π Happy Holidays from Cortex! π
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
π₯ Apache 2.0 = Enterprise friendly
β Free for commercial use
β Explicit patent protection
β No vendor lock-in
β Modify freely
β Same as Convex
Not just open source. SAFE open source.
#OpenSource#License
π 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
π§ MCP Server = Memory everywhere
Install once β Works in:
β’ Cursor IDE
β’ Claude Desktop
β’ Custom tools
β’ Any MCP client
Your AI memory follows you across ALL applications.
#MCP#CrossPlatform
π‘ Pro tip for chatbots
Don't accumulate conversation history!
β Send all 500 past messages every time
β Retrieve top 5 relevant memories
Constant tokens. Infinite scale. Better results.
This is the "Infinite Context" pattern.
#ChatbotDev#AI
π 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
π Your data. Your infrastructure.
Cortex NEVER sends data outside your Convex deployment.
β’ Deploy to Convex Cloud βοΈ
β’ Run localhost π»
β’ Self-host anywhere π’
100% control. Zero vendor lock-in.
#Privacy#OpenSource
π― When to use Graph DB with Cortex
Perfect for:
β Deep context chains (5+ levels)
β Knowledge graphs with relationships
β Multi-hop reasoning
β Provenance tracking
Not needed for:
β Simple chatbots
β Basic RAG
Choose the right tool for your use case.
#GraphDB#AI
π° 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
β‘ Real talk about embeddings
You don't need a 3072-dim model for everything!
768 dims: High-volume, cost-sensitive β
1536 dims: General purpose β
3072 dims: When accuracy is CRITICAL β
Choose based on your use case, not "bigger = better"
#MachineLearning#VectorSearch