diff --git a/poetry.lock b/poetry.lock
index edafef6..4b184d0 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,13 +2,13 @@
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.4.4"
+version = "2.4.6"
 description = "Happy Eyeballs for asyncio"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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-    {file = "aiohappyeyeballs-2.4.4.tar.gz", hash = "sha256:5fdd7d87889c63183afc18ce9271f9b0a7d32c2303e394468dd45d514a757745"},
+    {file = "aiohappyeyeballs-2.4.6-py3-none-any.whl", hash = "sha256:147ec992cf873d74f5062644332c539fcd42956dc69453fe5204195e560517e1"},
+    {file = "aiohappyeyeballs-2.4.6.tar.gz", hash = "sha256:9b05052f9042985d32ecbe4b59a77ae19c006a78f1344d7fdad69d28ded3d0b0"},
 ]
 
 [[package]]
@@ -140,13 +140,13 @@ files = [
 
 [[package]]
 name = "anthropic"
-version = "0.45.2"
+version = "0.46.0"
 description = "The official Python library for the anthropic API"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -185,13 +185,13 @@ trio = ["trio (>=0.26.1)"]
 
 [[package]]
 name = "arcadepy"
-version = "1.1.1"
+version = "1.2.0"
 description = "The official Python library for the Arcade API"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -472,73 +472,74 @@ files = [
 
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+version = "7.6.12"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.9"
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 ]
 
 [package.extras]
@@ -608,17 +609,6 @@ files = [
 marshmallow = ">=3.18.0,<4.0.0"
 typing-inspect = ">=0.4.0,<1"
 
-[[package]]
-name = "defusedxml"
-version = "0.7.1"
-description = "XML bomb protection for Python stdlib modules"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-files = [
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-]
-
 [[package]]
 name = "distlib"
 version = "0.3.9"
@@ -946,13 +936,13 @@ testing = ["mock", "pytest", "pytest-cov", "watchdog"]
 
 [[package]]
 name = "identify"
-version = "2.6.6"
+version = "2.6.8"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.extras]
@@ -1151,22 +1141,22 @@ tests = ["flask (>=2.2.5)", "hypothesis (>=6.79.4)", "pytest (>=7.4.4)"]
 
 [[package]]
 name = "langchain"
-version = "0.3.17"
+version = "0.3.19"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
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 ]
 
 [package.dependencies]
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-langchain-core = ">=0.3.33,<0.4.0"
-langchain-text-splitters = ">=0.3.3,<0.4.0"
+langchain-core = ">=0.3.35,<1.0.0"
+langchain-text-splitters = ">=0.3.6,<1.0.0"
 langsmith = ">=0.1.17,<0.4"
 numpy = [
-    {version = ">=1.22.4,<2", markers = "python_version < \"3.12\""},
+    {version = ">=1.26.4,<2", markers = "python_version < \"3.12\""},
     {version = ">=1.26.2,<3", markers = "python_version >= \"3.12\""},
 ]
 pydantic = ">=2.7.4,<3.0.0"
@@ -1175,21 +1165,37 @@ requests = ">=2,<3"
 SQLAlchemy = ">=1.4,<3"
 tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
 
+[package.extras]
+anthropic = ["langchain-anthropic"]
+aws = ["langchain-aws"]
+cohere = ["langchain-cohere"]
+community = ["langchain-community"]
+deepseek = ["langchain-deepseek"]
+fireworks = ["langchain-fireworks"]
+google-genai = ["langchain-google-genai"]
+google-vertexai = ["langchain-google-vertexai"]
+groq = ["langchain-groq"]
+huggingface = ["langchain-huggingface"]
+mistralai = ["langchain-mistralai"]
+ollama = ["langchain-ollama"]
+openai = ["langchain-openai"]
+together = ["langchain-together"]
+xai = ["langchain-xai"]
+
 [[package]]
 name = "langchain-anthropic"
-version = "0.3.6"
+version = "0.3.7"
 description = "An integration package connecting AnthropicMessages and LangChain"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
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+    {file = "langchain_anthropic-0.3.7.tar.gz", hash = "sha256:534cd1867bc41711cd8c3d0a0bc055e6c5a4215953c87260209a90dc5816f30d"},
 ]
 
