> ## Documentation Index > Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt > Use this file to discover all available pages before exploring further. # Migrate from Open Source to Platform > Migrate your Mem0 Open Source implementation to Mem0 Platform for managed infrastructure and advanced features. ## Overview | Scope | Effort | Downtime | | --------------------- | --------------- | ---------------------------- | | Infrastructure & Code | Low (\~30 mins) | None (Parallel run possible) | Using Mem0 Open Source with **hosted Qdrant**? You can migrate your existing memories to Mem0 Platform with a one-line script below. **Why migrate to Platform?** * **Time to Market**: Set up in 5 minutes vs 30+ minutes for OSS configuration * **Enterprise Ready**: Audit logs, workspace governance, and dedicated support * **Advanced Features**: Webhooks, memory export, analytics dashboard, custom categories * **Multi-tenancy**: Organizations, projects, and team management out of the box * **Zero Infrastructure**: No vector database, LLM provider, or maintenance overhead * **Enhanced Search**: Reranking, keyword expansion, and advanced filters * **Production Grade**: Auto-scaling, high availability, dedicated support ### Plan 1. **Sign up**: Create an account on Mem0 Platform. 2. **Get API Key**: Navigate to **Settings > API Keys** and generate a new key. 3. **Review Usage**: Identify where you instantiate `Memory` and where you call `search` or `get_all`. ## Migrate with Agent Skill Paste this prompt into your coding agent. It uses a migration skill to produce a plan; once you review and approve it, the agent implements the changes. ```text theme={null} Migrate my project from Mem0 OSS to the Mem0 Platform SDK using the mem0-oss-to-platform skill in the mem0ai/mem0 repo, at skills/mem0-oss-to-platform/ Get the skill whichever way is easiest: - install it: npx skills add https://github.com/mem0ai/mem0 --skill mem0-oss-to-platform - if the mem0 repo is cloned locally, read it from skills/mem0-oss-to-platform/ - otherwise fetch that folder from github.com/mem0ai/mem0 (SKILL.md + references/) Then read SKILL.md and begin the migration. ``` ## Migrate ### 1. Import Memories Into Platform If your Mem0 Open Source setup uses **hosted Qdrant** as the vector store, you can import your existing memories to Mem0 Platform with one command: ```bash theme={null} curl -fsSL https://raw.githubusercontent.com/mem0ai/mem0/main/scripts/oss-to-platform-migrate.sh | bash ``` This migration script currently supports **hosted Qdrant only**. Support for local Qdrant, pgvector, and other vector stores is coming soon. If you are using a different vector store and want to migrate to Platform, please contact Mem0 support and we’ll send you a custom migration script for your setup. ### 2. Install or Update SDK Ensure you have the latest version of the SDK, which supports both OSS and Platform clients. ```bash theme={null} pip install mem0ai --upgrade ``` ### 3. Update Initialization Switch from the local `Memory` class to the managed `MemoryClient`. ```python Open Source (Old) theme={null} from mem0 import Memory config = { "vector_store": { "provider": "qdrant", "config": {"host": "localhost", "port": 6333} }, "llm": { "provider": "openai", "config": {"model": "gpt-4"} } } m = Memory.from_config(config) ``` ```python Platform (New) theme={null} from mem0 import MemoryClient import os # Set MEM0_API_KEY in environment or pass explicitly client = MemoryClient(api_key="m0-...") ``` Run `client.get_all(filters={"user_id": "test_connection"})` to verify your API key works. It should return an empty list or valid results. ### 4. Update Retrieval Calls (Critical) **Critical Change**: Platform uses v2 endpoints that require filtering parameters to be nested inside a `filters` dictionary. The `limit` parameter has been removed in favor of `top_k` across all SDKs. Update any code using `limit=` to use `top_k=` instead. | Method | Open Source | Platform | | -------------- | --------------------------------- | --------------------------------------------------- | | `search()` | `m.search(query, user_id="alex")` | `client.search(query, filters={"user_id": "alex"})` | | `get_all()` | `m.get_all(user_id="alex")` | `client.get_all(filters={"user_id": "alex"})` | | `add()` | `m.add(memory, user_id="alex")` | `client.add(memory, user_id="alex")` | | `delete()` | `m.delete(memory_id)` | `client.delete(memory_id)` | | `delete_all()` | `m.delete_all(user_id="alex")` | `client.delete_all(user_id="alex")` | Note: `add()` and `delete()` methods remain unchanged. The `update()` method is not available in Platform - use delete + add pattern instead. ```python Open Source (Old) theme={null} # Basic search with user filter results = m.search("user's preferences", user_id="alex") # Search with multiple filters results = m.search("meeting notes", user_id="alex", agent_id="assistant") ``` ```python Platform (New) theme={null} # Basic search with user filter in filters dict results = client.search("user's preferences", filters={"user_id": "alex"}) # Search with multiple filters results = client.search("meeting notes", filters={ "AND": [ {"user_id": "alex"}, {"agent_id": "assistant"} ] }) ``` ```python Open Source (Old) theme={null} # Get all memories for a user memories = m.get_all(user_id="alex", top_k=10) # Get memories with pagination memories = m.get_all(user_id="alex", top_k=5, offset=10) ``` ```python Platform (New) theme={null} # Get all memories for a user memories = client.get_all(filters={"user_id": "alex"}, top_k=10) # Get memories with pagination memories = client.get_all(filters={"user_id": "alex"}, top_k=5, offset=10) ``` ```python Open Source (Old) theme={null} # Add a simple memory m.add("Loves