diff --git a/docs/docs/integrations/graphs/memgraph.ipynb b/docs/docs/integrations/graphs/memgraph.ipynb
index 1040d43f9e5..4ccfb6989b8 100644
--- a/docs/docs/integrations/graphs/memgraph.ipynb
+++ b/docs/docs/integrations/graphs/memgraph.ipynb
@@ -38,7 +38,7 @@
     "To use LangChain, install and import all the necessary packages. We'll use the package manager [pip](https://pip.pypa.io/en/stable/installation/), along with the `--user` flag, to ensure proper permissions. If you've installed Python 3.4 or a later version, `pip` is included by default. You can install all the required packages using the following command:\n",
     "\n",
     "```\n",
-    "pip install langchain langchain-openai neo4j --user\n",
+    "pip install langchain langchain-openai langchain-memgraph --user\n",
     "```\n",
     "\n",
     "You can either run the provided code blocks in this notebook or use a separate Python file to experiment with Memgraph and LangChain."
@@ -57,24 +57,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
     "\n",
-    "from langchain_community.chains.graph_qa.memgraph import MemgraphQAChain\n",
-    "from langchain_community.graphs import MemgraphGraph\n",
     "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain_memgraph.chains.graph_qa import MemgraphQAChain\n",
+    "from langchain_memgraph.graphs.memgraph import Memgraph\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
     "url = os.environ.get(\"MEMGRAPH_URI\", \"bolt://localhost:7687\")\n",
     "username = os.environ.get(\"MEMGRAPH_USERNAME\", \"\")\n",
     "password = os.environ.get(\"MEMGRAPH_PASSWORD\", \"\")\n",
     "\n",
-    "graph = MemgraphGraph(\n",
-    "    url=url, username=username, password=password, refresh_schema=False\n",
-    ")"
+    "graph = Memgraph(url=url, username=username, password=password, refresh_schema=False)"
    ]
   },
   {
diff --git a/docs/docs/integrations/providers/memgraph.mdx b/docs/docs/integrations/providers/memgraph.mdx
new file mode 100644
index 00000000000..a64203a85ba
--- /dev/null
+++ b/docs/docs/integrations/providers/memgraph.mdx
@@ -0,0 +1,40 @@
+# Memgraph
+
+>Memgraph is a high-performance, in-memory graph database that is optimized for real-time queries and analytics.
+>Get started with Memgraph by visiting [their website](https://memgraph.com/).
+
+## Installation and Setup
+
+- Install the Python SDK with `pip install langchain-memgraph`
+
+## MemgraphQAChain
+
+There exists a wrapper around Memgraph graph database that allows you to generate Cypher statements based on the user input
+and use them to retrieve relevant information from the database.
+
+```python
+from langchain_memgraph.chains.graph_qa import MemgraphQAChain
+from langchain_memgraph.graphs.memgraph import Memgraph
+```
+
+See a [usage example](/docs/integrations/graphs/memgraph)
+
+## Constructing a Knowledge Graph from unstructured data
+
+You can use the integration to construct a knowledge graph from unstructured data.
+
+```python
+from langchain_memgraph.graphs.memgraph import Memgraph
+from langchain_experimental.graph_transformers import LLMGraphTransformer
+```
+
+See a [usage example](/docs/integrations/graphs/memgraph)
+
+## Memgraph Tools and Toolkit
+
+Memgraph also provides a toolkit that allows you to interact with the Memgraph database.
+See a [usage example](/docs/integrations/tools/memgraph).
+
+```python
+from langchain_memgraph import MemgraphToolkit
+```
diff --git a/docs/docs/integrations/tools/memgraph.ipynb b/docs/docs/integrations/tools/memgraph.ipynb
new file mode 100644
index 00000000000..0797e30309b
--- /dev/null
+++ b/docs/docs/integrations/tools/memgraph.ipynb
@@ -0,0 +1,215 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Memgraph\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# MemgraphToolkit\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "This will help you getting started with the Memgraph [toolkit](/docs/concepts/tools/#toolkits). \n",
+    "\n",
+    "Tools within `MemgraphToolkit` are designed for the interaction with the `Memgraph` database.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To be able tot follow the steps below, make sure you have a running Memgraph instance on your local host. For more details on how to run Memgraph, take a look at [Memgraph docs](https://memgraph.com/docs/getting-started)\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing from runs of individual tools, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "This toolkit lives in the `langchain-memgraph` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-memgraph "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our toolkit:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import init_chat_model\n",
+    "from langchain_memgraph import MemgraphToolkit\n",
+    "from langchain_memgraph.graphs.memgraph import Memgraph\n",
+    "\n",
+    "db = Memgraph(url=url, username=username, password=password)\n",
+    "\n",
+    "llm = init_chat_model(\"gpt-4o-mini\", model_provider=\"openai\")\n",
+    "\n",
+    "toolkit = MemgraphToolkit(\n",
+    "    db=db,  # Memgraph instance\n",
+    "    llm=llm,  # LLM chat model for LLM operations\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c5f2839-4020-424e-9fc9-07777eede442",
+   "metadata": {},
+   "source": [
+    "## Tools\n",
+    "\n",
+    "View available tools:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51a60dbe-9f2e-4e04-bb62-23968f17164a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toolkit.get_tools()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "608af19d",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "Tools can be individually called by passing an arguments, for QueryMemgraphTool it would be: \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffa944db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_memgraph.tools import QueryMemgraphTool\n",
+    "\n",
+    "# Rest of the code omitted for brevity\n",
+    "\n",
+    "tool.invoke({QueryMemgraphTool({\"query\": \"MATCH (n) RETURN n LIMIT 5\"})})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfe8aad4-8626-4330-98a9-7ea1ca5d2e0e",
+   "metadata": {},
+   "source": [
+    "## Use within an agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "310bf18e-6c9a-4072-b86e-47bc1fcca29d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "agent_executor = create_react_agent(llm, tools)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23e11cc9-abd6-4855-a7eb-799f45ca01ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "example_query = \"MATCH (n) RETURN n LIMIT 1\"\n",
+    "\n",
+    "events = agent_executor.stream(\n",
+    "    {\"messages\": [(\"user\", example_query)]},\n",
+    "    stream_mode=\"values\",\n",
+    ")\n",
+    "for event in events:\n",
+    "    event[\"messages\"][-1].pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29ca615b",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For more details on API visit [Memgraph integration docs](https://memgraph.com/docs/ai-ecosystem/integrations#langchain)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/libs/packages.yml b/libs/packages.yml
index b3d79811c2c..af7aee18995 100644
--- a/libs/packages.yml
+++ b/libs/packages.yml
@@ -551,3 +551,6 @@ packages:
   provider_page: naver
   path: .
   repo: e7217/langchain-naver-community
+- name: langchain-memgraph
+  path: .
+  repo: memgraph/langchain-memgraph