diff --git a/docs/docs/examples/vector_stores/ObjectBoxIndexDemo.ipynb b/docs/docs/examples/vector_stores/ObjectBoxIndexDemo.ipynb
new file mode 100644
index 0000000000000..2c7604e374cab
--- /dev/null
+++ b/docs/docs/examples/vector_stores/ObjectBoxIndexDemo.ipynb
@@ -0,0 +1,294 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ObjectBox VectorStore Demo\n",
+    "\n",
+    "This notebook will demonstrate the use of [ObjectBox](https://objectbox.io/) as an efficient, on-device vector-store with LlamaIndex. We will consider a simple RAG use-case where given a document, the user can ask questions and get relevant answers from a LLM in natural language. The RAG pipeline will be configured along the following verticals:\n",
+    "\n",
+    "* A builtin [`SimpleDirectoryReader` reader](https://docs.llamaindex.ai/en/stable/examples/data_connectors/simple_directory_reader/) from LlamaIndex\n",
+    "* A builtin [`SentenceSplitter` node-parser](https://docs.llamaindex.ai/en/stable/api_reference/node_parsers/sentence_splitter/) from LlamaIndex\n",
+    "* Models from [HuggingFace as embedding providers](https://docs.llamaindex.ai/en/stable/examples/embeddings/huggingface/)\n",
+    "* [ObjectBox](https://objectbox.io/) as NoSQL and vector datastore\n",
+    "* Google's [Gemini](https://docs.llamaindex.ai/en/stable/examples/llm/gemini/) as a remote LLM service\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1) Installing dependencies\n",
+    "\n",
+    "We install integrations for HuggingFace and Gemini to use along with LlamaIndex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.6 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
+      "\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m1.5/1.6 MB\u001b[0m \u001b[31m40.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m25.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.0/4.0 MB\u001b[0m \u001b[31m44.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m38.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m37.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.4/76.4 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.3/49.3 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25h"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install llama_index_vector_stores_objectbox --quiet\n",
+    "!pip install llama-index --quiet\n",
+    "!pip install llama-index-embeddings-huggingface --quiet\n",
+    "!pip install llama-index-llms-gemini --quiet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2) Downloading the documents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!mkdir -p 'data/paul_graham/'\n",
+    "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3) Setup a LLM for RAG (Gemini)\n",
+    "\n",
+    "We use Google Gemini's cloud-based API as a LLM. You can get an API-key from the [console](https://aistudio.google.com/app/apikey)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.llms.gemini import Gemini\n",
+    "import getpass\n",
+    "\n",
+    "gemini_key_api = getpass.getpass(\"Gemini API Key: \")\n",
+    "gemini_llm = Gemini(api_key=gemini_key_api)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4) Setup an embedding model for RAG (HuggingFace `bge-small-en-v1.5`)\n",
+    "\n",
+    "HuggingFace hosts a variety of embedding models, which could be observed from the [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
+    "\n",
+    "hf_embedding = HuggingFaceEmbedding(model_name=\"BAAI/bge-base-en-v1.5\")\n",
+    "embedding_dim = 384"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5) Prepare documents and nodes\n",
+    "\n",
+    "In a RAG pipeline, the first step is to read the given documents. We use the `SimpleDirectoryReader` that selects the best file reader by checking the file extension from the directory.\n",
+    "\n",
+    "Next, we produce chunks (text subsequences) from the contents read by the `SimpleDirectoryReader` from the documents. A `SentenceSplitter` is a text-splitter that preserves sentence boundaries while splitting the text into chunks of size `chunk_size`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.core import SimpleDirectoryReader\n",
+    "from llama_index.core.node_parser import SentenceSplitter\n",
+    "\n",
+    "reader = SimpleDirectoryReader(\"./data/paul_graham\")\n",
+    "documents = reader.load_data()\n",
+    "\n",
+    "node_parser = SentenceSplitter(chunk_size=512, chunk_overlap=20)\n",
+    "nodes = node_parser.get_nodes_from_documents(documents)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6) Configure `ObjectBoxVectorStore`\n",
+    "\n",
+    "The `ObjectBoxVectorStore` can be initialized with several options:\n",
+    "\n",
+    "- `embedding_dim` (required): The dimensions of the embeddings that the vector DB will hold\n",
+    "- `distance_type`: Choose from `COSINE`, `DOT_PRODUCT`, `DOT_PRODUCT_NON_NORMALIZED` and `EUCLIDEAN`\n",