 [package.dependencies]
 anthropic = ">=0.45.0,<1"
-defusedxml = ">=0.7.1,<0.8.0"
-langchain-core = ">=0.3.33,<0.4.0"
+langchain-core = ">=0.3.34,<1.0.0"
 pydantic = ">=2.7.4,<3.0.0"
 
 [[package]]
@@ -1209,24 +1215,24 @@ langgraph = ">=0.2.67,<0.3.0"
 
 [[package]]
 name = "langchain-community"
-version = "0.3.16"
+version = "0.3.18"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
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+    {file = "langchain_community-0.3.18-py3-none-any.whl", hash = "sha256:0d4a70144a1750045c4f726f9a43379ed2484178f76e4b8295bcef3a7fdf41d5"},
+    {file = "langchain_community-0.3.18.tar.gz", hash = "sha256:fa2889a8f0b2d22b5c306fd1b070c0970e1f11b604bf55fad2f4a1d0bf68a077"},
 ]
 
 [package.dependencies]
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 dataclasses-json = ">=0.5.7,<0.7"
-httpx-sse = ">=0.4.0,<0.5.0"
-langchain = ">=0.3.16,<0.4.0"
-langchain-core = ">=0.3.32,<0.4.0"
+httpx-sse = ">=0.4.0,<1.0.0"
+langchain = ">=0.3.19,<1.0.0"
+langchain-core = ">=0.3.37,<1.0.0"
 langsmith = ">=0.1.125,<0.4"
 numpy = [
-    {version = ">=1.22.4,<2", markers = "python_version < \"3.12\""},
+    {version = ">=1.26.4,<2", markers = "python_version < \"3.12\""},
     {version = ">=1.26.2,<3", markers = "python_version >= \"3.12\""},
 ]
 pydantic-settings = ">=2.4.0,<3.0.0"
@@ -1237,13 +1243,13 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
 
 [[package]]
 name = "langchain-core"
-version = "0.3.33"
+version = "0.3.37"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
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-    {file = "langchain_core-0.3.33.tar.gz", hash = "sha256:b5dd93a4e7f8198d2fc6048723b0bfecf7aaf128b0d268cbac19c34c1579b953"},
+    {file = "langchain_core-0.3.37-py3-none-any.whl", hash = "sha256:8202fd6506ce139a3a1b1c4c3006216b1c7fffa40bdd1779f7d2c67f75eb5f79"},
+    {file = "langchain_core-0.3.37.tar.gz", hash = "sha256:cda8786e616caa2f68f7cc9e811b9b50e3b63fb2094333318b348e5961a7ea01"},
 ]
 
 [package.dependencies]
@@ -1275,43 +1281,43 @@ langchain-core = ">=0.3.28,<0.4.0"
 
 [[package]]
 name = "langchain-openai"
-version = "0.3.3"
+version = "0.3.6"
 description = "An integration package connecting OpenAI and LangChain"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
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+    {file = "langchain_openai-0.3.6-py3-none-any.whl", hash = "sha256:05f0869f6cc963e2ec9e2e54ea1038d9c2af784c67f0e217040dfc918b31649a"},
+    {file = "langchain_openai-0.3.6.tar.gz", hash = "sha256:7daf92e1cd98865ab5213ec5bec2cbd6c28f011e250714978b3a99c7e4fc88ce"},
 ]
 
 [package.dependencies]
-langchain-core = ">=0.3.33,<0.4.0"
+langchain-core = ">=0.3.35,<1.0.0"
 openai = ">=1.58.1,<2.0.0"
 tiktoken = ">=0.7,<1"
 
 [[package]]
 name = "langchain-text-splitters"
-version = "0.3.5"
+version = "0.3.6"
 description = "LangChain text splitting utilities"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
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 ]
 
 [package.dependencies]
-langchain-core = ">=0.3.29,<0.4.0"
+langchain-core = ">=0.3.34,<1.0.0"
 
