diff --git a/docs/extras/use_cases/more/graph/graph_falkordb_qa.ipynb b/docs/extras/use_cases/more/graph/graph_falkordb_qa.ipynb
new file mode 100644
index 0000000000..c43e232975
--- /dev/null
+++ b/docs/extras/use_cases/more/graph/graph_falkordb_qa.ipynb
@@ -0,0 +1,154 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# FalkorDBQAChain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook shows how to use LLMs to provide a natural language interface to FalkorDB database.\n",
+    "\n",
+    "FalkorDB is a low latency property graph database management system. You can simply run its docker locally:\n",
+    "\n",
+    "```bash\n",
+    "docker run -p 6379:6379 -it --rm falkordb/falkordb:edge\n",
+    "```\n",
+    "\n",
+    "Once launched, you can simply start creating a database on the local machine and connect to it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.graphs import FalkorDBGraph\n",
+    "from langchain.chains import FalkorDBQAChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph = FalkorDBGraph(database=\"movies\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "graph.query(\n",
+    "    \"\"\"\n",
+    "MERGE (m:Movie {name:\"Top Gun\"})\n",
+    "WITH m\n",
+    "UNWIND [\"Tom Cruise\", \"Val Kilmer\", \"Anthony Edwards\", \"Meg Ryan\"] AS actor\n",
+    "MERGE (a:Actor {name:actor})\n",
+    "MERGE (a)-[:ACTED_IN]->(m)\n",
+    "\"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph.refresh_schema()\n",
+    "import os\n",
+    "os.environ['OPENAI_API_KEY']='API_KEY_HERE'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = FalkorDBQAChain.from_llm(\n",
+    "    ChatOpenAI(temperature=0), graph=graph, verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new FalkorDBQAChain chain...\u001b[0m\n",
+      "Generated Cypher:\n",
+      "\u001b[32;1m\u001b[1;3mMATCH (:Movie {title: 'Top Gun'})<-[:ACTED_IN]-(actor:Person)\n",
+      "RETURN actor.name AS output\u001b[0m\n",
+      "Full Context:\n",
+      "\u001b[32;1m\u001b[1;3m[]\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The actor who played in Top Gun is Tom Cruise.'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run(\"Who played in Top Gun?\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "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": "3.11.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/libs/langchain/langchain/chains/__init__.py b/libs/langchain/langchain/chains/__init__.py
index 8564d6851a..4bb5242729 100644
--- a/libs/langchain/langchain/chains/__init__.py
+++ b/libs/langchain/langchain/chains/__init__.py
@@ -36,6 +36,7 @@ from langchain.chains.flare.base import FlareChain
 from langchain.chains.graph_qa.arangodb import ArangoGraphQAChain
 from langchain.chains.graph_qa.base import GraphQAChain
 from langchain.chains.graph_qa.cypher import GraphCypherQAChain
+from langchain.chains.graph_qa.falkordb import FalkorDBQAChain
 from langchain.chains.graph_qa.hugegraph import HugeGraphQAChain
 from langchain.chains.graph_qa.kuzu import KuzuQAChain
 from langchain.chains.graph_qa.nebulagraph import NebulaGraphQAChain
@@ -85,6 +86,7 @@ __all__ = [
     "ConstitutionalChain",
     "ConversationChain",
     "ConversationalRetrievalChain",
+    "FalkorDBQAChain",
     "FlareChain",
     "GraphCypherQAChain",
     "GraphQAChain",
diff --git a/libs/langchain/langchain/chains/graph_qa/falkordb.py b/libs/langchain/langchain/chains/graph_qa/falkordb.py
new file mode 100644
index 0000000000..b5ad8da487
--- /dev/null
+++ b/libs/langchain/langchain/chains/graph_qa/falkordb.py
@@ -0,0 +1,141 @@
+"""Question answering over a graph."""
+from __future__ import annotations
+
+import re
+from typing import Any, Dict, List, Optional
+
+from langchain.base_language import BaseLanguageModel
+from langchain.callbacks.manager import CallbackManagerForChainRun
+from langchain.chains.base import Chain
+from langchain.chains.graph_qa.prompts import CYPHER_GENERATION_PROMPT, CYPHER_QA_PROMPT
+from langchain.chains.llm import LLMChain
+from langchain.graphs import FalkorDBGraph
+from langchain.pydantic_v1 import Field
+from langchain.schema import BasePromptTemplate
+
+INTERMEDIATE_STEPS_KEY = "intermediate_steps"
+
+
+def extract_cypher(text: str) -> str:
+    """
+    Extract Cypher code from a text.