+    "- `db_directory`: The path of the directory where the `.mdb` ObjectBox database file should be created\n",
+    "- `clear_db`: Deletes the existing database file if it exists on `db_directory`\n",
+    "- `do_log`: Enables logging from the ObjectBox integration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.vector_stores.objectbox import ObjectBoxVectorStore\n",
+    "from llama_index.core import StorageContext, VectorStoreIndex, Settings\n",
+    "from objectbox import VectorDistanceType\n",
+    "\n",
+    "vector_store = ObjectBoxVectorStore(\n",
+    "    embedding_dim,\n",
+    "    distance_type=VectorDistanceType.COSINE,\n",
+    "    db_directory=\"obx_data\",\n",
+    "    clear_db=False,\n",
+    "    do_log=True,\n",
+    ")\n",
+    "\n",
+    "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+    "\n",
+    "Settings.llm = gemini_llm\n",
+    "Settings.embed_model = hf_embedding\n",
+    "\n",
+    "index = VectorStoreIndex(nodes=nodes, storage_context=storage_context)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7) Chat with the document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_engine = index.as_query_engine()\n",
+    "response = query_engine.query(\"Who is Paul Graham?\")\n",
+    "print(response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optional: Configuring `ObjectBoxVectorStore` as a retriever\n",
+    "\n",
+    "A LlamaIndex [retriever](https://docs.llamaindex.ai/en/stable/module_guides/querying/retriever/) is responsible for fetching similar chunks from a vector DB given a query.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = index.as_retriever()\n",
+    "response = retriever.retrieve(\"What did the author do growing up?\")\n",
+    "\n",
+    "for node in response:\n",
+    "    print(\"Retrieved chunk text:\\n\", node.node.get_text())\n",
+    "    print(\"Retrieved chunk metadata:\\n\", node.node.get_metadata_str())\n",
+    "    print(\"\\n\\n\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optional: Removing chunks associated with a single query using `delete_nodes`\n",
+    "\n",
+    "We can use the `ObjectBoxVectorStore.delete_nodes` method to remove chunks (nodes) from the vector DB providing a list containing node IDs as an argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = retriever.retrieve(\"What did the author do growing up?\")\n",
+    "\n",
+    "node_ids = []\n",
+    "for node in response:\n",
+    "    node_ids.append(node.node_id)\n",
+    "print(f\"Nodes to be removed: {node_ids}\")\n",
+    "\n",
+    "print(f\"No. of vectors before deletion: {vector_store.count()}\")\n",
+    "vector_store.delete_nodes(node_ids)\n",
+    "print(f\"No. of vectors after deletion: {vector_store.count()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optional: Removing a single document from the vector DB\n",
+    "\n",
+    "The `ObjectBoxVectorStore.delete` method can be used to remove chunks (nodes) associated with a single document whose `id_` is provided as an argument.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "document = documents[0]\n",
+    "print(f\"Document to be deleted {document.id_}\")\n",
+    "\n",
+    "print(f\"No. of vectors before deletion: {vector_store.count()}\")\n",
+    "vector_store.delete(document.id_)\n",
+    "print(f\"No. of vectors after document deletion: {vector_store.count()}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/.gitignore b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/.gitignore
new file mode 100644
index 0000000000000..c23ad90c75550
--- /dev/null
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/.gitignore
@@ -0,0 +1,157 @@
+llama_index/_static
+.DS_Store
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+bin/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+etc/
+include/
+lib/
+lib64/
+parts/
+sdist/
+share/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+.ruff_cache
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+notebooks/
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+pyvenv.cfg
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# Jetbrains
+.idea
+modules/
+*.swp
+
+# VsCode
+.vscode
+
+# pipenv
+Pipfile
+Pipfile.lock
+
+# pyright
+pyrightconfig.json
+
+# ObjectBox specific files
+llama_index/vector_stores/objectbox/objectbox-model.json
+objectbox
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/BUILD
new file mode 100644
index 0000000000000..0896ca890d8bf
--- /dev/null
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/BUILD
@@ -0,0 +1,3 @@
+poetry_requirements(
+    name="poetry",
+)
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/Makefile b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/Makefile
new file mode 100644
index 0000000000000..b9eab05aa3706
--- /dev/null
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/Makefile
@@ -0,0 +1,17 @@
+GIT_ROOT ?= $(shell git rev-parse --show-toplevel)
+
+help:	## Show all Makefile targets.
