputs_class: Type[TO] = ReconstructorOutputs
+
+
+class BaseMetric(AssetNode[TI, TO]):
+    functionType: FunctionType = FunctionType.METRIC
+
+    def build_label(self):
+        return f"METRIC({self.number})"
+
+
+class MetricInputs(Inputs):
+
+    hypotheses: InputParam = None
+    references: InputParam = None
+    sources: InputParam = None
+
+    def __init__(self, node: Node):
+        super().__init__(node)
+        self.hypotheses = self.create_param("hypotheses")
+        self.references = self.create_param("references")
+        self.sources = self.create_param("sources")
+
+
+class MetricOutputs(Outputs):
+
+    data: OutputParam = None
+
+    def __init__(self, node: Node):
+        super().__init__(node)
+        self.data = self.create_param("data")
+
+
+class BareMetric(BaseMetric[MetricInputs, MetricOutputs]):
+    pass
diff --git a/aixplain/modules/pipeline/designer/pipeline.py b/aixplain/modules/pipeline/designer/pipeline.py
index 5304d202..b2ebd19b 100644
--- a/aixplain/modules/pipeline/designer/pipeline.py
+++ b/aixplain/modules/pipeline/designer/pipeline.py
@@ -13,6 +13,7 @@
     Route,
     BareReconstructor,
     BareSegmentor,
+    BareMetric
 )
 from .enums import NodeType, RouteType, Operation
 
@@ -326,3 +327,14 @@ def bare_segmentor(self, *args, **kwargs) -> BareSegmentor:
         :return: the node
         """
         return BareSegmentor(*args, pipeline=self, **kwargs)
+
+    def metric(self, *args, **kwargs) -> BareMetric:
+        """
+        Shortcut to create an metric node for the current pipeline.
+        All params will be passed as keyword arguments to the node
+        constructor.
+
+        :param kwargs: keyword arguments
+        :return: the node
+        """
+        return BareMetric(*args, pipeline=self, **kwargs)
diff --git a/aixplain/modules/pipeline/generate.py b/aixplain/modules/pipeline/generate.py
index c71e8ae6..a64917c1 100644
--- a/aixplain/modules/pipeline/generate.py
+++ b/aixplain/modules/pipeline/generate.py
@@ -31,6 +31,7 @@
     AssetNode,
     BaseReconstructor,
     BaseSegmentor,
+    BaseMetric
 )
 from .default import DefaultPipeline
 from aixplain.modules import asset
@@ -160,6 +161,8 @@ def populate_specs(functions: list):
             base_class = "BaseSegmentor"
         elif is_reconstructor:
             base_class = "BaseReconstructor"
+        elif "metric" in function_name.split("_"):  # noqa: Advise a better distinguisher please
+            base_class = "BaseMetric"
 
         spec = {
             "id": function["id"],
diff --git a/aixplain/modules/pipeline/pipeline.py b/aixplain/modules/pipeline/pipeline.py
index 36bc643d..e5675e4b 100644
--- a/aixplain/modules/pipeline/pipeline.py
+++ b/aixplain/modules/pipeline/pipeline.py
@@ -14,6 +14,7 @@
     AssetNode,
     BaseReconstructor,
     BaseSegmentor,
+    BaseMetric
 )
 from .default import DefaultPipeline
 from aixplain.modules import asset
@@ -907,7 +908,7 @@ def __init__(self, node=None):
         self.data = self.create_param(code="data", data_type=DataType.TEXT)
 
 
-class ReferencelessAudioGenerationMetric(AssetNode[ReferencelessAudioGenerationMetricInputs, ReferencelessAudioGenerationMetricOutputs]):
+class ReferencelessAudioGenerationMetric(BaseMetric[ReferencelessAudioGenerationMetricInputs, ReferencelessAudioGenerationMetricOutputs]):
     """
     The Referenceless Audio Generation Metric is a tool designed to evaluate the
 quality of generated audio content without the need for a reference or original
@@ -1080,7 +1081,7 @@ def __init__(self, node=None):
         self.data = self.create_param(code="data", data_type=DataType.TEXT)
 
 
-class AudioGenerationMetric(AssetNode[AudioGenerationMetricInputs, AudioGenerationMetricOutputs]):
+class AudioGenerationMetric(BaseMetric[AudioGenerationMetricInputs, AudioGenerationMetricOutputs]):
     """
     The Audio Generation Metric is a quantitative measure used to evaluate the
 quality, accuracy, and overall performance of audio generated by artificial
@@ -1471,7 +1472,7 @@ def __init__(self, node=None):
         self.data = self.create_param(code="data", data_type=DataType.TEXT)
 
 
-class MetricAggregation(AssetNode[MetricAggregationInputs, MetricAggregationOutputs]):
+class MetricAggregation(BaseMetric[MetricAggregationInputs, MetricAggregationOutputs]):
     """
     Metric Aggregation is a function that computes and summarizes numerical data by
 applying statistical operations, such as averaging, summing, or finding the
@@ -1790,7 +1791,7 @@ def __init__(self, node=None):
         self.data = self.create_param(code="data", data_type=DataType.TEXT)
 