+    Args:
+        text: Text to extract Cypher code from.
+
+    Returns:
+        Cypher code extracted from the text.
+    """
+    # The pattern to find Cypher code enclosed in triple backticks
+    pattern = r"```(.*?)```"
+
+    # Find all matches in the input text
+    matches = re.findall(pattern, text, re.DOTALL)
+
+    return matches[0] if matches else text
+
+
+class FalkorDBQAChain(Chain):
+    """Chain for question-answering against a graph by generating Cypher statements."""
+
+    graph: FalkorDBGraph = Field(exclude=True)
+    cypher_generation_chain: LLMChain
+    qa_chain: LLMChain
+    input_key: str = "query"  #: :meta private:
+    output_key: str = "result"  #: :meta private:
+    top_k: int = 10
+    """Number of results to return from the query"""
+    return_intermediate_steps: bool = False
+    """Whether or not to return the intermediate steps along with the final answer."""
+    return_direct: bool = False
+    """Whether or not to return the result of querying the graph directly."""
+
+    @property
+    def input_keys(self) -> List[str]:
+        """Return the input keys.
+
+        :meta private:
+        """
+        return [self.input_key]
+
+    @property
+    def output_keys(self) -> List[str]:
+        """Return the output keys.
+
+        :meta private:
+        """
+        _output_keys = [self.output_key]
+        return _output_keys
+
+    @property
+    def _chain_type(self) -> str:
+        return "graph_cypher_chain"
+
+    @classmethod
+    def from_llm(
+        cls,
+        llm: BaseLanguageModel,
+        *,
+        qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT,
+        cypher_prompt: BasePromptTemplate = CYPHER_GENERATION_PROMPT,
+        **kwargs: Any,
+    ) -> FalkorDBQAChain:
+        """Initialize from LLM."""
+        qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
+        cypher_generation_chain = LLMChain(llm=llm, prompt=cypher_prompt)
+
+        return cls(
+            qa_chain=qa_chain,
+            cypher_generation_chain=cypher_generation_chain,
+            **kwargs,
+        )
+
+    def _call(
+        self,
+        inputs: Dict[str, Any],
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
+        """Generate Cypher statement, use it to look up in db and answer question."""
+        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
+        callbacks = _run_manager.get_child()
+        question = inputs[self.input_key]
+
+        intermediate_steps: List = []
+
+        generated_cypher = self.cypher_generation_chain.run(
+            {"question": question, "schema": self.graph.schema}, callbacks=callbacks
+        )
+
+        # Extract Cypher code if it is wrapped in backticks
+        generated_cypher = extract_cypher(generated_cypher)
+
+        _run_manager.on_text("Generated Cypher:", end="\n", verbose=self.verbose)
+        _run_manager.on_text(
+            generated_cypher, color="green", end="\n", verbose=self.verbose
+        )
+
+        intermediate_steps.append({"query": generated_cypher})
+
+        # Retrieve and limit the number of results
+        context = self.graph.query(generated_cypher)[: self.top_k]
+
+        if self.return_direct:
+            final_result = context
+        else:
+            _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
+            _run_manager.on_text(
+                str(context), color="green", end="\n", verbose=self.verbose
+            )
+
+            intermediate_steps.append({"context": context})
+
+            result = self.qa_chain(
+                {"question": question, "context": context},
+                callbacks=callbacks,
+            )
+            final_result = result[self.qa_chain.output_key]
+
+        chain_result: Dict[str, Any] = {self.output_key: final_result}
+        if self.return_intermediate_steps:
+            chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps
+
+        return chain_result
diff --git a/libs/langchain/langchain/graphs/__init__.py b/libs/langchain/langchain/graphs/__init__.py
index 9699750db2..b2a43168c9 100644
--- a/libs/langchain/langchain/graphs/__init__.py
+++ b/libs/langchain/langchain/graphs/__init__.py
@@ -1,6 +1,7 @@
 """**Graphs** provide a natural language interface to graph databases."""