+	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}'
+
+format:	## Run code autoformatters (black).
+	pre-commit install
+	git ls-files | xargs pre-commit run black --files
+
+lint:	## Run linters: pre-commit (black, ruff, codespell) and mypy
+	pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files
+
+test:	## Run tests via pytest.
+	pytest tests
+
+watch-docs:	## Build and watch documentation.
+	sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/README.md b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/README.md
new file mode 100644
index 0000000000000..6b27928a27185
--- /dev/null
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/README.md
@@ -0,0 +1,126 @@
+# ObjectBox VectorStore For LlamaIndex
+
+### About
+
+This package contains the [ObjectBox](https://objectbox.io/) integrations for [LlamaIndex](https://www.llamaindex.ai/)
+
+### Getting Started
+
+Install the `llama-index-vector-stores-objectbox` package from PyPI via pip.
+
+```commandline
+pip install llama-index-vector-stores-objectbox
+```
+
+You can import the ObjectBox vector-store with `from llama_index.vector_stores.objectbox import ObjectBoxVectorStore` and start using it,
+
+```python
+from llama_index.vector_stores.objectbox import ObjectBoxVectorStore
+from objectbox import VectorDistanceType
+
+embedding_dim = 384  # size of the embeddings to be stored
+
+vector_store = ObjectBoxVectorStore(
+    embedding_dim,
+    distance_type=VectorDistanceType.COSINE,
+    db_directory="obx_data",
+    clear_db=False,
+    do_log=True,
+)
+```
+
+- `embedding_dim` (required): The dimensions of the embeddings that the vector DB will hold
+- `distance_type`: Choose from `COSINE`, `DOT_PRODUCT`, `DOT_PRODUCT_NON_NORMALIZED` and `EUCLIDEAN`
+- `db_directory`: The path of the directory where the `.mdb` ObjectBox database file should be created
+- `clear_db`: Deletes the existing database file if it exists on `db_directory`
+- `do_log`: Enables logging from the ObjectBox integration
+
+### A complete RAG example
+
+Along the `llama-index-vector-stores-objectbox`, install the following packages,
+
+```commandline
+pip install llama-index --quiet
+pip install llama-index-embeddings-huggingface --quiet
+pip install llama-index-llms-gemini --quiet
+```
+
+Download a sample text file,
+
+```commandline
+mkdir -p 'data/paul_graham/'
+wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'
+```
+
+This example will require a Gemini API key. You can get an API-key from the [Gemini developer console](https://aistudio.google.com/app/apikey). Execute the following Python script to generate an answer for `Who is Paul Graham?` from the text file,
+
+```python
+from llama_index.llms.gemini import Gemini
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.vector_stores.objectbox import ObjectBoxVectorStore
+from llama_index.core import StorageContext, VectorStoreIndex, Settings
+from llama_index.core import SimpleDirectoryReader
+from llama_index.core.node_parser import SentenceSplitter
+from objectbox import VectorDistanceType
+import getpass
+
+gemini_key_api = getpass.getpass("Gemini API Key: ")
+gemini_llm = Gemini(api_key=gemini_key_api)
+
+# Configure embedding model from HuggingFace
+hf_embedding = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5")
+embedding_dim = 384
+
+# Setup file reader and text splitter
+reader = SimpleDirectoryReader("./data/paul_graham")
+documents = reader.load_data()
+
+node_parser = SentenceSplitter(chunk_size=512, chunk_overlap=20)
+nodes = node_parser.get_nodes_from_documents(documents)
+
+# Configure ObjectBox as a vector-store
+vector_store = ObjectBoxVectorStore(
+    embedding_dim,
+    distance_type=VectorDistanceType.COSINE,
+    db_directory="obx_data",
+    clear_db=False,
+    do_log=True,
+)
+
+storage_context = StorageContext.from_defaults(vector_store=vector_store)
+
+Settings.llm = gemini_llm
+Settings.embed_model = hf_embedding
+
+index = VectorStoreIndex(nodes=nodes, storage_context=storage_context)
+
+query_engine = index.as_query_engine()
+response = query_engine.query("Who is Paul Graham?")