 
-class ReferencelessTextGenerationMetric(AssetNode[ReferencelessTextGenerationMetricInputs, ReferencelessTextGenerationMetricOutputs]):
+class ReferencelessTextGenerationMetric(BaseMetric[ReferencelessTextGenerationMetricInputs, ReferencelessTextGenerationMetricOutputs]):
     """
     The Referenceless Text Generation Metric is a method for evaluating the quality
 of generated text without requiring a reference text for comparison, often
@@ -1830,7 +1831,7 @@ def __init__(self, node=None):
         self.data = self.create_param(code="data", data_type=DataType.TEXT)
 
 
-class TextGenerationMetricDefault(AssetNode[TextGenerationMetricDefaultInputs, TextGenerationMetricDefaultOutputs]):
+class TextGenerationMetricDefault(BaseMetric[TextGenerationMetricDefaultInputs, TextGenerationMetricDefaultOutputs]):
     """
     The "Text Generation Metric Default" function provides a standard set of
 evaluation metrics for assessing the quality and performance of text generation
@@ -2130,7 +2131,7 @@ def __init__(self, node=None):
         self.data = self.create_param(code="data", data_type=DataType.TEXT)
 
 
-class TextGenerationMetric(AssetNode[TextGenerationMetricInputs, TextGenerationMetricOutputs]):
+class TextGenerationMetric(BaseMetric[TextGenerationMetricInputs, TextGenerationMetricOutputs]):
     """
     A Text Generation Metric is a quantitative measure used to evaluate the quality
 and effectiveness of text produced by natural language processing models, often
@@ -2981,7 +2982,7 @@ def __init__(self, node=None):
         self.data = self.create_param(code="data", data_type=DataType.TEXT)
 
 
-class ReferencelessTextGenerationMetricDefault(AssetNode[ReferencelessTextGenerationMetricDefaultInputs, ReferencelessTextGenerationMetricDefaultOutputs]):
+class ReferencelessTextGenerationMetricDefault(BaseMetric[ReferencelessTextGenerationMetricDefaultInputs, ReferencelessTextGenerationMetricDefaultOutputs]):
     """
     The Referenceless Text Generation Metric Default is a function designed to
 evaluate the quality of generated text without relying on reference texts for
@@ -3665,7 +3666,7 @@ def __init__(self, node=None):
         self.data = self.create_param(code="data", data_type=DataType.NUMBER)
 