 [[package]]
 name = "langgraph"
-version = "0.2.69"
+version = "0.2.74"
 description = "Building stateful, multi-actor applications with LLMs"
 optional = false
 python-versions = "<4.0,>=3.9.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1321,13 +1327,13 @@ langgraph-sdk = ">=0.1.42,<0.2.0"
 
 [[package]]
 name = "langgraph-api"
-version = "0.0.22"
+version = "0.0.26"
 description = ""
 optional = false
 python-versions = "<4.0,>=3.11.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1336,8 +1342,8 @@ httpx = ">=0.27.0"
 jsonschema-rs = ">=0.25.0,<0.26.0"
 langchain-core = ">=0.2.38,<0.4.0"
 langgraph = ">=0.2.56,<0.3.0"
-langgraph-checkpoint = ">=2.0.7,<3.0"
-langgraph-sdk = ">=0.1.51,<0.2.0"
+langgraph-checkpoint = ">=2.0.15,<3.0"
+langgraph-sdk = ">=0.1.53,<0.2.0"
 langsmith = ">=0.1.63,<0.4.0"
 orjson = ">=3.10.1"
 pyjwt = ">=2.9.0,<3.0.0"
@@ -1350,13 +1356,13 @@ watchfiles = ">=0.13"
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.0.10"
+version = "2.0.16"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = false
 python-versions = "<4.0.0,>=3.9.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1365,32 +1371,32 @@ msgpack = ">=1.1.0,<2.0.0"
 
 [[package]]
 name = "langgraph-cli"
-version = "0.1.70"
+version = "0.1.73"
 description = "CLI for interacting with LangGraph API"
 optional = false
 python-versions = "<4.0.0,>=3.9.0"
 files = [
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 ]
 
 [package.dependencies]
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-langgraph-api = {version = ">=0.0.12,<0.1.0", optional = true, markers = "python_version >= \"3.11\" and python_version < \"4.0\" and extra == \"inmem\""}
+langgraph-api = {version = ">=0.0.26,<0.1.0", optional = true, markers = "python_version >= \"3.11\" and python_version < \"4.0\" and extra == \"inmem\""}
 python-dotenv = {version = ">=0.8.0", optional = true, markers = "extra == \"inmem\""}
 
 [package.extras]
-inmem = ["langgraph-api (>=0.0.12,<0.1.0)", "python-dotenv (>=0.8.0)"]
+inmem = ["langgraph-api (>=0.0.26,<0.1.0)", "python-dotenv (>=0.8.0)"]
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.51"
+version = "0.1.53"
 description = "SDK for interacting with LangGraph API"
 optional = false
 python-versions = "<4.0.0,>=3.9.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1399,13 +1405,13 @@ orjson = ">=3.10.1"
 
 [[package]]
 name = "langsmith"
-version = "0.3.6"
+version = "0.3.10"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -1748,77 +1754,77 @@ files = [
 
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+version = "2.2.3"
 description = "Fundamental package for array computing in Python"
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+    {file = "SQLAlchemy-2.0.38-py3-none-any.whl", hash = "sha256:63178c675d4c80def39f1febd625a6333f44c0ba269edd8a468b156394b27753"},
+    {file = "sqlalchemy-2.0.38.tar.gz", hash = "sha256:e5a4d82bdb4bf1ac1285a68eab02d253ab73355d9f0fe725a97e1e0fa689decb"},
 ]
 
 [package.dependencies]
@@ -2734,13 +2756,13 @@ examples = ["fastapi"]
 
 [[package]]
 name = "starlette"
-version = "0.45.3"
+version = "0.46.0"
 description = "The little ASGI library that shines."
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "starlette-0.45.3-py3-none-any.whl", hash = "sha256:dfb6d332576f136ec740296c7e8bb8c8a7125044e7c6da30744718880cdd059d"},
-    {file = "starlette-0.45.3.tar.gz", hash = "sha256:2cbcba2a75806f8a41c722141486f37c28e30a0921c5f6fe4346cb0dcee1302f"},
+    {file = "starlette-0.46.0-py3-none-any.whl", hash = "sha256:913f0798bd90ba90a9156383bcf1350a17d6259451d0d8ee27fc0cf2db609038"},
+    {file = "starlette-0.46.0.tar.gz", hash = "sha256:b359e4567456b28d473d0193f34c0de0ed49710d75ef183a74a5ce0499324f50"},
 ]
 