+print(response)
+```
+
+### License
+
+```text
+MIT License
+
+Copyright (c) 2024 ObjectBox, Ltd.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+```
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/llama_index/vector_stores/objectbox/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/llama_index/vector_stores/objectbox/BUILD
new file mode 100644
index 0000000000000..db46e8d6c978c
--- /dev/null
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/llama_index/vector_stores/objectbox/BUILD
@@ -0,0 +1 @@
+python_sources()
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/llama_index/vector_stores/objectbox/__init__.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/llama_index/vector_stores/objectbox/__init__.py
new file mode 100644
index 0000000000000..81958b99865c1
--- /dev/null
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/llama_index/vector_stores/objectbox/__init__.py
@@ -0,0 +1,3 @@
+from llama_index.vector_stores.objectbox.base import ObjectBoxVectorStore
+
+__all__ = ["ObjectBoxVectorStore"]
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/llama_index/vector_stores/objectbox/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/llama_index/vector_stores/objectbox/base.py
new file mode 100644
index 0000000000000..b94598f1d1b3b
--- /dev/null
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/llama_index/vector_stores/objectbox/base.py
@@ -0,0 +1,258 @@
+import logging
+import os
+import shutil
+import sys
+import time
+from typing import Any, List, Optional
+
+from llama_index.core.schema import BaseNode, MetadataMode, TextNode
+from llama_index.core.vector_stores import MetadataFilters
+from llama_index.core.vector_stores.types import (
+    BasePydanticVectorStore,
+    VectorStoreQuery,
+    VectorStoreQueryResult,
+)
+from objectbox import Store, Model, Box
+from objectbox.model.entity import Entity
+from objectbox.model.properties import (
+    VectorDistanceType,
+    HnswIndex,
+    Id,
+    Property,
+    PropertyType,
+    String,
+    Float32Vector,
+)
+from objectbox.query import Query
+from pydantic import PrivateAttr
+
+# from docs.prepare_for_build import file_name
+
+DIRECTORY = "objectbox"
+_logger = logging.getLogger(__name__)
+handler = logging.StreamHandler(stream=sys.stdout)
+_logger.addHandler(handler)
+_logger.setLevel(logging.INFO)
+
+
+class ObjectBoxVectorStore(BasePydanticVectorStore):
+    """ObjectBox vector store.
+
+    In this vector store, embeddings are stored within a ObjectBox `Box` (collection).
+
+    During query time, the index uses ObjectBox to query for the top-K most similar nodes.
+
+    Args:
+        embedding_dimensions (int): Number of elements in the embedding to be stored
+        distance_type (objectbox.model.properties.VectorDistanceType):
+            Distance metric to be used for vector search
+        db_directory (str): File path where ObjectBox database files will be stored
+        clear_db (bool): Whether to delete any existing database on `db_directory`
+        do_log (bool): enable/disable logging
+
+    Examples:
+        `pip install llama-index-vector-stores-objectbox`
+
+        ```python
+        from llama_index.vector_stores.objectbox import ObjectBoxVectorStore
+        from objectbox import VectorDistanceType
+
+        vector_store = ObjectBoxVectorStore(
+            embedding_dim,
+            distance_type=VectorDistanceType.COSINE,
+            db_directory="obx_data",
+            clear_db=False,
+            do_log=True
+        )
+        ```
+    """
+
+    stores_text: bool = True
+    embedding_dimensions: int
+    distance_type: VectorDistanceType = VectorDistanceType.EUCLIDEAN
+    db_directory: Optional[str] = None
+    clear_db: Optional[bool] = False
+    do_log: Optional[bool] = False
+
+    _store: Store = PrivateAttr()
+    _entity_class: Entity = PrivateAttr()
+    _box: Box = PrivateAttr()
+
+    def __init__(
+        self,
+        embedding_dimensions: int,
+        distance_type: VectorDistanceType = VectorDistanceType.EUCLIDEAN,
+        db_directory: Optional[str] = None,
+        clear_db: Optional[bool] = False,
+        do_log: Optional[bool] = False,
+        **data: Any,
+    ):
+        super().__init__(
+            embedding_dimensions=embedding_dimensions,
+            distance_type=distance_type,
+            db_directory=db_directory,
+            clear_db=clear_db,
+            do_log=do_log,
+            **data,
+        )
+        self._entity_class = self._create_entity_class()
+        self._store = self._create_box_store()
+
+        self._box = self._store.box(self._entity_class)
+
+    @property
+    def client(self) -> Any:
+        return self._box
+
+    def add(self, nodes: List[BaseNode], **kwargs: Any) -> List[str]:
+        ids: list[str] = []
+        start = time.perf_counter()
+        with self._store.write_tx():
+            for node in nodes:
