diff --git a/pyproject.toml b/pyproject.toml
index 6dddc623809..5bae004fda7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -104,7 +104,7 @@ dependencies = [
     "yfinance>=0.2.40",
     "langchain-google-community==1.0.7",
     "wolframalpha>=5.1.3",
-    "astra-assistants>=2.1.2",
+    "astra-assistants>=2.1.4",
     "composio-langchain==0.5.9",
     "spider-client>=0.0.27",
     "nltk>=3.9.1",
diff --git a/src/backend/base/langflow/components/astra_assistants/__init__.py b/src/backend/base/langflow/components/astra_assistants/__init__.py
index 94ef605cc27..909a5f88fd7 100644
--- a/src/backend/base/langflow/components/astra_assistants/__init__.py
+++ b/src/backend/base/langflow/components/astra_assistants/__init__.py
@@ -1,3 +1,4 @@
+from .astra_assistant_manager import AstraAssistantManager
 from .create_assistant import AssistantsCreateAssistant
 from .create_thread import AssistantsCreateThread
 from .dotenv import Dotenv
@@ -7,6 +8,7 @@
 from .run import AssistantsRun
 
 __all__ = [
+    "AstraAssistantManager",
     "AssistantsCreateAssistant",
     "AssistantsGetAssistantName",
     "AssistantsListAssistants",
diff --git a/src/backend/base/langflow/components/astra_assistants/astra_assistant_manager.py b/src/backend/base/langflow/components/astra_assistants/astra_assistant_manager.py
new file mode 100644
index 00000000000..aec2e9ddda7
--- /dev/null
+++ b/src/backend/base/langflow/components/astra_assistants/astra_assistant_manager.py
@@ -0,0 +1,134 @@
+import asyncio
+
+from astra_assistants.astra_assistants_manager import AssistantManager
+
+from langflow.components.astra_assistants.util import (
+    get_patched_openai_client,
+    litellm_model_names,
+    tool_names,
+    tools_and_names,
+)
+from langflow.custom.custom_component.component_with_cache import ComponentWithCache
+from langflow.inputs import DropdownInput, MultilineInput, StrInput
+from langflow.schema.message import Message
+from langflow.template import Output
+
+
+class AstraAssistantManager(ComponentWithCache):
+    display_name = "Astra Assistant Manager"
+    description = "Manages Assistant Interactions"
+    icon = "bot"
+
+    inputs = [
+        StrInput(
+            name="instructions",
+            display_name="Instructions",
+            info="Instructions for the assistant, think of these as the system prompt.",
+        ),
+        DropdownInput(
+            name="model_name",
+            display_name="Model Name",
+            advanced=False,
+            options=litellm_model_names,
+            value="gpt-4o-mini",
+        ),
+        DropdownInput(
+            display_name="Tool",
+            name="tool",
+            options=tool_names,
+        ),
+        MultilineInput(
+            name="user_message",
+            display_name="User Message",
+            info="User message to pass to the run.",
+        ),
+        MultilineInput(
+            name="input_thread_id",
+            display_name="Thread ID (optional)",
+            info="ID of the thread",
+        ),
+        MultilineInput(
+            name="input_assistant_id",
+            display_name="Assistant ID (optional)",
+            info="ID of the assistant",
+        ),
+        MultilineInput(
+            name="env_set",
+            display_name="Environment Set",
+            info="Dummy input to allow chaining with Dotenv Component.",
+        ),
+    ]
+
+    outputs = [
+        Output(display_name="Assistant Response", name="assistant_response", method="get_assistant_response"),
+        Output(display_name="Tool output", name="tool_output", method="get_tool_output"),
+        Output(display_name="Thread Id", name="output_thread_id", method="get_thread_id"),
+        Output(display_name="Assistant Id", name="output_assistant_id", method="get_assistant_id"),
+    ]
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.lock = asyncio.Lock()
+        self.initialized = False
+        self.assistant_response = None
+        self.tool_output = None
+        self.thread_id = None
+        self.assistant_id = None
+        self.client = get_patched_openai_client(self._shared_component_cache)
+
+    async def get_assistant_response(self) -> Message:
+        await self.initialize()
+        return self.assistant_response
+
+    async def get_tool_output(self) -> Message:
+        await self.initialize()
+        return self.tool_output
+
+    async def get_thread_id(self) -> Message:
+        await self.initialize()
+        return self.thread_id
+
+    async def get_assistant_id(self) -> Message:
+        await self.initialize()
+        return self.assistant_id
+
+    async def initialize(self):
+        async with self.lock:
+            if not self.initialized:
+                await self.process_inputs()
+                self.initialized = True
+
+    async def process_inputs(self):
+        print(f"env_set is {self.env_set}")
+        print(self.tool)
+        tools = []
+        tool_obj = None
+        if self.tool is not None and self.tool != "":
+            tool_cls = tools_and_names[self.tool]
+            tool_obj = tool_cls()
+            tools.append(tool_obj)
+        assistant_id = None
+        thread_id = None
+        if self.input_assistant_id:
+            assistant_id = self.input_assistant_id
+        if self.input_thread_id:
+            thread_id = self.input_thread_id
+        assistant_manager = AssistantManager(
+            instructions=self.instructions,
+            model=self.model_name,
+            name="managed_assistant",
+            tools=tools,
+            client=self.client,
+            thread_id=thread_id,
+            assistant_id=assistant_id,
+        )
+
+        content = self.user_message
+        result = await assistant_manager.run_thread(content=content, tool=tool_obj)
+        self.assistant_response = Message(text=result["text"])
+        if "decision" in result:
+            self.tool_output = Message(text=str(result["decision"].is_complete))
+        else:
+            self.tool_output = Message(text=result["text"])
+        self.thread_id = Message(text=assistant_manager.thread.id)
+        self.assistant_id = Message(text=assistant_manager.assistant.id)
diff --git a/src/backend/base/langflow/components/astra_assistants/create_assistant.py b/src/backend/base/langflow/components/astra_assistants/create_assistant.py
index 67a3eb6b814..e69a2b7f08a 100644
--- a/src/backend/base/langflow/components/astra_assistants/create_assistant.py
+++ b/src/backend/base/langflow/components/astra_assistants/create_assistant.py
@@ -1,17 +1,14 @@
-from astra_assistants import patch  # type: ignore
-from openai import OpenAI
-
-from langflow.custom import Component
+from langflow.components.astra_assistants.util import get_patched_openai_client
+from langflow.custom.custom_component.component_with_cache import ComponentWithCache
 from langflow.inputs import MultilineInput, StrInput
 from langflow.schema.message import Message
 from langflow.template import Output
 
