Launch
Introducing Knol: Context Engineering for AI Applications
Today we're open-sourcing Knol — a Rust-native context engineering platform that gives LLM applications persistent memory with sub-5ms latency, powered by a single PostgreSQL database.
Blog
Updates, technical deep dives, and research from the Knol team.
Strategy
Why Context Engineering Replaces "Memory" in AI
The industry is shifting from simple memory to context engineering — assembling the right information at the right time. Here's why this matters and how Knol is built for it.
Technical
How Hybrid Retrieval Works: Vector + BM25 + Graph Fusion
A deep dive into Knol's adaptive retrieval engine: intent classification, Reciprocal Rank Fusion, and how we combine three search signals for sub-5ms results.
Technical
75% LLM Cost Reduction: The 7-Layer Optimization Pipeline
How Knol's extraction pipeline uses prompt caching, batching, model routing, and deduplication to cut LLM costs by 75% without sacrificing quality.
Research
Memory Decay, Conflict Detection, and Temporal Knowledge Graphs
Why we modeled Knol's memory system after human cognition — with decay scoring, conflict resolution, and bi-temporal knowledge graphs.
Guide
From Mem0 to Knol: A Migration Guide
Step-by-step guide for teams migrating from Mem0 or Zep to Knol. Same API patterns, better performance, no vendor lock-in.
Tutorial
Give Claude Persistent Memory with Knol MCP Server
Step-by-step guide to setting up Knol as an MCP server for Claude Desktop. Your AI assistant will remember users, preferences, and context across every session.
Comparison
Knol vs Mem0: A Technical Comparison for AI Memory
An honest technical comparison between Knol and Mem0 covering architecture, performance, features, and total cost of ownership for production AI memory.
Tutorial
Deploy AI Memory on PostgreSQL in 60 Seconds
The fastest way to add persistent memory to your AI application. Three commands, one PostgreSQL database, sub-5ms latency. No Qdrant, no Neo4j, no complexity.