 
 from langchain.graphs.arangodb_graph import ArangoGraph
+from langchain.graphs.falkordb_graph import FalkorDBGraph
 from langchain.graphs.hugegraph import HugeGraph
 from langchain.graphs.kuzu_graph import KuzuGraph
 from langchain.graphs.memgraph_graph import MemgraphGraph
@@ -20,4 +21,5 @@ __all__ = [
     "HugeGraph",
     "RdfGraph",
     "ArangoGraph",
+    "FalkorDBGraph",
 ]
diff --git a/libs/langchain/langchain/graphs/falkordb_graph.py b/libs/langchain/langchain/graphs/falkordb_graph.py
new file mode 100644
index 0000000000..189189f641
--- /dev/null
+++ b/libs/langchain/langchain/graphs/falkordb_graph.py
@@ -0,0 +1,67 @@
+from typing import Any, Dict, List
+
+node_properties_query = """
+MATCH (n)
+UNWIND labels(n) as l
+UNWIND keys(n) as p
+RETURN {label:l, properties: collect(distinct p)} AS output
+"""
+
+rel_properties_query = """
+MATCH ()-[r]->()
+UNWIND keys(r) as p
+RETURN {type:type(r), properties: collect(distinct p)} AS output
+"""
+
+rel_query = """
+MATCH (n)-[r]->(m)
+WITH labels(n)[0] AS src, labels(m)[0] AS dst, type(r) AS type
+RETURN DISTINCT "(:" + src + ")-[:" + type + "]->(:" + dst + ")" AS output
+"""
+
+
+class FalkorDBGraph:
+    """FalkorDB wrapper for graph operations."""
+
+    def __init__(
+        self, database: str, host: str = "localhost", port: int = 6379
+    ) -> None:
+        """Create a new FalkorDB graph wrapper instance."""
+        try:
+            import redis
+            from redis.commands.graph import Graph
+        except ImportError:
+            raise ImportError(
+                "Could not import redis python package. "
+                "Please install it with `pip install redis`."
+            )
+
+        self._driver = redis.Redis(host=host, port=port)
+        self._graph = Graph(self._driver, database)
+
+        try:
+            self.refresh_schema()
+        except Exception as e:
+            raise ValueError(f"Could not refresh schema. Error: {e}")
+
+    @property
+    def get_schema(self) -> str:
+        """Returns the schema of the FalkorDB database"""
+        return self.schema
+
+    def refresh_schema(self) -> None:
+        """Refreshes the schema of the FalkorDB database"""
+        self.schema = (
+            f"Node properties: {node_properties_query}\n"
+            f"Relationships properties: {rel_properties_query}\n"
+            f"Relationships: {rel_query}\n"
+        )
+
+    def query(self, query: str, params: dict = {}) -> List[Dict[str, Any]]:
+        """Query FalkorDB database."""
+
+        try:
+            data = self._graph.query(query, params)
+            return data.result_set
+        except Exception as e:
+            raise ValueError("Generated Cypher Statement is not valid\n" f"{e}")
diff --git a/libs/langchain/tests/integration_tests/graphs/test_falkordb.py b/libs/langchain/tests/integration_tests/graphs/test_falkordb.py
new file mode 100644
index 0000000000..de6c77a493
--- /dev/null
+++ b/libs/langchain/tests/integration_tests/graphs/test_falkordb.py
@@ -0,0 +1,34 @@
+import unittest
+from typing import Any
+from unittest.mock import MagicMock, patch
+
+from langchain.graphs import FalkorDBGraph
+
+
+class TestFalkorDB(unittest.TestCase):
+    def setUp(self) -> None:
+        self.host = "localhost"
+        self.graph = "test_falkordb"
+        self.port = 6379
+
+    @patch("redis.Redis")
+    def test_init(self, mock_client: Any) -> None:
+        mock_client.return_value = MagicMock()
+        FalkorDBGraph(database=self.graph, host=self.host, port=self.port)
+
+    @patch("redis.Redis")
+    def test_execute(self, mock_client: Any) -> None:
+        mock_client.return_value = MagicMock()
+        graph = FalkorDBGraph(database=self.graph, host=self.host, port=self.port)
+
+        query = "RETURN 1"
+        result = graph.query(query)
+        self.assertIsInstance(result, MagicMock)
+
+    @patch("redis.Redis")
+    def test_refresh_schema(self, mock_client: Any) -> None:
+        mock_client.return_value = MagicMock()
+        graph = FalkorDBGraph(database=self.graph, host=self.host, port=self.port)
+
+        graph.refresh_schema()
+        self.assertNotEqual(graph.get_schema, "")