 
-class AssistantsCreateAssistant(Component):
+class AssistantsCreateAssistant(ComponentWithCache):
     icon = "bot"
     display_name = "Create Assistant"
     description = "Creates an Assistant and returns it's id"
-    client = patch(OpenAI())
 
     inputs = [
         StrInput(
@@ -46,6 +43,10 @@ class AssistantsCreateAssistant(Component):
         Output(display_name="Assistant ID", name="assistant_id", method="process_inputs"),
     ]
 
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.client = get_patched_openai_client(self._shared_component_cache)
+
     def process_inputs(self) -> Message:
         print(f"env_set is {self.env_set}")
         assistant = self.client.beta.assistants.create(
diff --git a/src/backend/base/langflow/components/astra_assistants/create_thread.py b/src/backend/base/langflow/components/astra_assistants/create_thread.py
index a869c5622df..7ddaab3056f 100644
--- a/src/backend/base/langflow/components/astra_assistants/create_thread.py
+++ b/src/backend/base/langflow/components/astra_assistants/create_thread.py
@@ -1,16 +1,13 @@
-from astra_assistants import patch  # type: ignore
-from openai import OpenAI
-
-from langflow.custom import Component
+from langflow.components.astra_assistants.util import get_patched_openai_client
+from langflow.custom.custom_component.component_with_cache import ComponentWithCache
 from langflow.inputs import MultilineInput
 from langflow.schema.message import Message
 from langflow.template import Output
 
 
-class AssistantsCreateThread(Component):
+class AssistantsCreateThread(ComponentWithCache):
     display_name = "Create Assistant Thread"
     description = "Creates a thread and returns the thread id"
-    client = patch(OpenAI())
 
     inputs = [
         MultilineInput(
@@ -24,6 +21,10 @@ class AssistantsCreateThread(Component):
         Output(display_name="Thread ID", name="thread_id", method="process_inputs"),
     ]
 
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.client = get_patched_openai_client(self._shared_component_cache)
+
     def process_inputs(self) -> Message:
         thread = self.client.beta.threads.create()
         thread_id = thread.id
diff --git a/src/backend/base/langflow/components/astra_assistants/get_assistant.py b/src/backend/base/langflow/components/astra_assistants/get_assistant.py
index 04d00cfe2dd..c77a3b2be67 100644
--- a/src/backend/base/langflow/components/astra_assistants/get_assistant.py
+++ b/src/backend/base/langflow/components/astra_assistants/get_assistant.py
@@ -1,16 +1,13 @@
-from astra_assistants import patch  # type: ignore
-from openai import OpenAI
-
-from langflow.custom import Component
+from langflow.components.astra_assistants.util import get_patched_openai_client
+from langflow.custom.custom_component.component_with_cache import ComponentWithCache
 from langflow.inputs import MultilineInput, StrInput
 from langflow.schema.message import Message
 from langflow.template import Output
 