 
-class ClassificationMetric(AssetNode[ClassificationMetricInputs, ClassificationMetricOutputs]):
+class ClassificationMetric(BaseMetric[ClassificationMetricInputs, ClassificationMetricOutputs]):
     """
     A Classification Metric is a quantitative measure used to evaluate the quality
 and effectiveness of classification models.
diff --git a/aixplain/modules/team_agent/__init__.py b/aixplain/modules/team_agent/__init__.py
new file mode 100644
index 00000000..420fc23a
--- /dev/null
+++ b/aixplain/modules/team_agent/__init__.py
@@ -0,0 +1,246 @@
+__author__ = "aiXplain"
+
+"""
+Copyright 2024 The aiXplain SDK authors
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+Author: Lucas Pavanelli and Thiago Castro Ferreira
+Date: August 15th 2024
+Description:
+    Team Agent Class
+"""
+
+import json
+import logging
+import time
+import traceback
+
+from aixplain.utils.file_utils import _request_with_retry
+from aixplain.enums.supplier import Supplier
+from aixplain.enums.asset_status import AssetStatus
+from aixplain.enums.storage_type import StorageType
+from aixplain.modules.model import Model
+from aixplain.modules.agent import Agent
+from typing import Dict, List, Text, Optional, Union
+from urllib.parse import urljoin
+
+from aixplain.utils import config
+
+
+class TeamAgent(Model):
+    """Advanced AI system capable of using multiple agents to perform a variety of tasks.
+
+    Attributes:
+        id (Text): ID of the Team Agent
+        name (Text): Name of the Team Agent
+        agents (List[Agent]): List of Agents that the Team Agent uses.
+        description (Text, optional): description of the Team Agent. Defaults to "".
+        llm_id (Text, optional): large language model. Defaults to GPT-4o (6646261c6eb563165658bbb1).
+        supplier (Text): Supplier of the Team Agent.
+        version (Text): Version of the Team Agent.
+        backend_url (str): URL of the backend.
+        api_key (str): The TEAM API key used for authentication.
+        cost (Dict, optional): model price. Defaults to None.
+        use_mentalist_and_inspector (bool): Use Mentalist and Inspector tools. Defaults to True.
+    """
+
+    def __init__(
+        self,
+        id: Text,
+        name: Text,
+        agents: List[Agent] = [],
+        description: Text = "",
+        llm_id: Text = "6646261c6eb563165658bbb1",
+        api_key: Optional[Text] = config.TEAM_API_KEY,
+        supplier: Union[Dict, Text, Supplier, int] = "aiXplain",
+        version: Optional[Text] = None,
+        cost: Optional[Dict] = None,
+        use_mentalist_and_inspector: bool = True,
+        status: AssetStatus = AssetStatus.ONBOARDING,
+        **additional_info,
+    ) -> None:
+        """Create a FineTune with the necessary information.
+
+        Args:
+            id (Text): ID of the Team Agent
+            name (Text): Name of the Team Agent
+            agents (List[Agent]): List of agents that the Team Agent uses.
+            description (Text, optional): description of the Team Agent. Defaults to "".
+            llm_id (Text, optional): large language model. Defaults to GPT-4o (6646261c6eb563165658bbb1).
+            supplier (Text): Supplier of the Team Agent.
+            version (Text): Version of the Team Agent.
+            backend_url (str): URL of the backend.
+            api_key (str): The TEAM API key used for authentication.
+            cost (Dict, optional): model price. Defaults to None.
+            use_mentalist_and_inspector (bool): Use Mentalist and Inspector tools. Defaults to True.
+        """
+        super().__init__(id, name, description, api_key, supplier, version, cost=cost)
+        self.additional_info = additional_info
+        self.agents = agents
+        self.llm_id = llm_id
+        self.use_mentalist_and_inspector = use_mentalist_and_inspector
+        if isinstance(status, str):
+            try:
+                status = AssetStatus(status)
+            except Exception:
+                status = AssetStatus.ONBOARDING
+        self.status = status
+
+    def run(
+        self,
+        data: Optional[Union[Dict, Text]] = None,
+        query: Optional[Text] = None,
+        session_id: Optional[Text] = None,
+        history: Optional[List[Dict]] = None,
+        name: Text = "model_process",
+        timeout: float = 300,
+        parameters: Dict = {},
+        wait_time: float = 0.5,
+        content: Optional[Union[Dict[Text, Text], List[Text]]] = None,
+    ) -> Dict:
+        """Runs a team agent call.
+
+        Args:
+            data (Optional[Union[Dict, Text]], optional): data to be processed by the team agent. Defaults to None.
+            query (Optional[Text], optional): query to be processed by the team agent. Defaults to None.
+            session_id (Optional[Text], optional): conversation Session ID. Defaults to None.
+            history (Optional[List[Dict]], optional): chat history (in case session ID is None). Defaults to None.
+            name (Text, optional): ID given to a call. Defaults to "model_process".
+            timeout (float, optional): total polling time. Defaults to 300.
+            parameters (Dict, optional): optional parameters to the model. Defaults to "{}".
+            wait_time (float, optional): wait time in seconds between polling calls. Defaults to 0.5.
+            content (Union[Dict[Text, Text], List[Text]], optional): Content inputs to be processed according to the query. Defaults to None.
+
+        Returns:
+            Dict: parsed output from model
+        """
+        start = time.time()
+        try:
+            response = self.run_async(
+                data=data,
+                query=query,
+                session_id=session_id,
+                history=history,
+                name=name,
+                parameters=parameters,
+                content=content,
+            )
+            if response["status"] == "FAILED":
+                end = time.time()
+                response["elapsed_time"] = end - start
+                return response
+            poll_url = response["url"]
+            end = time.time()
+            response = self.sync_poll(poll_url, name=name, timeout=timeout, wait_time=wait_time)
+            return response
+        except Exception as e:
+            msg = f"Error in request for {name} - {traceback.format_exc()}"
+            logging.error(f"Team Agent Run: Error in running for {name}: {e}")
+            end = time.time()
+            return {"status": "FAILED", "error": msg, "elapsed_time": end - start}
+
+    def run_async(
+        self,
+        data: Optional[Union[Dict, Text]] = None,
+        query: Optional[Text] = None,
+        session_id: Optional[Text] = None,
+        history: Optional[List[Dict]] = None,
+        name: Text = "model_process",
+        parameters: Dict = {},
+        content: Optional[Union[Dict[Text, Text], List[Text]]] = None,
+    ) -> Dict:
+        """Runs asynchronously a Team Agent call.
+
+        Args:
+            data (Optional[Union[Dict, Text]], optional): data to be processed by the Team Agent. Defaults to None.
+            query (Optional[Text], optional): query to be processed by the Team Agent. Defaults to None.
+            session_id (Optional[Text], optional): conversation Session ID. Defaults to None.
+            history (Optional[List[Dict]], optional): chat history (in case session ID is None). Defaults to None.
+            name (Text, optional): ID given to a call. Defaults to "model_process".
+            parameters (Dict, optional): optional parameters to the model. Defaults to "{}".
+            content (Union[Dict[Text, Text], List[Text]], optional): Content inputs to be processed according to the query. Defaults to None.
+
+        Returns:
+            dict: polling URL in response
+        """
+        from aixplain.factories.file_factory import FileFactory
+
+        assert data is not None or query is not None, "Either 'data' or 'query' must be provided."
+        if data is not None:
+            if isinstance(data, dict):
+                assert "query" in data and data["query"] is not None, "When providing a dictionary, 'query' must be provided."
+                query = data.get("query")
+                if session_id is None:
+                    session_id = data.get("session_id")
+                if history is None:
+                    history = data.get("history")
+                if content is None:
+                    content = data.get("content")
+            else:
+                query = data
+
+        # process content inputs
+        if content is not None:
+            assert FileFactory.check_storage_type(query) == StorageType.TEXT, "When providing 'content', query must be text."
+
+            if isinstance(content, list):
+                assert len(content) <= 3, "The maximum number of content inputs is 3."
+                for input_link in content:
+                    input_link = FileFactory.to_link(input_link)
+                    query += f"\n{input_link}"
+            elif isinstance(content, dict):
+                for key, value in content.items():
+                    assert "{{" + key + "}}" in query, f"Key '{key}' not found in query."
+                    value = FileFactory.to_link(value)
+                    query = query.replace("{{" + key + "}}", f"'{value}'")
+
+        headers = {"x-api-key": self.api_key, "Content-Type": "application/json"}
+
+        payload = {"id": self.id, "query": FileFactory.to_link(query), "sessionId": session_id, "history": history}
+        payload.update(parameters)
+        payload = json.dumps(payload)
+
+        r = _request_with_retry("post", self.url, headers=headers, data=payload)
+        logging.info(f"Team Agent Run Async: Start service for {name} - {self.url} - {payload} - {headers}")
+
+        resp = None
+        try:
+            resp = r.json()
+            logging.info(f"Result of request for {name} - {r.status_code} - {resp}")
+
+            poll_url = resp["data"]
+            response = {"status": "IN_PROGRESS", "url": poll_url}
+        except Exception:
+            response = {"status": "FAILED"}
+            msg = f"Error in request for {name} - {traceback.format_exc()}"
+            logging.error(f"Team Agent Run Async: Error in running for {name}: {resp}")
+            if resp is not None:
+                response["error"] = msg
+        return response
+
+    def delete(self) -> None:
+        """Delete Corpus service"""
+        try:
+            url = urljoin(config.BACKEND_URL, f"sdk/agent-communities/{self.id}")
+            headers = {"x-api-key": config.TEAM_API_KEY, "Content-Type": "application/json"}
+            logging.debug(f"Start service for DELETE Team Agent  - {url} - {headers}")
+            r = _request_with_retry("delete", url, headers=headers)
+            if r.status_code != 200:
+                raise Exception()
+        except Exception:
+            message = (
+                f"Team Agent Deletion Error (HTTP {r.status_code}): Make sure the Team Agent exists and you are the owner."
+            )
+            logging.error(message)
+            raise Exception(f"{message}")
diff --git a/aixplain/processes/data_onboarding/onboard_functions.py b/aixplain/processes/data_onboarding/onboard_functions.py
index 35a64e12..01a3fe9b 100644
--- a/aixplain/processes/data_onboarding/onboard_functions.py
+++ b/aixplain/processes/data_onboarding/onboard_functions.py
@@ -325,9 +325,9 @@ def create_data_asset(payload: Dict, data_asset_type: Text = "corpus", api_key:
             response = r.json()
             msg = response["message"]
             error_msg = f"Data Asset Onboarding Error: {msg}"
-        except Exception as e:
+        except Exception:
             error_msg = (
-                f"Data Asset Onboarding Error: Failure on creating the {data_asset_type}. Please contant the administrators."
+                f"Data Asset Onboarding Error: Failure on creating the {data_asset_type}. Please contact the administrators."
             )
         return {"success": False, "error": error_msg}
 
