Updated docs for agent_id and run_id (#3294)

docs/open-source/features/async-memory.mdx

@@ -68,10 +68,15 @@ await memory.search(
 
 #### List memories
 
-List all memories for a `user_id`, `agent_id`, or `run_id`:
+List all memories for a `user_id`, `agent_id`, and/or `run_id`:
 
 ```python Python
 await memory.get_all(user_id="alice")
+
+# Get memories with agent and run context
+await memory.get_all(user_id="alice", agent_id="assistant")
+await memory.get_all(user_id="alice", run_id="session-001")
+await memory.get_all(user_id="alice", agent_id="assistant", run_id="session-001")
 ```
 
 #### Get specific memory
@@ -111,6 +116,35 @@ await memory.delete_all(user_id="alice")
 
 Note: At least one filter (user_id, agent_id, or run_id) is required when using delete_all.
 
+### Advanced Memory Organization
+
+AsyncMemory supports the same three-parameter organization system as the synchronous Memory class:
+
+```python Python
+# Store memories with full context
+await memory.add(
+    messages=[{"role": "user", "content": "I prefer vegetarian food"}],
+    user_id="alice",
+    agent_id="diet-assistant",
+    run_id="consultation-001"
+)
+
+# Retrieve memories with different scopes
+all_user_memories = await memory.get_all(user_id="alice")
+agent_memories = await memory.get_all(user_id="alice", agent_id="diet-assistant")
+session_memories = await memory.get_all(user_id="alice", run_id="consultation-001")
+specific_memories = await memory.get_all(
+    user_id="alice", 
+    agent_id="diet-assistant", 
+    run_id="consultation-001"
+)
+
+# Search with context
+general_search = await memory.search("What do you know about me?", user_id="alice")
+agent_search = await memory.search("What do you know about me?", user_id="alice", agent_id="diet-assistant")
+session_search = await memory.search("What do you know about me?", user_id="alice", run_id="consultation-001")
+```
+
 #### Memory History
 
 Get the history of changes for a specific memory:

docs/open-source/features/overview.mdx

@@ -0,0 +1,57 @@
+---
+title: Overview
+description: 'Build powerful AI applications with self-improving memory using Mem0 open-source'
+icon: "eye"
+iconType: "solid"
+---
+
+## Welcome to Mem0 Open Source
+
+Mem0 is a self-improving memory layer for LLM applications that enables personalized AI experiences while saving costs and delighting users. The open-source version gives you complete control over your memory infrastructure.
+
+## Why Choose Mem0 Open Source?
+
+Mem0 open-source provides a powerful, flexible foundation for AI memory management with these key advantages:
+
+1. **Complete Control**: Deploy and manage your own memory infrastructure with full customization capabilities. Perfect for organizations that need data sovereignty and custom integrations.
+
+2. **Flexible Architecture**: Choose from multiple vector databases (Pinecone, Qdrant, Weaviate, Chroma, PGVector), graph stores (Neo4j, Memgraph), and embedding models to fit your specific needs.
+
+3. **Advanced Memory Organization**: Organize memories using `user_id`, `agent_id`, and `run_id` parameters for sophisticated multi-agent, multi-session applications with precise context control.
+
+4. **Rich Integration Ecosystem**: Seamlessly integrate with popular frameworks like LangChain, LlamaIndex, AutoGen, CrewAI, and Vercel AI SDK.
+
+## Core Features
+
+### Memory Management
+- **Synchronous & Asynchronous Operations**: Choose between sync and async memory operations based on your application needs
+- **Smart Memory Retrieval**: Intelligent search and retrieval with semantic understanding
+- **Memory Persistence**: Long-term storage with automatic optimization and cleanup
+
+### Advanced Organization
+- **User Context**: Organize memories by user for personalized experiences
+- **Agent Isolation**: Separate memories by AI agent for specialized knowledge domains
+- **Session Tracking**: Use run IDs to maintain context across different conversation sessions
+
+### Flexible Storage
+- **Vector Databases**: Support for Pinecone, Qdrant, Weaviate, Chroma, and PGVector
+- **Graph Stores**: Neo4j and Memgraph integration for relationship-based memory
+- **Embedding Models**: Multiple embedding providers for optimal performance
+
+## Getting Started
+
+Choose your preferred approach:
+
+- **[Python Quickstart](../python-quickstart)**: Get started with Python SDK
+- **[Node.js Quickstart](../node-quickstart)**: Use Mem0 with Node.js/TypeScript
+- **[Examples](../examples)**: Explore real-world use cases and implementations
+
+## Next Steps
+
+- Explore [specific features](./async-memory) in detail
+- Learn about [graph memory](../graph_memory/overview) capabilities
+- Set up [vector databases](../components/vectordbs/overview) and [LLM integrations](../components/llms/overview)
+- Check out our [examples](../examples) for practical implementations
+- Join our [Discord community](https://mem0.dev/DiD) for support
+
+We're excited to see what you'll build with Mem0 open-source. Let's create smarter, more personalized AI experiences together!