 
-class AssistantsGetAssistantName(Component):
+class AssistantsGetAssistantName(ComponentWithCache):
     display_name = "Get Assistant name"
     description = "Assistant by id"
-    client = patch(OpenAI())
 
     inputs = [
         StrInput(
@@ -29,6 +26,10 @@ class AssistantsGetAssistantName(Component):
         Output(display_name="Assistant Name", name="assistant_name", method="process_inputs"),
     ]
 
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.client = get_patched_openai_client(self._shared_component_cache)
+
     def process_inputs(self) -> Message:
         assistant = self.client.beta.assistants.retrieve(
             assistant_id=self.assistant_id,
diff --git a/src/backend/base/langflow/components/astra_assistants/list_assistants.py b/src/backend/base/langflow/components/astra_assistants/list_assistants.py
index 70b040c66f4..2db5e106446 100644
--- a/src/backend/base/langflow/components/astra_assistants/list_assistants.py
+++ b/src/backend/base/langflow/components/astra_assistants/list_assistants.py
@@ -1,20 +1,21 @@
-from astra_assistants import patch  # type: ignore
-from openai import OpenAI
-
-from langflow.custom import Component
+from langflow.components.astra_assistants.util import get_patched_openai_client
+from langflow.custom.custom_component.component_with_cache import ComponentWithCache
 from langflow.schema.message import Message
 from langflow.template.field.base import Output
 
 
-class AssistantsListAssistants(Component):
+class AssistantsListAssistants(ComponentWithCache):
     display_name = "List Assistants"
     description = "Returns a list of assistant id's"
-    client = patch(OpenAI())
 
     outputs = [
         Output(display_name="Assistants", name="assistants", method="process_inputs"),
     ]
 
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.client = get_patched_openai_client(self._shared_component_cache)
+
     def process_inputs(self) -> Message:
         assistants = self.client.beta.assistants.list().data
         id_list = [assistant.id for assistant in assistants]
diff --git a/src/backend/base/langflow/components/astra_assistants/run.py b/src/backend/base/langflow/components/astra_assistants/run.py
index e7372b2d88b..ac1b697f44a 100644
--- a/src/backend/base/langflow/components/astra_assistants/run.py
+++ b/src/backend/base/langflow/components/astra_assistants/run.py
@@ -4,17 +4,22 @@
 from openai import OpenAI
 from openai.lib.streaming import AssistantEventHandler
 
-from langflow.custom import Component
+from langflow.components.astra_assistants.util import get_patched_openai_client
+from langflow.custom.custom_component.component_with_cache import ComponentWithCache
 from langflow.inputs import MultilineInput
 from langflow.schema import dotdict
 from langflow.schema.message import Message
 from langflow.template import Output
 
 
-class AssistantsRun(Component):
+class AssistantsRun(ComponentWithCache):
     display_name = "Run Assistant"
     description = "Executes an Assistant Run against a thread"
-    client = patch(OpenAI())
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.client = get_patched_openai_client(self._shared_component_cache)
+        self.thread_id = None
 