@@ -352,7 +352,7 @@ def is_data(data_id: Text) -> bool:
         if "id" in resp:
             return True
         return False
-    except:
+    except Exception:
         return False
 
 
@@ -379,13 +379,13 @@ def split_data(paths: List, split_rate: List[float], split_labels: List[Text]) -
 
             if column_name is not None:
                 break
-        except Exception as e:
+        except Exception:
             message = f'Data Asset Onboarding Error: Local file "{path}" not found.'
             logging.exception(message)
             raise Exception(message)
 
     if column_name is None:
-        message = f"Data Asset Onboarding Error: All split names are used."
+        message = "Data Asset Onboarding Error: All split names are used."
         raise Exception(message)
 
     for path in paths:
diff --git a/tests/functional/agent/agent_functional_test.py b/tests/functional/agent/agent_functional_test.py
index 0acdb5be..0d433899 100644
--- a/tests/functional/agent/agent_functional_test.py
+++ b/tests/functional/agent/agent_functional_test.py
@@ -20,6 +20,7 @@
 
 load_dotenv()
 from aixplain.factories import AgentFactory
+from aixplain.enums.function import Function
 from aixplain.enums.supplier import Supplier
 
 import pytest
@@ -55,7 +56,9 @@ def test_end2end(run_input_map):
         for tool in run_input_map["pipeline_tools"]:
             tools.append(AgentFactory.create_pipeline_tool(pipeline=tool["pipeline_id"], description=tool["description"]))
     print(f"Creating agent with tools: {tools}")
-    agent = AgentFactory.create(name=run_input_map["agent_name"], llm_id=run_input_map["llm_id"], tools=tools)
+    agent = AgentFactory.create(
+        name=run_input_map["agent_name"], description=run_input_map["agent_name"], llm_id=run_input_map["llm_id"], tools=tools
+    )
     print(f"Agent created: {agent.__dict__}")
     print("Running agent")
     response = agent.run(data=run_input_map["query"])
@@ -79,5 +82,10 @@ def test_list_agents():
 