+                if node.embedding is None:
+                    _logger.info("A node with no embedding was found ")
+                    continue
+                self._box.put(
+                    self._entity_class(
+                        node_id=node.node_id,
+                        doc_id=node.ref_doc_id if node.ref_doc_id is not None else "",
+                        text=node.get_content(metadata_mode=MetadataMode.NONE),
+                        metadata=node.metadata,
+                        embeddings=node.embedding,
+                    )
+                )
+                ids.append(node.node_id)
+            if self.do_log:
+                end = time.perf_counter()
+                _logger.info(
+                    f"ObjectBox stored {len(ids)} nodes in {end - start} seconds"
+                )
+            return ids
+
+    def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
+        self._box.query(self._entity_class.doc_id.equals(ref_doc_id)).build().remove()
+
+    def delete_nodes(
+        self,
+        node_ids: Optional[List[str]] = None,
+        filters: Optional[MetadataFilters] = None,
+        **delete_kwargs: Any,
+    ) -> None:
+        if filters is not None:
+            raise NotImplementedError(
+                "ObjectBox does not yet support delete_nodes() with metadata filters - contact us if you need this feature"
+            )
+        if node_ids is not None:
+            query_obj = self._box.query(
+                self._entity_class.node_id.equals("node_id").alias("node_id")
+            ).build()
+            for node_id in node_ids:
+                query_obj.set_parameter_alias_string("node_id", node_id)
+                query_obj.remove()
+
+    def get_nodes(
+        self,
+        node_ids: Optional[List[str]] = None,
+        filters: Optional[MetadataFilters] = None,
+    ) -> List[BaseNode]:
+        if filters is not None:
+            raise NotImplementedError(
+                "ObjectBox does not yet support get_nodes() with metadata filters - contact us if you need this feature"
+            )
+        if node_ids is not None:
+            retrieved_nodes: list[BaseNode] = []
+            with self._store.read_tx():
+                query_obj = self._box.query(
+                    self._entity_class.node_id.equals("node_id").alias("node_id")
+                ).build()
+                for node_id in node_ids:
+                    try:
+                        query_obj.set_parameter_alias_string("node_id", node_id)
+                        entities = query_obj.find()
+                        if len(entities) == 0:
+                            _logger.info(f"No entity with id = {node_id} was found")
+                            continue
+                        retrieved_nodes.append(
+                            TextNode(
+                                text=entities[0].text,
+                                id_=entities[0].node_id,
+                                metadata=entities[0].metadata,
+                            )
+                        )
+                    except ValueError:
+                        raise ValueError(f"Invalid node id: {node_id}")
+                return retrieved_nodes
+        else:
+            raise ValueError("node_ids cannot be None")
+
+    def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
+        if query.filters is not None:
+            raise NotImplementedError(
+                "ObjectBox does not yet support query() with metadata filters - contact us if you need this feature"
+            )
+
+        query_embedding = query.query_embedding
+        n_results = query.similarity_top_k
+
+        nodes: list[TextNode] = []
+        similarities: list[float] = []
+        ids: list[str] = []
+
+        start = time.perf_counter()
+        query: Query = self._box.query(
+            self._entity_class.embeddings.nearest_neighbor(query_embedding, n_results)
+        ).build()
+        results: list[tuple[Entity, float]] = query.find_with_scores()
+        end = time.perf_counter()
+
+        if self.do_log:
+            _logger.info(
+                f"ObjectBox retrieved {len(results)} vectors in {end - start} seconds"
+            )
+
+        for entity, score in results:
+            node = TextNode(
+                text=entity.text, id_=entity.node_id, metadata=entity.metadata
+            )
+            ids.append(entity.node_id)
+            nodes.append(node)
+            similarities.append(score)
+
+        return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)
+
+    def count(self) -> int:
+        return self._box.count()
+
+    def clear(self) -> None:
+        self._box.remove_all()
+
+    def close(self):
+        self._store.close()
+
+    def _create_entity_class(self) -> Entity:
+        """Dynamically define an Entity class according to the parameters."""
