| title | description |
|---|---|
Introduction |
Build personalized AI experiences with MemSync's portable long-term memory |
MemSync is a portable memory platform that elevates AI applications from stateless interactions to intelligent, context-aware experiences with persistent long-term memory. It also enables users to seamlessly transfer context and data across different applications into your app.
Traditional AI applications don't have cross-session context. MemSync provides state-of-the-art memory infrastructure that can:
- Extract meaningful facts from conversations automatically
- Store long-term memories in organized, searchable formats
- Provide semantic search to find relevant context instantly
- Build user profiles that evolve over time
- Import external cross-application context from other AI chat and social media apps
Get started with MemSync in minutes:
Set up MemSync and make your first API call in under 5 minutes Explore the complete API documentation with examples Learn about semantic and episodic memories, how they're extracted and stored Organize memories into categories like work, hobbies, relationships, and more Find relevant memories using natural language queries with vector search Auto-generate user bios and profiles from accumulated memoriesConnect MemSync with your existing tools and data sources:
Import context from LinkedIn, Twitter, and Reddit Process PDFs, Word docs, and other files Import conversations from ChatGPT and other platforms- Remember customer preferences and past issues
- Provide contextual support based on interaction history
- Track customer journey and satisfaction
- Remember customer preferences and past issues
- Provide contextual support based on interaction history
- Track customer journey and satisfaction
- Track learning progress and preferences
- Personalize curriculum based on user goals
- Remember areas of difficulty and success
Ready to build your first personalized AI with memory retrieval?
# Get relevant memories for context
memories = memsync.search_memories(
query="What's the latest news related to my educational interests?",
user_id="user123",
limit=5
)
# Use memories to personalize response
context = "\n".join([m.memory for m in memories])
response = llm.generate(context=context, message=user_message)<Card title="Start Building" icon="hammer" href="/quickstart"
Follow our quickstart guide to set up MemSync and make your first API call