 def test_fail_non_existent_llm():
     with pytest.raises(Exception) as exc_info:
-        AgentFactory.create(name="Test Agent", llm_id="non_existent_llm", tools=[])
+        AgentFactory.create(
+            name="Test Agent",
+            description="Test description",
+            llm_id="non_existent_llm",
+            tools=[AgentFactory.create_model_tool(function=Function.TRANSLATION)],
+        )
     assert str(exc_info.value) == "Large Language Model with ID 'non_existent_llm' not found."
diff --git a/tests/functional/agent/data/agent_test_end2end.json b/tests/functional/agent/data/agent_test_end2end.json
index 94bfc94b..595d22a9 100644
--- a/tests/functional/agent/data/agent_test_end2end.json
+++ b/tests/functional/agent/data/agent_test_end2end.json
@@ -1,6 +1,6 @@
 [
     {
-        "agent_name": "[TEST] Translation agent", 
+        "agent_name": "TEST Translation agent", 
         "llm_id": "6626a3a8c8f1d089790cf5a2",
         "llm_name": "Groq Llama 3 70B",
         "query": "Who is the president of Brazil right now? Translate to pt",
diff --git a/tests/functional/model/run_model_test.py b/tests/functional/model/run_model_test.py
index 79979357..47f351bb 100644
--- a/tests/functional/model/run_model_test.py
+++ b/tests/functional/model/run_model_test.py
@@ -1,20 +1,31 @@
 __author__ = "thiagocastroferreira"
 
-import pytest
 
 from aixplain.enums import Function
 from aixplain.factories import ModelFactory
 from aixplain.modules import LLM
+from datetime import datetime, timedelta, timezone
+
+
+def pytest_generate_tests(metafunc):
+    if "llm_model" in metafunc.fixturenames:
+        four_weeks_ago = datetime.now(timezone.utc) - timedelta(weeks=4)
+        models = ModelFactory.list(function=Function.TEXT_GENERATION)["results"]
+
+        predefined_models = ["Groq Llama 3 70B", "Chat GPT 3.5", "GPT-4o", "GPT 4 (32k)"]
+        recent_models = [model for model in models if model.created_at and model.created_at >= four_weeks_ago]
+        combined_models = recent_models + [
+            ModelFactory.list(query=model, function=Function.TEXT_GENERATION)["results"][0] for model in predefined_models
+        ]
+        metafunc.parametrize("llm_model", combined_models)
 
 
-@pytest.mark.parametrize("llm_model", ["Groq Llama 3 70B", "Chat GPT 3.5", "GPT-4o", "GPT 4 (32k)"])
 def test_llm_run(llm_model):
     """Testing LLMs with history context"""
-    model = ModelFactory.list(query=llm_model, function=Function.TEXT_GENERATION)["results"][0]
 
-    assert isinstance(model, LLM)
+    assert isinstance(llm_model, LLM)
 
-    response = model.run(
+    response = llm_model.run(
         data="What is my name?",
         history=[{"role": "user", "content": "Hello! My name is Thiago."}, {"role": "assistant", "content": "Hello!"}],
     )
diff --git a/tests/functional/pipelines/create_test.py b/tests/functional/pipelines/create_test.py
index 6431bd41..6cf3d718 100644
--- a/tests/functional/pipelines/create_test.py
+++ b/tests/functional/pipelines/create_test.py
@@ -54,7 +54,8 @@ def test_update_pipeline():
     pipeline_name = str(uuid4())
     pipeline = PipelineFactory.create(name=pipeline_name, pipeline=pipeline_dict)
 
-    pipeline.update(pipeline=pipeline_json, save_as_asset=True)
+    pipeline.update(pipeline=pipeline_json, save_as_asset=True, name="NEW NAME")
+    assert pipeline.name == "NEW NAME"
     assert isinstance(pipeline, Pipeline)
     assert pipeline.id != ""
     pipeline.delete()
diff --git a/tests/functional/pipelines/designer_test.py b/tests/functional/pipelines/designer_test.py
index 62f42f7e..d8caaf35 100644
--- a/tests/functional/pipelines/designer_test.py
+++ b/tests/functional/pipelines/designer_test.py
@@ -1,7 +1,7 @@
 import pytest
 
 from aixplain.enums import DataType
-from aixplain.factories import PipelineFactory
+from aixplain.factories import PipelineFactory, DatasetFactory
 from aixplain.modules.pipeline.designer import (
     Link,
     Operation,
@@ -110,9 +110,7 @@ def test_routing_pipeline(pipeline):
     translation = pipeline.asset(TRANSLATION_ASSET)
     speech_recognition = pipeline.asset(SPEECH_RECOGNITION_ASSET)
 
-    input.route(
-        translation.inputs.text, speech_recognition.inputs.source_audio
-    )
+    input.route(translation.inputs.text, speech_recognition.inputs.source_audio)
 
     translation.use_output("data")
     speech_recognition.use_output("data")
@@ -135,17 +133,11 @@ def test_scripting_pipeline(pipeline):
 
     input = pipeline.input()
 
-    segmentor = pipeline.speaker_diarization_audio(
-        asset_id=SPEAKER_DIARIZATION_AUDIO_ASSET
-    )
+    segmentor = pipeline.speaker_diarization_audio(asset_id=SPEAKER_DIARIZATION_AUDIO_ASSET)
 
-    speech_recognition = pipeline.speech_recognition(
-        asset_id=SPEECH_RECOGNITION_ASSET
-    )
+    speech_recognition = pipeline.speech_recognition(asset_id=SPEECH_RECOGNITION_ASSET)
 
-    script = pipeline.script(
-        script_path="tests/functional/pipelines/data/script.py"
-    )
+    script = pipeline.script(script_path="tests/functional/pipelines/data/script.py")
     script.inputs.create_param(code="transcripts", data_type=DataType.TEXT)
     script.inputs.create_param(code="speakers", data_type=DataType.LABEL)
     script.outputs.create_param(code="data", data_type=DataType.TEXT)
@@ -177,9 +169,7 @@ def test_decision_pipeline(pipeline):
 
     input = pipeline.input()
 