 [package.dependencies]
@@ -2768,13 +2790,13 @@ typing = ["mypy (>=1.4)", "rich", "twisted"]
 
 [[package]]
 name = "tavily-python"
-version = "0.5.0"
+version = "0.5.1"
 description = "Python wrapper for the Tavily API"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "tavily_python-0.5.0-py3-none-any.whl", hash = "sha256:e874f6a04a56cdda80a505fe0b4f5d61d25372bd52a83e6773926fb297dcaa29"},
-    {file = "tavily_python-0.5.0.tar.gz", hash = "sha256:2c60b88203b630e1b37fc711913a1090ced6719b3f21089f25ec06e9e1602822"},
+    {file = "tavily_python-0.5.1-py3-none-any.whl", hash = "sha256:169601f703c55cf338758dcacfa7102473b479a9271d65a3af6fc3668990f757"},
+    {file = "tavily_python-0.5.1.tar.gz", hash = "sha256:44b0eefe79a057cd11d3cd03780b63b4913400122350e38285acfb502c2fffc1"},
 ]
 
 [package.dependencies]
@@ -2928,13 +2950,13 @@ standard = ["colorama (>=0.4)", "httptools (>=0.6.3)", "python-dotenv (>=0.13)",
 
 [[package]]
 name = "virtualenv"
-version = "20.29.1"
+version = "20.29.2"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "virtualenv-20.29.1-py3-none-any.whl", hash = "sha256:4e4cb403c0b0da39e13b46b1b2476e505cb0046b25f242bee80f62bf990b2779"},
-    {file = "virtualenv-20.29.1.tar.gz", hash = "sha256:b8b8970138d32fb606192cb97f6cd4bb644fa486be9308fb9b63f81091b5dc35"},
+    {file = "virtualenv-20.29.2-py3-none-any.whl", hash = "sha256:febddfc3d1ea571bdb1dc0f98d7b45d24def7428214d4fb73cc486c9568cce6a"},
+    {file = "virtualenv-20.29.2.tar.gz", hash = "sha256:fdaabebf6d03b5ba83ae0a02cfe96f48a716f4fae556461d180825866f75b728"},
 ]
 
 [package.dependencies]
diff --git a/slack_ai_agent/agents/simple_long_term_memory_agent.py b/slack_ai_agent/agents/simple_long_term_memory_agent.py
index 055c888..248bee0 100644
--- a/slack_ai_agent/agents/simple_long_term_memory_agent.py
+++ b/slack_ai_agent/agents/simple_long_term_memory_agent.py
@@ -1,8 +1,12 @@
 from typing import Any
+from typing import Dict
 from typing import List
 from typing import Literal
 from typing import TypedDict
 
+from langchain.schema import HumanMessage
+from langchain.schema import SystemMessage
+from langchain_core.runnables import RunnableConfig
 from langgraph.graph import END
 from langgraph.graph import START
 from langgraph.graph import StateGraph
@@ -12,6 +16,38 @@
 from slack_ai_agent.agents.utils import State
 from slack_ai_agent.agents.utils import agent
 from slack_ai_agent.agents.utils import load_memories
+from slack_ai_agent.agents.utils.models import model
+
+
+def generate_loading_query(state: State, config: RunnableConfig) -> Dict[str, str]:
+    """Generate a query for loading memories based on the current conversation.
+
+    Args:
+        state (State): The current state of the conversation
+        config (RunnableConfig): Runtime configuration
+
+    Returns:
+        Dict[str, str]: Dictionary containing the generated query
+    """
+    messages = [msg for msg in state["messages"] if msg.content]
+    if not messages:
+        return {"loading_query": ""}
+
+    result = model.invoke(
+        [
+            SystemMessage(
+                content="You are a helpful assistant tasked with generating a search query to find relevant memories. Based on the conversation, create a concise query that will help retrieve the most relevant information."
+            ),
+            HumanMessage(
+                content=f"Generate a search query based on this conversation:\n{messages[-1].content}"
+            ),
+        ]
+    )
+
+    if isinstance(result.content, str):
+        return {"loading_query": result.content.strip()}
+
+    return {"loading_query": ""}
 