coffee", user_id="alex") # Add memory with metadata m.add("Completed marathon", user_id="alex", metadata={"category": "achievement"}) ``` ```python Platform (New) theme={null} # Add a simple memory (no change) client.add("Loves coffee", user_id="alex") # Add memory with metadata (no change) client.add("Completed marathon", user_id="alex", metadata={"category": "achievement"}) ``` ```python Open Source (Old) theme={null} # Delete specific memory m.delete(memory_id="mem_123") # Delete all memories for user m.delete_all(user_id="alex") ``` ```python Platform (New) theme={null} # Delete specific memory (no change) client.delete(memory_id="mem_123") # Delete all memories for user (no change) client.delete_all(user_id="alex") ``` ```python Open Source (Old) theme={null} # Update memory content m.update(memory_id="mem_123", new_memory="Updated content") ``` ```python Platform (New) theme={null} # Update memory (not available in Platform) # Use delete + add pattern instead client.delete(memory_id="mem_123") client.add("Updated content", user_id="alex") ``` ## Platform-Exclusive Features The Platform introduces powerful capabilities not available in OSS: **Why it matters**: Manage multiple teams and projects with hierarchical access control. ```python theme={null} # Create an organization org = client.organizations.create(name="Acme Corp") # Create projects within the organization project = client.projects.create( name="Customer Support Bot", org_id=org.id ) # Add team members client.organizations.add_member( org_id=org.id, email="team@acme.com", role="admin" ) ``` **Why it matters**: Instantly react to memory changes in your application. Build features like notifications, audit logs, or sync with external systems. ```python theme={null} # Create webhook for memory events webhook = client.webhooks.create( project_id="proj_123", name="Memory Events", url="https://your-app.com/webhooks/mem0", events=["memory_add", "memory_delete"] ) # Webhook payload example: # { # "event": "memory_add", # "memory_id": "mem_456", # "user_id": "user_789", # "memory": "User prefers dark mode", # "timestamp": "2024-01-15T10:30:00Z" # } ``` **Why it matters**: Export your data for compliance, analytics, or migration with custom schemas and filters. ```python theme={null} # Export memories with custom schema export_job = client.memories.export( filters={ "AND": [ {"user_id": "user_123"}, {"created_at": {"gte": "2024-01-01"}} ] }, output_format="json", schema={ "memory": str, "categories": list[str], "timestamp": str } ) # Download when ready if client.memories.get_export(export_job.id).status == "completed": data = client.memories.download_export(export_job.id) ``` **Why it matters**: Get better search results with AI-powered reranking and keyword expansion. ```python theme={null} # Search with reranking for better results results = client.search( "user preferences", filters={"user_id": "alex"}, rerank=True, # Platform exclusive top_k=5 ) # Search with keyword expansion results = client.search( "coffee order", filters={"user_id": "alex"}, keywords=["latte", "espresso", "cappuccino"], expand_keywords=True ) ``` **Why it matters**: Use domain-specific categories instead of generic ones for better organization. ```python theme={null} # Set custom categories for your project client.projects.update_categories( project_id="proj_123", categories=[ "Customer Preferences", "Product Feedback", "Support Issues", "Feature Requests" ] ) # Memories will use these categories client.add( "User wants dark mode in dashboard", user_id="alex", categories=["Customer Preferences"] ) ``` **Why it matters**: Track all memory operations for audit trails, usage analytics, and debugging. ```python theme={null} # Get audit trail of all memory operations events = client.events.list( filters={ "AND": [ {"user_id": "alex"}, {"event_type": "memory_add"}, {"timestamp": {"gte": "2024-01-01"}} ] }, top_k=100 ) # Monitor usage patterns for event in events: print(f"{event.timestamp}: {event.event_type} - {event.memory_id}") ``` ## Summary of Changes | Feature | Open Source | Platform | Action Required | | ------------------ | --------------------------------- | ------------------------------------------------ | ----------------------------------------- | | **Initialization** | `Memory.from_config(config)` | `MemoryClient(api_key)` | Replace config object with API key | | **Search Method** | `m.search(query, user_id="x")` | `client.search(query, filters={"user_id": "x"})` | Move filtering params into `filters` dict | | **Get All Method** | `m.get_all(user_id="x")` | `client.get_all(filters={"user_id": "x"})` | Move filtering params into `filters` dict | | **Add Method** | `m.add(memory, user_id="x")` | `client.add(memory, user_id="x")` | No change | | **Delete Method** | `m.delete(memory_id)` | `client.delete(memory_id)` | No change | | **Delete All** | `m.delete_all(user_id="x")` | `client.delete_all(user_id="x")` | No change | | **Update Method** | `m.update(memory_id, new_memory)` | Use delete + add pattern | Replace with delete then add | | **Config** | Local vector store + LLM config | Managed cloud infrastructure | Remove local config setup | ## Rollback plan If you encounter issues, you can revert immediately by switching your import back. 1. **Revert Code**: Change `MemoryClient` back to `Memory`. 2. **Restore Config**: Uncomment your local vector store and LLM configuration. 3. **Verify**: Ensure your local vector database is still running and accessible. ## Next Steps * Platform Dashboard - Monitor usage and manage settings. * [Webhooks Setup](/platform/features/webhooks) - Configure real-time event notifications. * [Organizations & Projects](/api-reference/organizations-projects) - Set up multi-tenancy for your team.