     def update_build_config(
         self,
diff --git a/src/backend/base/langflow/components/astra_assistants/tools/ReAct.py b/src/backend/base/langflow/components/astra_assistants/tools/ReAct.py
new file mode 100644
index 00000000000..813396968d2
--- /dev/null
+++ b/src/backend/base/langflow/components/astra_assistants/tools/ReAct.py
@@ -0,0 +1,145 @@
+from astra_assistants.tools.tool_interface import ToolInterface
+from openai import BaseModel
+from pydantic import Field
+
+
+class ReActCompletionDecider(BaseModel):
+    """
+    This tool evaluates whether the task is complete and provides logic to explain why the task is done or not.
+    """
+
+    logic: str = Field(..., description="Explanation of why the task is considered complete or incomplete.")
+    is_complete: bool = Field(..., description="Boolean indicating whether the task is complete (True) or not (False).")
+
+    @classmethod
+    def check_completion(cls, logic: str) -> bool:
+        """
+        Determines whether the task is complete based on the provided logic.
+        Returns True if the task is complete, otherwise False.
+        """
+        return "final answer" in logic.lower() or "no further actions" in logic.lower()
+
+    class Config:
+        schema_extra = {
+            "example": [
+                {
+                    "logic": "The list comprehension has been correctly implemented and tested. There are no further "
+                    "actions required.",
+                    "is_complete": True,
+                },
+                {
+                    "logic": "The list comprehension has been implemented, but I need to test if it behaves the same "
+                    "as the for loop.",
+                    "is_complete": False,
+                },
+                {
+                    "logic": "I have verified that the list comprehension achieves the same results as the original "
+                    "for loop. No further actions are needed.",
+                    "is_complete": True,
+                },
+                {
+                    "logic": "I need to rewrite the for loop with list comprehension and check for correctness.",
+                    "is_complete": False,
+                },
+            ]
+        }
+
+
+class ReActThoughtGenerator(BaseModel):
+    """
+    ReAct Chain of Thought Tool / Function, this function provides some additional context  to help answer questions.
+    It can be called multiple times to get better context. Continue to call it until the thought process is complete.
+    Then use the context to answer the question.
+    """
+
+    thought: str = Field(
+        ..., description="Your current stream of consciousness step by step thoughts as you analyze this question."
+    )
+    action: str = Field(..., description="Actions that need to be taken to complete the thought.")
+    answer: str | None = Field(
+        ...,
+        description="A string that represents the final answer to the question. Only provide this if you are sure of "
+        "the answer.",
+    )
+
+    class Config:
+        schema_extra = {
+            "example": [
+                {
+                    "thought": "I want to refactor a function that currently uses a `for` loop to generate a list. \
+                    Using list comprehension will make the code more concise.",
+                    "action": "Analyze the function to determine what the `for` loop does, and how it can be "
+                    "rewritten in a list comprehension.",
+                    "answer": None,
+                },
+                {
+                    "thought": "The function is currently looping through a list of numbers, squaring each number, \
+                    and appending it to a new list using a `for` loop. A list comprehension can achieve the same "
+                    "result in a single line.",
+                    "action": "Rewrite the `for` loop as a list comprehension that performs the same operation.",
+                    "answer": None,
+                },
+                {
+                    "thought": "The `for` loop is iterating over a list called `numbers`, squaring each element and "
+                    "appending it to a new list `squared_numbers`. \
+                    The list comprehension would look like this: `squared_numbers = [x ** 2 for x in numbers]`.",
+                    "action": "Check that the list comprehension is syntactically correct and performs the same "
+                    "operation as the original loop.",
+                    "answer": None,
+                },
+                {
+                    "thought": "The refactored list comprehension correctly squares each element of the list and "
+                    "returns the same result as the original `for` loop. \
+                    The code is now more concise and easier to read.",
+                    "action": "None",
+                    "answer": "The final refactored code is: `squared_numbers = [x ** 2 for x in numbers]`. This "
+                    "replaces the `for` loop in a more concise manner.",
+                },
+            ]
+        }
+
+    def to_string(self):
+        if self.answer:
+            return f"Thought: {self.thought}\n" f"action: {self.action}\n" f"answer: {self.answer}"
+        return f"Thought: {self.thought}\n" f"action: {self.action}\n"
+
+
+# Define the chain-of-thought tool
+class ReActThoughtTool(ToolInterface):
+    def __init__(self):
+        self.thought_process = []
+        self.current_thought = None
+
+    def set_initial_thought(self, thought: ReActThoughtGenerator):
+        """Initialize the chain of thought."""
+        self.current_thought = thought
+        self.thought_process.append(thought)
+
+    def call(self, cot: ReActThoughtGenerator):
+        """Executes the chain of thought process until it's complete."""
+        instructions = f"## Context:\n" f"{cot.to_string()}\n"
+
+        print(f"providing instructions: \n{instructions}")
+        return {"output": instructions, "cot": cot, "tool": self.__class__.__name__}
+
+    # Define the chain-of-thought tool
+
+
+class ReActDeciderTool(ToolInterface):
+    def call(self, decision: ReActCompletionDecider):
+        """Executes the chain of thought process until it's complete."""
+        if decision.is_complete:
+            instructions = (
+                f"## Context:\n"
+                f"{decision.logic}\n"
+                f"## Instructions: now provide your final answer (and only the final answer)\n"
+            )
+        else:
+            instructions = (
+                f"## Context:\n"
+                f"{decision.logic}\n"
+                f"## Instructions: the task is not complete, continue working on it\n"
+            )
+
+        print(f"providing instructions: \n{instructions}")
+        return {"output": instructions, "decision": decision, "tool": self.__class__.__name__}