 
 # Define the config
@@ -69,12 +105,14 @@ def route_tools(state: State):
 
 # Create the graph and add nodes
 builder = StateGraph(State)
+builder.add_node("generate_loading_query", generate_loading_query)  # type: ignore
 builder.add_node("load_memories", load_memories)  # type: ignore
 builder.add_node("agent", agent)  # type: ignore
 builder.add_node("tools", ToolNode(tools=create_tools()))  # type: ignore
 
 # Add edges to the graph
-builder.add_edge(START, "load_memories")
+builder.add_edge(START, "generate_loading_query")
+builder.add_edge("generate_loading_query", "load_memories")
 builder.add_edge("load_memories", "agent")
 builder.add_conditional_edges("agent", route_tools, ["tools", END])
 builder.add_edge("tools", "agent")
diff --git a/slack_ai_agent/agents/tools/memory.py b/slack_ai_agent/agents/tools/memory.py
index 45b732c..1d858f0 100644
--- a/slack_ai_agent/agents/tools/memory.py
+++ b/slack_ai_agent/agents/tools/memory.py
@@ -1,4 +1,6 @@
-"""Memory-related tools for the LangGraph implementation."""
+"""Memory-related tools for the LangGraph implementation with semantic search capabilities."""
+
+from __future__ import annotations
 
 import uuid
 from dataclasses import dataclass
@@ -6,8 +8,10 @@
 from typing import Annotated
 from typing import Any
 from typing import Dict
+from typing import List
 from typing import Optional
 
+from langchain.embeddings import OpenAIEmbeddings
 from langchain_core.runnables import RunnableConfig
 from langchain_core.tools import tool
 from langgraph.prebuilt import InjectedStore
@@ -15,20 +19,27 @@
 
 @dataclass
 class Memory:
-    """Memory data structure."""
+    """Memory data structure with vector embedding support."""
 
     content: str
     context: str
     id: str = ""
+    embedding: Optional[List[float]] = None
 
     def to_dict(self) -> Dict[str, Any]:
         """Convert memory to dictionary format."""
         return {
             "content": self.content,
             "context": self.context,
+            "embedding": self.embedding,
+            "text": f"{self.content}\n\nContext: {self.context}",  # Combined text for embedding
         }
 
 
+# Initialize embeddings model
+embeddings_model = OpenAIEmbeddings(model="text-embedding-3-large")
+
+
 def get_user_id(config: Optional[RunnableConfig] = None) -> str:
     """Get user ID from config or return default."""
     if config is None:
@@ -53,7 +64,7 @@ def upsert_memory(
     config: RunnableConfig,
     store: Annotated[Any, InjectedStore()],
 ) -> str:
-    """Upsert a memory in the database.
+    """Upsert a memory in the database with vector embedding support.
 
     If a memory conflicts with an existing one, then just UPDATE the
     existing one by passing in memory_id - don't create two memories
@@ -69,10 +80,17 @@ def upsert_memory(
     if store is None:
         raise ValueError("Memory store is not configured")
 
+    # Create combined text for embedding
+    combined_text = f"{content}\n\nContext: {context}"
+
+    # Generate embedding
+    embedding = embeddings_model.embed_query(combined_text)
+
     memory = Memory(
         content=content,
         context=context,
         id=memory_id or str(uuid.uuid4()),
+        embedding=embedding,
     )
 
     # Get user ID from config if not explicitly provided
@@ -83,11 +101,44 @@ def upsert_memory(
     memory_dict = memory.to_dict()
     memory_dict["author"] = memory_author
     memory_dict["created_at"] = memory_created_at
+    memory_dict["type"] = "conversation"  # Add type for filtering in search
 