-    sentiment_analysis = pipeline.sentiment_analysis(
-        asset_id=SENTIMENT_ANALYSIS_ASSET
-    )
+    sentiment_analysis = pipeline.sentiment_analysis(asset_id=SENTIMENT_ANALYSIS_ASSET)
 
     positive_output = pipeline.output()
     negative_output = pipeline.output()
@@ -220,19 +210,15 @@ def test_decision_pipeline(pipeline):
 def test_reconstructing_pipeline(pipeline):
     input = pipeline.input()
 
-    segmentor = pipeline.speaker_diarization_audio(
-        asset_id="62fab6ecb39cca09ca5bc365"
-    )
+    segmentor = pipeline.speaker_diarization_audio(asset_id="62fab6ecb39cca09ca5bc365")
 
-    speech_recognition = pipeline.speech_recognition(
-        asset_id="60ddefab8d38c51c5885ee38"
-    )
+    speech_recognition = pipeline.speech_recognition(asset_id="60ddefab8d38c51c5885ee38")
 
-    reconstructor = pipeline.bare_reconstructor()
+    reconstructor = pipeline.text_reconstruction(asset_id="636cf7ab0f8ddf0db97929e4")
 
     input.outputs.input.link(segmentor.inputs.audio)
     segmentor.outputs.audio.link(speech_recognition.inputs.source_audio)
-    speech_recognition.outputs.data.link(reconstructor.inputs.data)
+    speech_recognition.outputs.data.link(reconstructor.inputs.text)
 
     reconstructor.use_output("data")
 
@@ -246,3 +232,43 @@ def test_reconstructing_pipeline(pipeline):
     assert len(output["data"]) > 0
     assert output["data"][0].get("segments") is not None
     assert len(output["data"][0]["segments"]) > 0
+
+
+def test_metric_pipeline(pipeline):
+
+    dataset = DatasetFactory.list(query="for_functional_tests")["results"][0]
+    data_asset_id = dataset.id
+    reference_id = dataset.target_data["pt"][0].id
+
+    # Instantiate input nodes
+    text_input_node = pipeline.input(label="TextInput")
+    reference_input_node = pipeline.input(label="ReferenceInput")
+
+    # Instantiate the metric node
+    translation_metric_node = pipeline.text_generation_metric(asset_id="639874ab506c987b1ae1acc6")
+
+    # Instantiate output node
+    score_output_node = pipeline.output()
+
+    # Link the nodes
+    text_input_node.link(translation_metric_node, from_param="input", to_param="hypotheses")
+
+    reference_input_node.link(translation_metric_node, from_param="input", to_param="references")
+
+    translation_metric_node.link(score_output_node, from_param="data", to_param="output")
+
+    translation_metric_node.inputs.score_identifier = "bleu"
+
+    # Save and run the pipeline
+    pipeline.save()
+
+    output = pipeline.run(
+        data={"TextInput": reference_id, "ReferenceInput": reference_id},
+        data_asset={"TextInput": data_asset_id, "ReferenceInput": data_asset_id},
+    )
+
+    assert output["status"] == "SUCCESS"
+    assert output.get("data") is not None
+    assert len(output["data"]) > 0
+    assert output["data"][0].get("segments") is not None
+    assert len(output["data"][0]["segments"]) > 0
diff --git a/tests/functional/team_agent/data/team_agent_test_end2end.json b/tests/functional/team_agent/data/team_agent_test_end2end.json
new file mode 100644
index 00000000..ed6437d2
--- /dev/null
+++ b/tests/functional/team_agent/data/team_agent_test_end2end.json
@@ -0,0 +1,32 @@
+[
+    {
+        "team_agent_name": "TEST Multi agent", 
+        "llm_id": "6626a3a8c8f1d089790cf5a2",
+        "llm_name": "Groq Llama 3 70B",
+        "query": "Who is the president of Brazil right now? Translate to pt and synthesize in audio",
+        "agents": [
+            {
+                "agent_name": "TEST Translation agent", 
+                "llm_id": "6626a3a8c8f1d089790cf5a2",
+                "llm_name": "Groq Llama 3 70B",
+                "model_tools": [
+                    {
+                        "function": "translation",
+                        "supplier": "AWS"
+                    }
+                ]
+            },
+            {
+                "agent_name": "TEST Speech Synthesis agent", 
+                "llm_id": "6626a3a8c8f1d089790cf5a2",
+                "llm_name": "Groq Llama 3 70B",
+                "model_tools": [
+                    {
+                        "function": "speech-synthesis",
+                        "supplier": "Google"
+                    }
+                ]
+            }
+        ]
+    }
+]
diff --git a/tests/functional/team_agent/team_agent_functional_test.py b/tests/functional/team_agent/team_agent_functional_test.py
new file mode 100644
index 00000000..46adfcbc
--- /dev/null
+++ b/tests/functional/team_agent/team_agent_functional_test.py
@@ -0,0 +1,94 @@
+__author__ = "lucaspavanelli"
+
+"""
+Copyright 2022 The aiXplain SDK authors
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+import json
+from dotenv import load_dotenv
+
+load_dotenv()
+from aixplain.factories import AgentFactory, TeamAgentFactory
+from aixplain.enums.function import Function
+from aixplain.enums.supplier import Supplier
+
+import pytest
+
+RUN_FILE = "tests/functional/team_agent/data/team_agent_test_end2end.json"
+
+
+def read_data(data_path):
+    return json.load(open(data_path, "r"))
+
+
+@pytest.fixture(scope="module", params=read_data(RUN_FILE))
+def run_input_map(request):
+    return request.param
+
+
+def test_end2end(run_input_map):
+    for agent in AgentFactory.list()["results"]:
+        agent.delete()
+
+    agents = []
+    for agent in run_input_map["agents"]:
+        tools = []
+        if "model_tools" in agent:
+            for tool in agent["model_tools"]:
+                for supplier in Supplier:
+                    if tool["supplier"] is not None and tool["supplier"].lower() in [
+                        supplier.value["code"].lower(),
+                        supplier.value["name"].lower(),
+                    ]:
+                        tool["supplier"] = supplier
+                        break
+                tools.append(AgentFactory.create_model_tool(**tool))
+        if "pipeline_tools" in agent:
+            for tool in agent["pipeline_tools"]:
+                tools.append(AgentFactory.create_pipeline_tool(pipeline=tool["pipeline_id"], description=tool["description"]))
+        print(f"Creating agent with tools: {tools}")
+        agent = AgentFactory.create(
+            name=agent["agent_name"], description=agent["agent_name"], llm_id=agent["llm_id"], tools=tools
+        )
+        agents.append(agent)
+
+    team_agent = TeamAgentFactory.create(
+        name=run_input_map["team_agent_name"],
+        agents=agents,
+        description=run_input_map["team_agent_name"],
+        llm_id=run_input_map["llm_id"],
+        use_mentalist_and_inspector=True,
+    )
+    print("Running team agent")
+    response = team_agent.run(data=run_input_map["query"])
+    print(f"Team Agent response: {response}")
+    assert response is not None
+    assert response["completed"] is True
+    assert response["status"].lower() == "success"
+    assert "data" in response
+    assert response["data"]["session_id"] is not None
+    assert response["data"]["output"] is not None
+    print("Deleting team agent")
+    team_agent.delete()
+
+
+def test_fail_non_existent_llm():
+    with pytest.raises(Exception) as exc_info:
+        AgentFactory.create(
+            name="Test Agent",
+            description="",
+            llm_id="non_existent_llm",
+            tools=[AgentFactory.create_model_tool(function=Function.TRANSLATION)],
+        )
+    assert str(exc_info.value) == "Large Language Model with ID 'non_existent_llm' not found."
diff --git a/tests/unit/agent_test.py b/tests/unit/agent_test.py
index 8a619011..1be0682e 100644
--- a/tests/unit/agent_test.py
+++ b/tests/unit/agent_test.py
@@ -8,21 +8,21 @@
 