diff --git a/src/backend/base/langflow/components/astra_assistants/tools/__init__.py b/src/backend/base/langflow/components/astra_assistants/tools/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/src/backend/base/langflow/components/astra_assistants/tools/util.py b/src/backend/base/langflow/components/astra_assistants/tools/util.py
new file mode 100644
index 00000000000..28dc3d11da7
--- /dev/null
+++ b/src/backend/base/langflow/components/astra_assistants/tools/util.py
@@ -0,0 +1,81 @@
+from enum import Enum
+from typing import (
+    TypedDict,
+    Union,
+    get_args,
+    get_origin,
+    get_type_hints,
+)
+
+from pydantic import BaseModel, create_model
+from typing_extensions import NotRequired
+
+
+def typed_dict_to_basemodel(name: str, typed_dict: type[TypedDict], created_models: dict = None) -> type[BaseModel]:
+    if created_models is None:
+        created_models = {}
+
+    # Check if this TypedDict has already been converted to avoid circular references
+    if name in created_models:
+        return created_models[name]
+
+    # Retrieve type hints (field types) from the TypedDict
+    hints = get_type_hints(typed_dict)
+    model_fields = {}
+
+    # Determine required and optional fields
+    required_keys = getattr(typed_dict, "__required_keys__", set())
+    # optional_keys = getattr(typed_dict, "__optional_keys__", set())
+
+    # Helper function to safely check subclass
+    def issubclass_safe(cls, classinfo):
+        try:
+            return issubclass(cls, classinfo)
+        except TypeError:
+            return False
+
+    # Function to process each type hint recursively
+    def process_hint(hint_type):
+        origin_inner = get_origin(hint_type)
+        args_inner = get_args(hint_type)
+
+        if isinstance(hint_type, type):
+            if issubclass_safe(hint_type, TypedDict):
+                nested_model_name = f"{hint_type.__name__}Model"
+                return typed_dict_to_basemodel(nested_model_name, hint_type, created_models)
+            if issubclass_safe(hint_type, BaseModel):
+                return hint_type  # Already a Pydantic model
+            if issubclass_safe(hint_type, Enum):
+                return hint_type  # Enums can be used directly
+
+        if origin_inner in {list, list} and len(args_inner) == 1:
+            elem_type = args_inner[0]
+            if isinstance(elem_type, type) and issubclass_safe(elem_type, TypedDict):
+                nested_model = typed_dict_to_basemodel(f"{elem_type.__name__}Model", elem_type, created_models)
+                return list[nested_model]
+            return list[elem_type]
+        if origin_inner is Union:
+            # Handle Optional (Union[..., NoneType])
+            non_none_args = [arg for arg in args_inner if arg is not type(None)]
+            if len(non_none_args) == 1:
+                return process_hint(non_none_args[0]) | None
+            return hint_type
+        if origin_inner is NotRequired:
+            # Handle NotRequired explicitly, treat as Optional
+            return args_inner[0] | None
+        return hint_type
+
+    for field, hint in hints.items():
+        processed_hint = process_hint(hint)
+
+        if field in required_keys:
+            model_fields[field] = (processed_hint, ...)
+        else:
+            # If the field is optional, set default to None
+            model_fields[field] = (processed_hint, None)
+
+    # Use Pydantic's create_model to dynamically create the BaseModel
+    model = create_model(name, **model_fields)
+
+    created_models[name] = model
+    return model
diff --git a/src/backend/base/langflow/components/astra_assistants/util.py b/src/backend/base/langflow/components/astra_assistants/util.py
new file mode 100644
index 00000000000..bc688f4cac5
--- /dev/null
+++ b/src/backend/base/langflow/components/astra_assistants/util.py
@@ -0,0 +1,63 @@
+import importlib
+import inspect
+import json
+import os
+import pkgutil
+import threading
+
+import astra_assistants.tools as astra_assistants_tools
+import requests
+from astra_assistants import OpenAI, patch
+from astra_assistants.tools.tool_interface import ToolInterface
+
+import langflow.components.astra_assistants.tools as langflow_assistant_tools
+
+client_lock = threading.Lock()
+client = None
+
+
+def get_patched_openai_client(shared_component_cache):
+    os.environ["ASTRA_ASSISTANTS_QUIET"] = "true"
+    client = shared_component_cache.get("client")
+    if client is None:
+        client = patch(OpenAI())
+        shared_component_cache.set("client", client)
+    return client
+
+
+url = "https://raw.githubusercontent.com/BerriAI/litellm/refs/heads/main/model_prices_and_context_window.json"
+response = requests.get(url)
+data = json.loads(response.text)
+
+# Extract the model names into a Python list
+litellm_model_names = []
+for model, _ in data.items():
+    if model != "sample_spec":
+        # litellm_model_names.append(f"{details['litellm_provider']}/{model}")
+        litellm_model_names.append(model)
+
+
+# To store the class names that extend ToolInterface
+tool_names = []
+tools_and_names = {}
+
+
+def tools_from_package(your_package):
+    # Iterate over all modules in the package
+    package_name = your_package.__name__
+    for module_info in pkgutil.iter_modules(your_package.__path__):
+        module_name = f"{package_name}.{module_info.name}"
+
+        # Dynamically import the module
+        module = importlib.import_module(module_name)
+
+        # Iterate over all members of the module
+        for name, obj in inspect.getmembers(module, inspect.isclass):
+            # Check if the class is a subclass of ToolInterface and is not ToolInterface itself
+            if issubclass(obj, ToolInterface) and obj is not ToolInterface:
+                tool_names.append(name)
+                tools_and_names[name] = obj
+
+
+tools_from_package(astra_assistants_tools)
+tools_from_package(langflow_assistant_tools)
diff --git a/src/backend/base/langflow/custom/custom_component/component_with_cache.py b/src/backend/base/langflow/custom/custom_component/component_with_cache.py
new file mode 100644
index 00000000000..90f440799da
--- /dev/null
+++ b/src/backend/base/langflow/custom/custom_component/component_with_cache.py
@@ -0,0 +1,8 @@
+from langflow.custom import Component
+from langflow.services.deps import get_shared_component_cache_service
+
+
+class ComponentWithCache(Component):
+    def __init__(self, **data):
+        super().__init__(**data)
+        self._shared_component_cache = get_shared_component_cache_service()
diff --git a/src/backend/base/langflow/services/deps.py b/src/backend/base/langflow/services/deps.py
index d43d9cc82ba..aa4370bee1f 100644
--- a/src/backend/base/langflow/services/deps.py
+++ b/src/backend/base/langflow/services/deps.py
@@ -209,6 +209,18 @@ def get_cache_service() -> CacheService:
     return get_service(ServiceType.CACHE_SERVICE, CacheServiceFactory())  # type: ignore
 
