diff --git a/docs/docs/integrations/providers/tensorlake.mdx b/docs/docs/integrations/providers/tensorlake.mdx
new file mode 100644
index 00000000000..9690dddaa7e
--- /dev/null
+++ b/docs/docs/integrations/providers/tensorlake.mdx
@@ -0,0 +1,96 @@
+# Tensorlake
+
+Tensorlake is the AI Data Cloud that reliably transforms data from unstructured sources into ingestion-ready formats for AI Applications. 
+
+The `langchain-tensorlake` package provides seamless integration between [Tensorlake](https://tensorlake.ai) and [LangChain](https://langchain.com), 
+enabling you to build sophisticated document processing agents with enhanced parsing features, like signature detection.
+
+## Tensorlake feature overview
+
+Tensorlake gives you tools to:
+- Extract: Schema-driven structured data extraction to pull out specific fields from documents.
+- Parse: Convert documents to markdown to build RAG/Knowledge Graph systems. 
+- Orchestrate: Build programmable workflows for large-scale ingestion and enrichment of Documents, Text, Audio, Video and more.
+
+Learn more at [docs.tensorlake.ai](https://docs.tensorlake.ai/introduction)
+
+---
+
+## Installation
+
+```bash
+pip install -U langchain-tensorlake
+```
+
+---
+
+## Examples 
+
+Follow a [full tutorial](https://docs.tensorlake.ai/examples/tutorials/real-estate-agent-with-langgraph-cli) on how to detect signatures in unstructured documents using the `langchain-tensorlake` tool.
+
+Or check out this [colab notebook](https://colab.research.google.com/drive/1VRWIPCWYnjcRtQL864Bqm9CJ6g4EpRqs?usp=sharing) for a quick start.
+
+--- 
+## Quick Start
+
+### 1. Set up your environment
+
+You should configure credentials for Tensorlake and OpenAI by setting the following environment variables:
+```
+export TENSORLAKE_API_KEY="your-tensorlake-api-key"
+export OPENAI_API_KEY = "your-openai-api-key"
+```
+
+Get your Tensorlake API key from the [Tensorlake Cloud Console](https://cloud.tensorlake.ai/). New users get 100 free credits.
+
+### 2. Import necessary packages
+
+```python
+from langchain_tensorlake import document_markdown_tool
+from langgraph.prebuilt import create_react_agent
+import asyncio
+import os
+```
+
+### 3. Build a Signature Detection Agent
+
+```python
+async def main(question):
+    # Create the agent with the Tensorlake tool
+    agent = create_react_agent(
+            model="openai:gpt-4o-mini",
+            tools=[document_markdown_tool],
+            prompt=(
+                """
+                I have a document that needs to be parsed. \n\nPlease parse this document and answer the question about it.
+                """
+            ),
+            name="real-estate-agent",
+        )
+    
+    # Run the agent
+    result = await agent.ainvoke({"messages": [{"role": "user", "content": question}]})
+
+    # Print the result
+    print(result["messages"][-1].content)
+```
+
+*Note:* We highly recommend using `openai` as the agent model to ensure the agent sets the right parsing parameters
+
+### 4. Example Usage
+
+```python
+# Define the path to the document to be parsed
+path = "path/to/your/document.pdf"
+
+# Define the question to be asked and create the agent
+question = f"What contextual information can you extract about the signatures in my document found at {path}?"
+
+if __name__ == "__main__":
+    asyncio.run(main(question))
+```
+
+## Need help?
+
+Reach out to us on [Slack](https://join.slack.com/t/tensorlakecloud/shared_invite/zt-32fq4nmib-gO0OM5RIar3zLOBm~ZGqKg) or on the 
+[package repository on GitHub](https://github.com/tensorlakeai/langchain-tensorlake) directly.
\ No newline at end of file
diff --git a/libs/packages.yml b/libs/packages.yml
index 80a69051116..163a8407421 100644
--- a/libs/packages.yml
+++ b/libs/packages.yml
@@ -692,4 +692,7 @@ packages:
   provider_page: ibm
 - name: langchain-greennode
   path: libs/greennode
-  repo: greennode-ai/langchain-greennode
\ No newline at end of file
+  repo: greennode-ai/langchain-greennode
+- name: langchain-tensorlake
+  path: .
+  repo: tensorlakeai/langchain-tensorlake