 
 def test_fail_no_data_query():
-    agent = Agent("123", "Test Agent")
+    agent = Agent("123", "Test Agent", "Sample Description")
     with pytest.raises(Exception) as exc_info:
         agent.run_async()
     assert str(exc_info.value) == "Either 'data' or 'query' must be provided."
 
 
 def test_fail_query_must_be_provided():
-    agent = Agent("123", "Test Agent")
+    agent = Agent("123", "Test Agent", "Sample Description")
     with pytest.raises(Exception) as exc_info:
         agent.run_async(data={})
     assert str(exc_info.value) == "When providing a dictionary, 'query' must be provided."
 
 
 def test_fail_query_as_text_when_content_not_empty():
-    agent = Agent("123", "Test Agent")
+    agent = Agent("123", "Test Agent", "Sample Description")
     with pytest.raises(Exception) as exc_info:
         agent.run_async(
             data={"query": "https://aixplain-platform-assets.s3.amazonaws.com/samples/en/CPAC1x2.wav"},
@@ -32,7 +32,7 @@ def test_fail_query_as_text_when_content_not_empty():
 
 
 def test_fail_content_exceed_maximum():
-    agent = Agent("123", "Test Agent")
+    agent = Agent("123", "Test Agent", "Sample Description")
     with pytest.raises(Exception) as exc_info:
         agent.run_async(
             data={"query": "Transcribe the audios:"},
@@ -47,14 +47,14 @@ def test_fail_content_exceed_maximum():
 
 
 def test_fail_key_not_found():
-    agent = Agent("123", "Test Agent")
+    agent = Agent("123", "Test Agent", "Sample Description")
     with pytest.raises(Exception) as exc_info:
         agent.run_async(data={"query": "Translate the text: {{input1}}"}, content={"input2": "Hello, how are you?"})
     assert str(exc_info.value) == "Key 'input2' not found in query."
 
 
 def test_sucess_query_content():
-    agent = Agent("123", "Test Agent")
+    agent = Agent("123", "Test Agent", "Sample Description")
     with requests_mock.Mocker() as mock:
         url = agent.url
         headers = {"x-api-key": config.TEAM_API_KEY, "Content-Type": "application/json"}
@@ -69,7 +69,10 @@ def test_sucess_query_content():
 def test_invalid_pipelinetool():
     with pytest.raises(Exception) as exc_info:
         AgentFactory.create(
-            name="Test", tools=[PipelineTool(pipeline="309851793", description="Test")], llm_id="6646261c6eb563165658bbb1"
+            name="Test",
+            description="Test Description",
+            tools=[PipelineTool(pipeline="309851793", description="Test")],
+            llm_id="6646261c6eb563165658bbb1",
         )
     assert str(exc_info.value) == "Pipeline Tool Unavailable. Make sure Pipeline '309851793' exists or you have access to it."
 