 
+def get_shared_component_cache_service() -> CacheService:
+    """
+    Retrieves the cache service from the service manager.
+
+    Returns:
+        The cache service instance.
+    """
+    from langflow.services.shared_component_cache.factory import SharedComponentCacheServiceFactory
+
+    return get_service(ServiceType.SHARED_COMPONENT_CACHE_SERVICE, SharedComponentCacheServiceFactory())  # type: ignore
+
+
 def get_session_service() -> SessionService:
     """
     Retrieves the session service from the service manager.
diff --git a/src/backend/base/langflow/services/schema.py b/src/backend/base/langflow/services/schema.py
index 173b8075039..c1ecec49376 100644
--- a/src/backend/base/langflow/services/schema.py
+++ b/src/backend/base/langflow/services/schema.py
@@ -9,6 +9,7 @@ class ServiceType(str, Enum):
 
     AUTH_SERVICE = "auth_service"
     CACHE_SERVICE = "cache_service"
+    SHARED_COMPONENT_CACHE_SERVICE = "shared_component_cache_service"
     SETTINGS_SERVICE = "settings_service"
     DATABASE_SERVICE = "database_service"
     CHAT_SERVICE = "chat_service"
diff --git a/src/backend/base/langflow/services/shared_component_cache/__init__.py b/src/backend/base/langflow/services/shared_component_cache/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/src/backend/base/langflow/services/shared_component_cache/factory.py b/src/backend/base/langflow/services/shared_component_cache/factory.py
new file mode 100644
index 00000000000..ddb51a0495c
--- /dev/null
+++ b/src/backend/base/langflow/services/shared_component_cache/factory.py
@@ -0,0 +1,15 @@
+from typing import TYPE_CHECKING
+
+from langflow.services.factory import ServiceFactory
+from langflow.services.shared_component_cache.service import SharedComponentCacheService
+
+if TYPE_CHECKING:
+    from langflow.services.settings.service import SettingsService
+
+
+class SharedComponentCacheServiceFactory(ServiceFactory):
+    def __init__(self):
+        super().__init__(SharedComponentCacheService)
+
+    def create(self, settings_service: "SettingsService"):
+        return SharedComponentCacheService(expiration_time=settings_service.settings.cache_expire)
diff --git a/src/backend/base/langflow/services/shared_component_cache/service.py b/src/backend/base/langflow/services/shared_component_cache/service.py
new file mode 100644
index 00000000000..ac1d44d37f6
--- /dev/null
+++ b/src/backend/base/langflow/services/shared_component_cache/service.py
@@ -0,0 +1,9 @@
+from langflow.services.cache import ThreadingInMemoryCache
+
+
+class SharedComponentCacheService(ThreadingInMemoryCache):
+    """
+    A caching service shared across components.
+    """
+
+    name = "shared_component_cache_service"
diff --git a/src/backend/tests/integration/components/assistants/test_assistants_components.py b/src/backend/tests/integration/components/assistants/test_assistants_components.py
index a7b9f3b8a04..0bf23e8ded2 100644
--- a/src/backend/tests/integration/components/assistants/test_assistants_components.py
+++ b/src/backend/tests/integration/components/assistants/test_assistants_components.py
@@ -1,9 +1,53 @@
 import pytest
+
+from langflow.components.astra_assistants import AstraAssistantManager
 from tests.integration.utils import run_single_component
 