+
+        @Entity()
+        class VectorEntity:
+            id = Id()
+            node_id = String()
+            doc_id = String()
+            text = String()
+            metadata = Property(dict, type=PropertyType.flex)
+            embeddings = Float32Vector(
+                index=HnswIndex(
+                    dimensions=self.embedding_dimensions,
+                    distance_type=self.distance_type,
+                )
+            )
+
+        return VectorEntity
+
+    def _create_box_store(self) -> Store:
+        """Registering the VectorEntity model and setting up objectbox database."""
+        db_path = DIRECTORY if self.db_directory is None else self.db_directory
+        if self.clear_db and os.path.exists(db_path):
+            shutil.rmtree(db_path)
+        model = Model()
+        model.entity(self._entity_class)
+        return Store(model=model, directory=db_path)
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/pyproject.toml
new file mode 100644
index 0000000000000..8db318fa324f4
--- /dev/null
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/pyproject.toml
@@ -0,0 +1,67 @@
+[build-system]
+build-backend = "poetry.core.masonry.api"
+requires = ["poetry-core"]
+
+[tool.codespell]
+check-filenames = true
+check-hidden = true
+skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb"
+
+[tool.llamahub]
+contains_example = false
+import_path = "llama_index.vector_stores.objectbox"
+
+[tool.llamahub.class_authors]
+ObjectBoxVectorStore = "ObjectBox"
+
+[tool.mypy]
+disallow_untyped_defs = true
+exclude = ["_static", "build", "examples", "notebooks", "venv"]
+ignore_missing_imports = true
+python_version = "3.8"
+
+[tool.poetry]
+authors = ["ObjectBox"]
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "License :: OSI Approved :: MIT License",
+]
+description = "Integration package connecting ObjectBox and LlamaIndex"
+exclude = ["**/BUILD"]
+license = "MIT"
+name = "llama-index-vector-stores-objectbox"
+readme = "README.md"
+version = "0.1.0a"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+llama-index-core = "^0.11.0"
+objectbox = "^4.0.0"
+
+[tool.poetry.group.dev.dependencies]
+ipython = "8.10.0"
+jupyter = "^1.0.0"
+mypy = "0.991"
+pre-commit = "3.2.0"
+pylint = "2.15.10"
+pytest = "7.2.1"
+pytest-mock = "3.11.1"
+ruff = "0.0.292"
+tree-sitter-languages = "^1.8.0"
+types-Deprecated = ">=0.1.0"
+types-PyYAML = "^6.0.12.12"
+types-protobuf = "^4.24.0.4"
+types-redis = "4.5.5.0"
+types-requests = "2.28.11.8"
+types-setuptools = "67.1.0.0"
+
+[tool.poetry.group.dev.dependencies.black]
+extras = ["jupyter"]
+version = "<=23.9.1,>=23.7.0"
+
+[tool.poetry.group.dev.dependencies.codespell]
+extras = ["toml"]
+version = ">=v2.2.6"
+
+[[tool.poetry.packages]]
+include = "llama_index/"
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/tests/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/tests/BUILD
new file mode 100644
index 0000000000000..dabf212d7e716
--- /dev/null
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/tests/BUILD
@@ -0,0 +1 @@
+python_tests()
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/tests/__init__.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/tests/__init__.py
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/tests/test_objectbox.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/tests/test_objectbox.py
new file mode 100644
index 0000000000000..a5f9eb8e7282a
--- /dev/null
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/tests/test_objectbox.py
@@ -0,0 +1,109 @@
+import os
+import shutil
+from typing import Sequence
+
+import pytest
+from llama_index.core.schema import TextNode, BaseNode
+from llama_index.core.vector_stores import VectorStoreQuery
+
+from llama_index.vector_stores.objectbox import ObjectBoxVectorStore
+
+
+EMBEDDING_DIM = 3
+
+
+@pytest.fixture()
+def vectorstore():
+    obx = ObjectBoxVectorStore(embedding_dimensions=EMBEDDING_DIM)
+    db_default_path = "objectbox"
+    assert os.path.exists(
+        db_default_path
+    ), f"Directory '{db_default_path}' does not exist."
