docs: Mem0 Integration (#1216)

Co-authored-by: Anush  <[email protected]>

qdrant-landing/content/documentation/frameworks/_index.md

@@ -19,6 +19,7 @@ weight: 20
 | [Langchain-Go](/documentation/frameworks/langchain-go/)       | Go framework for building context-aware, reasoning applications using LLMs.                          |
 | [Langchain4j](/documentation/frameworks/langchain4j/)         | Java framework for building context-aware, reasoning applications using LLMs.                        |
 | [LlamaIndex](/documentation/frameworks/llama-index/)          | A data framework for building LLM applications with modular integrations.                            |
+| [Mem0](/documentation/frameworks/mem0/)                       | Self-improving memory layer for LLM applications, enabling personalized AI experiences.              |
 | [MemGPT](/documentation/frameworks/memgpt/)                   | System to build LLM agents with long term memory & custom tools                                      |
 | [Pandas-AI](/documentation/frameworks/pandas-ai/)             | Python library to query/visualize your data (CSV, XLSX, PostgreSQL, etc.) in natural language        |
 | [Semantic Router](/documentation/frameworks/semantic-router/) | Python library to build a decision-making layer for AI applications using vector search.             |

qdrant-landing/content/documentation/frameworks/mem0.md

@@ -0,0 +1,66 @@
+---
+title: Mem0
+---
+
+![Mem0 Logo](/documentation/frameworks/mem0/mem0-banner.png)
+
+[Mem0](https://mem0.ai) is a self-improving memory layer for LLM applications, enabling personalized AI experiences that save costs and delight users. Mem0 remembers user preferences, adapts to individual needs, and continuously improves over time, ideal for chatbots and AI systems.
+
+Mem0 supports various vector store providers, including Qdrant, for efficient data handling and search capabilities.
+
+## Installation
+
+To install Mem0 with Qdrant support, use the following command:
+
+```sh
+pip install mem0ai
+```
+
+## Usage
+
+Here's a basic example of how to use Mem0 with Qdrant:
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "qdrant",
+        "config": {
+            "collection_name": "test",
+            "host": "localhost",
+            "port": 6333,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+## Configuration
+
+When configuring Mem0 to use Qdrant as the vector store, you can specify [various parameters](https://docs.mem0.ai/components/vectordbs/dbs/qdrant#config) in the `config` dictionary.
+
+## Advanced Usage
+
+Mem0 provides additional functionality for managing and querying your vector data. Here are some examples:
+
+```python
+# Search memories
+related_memories = m.search(query="What are Alice's hobbies?", user_id="alice")
+
+# Update existing memory
+result = m.update(memory_id="m1", data="Likes to play tennis on weekends")
+
+# Get memory history
+history = m.history(memory_id="m1")
+```
+
+## Further Reading
+
+- [Mem0 GitHub Repository](https://github.com/mem0ai/mem0)
+- [Mem0 Documentation](https://docs.mem0.ai/).