 
 @pytest.mark.api_key_required
-async def test_list_assistants():
+@pytest.mark.asyncio
+async def test_manager():
+    system_prompt = "you're a very detailed ascii artist"
+    user_message = "draw a cat eating ice cream"
+    results = await iterate(system_prompt, user_message)
+    is_complete = bool(results["tool_output"].data["text"])
+    i = 0
+    while not is_complete and i < 10:
+        user_message = "continue"
+        results = await iterate(system_prompt, user_message)
+        is_complete = bool(results["tool_output"].data["text"])
+        i += 1
+
+
+async def iterate(system_prompt, user_message):
+    results = await run_single_component(
+        AstraAssistantManager,
+        inputs={
+            "instructions": system_prompt,
+            "model_name": "gpt-4o-mini",
+            "user_message": user_message,
+            "tool": "ReActThoughtTool",
+        },
+    )
+    assert results["assistant_response"].text is not None
+    print(results["assistant_response"].text)
+    thread_id = results["output_thread_id"].text
+    results = await run_single_component(
+        AstraAssistantManager,
+        inputs={
+            "instructions": system_prompt,
+            "model_name": "gpt-4o-mini",
+            "user_message": "check to see if you are finished, do at least a few iterations",
+            "tool": "ReActDeciderTool",
+            "input_thread_id": thread_id,
+        },
+    )
+    assert results["assistant_response"].text is not None
+    print(results["assistant_response"].text)
+    return results
+
+
+async def list_assistants():
     from langflow.components.astra_assistants import AssistantsListAssistants
 
     results = await run_single_component(
@@ -28,13 +72,14 @@ async def test_create_assistants():
     )
     assistant_id = results["assistant_id"].text
     assert assistant_id is not None
-    await test_list_assistants()
+    await list_assistants()
     await get_assistant_name(assistant_id)
     thread_id = await test_create_thread()
     await run_assistant(assistant_id, thread_id)
 
 
 @pytest.mark.api_key_required
+@pytest.mark.asyncio
 async def test_create_thread():
     from langflow.components.astra_assistants import AssistantsCreateThread
 
diff --git a/src/backend/tests/unit/graph/graph/test_cycles.py b/src/backend/tests/unit/graph/graph/test_cycles.py
index 573ec6f0356..aa2e0a50ea8 100644
--- a/src/backend/tests/unit/graph/graph/test_cycles.py
+++ b/src/backend/tests/unit/graph/graph/test_cycles.py
@@ -2,6 +2,7 @@
 
 import pytest
 
+from langflow.components.astra_assistants import AstraAssistantManager
 from langflow.components.inputs.ChatInput import ChatInput
 from langflow.components.models.OpenAIModel import OpenAIModelComponent
 from langflow.components.outputs.ChatOutput import ChatOutput
@@ -210,3 +211,77 @@ def test_updated_graph_with_prompts():
     assert "chat_output_1" in results_ids, f"Expected outputs not in results: {results_ids}"
 