+    filepath = os.path.join(db_default_path, "data.mdb")
+    assert os.path.isfile(
+        filepath
+    ), f"File '{db_default_path}' not found in '{db_default_path}'"
+    return obx
+
+
+@pytest.fixture()
+def node_embeddings() -> Sequence[BaseNode]:
+    return [
+        TextNode(
+            id_="e8671c2d-8ee3-4f95-9730-7832f0115560",
+            text="test1",
+            embedding=[1.2, 0.3, -0.9],
+        ),
+        TextNode(
+            id_="d0db4ed6-da16-4769-bf19-d1c06267a5f6",
+            text="test2",
+            embedding=[0.1, 0.0, 0.0],
+        ),
+        TextNode(
+            id_="8601b27c-376e-48dd-a252-e61e01f29069",
+            text="test3",
+            embedding=[-2.3, 1.2, -6.7],
+        ),
+    ]
+
+
+def test_add(vectorstore: ObjectBoxVectorStore, node_embeddings: Sequence[BaseNode]):
+    node_ids = vectorstore.add(node_embeddings)
+    retrieved_nodes = vectorstore.get_nodes(node_ids)
+    assert len(retrieved_nodes) == len(node_embeddings)
+
+
+def test_query(vectorstore: ObjectBoxVectorStore, node_embeddings: Sequence[BaseNode]):
+    vectorstore.add(node_embeddings)
+    search_result = vectorstore.query(
+        VectorStoreQuery(query_embedding=[0.15, 0.001, -0.01], similarity_top_k=1)
+    )
+    assert len(search_result.ids) == 1
+    assert search_result.nodes[0].id_ == "d0db4ed6-da16-4769-bf19-d1c06267a5f6"
+
+
+def test_get_nodes(
+    vectorstore: ObjectBoxVectorStore, node_embeddings: Sequence[BaseNode]
+):
+    vectorstore.add(node_embeddings)
+    retrieved_nodes = vectorstore.get_nodes(
+        node_ids=["8601b27c-376e-48dd-a252-e61e01f29069"]
+    )
+    assert len(retrieved_nodes) == 1
+    assert retrieved_nodes[0].id_ == "8601b27c-376e-48dd-a252-e61e01f29069"
+
+
+def test_count(vectorstore: ObjectBoxVectorStore, node_embeddings: Sequence[BaseNode]):
+    vectorstore.add(node_embeddings)
+    assert vectorstore.count() == len(node_embeddings)
+
+
+def test_delete_nodes(
+    vectorstore: ObjectBoxVectorStore, node_embeddings: Sequence[BaseNode]
+):
+    node_ids = vectorstore.add(node_embeddings)
+    node_ids_to_be_deleted = node_ids[0:2]
+    vectorstore.delete_nodes(node_ids_to_be_deleted)
+    assert vectorstore.count() == 1
+
+
+def test_clear(vectorstore: ObjectBoxVectorStore, node_embeddings: Sequence[BaseNode]):
+    node_ids = vectorstore.add(node_embeddings)
+    vectorstore.clear()
+    retrieved_nodes = vectorstore.get_nodes(node_ids)
+    assert len(retrieved_nodes) == 0
+
+
+def remove_test_dir(test_dir: str):
+    if os.path.exists(test_dir):
+        shutil.rmtree(test_dir)
+
+
+@pytest.fixture(autouse=True)
+def auto_cleanup(vectorstore: ObjectBoxVectorStore):
+    yield  # run the test function
+    vectorstore.close()
+    os.remove(
+        "llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/llama_index/vector_stores/objectbox/objectbox-model.json"
+    )
+    remove_test_dir("objectbox")
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/tests/test_vector_stores_objectbox.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/tests/test_vector_stores_objectbox.py
new file mode 100644
index 0000000000000..1d60479ccb0aa
--- /dev/null
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-objectbox/tests/test_vector_stores_objectbox.py
@@ -0,0 +1,8 @@
+from llama_index.core.vector_stores.types import BasePydanticVectorStore
+from llama_index.vector_stores.objectbox import ObjectBoxVectorStore
+
+
+def test_class():
+    """Ensures that BasePydanticVectorStore is one of the parent classes of ObjectBoxVectorStore."""
+    names_of_base_classes = [b.__name__ for b in ObjectBoxVectorStore.__mro__]
+    assert BasePydanticVectorStore.__name__ in names_of_base_classes