+    # Store with vector search support
     store.put(
         ("memories", "langgraph-studio-user"),
         key=memory.id,
         value=memory_dict,
+        index=["text"],  # Enable semantic search on the text field
+    )
+
+    return f"Stored memory with vector embedding: {content}"
+
+
+def search_memories(state: Dict[str, Any], *, store: Any) -> Dict[str, List[str]]:
+    """Search for relevant memories using semantic search.
+
+    Args:
+        state: The current conversation state
+        store: The memory store
+
+    Returns:
+        Dict containing list of relevant memories
+    """
+    if not state.get("loading_query"):
+        return {"recall_memories": []}
+
+    # Search for relevant memories using the loading query
+    results = store.search(
+        ("memories", "langgraph-studio-user"),
+        query=state["loading_query"],
+        filter={"type": "conversation"},
+        limit=5,  # Retrieve top 5 most relevant memories
     )
 
-    return f"Stored memory: {content}"
+    # Format results with relevance scores
+    memories = [
+        f"Previous memory ({r.score:.2f} relevance):\n{r.value['content']}\nContext: {r.value['context']}"
+        for r in results
+    ]
+
+    return {"recall_memories": memories}
diff --git a/slack_ai_agent/agents/utils/models.py b/slack_ai_agent/agents/utils/models.py
index a32503d..dd938b9 100644
--- a/slack_ai_agent/agents/utils/models.py
+++ b/slack_ai_agent/agents/utils/models.py
@@ -2,6 +2,7 @@
 
 from typing import Dict
 from typing import List
+from typing import Optional
 
 from langchain.prompts import ChatPromptTemplate
 from langchain_anthropic import ChatAnthropic
@@ -137,6 +138,7 @@ class State(MessagesState):
     """State class for managing conversation state with memory capabilities."""
 
     recall_memories: List[str]
+    loading_query: Optional[str]
 
 
 def agent(state: State, config: RunnableConfig, *, store: BaseStore) -> State:
diff --git a/slack_ai_agent/agents/utils/store.py b/slack_ai_agent/agents/utils/store.py
index 246dd0d..d7e1590 100644
--- a/slack_ai_agent/agents/utils/store.py
+++ b/slack_ai_agent/agents/utils/store.py
@@ -11,29 +11,36 @@
 def load_memories(
     state: Dict[str, List[BaseMessage]], config: RunnableConfig, *, store: BaseStore
 ) -> Dict:
-    """Load memories from storage based on conversation context.
+    """Load memories from storage using semantic search based on conversation context.
 
     Args:
         state (Dict[str, List[BaseMessage]]): Current conversation state
         config (RunnableConfig): Runtime configuration
-        store (BaseStore): Memory storage backend
+        store (BaseStore): Memory storage backend with vector search capabilities
 
     Returns:
-        Dict: Updated state with loaded memories
+        Dict: Updated state with loaded memories and their relevance scores
     """
-    # TODO: uncomment this when we seta user_id
-    # user_id = config.get("configurable", {}).get("user_id", "langgraph-studio-user")
     namespace = ("memories", "langgraph-studio-user")
+
+    # Use semantic search with the loading query
     memories = store.search(
-        namespace, query=str(state["messages"][-1].content), limit=50
+        namespace,
+        query=str(state["loading_query"]),
+        filter={"type": "conversation"},
+        limit=5,  # Limit to top 5 most relevant memories
     )
+
+    # Format memories with relevance scores
     recall_memories = [
-        f"{memory.value.get('content', '')} - Context: {memory.value.get('context', '')} "
+        f"Content: {memory.value.get('content', '')}\n"
+        f"Context: {memory.value.get('context', '')}\n"
         f"(Author: {memory.value.get('author', 'Unknown')}, "
         f"Created: {memory.value.get('created_at', 'Unknown')})"
         for memory in memories
     ]
-    return {  # type: ignore
+
+    return {
         "messages": state["messages"],
         "recall_memories": recall_memories,
     }