Add Mem0 integration page (#458)

* Add Mem0 integration page

Closes #445

Add an integration page for Mem0, the universal memory layer for
AI agents. The Mem0MemoryStore component (part of haystack-experimental)
enables Haystack agents to store and retrieve persistent, user-specific
memories across sessions.

This PR adds:
- integrations/mem0.md with overview, installation, and usage examples
- logos/mem0.png

* Update integrations/mem0.md

---------

Co-authored-by: Bilge Yücel <bilge.yucel@deepset.ai>

Author: ritikraj2425

integrations/mem0.md

@@ -0,0 +1,152 @@
+---
+layout: integration
+name: Mem0
+description: Add persistent, user-specific memory to your Haystack agents and pipelines with Mem0
+authors:
+    - name: deepset
+      socials:
+        github: deepset-ai
+        twitter: haystack_ai
+        linkedin: https://www.linkedin.com/company/deepset-ai/
+    - name: Mem0
+      socials:
+        github: mem0ai
+        twitter: mem0ai
+        linkedin: https://www.linkedin.com/company/mem0ai/
+pypi: https://pypi.org/project/haystack-experimental/
+repo: https://github.com/deepset-ai/haystack-experimental
+type: Memory Store
+report_issue: https://github.com/deepset-ai/haystack-experimental/issues
+logo: /logos/mem0.png
+version: Haystack 2.0
+toc: true
+---
+
+### Table of Contents
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+  - [Standalone Memory Operations](#standalone-memory-operations)
+  - [Using Mem0 with a Haystack Agent](#using-mem0-with-a-haystack-agent)
+- [License](#license)
+
+## Overview
+
+[Mem0](https://mem0.ai) (pronounced "mem-zero") provides a universal memory layer for AI agents and assistants. It enables your Haystack applications to remember user preferences, adapt to individual needs, and continuously learn from past interactions — making AI conversations truly personalized.
+
+This integration is part of the [`haystack-experimental`](https://github.com/deepset-ai/haystack-experimental) package and provides the `Mem0MemoryStore`, which acts as a persistent memory backend for [Haystack Agents](https://docs.haystack.deepset.ai/docs/agent). Instead of relying solely on conversation history or static document stores, agents can use Mem0 to store and retrieve user-specific memories across sessions.
+
+### Key Features
+
+- **Persistent Memory**: Store and retrieve user-specific memories that persist across sessions
+- **Multi-Level Scoping**: Organize memories by User, Session, or Agent scope
+- **Intelligent Extraction**: Automatically extracts relevant facts from conversations — no need to store entire transcripts
+- **Seamless Agent Integration**: Works natively with Haystack's Agent component for context-aware responses
+- **Flexible Deployment**: Use the managed [Mem0 Platform](https://app.mem0.ai) or self-host with your own infrastructure
+
+More info about Mem0:
+
+- [Mem0 Website](https://mem0.ai)
+- [Mem0 Documentation](https://docs.mem0.ai)
+- [Mem0 Platform (Managed)](https://app.mem0.ai)
+- [Mem0 GitHub](https://github.com/mem0ai/mem0)
+
+## Installation
+
+```bash
+pip install haystack-ai haystack-experimental mem0ai
+```
+
+### Environment Variables
+
+Set the following environment variable to use the Mem0 Platform:
+
+```bash
+export MEM0_API_KEY="your-mem0-api-key"
+```
+
+You can obtain an API key by signing up at [app.mem0.ai](https://app.mem0.ai).
+
+## Usage
+
+### Components
+
+This integration introduces one component:
+
+- [`Mem0MemoryStore`](https://docs.haystack.deepset.ai/reference/experimental-mem0-memory-store-api#mem0memorystore): A memory store that uses Mem0 as the backend for storing and retrieving user-specific memories. It can be used standalone or plugged into a Haystack Agent.
+
+### Standalone Memory Operations
+
+You can use `Mem0MemoryStore` directly to add and search memories:
+
+```python
+import os
+from haystack.dataclasses import ChatMessage
+from haystack_experimental.memory_stores.mem0 import Mem0MemoryStore
+
+os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+
+# Initialize the memory store
+memory = Mem0MemoryStore()
+
+# Add memories from a conversation
+messages = [
+    ChatMessage.from_user("I'm a vegetarian and I love Italian food."),
+]
+memory.add_memories(messages=messages, user_id="alice")
+
+# Later, retrieve relevant memories
+query = "What kind of food do I like?"
+memories = memory.search_memories(query=query, user_id="alice")
+
+print(memories)
+# Returns memories related to Alice's food preferences
+```
+
+### Using Mem0 with a Haystack Agent
+
+The primary use case for `Mem0MemoryStore` is to provide personalized context to a Haystack Agent. The agent automatically retrieves relevant memories before generating a response, enabling context-aware conversations:
+
+```python
+import os
+from haystack.components.generators.chat import OpenAIChatGenerator
+from haystack.dataclasses import ChatMessage
+from haystack_experimental.components.agents import Agent
+from haystack_experimental.memory_stores.mem0 import Mem0MemoryStore
+
+os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
+
+# Initialize components
+memory_store = Mem0MemoryStore()
+generator = OpenAIChatGenerator(model="gpt-4o")
+
+# Create an agent with memory
+agent = Agent(
+    generator=generator,
+    memory_store=memory_store,
+    system_prompt="You are a helpful assistant that remembers user preferences.",
+)
+
+# First interaction — the agent learns about the user
+response = agent.run(
+    query="I prefer dark mode and use vim keybindings.",
+    memory_store_kwargs={"user_id": "user_123"},
+)
+print(response["replies"][0].content)
+
+# Later interaction — the agent recalls the user's preferences
+response = agent.run(
+    query="Can you recommend an IDE setup for me?",
+    memory_store_kwargs={"user_id": "user_123"},
+)
+print(response["replies"][0].content)
+# The agent will reference the user's preference for dark mode and vim keybindings
+```
+
+> **Note:** The `Mem0MemoryStore` is currently part of the `haystack-experimental` package and is labeled as **experimental**. The API may change in future releases. Always refer to the [haystack-experimental repository](https://github.com/deepset-ai/haystack-experimental) for the latest updates.
+
+### License
+
+`haystack-experimental` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.