๐ Happy Holidays from Cortex! ๐
Building AI agents that never forget?
We're here to help. Star the repo, join discussions, share your use case.
Together, we're making AI memory accessible to everyone.
#HappyHolidays#Community
๐ฅ Apache 2.0 = Enterprise friendly
โ Free for commercial use
โ Explicit patent protection
โ No vendor lock-in
โ Modify freely
โ Same as Convex
Not just open source. SAFE open source.
#OpenSource#License
๐ Real-world use case: Customer Support
500 tickets over 5 years = 500K tokens accumulated
With Cortex: Search returns 5 most relevant facts from 5 YEARS in 40 tokens.
Perfect recall. Minimal cost. Happy customers.
#CustomerSupport#AI
๐ง MCP Server = Memory everywhere
Install once โ Works in:
โข Cursor IDE
โข Claude Desktop
โข Custom tools
โข Any MCP client
Your AI memory follows you across ALL applications.
#MCP#CrossPlatform
๐ก Pro tip for chatbots
Don't accumulate conversation history!
โ Send all 500 past messages every time
โ Retrieve top 5 relevant memories
Constant tokens. Infinite scale. Better results.
This is the "Infinite Context" pattern.
#ChatbotDev#AI
๐ Context Chains for multi-agent systems
Supervisor can see subordinate context.
Specialist reads manager's workflow.
Clean hierarchies. No information silos.
Read-only. Limited. Audited. Secure.
Perfect for complex agent orchestration.
#MultiAgent#Architecture
๐ Your data. Your infrastructure.
Cortex NEVER sends data outside your Convex deployment.
โข Deploy to Convex Cloud โ๏ธ
โข Run localhost ๐ป
โข Self-host anywhere ๐ข
100% control. Zero vendor lock-in.
#Privacy#OpenSource
๐ฏ When to use Graph DB with Cortex
Perfect for:
โ Deep context chains (5+ levels)
โ Knowledge graphs with relationships
โ Multi-hop reasoning
โ Provenance tracking
Not needed for:
โ Simple chatbots
โ Basic RAG
Choose the right tool for your use case.
#GraphDB#AI
๐ฐ Cost comparison for 1000 exchanges:
Traditional (accumulation): $1.50/request
Summarization: $0.30/request
Cortex (facts only): $0.0024/request
Cortex (hybrid): $0.012/request
Up to 99% savings. Use smaller models. Same results.
#CostOptimization#AI
โก Real talk about embeddings
You don't need a 3072-dim model for everything!
768 dims: High-volume, cost-sensitive โ
1536 dims: General purpose โ
3072 dims: When accuracy is CRITICAL โ
Choose based on your use case, not "bigger = better"
#MachineLearning#VectorSearch