     print(f"Execution completed with results: {results_ids}")
+
+
+@pytest.mark.api_key_required
+def test_react():
+    # system_prompt = "you're a very detailed ascii artist"
+    # user_message = "draw a cat eating ice cream"
+
+    system_prompt = "you're a helpful spelling assistant that always breaks down words into letters to avoid tokenization issues, i.e. puppies --> p | u | p | p | i | e | s"
+    user_message = "how many r's are in the word strawberry"
+
+    # Chat input initialization
+    chat_input = ChatInput(_id="chat_input").set(input_value=user_message)
+
+    router = ConditionalRouterComponent(_id="router")
+
+    # First prompt: Guessing game with hints
+    prompt_component_1 = PromptComponent(_id="prompt_component_1").set(
+        template="Original prompt: {original_prompt}\nNew instructions: {new_prompt}",
+        original_prompt=chat_input.message_response,
+        new_prompt=router.false_response,
+    )
+
+    # First Assistant Manager component (ThoughtGenerator)
+    assistant_component_1 = AstraAssistantManager(_id="assistant_component_1")
+    assistant_component_1.set(
+        instructions=system_prompt,
+        model_name="gpt-4o-mini",
+        tool="ReActThoughtTool",
+        user_message=prompt_component_1.build_prompt,
+    )
+
+    # Second Assistant Manager component (DeciderTool)
+    assistant_component_2 = AstraAssistantManager(_id="assistant_component_2")
+    assistant_component_2.set(
+        instructions="determine if you are done, do more iterations if at all unsure",
+        model_name="gpt-4o-mini",
+        tool="ReActDeciderTool",
+        input_thread_id=assistant_component_1.get_thread_id,
+        user_message=assistant_component_1.get_assistant_response,
+    )
+
+    # Conditional router based on agent response
+    router.set(
+        input_text=assistant_component_2.get_tool_output,
+        match_text=str(True),
+        operator="equals",
+        message=assistant_component_1.get_assistant_response,
+    )
+
+    # chat output for the final OpenAI response
+    chat_output_1 = ChatOutput(_id="chat_output_1")
+    chat_output_1.set(input_value=router.true_response)
+
+    # Build the graph without concatenate
+    graph = Graph(chat_input, chat_output_1)
+
+    # Assertions for graph cyclicity and correctness
+    assert graph.is_cyclic is True, "Graph should contain cycles."
+
+    # Run and validate the execution of the graph
+    results = []
+    max_iterations = 20
+    snapshots = [graph.get_snapshot()]
+
+    for result in graph.start(max_iterations=max_iterations, config={"output": {"cache": False}}):
+        snapshots.append(graph.get_snapshot())
+        results.append(result)
+
+    assert len(snapshots) > 2, "Graph should have more than one snapshot"
+    # Extract the vertex IDs for analysis
+    results_ids = [result.vertex.id for result in results if hasattr(result, "vertex")]
+    assert "chat_output_1" in results_ids, f"Expected outputs not in results: {results_ids}"
+
+    print(f"Execution completed with results: {results_ids}")
diff --git a/uv.lock b/uv.lock
index 4bc918ab2f6..540248dd460 100644
--- a/uv.lock
+++ b/uv.lock
@@ -33,16 +33,16 @@ wheels = [
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.4.0"
+version = "2.4.3"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/2d/f7/22bba300a16fd1cad99da1a23793fe43963ee326d012fdf852d0b4035955/aiohappyeyeballs-2.4.0.tar.gz", hash = "sha256:55a1714f084e63d49639800f95716da97a1f173d46a16dfcfda0016abb93b6b2", size = 16786 }
+sdist = { url = "https://files.pythonhosted.org/packages/bc/69/2f6d5a019bd02e920a3417689a89887b39ad1e350b562f9955693d900c40/aiohappyeyeballs-2.4.3.tar.gz", hash = "sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586", size = 21809 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/18/b6/58ea188899950d759a837f9a58b2aee1d1a380ea4d6211ce9b1823748851/aiohappyeyeballs-2.4.0-py3-none-any.whl", hash = "sha256:7ce92076e249169a13c2f49320d1967425eaf1f407522d707d59cac7628d62bd", size = 12155 },
+    { url = "https://files.pythonhosted.org/packages/f7/d8/120cd0fe3e8530df0539e71ba9683eade12cae103dd7543e50d15f737917/aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572", size = 14742 },
 ]
 
 [[package]]
 name = "aiohttp"
-version = "3.10.6"
+version = "3.10.8"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -53,68 +53,68 @@ dependencies = [
     { name = "multidict" },
     { name = "yarl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/2b/97/15c51bbfcc184bcb4d473b7b02e7b54b6978e0083556a9cd491875cf11f7/aiohttp-3.10.6.tar.gz", hash = "sha256:d2578ef941be0c2ba58f6f421a703527d08427237ed45ecb091fed6f83305336", size = 7538429 }
-wheels = [
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