@@ -80,6 +83,12 @@ def test_invalid_modeltool():
     assert str(exc_info.value) == "Model Tool Unavailable. Make sure Model '309851793' exists or you have access to it."
 
 
+def test_invalid_agent_name():
+    with pytest.raises(Exception) as exc_info:
+        AgentFactory.create(name="[Test]", description="", tools=[], llm_id="6646261c6eb563165658bbb1")
+    assert str(exc_info.value) == "Agent Creation Error: Agent name must not contain special characters."
+
+
 def test_create_agent():
     from aixplain.enums import Supplier
 
diff --git a/tests/unit/model_test.py b/tests/unit/model_test.py
index ff44d821..c52bb950 100644
--- a/tests/unit/model_test.py
+++ b/tests/unit/model_test.py
@@ -71,7 +71,6 @@ def test_failed_poll():
         (501, "Status 501: Unspecified error: An unspecified error occurred while processing your request."),
     ],
 )
-
 def test_run_async_errors(status_code, error_message):
     base_url = config.MODELS_RUN_URL
     model_id = "model-id"
@@ -83,4 +82,3 @@ def test_run_async_errors(status_code, error_message):
         response = test_model.run_async(data="input_data")
     assert response["status"] == "FAILED"
     assert response["error_message"] == error_message
-
diff --git a/tests/unit/team_agent_test.py b/tests/unit/team_agent_test.py
new file mode 100644
index 00000000..fd738c04
--- /dev/null
+++ b/tests/unit/team_agent_test.py
@@ -0,0 +1,73 @@
+import pytest
+import requests_mock
+from aixplain.modules import TeamAgent
+from aixplain.factories import TeamAgentFactory
+from aixplain.utils import config
+
+
+def test_fail_no_data_query():
+    team_agent = TeamAgent("123", "Test Team Agent")
+    with pytest.raises(Exception) as exc_info:
+        team_agent.run_async()
+    assert str(exc_info.value) == "Either 'data' or 'query' must be provided."
+
+
+def test_fail_query_must_be_provided():
+    team_agent = TeamAgent("123", "Test Team Agent")
+    with pytest.raises(Exception) as exc_info:
+        team_agent.run_async(data={})
+    assert str(exc_info.value) == "When providing a dictionary, 'query' must be provided."
+
+
+def test_fail_query_as_text_when_content_not_empty():
+    team_agent = TeamAgent("123", "Test Team Agent")
+    with pytest.raises(Exception) as exc_info:
+        team_agent.run_async(
+            data={"query": "https://aixplain-platform-assets.s3.amazonaws.com/samples/en/CPAC1x2.wav"},
+            content=["https://aixplain-platform-assets.s3.amazonaws.com/samples/en/CPAC1x2.wav"],
+        )
+    assert str(exc_info.value) == "When providing 'content', query must be text."
+
+
+def test_fail_content_exceed_maximum():
+    team_agent = TeamAgent("123", "Test Team Agent")
+    with pytest.raises(Exception) as exc_info:
+        team_agent.run_async(
+            data={"query": "Transcribe the audios:"},
+            content=[
+                "https://aixplain-platform-assets.s3.amazonaws.com/samples/en/CPAC1x2.wav",
+                "https://aixplain-platform-assets.s3.amazonaws.com/samples/en/CPAC1x2.wav",
+                "https://aixplain-platform-assets.s3.amazonaws.com/samples/en/CPAC1x2.wav",
+                "https://aixplain-platform-assets.s3.amazonaws.com/samples/en/CPAC1x2.wav",
+            ],
+        )
+    assert str(exc_info.value) == "The maximum number of content inputs is 3."
+
+
+def test_fail_key_not_found():
+    team_agent = TeamAgent("123", "Test Team Agent")
+    with pytest.raises(Exception) as exc_info:
+        team_agent.run_async(data={"query": "Translate the text: {{input1}}"}, content={"input2": "Hello, how are you?"})
+    assert str(exc_info.value) == "Key 'input2' not found in query."
+
+
+def test_sucess_query_content():
+    team_agent = TeamAgent("123", "Test Team Agent")
+    with requests_mock.Mocker() as mock:
+        url = team_agent.url
+        headers = {"x-api-key": config.TEAM_API_KEY, "Content-Type": "application/json"}
+        ref_response = {"data": "Hello, how are you?", "status": "IN_PROGRESS"}
+        mock.post(url, headers=headers, json=ref_response)
+
+        response = team_agent.run_async(
+            data={"query": "Translate the text: {{input1}}"}, content={"input1": "Hello, how are you?"}
+        )
+    assert response["status"] == ref_response["status"]
+    assert response["url"] == ref_response["data"]
+
+
+def test_fail_number_agents():
+    with pytest.raises(Exception) as exc_info:
+        TeamAgentFactory.create(name="Test Team Agent", agents=[])
+
+    assert str(exc_info.value) == "TeamAgent Onboarding Error: At least one agent must be provided."