Technical insights on AI memory and agent architecture
Five sprints to get from 5-second recall latency to sub-100ms on keyword queries. OOM kills, a broken analytics table, BM25 at 20ms, and the cold path we haven't solved yet.
A real account of building self-improving memory consolidation: 6 phases, 5 days, 7 merges, and the prime directive that kept us from corrupting 1,979 memories.
Real dogfooding: a 9-agent OpenClaw fleet, 1,979 memories in the thomas namespace, a namespace isolation bug we only found by using our own product. What works and what still needs a human.
Configuration wizards aren't just UX polish. For AI agent infrastructure, they're the difference between setup that works and setup that silently fails. What we learned building the 0Latency config wizard.
Adding PostHog analytics to our memory API killed the upstream nginx connection. Here's what happened, why it happened, and the pre-deploy verification script we wrote after.
Learn how 0Latency's L0/L1/L2 tiered memory loading cuts token costs by 40-60% while improving response quality. Smart context budgeting for AI agents.
Build AI knowledge graphs without Neo4j. 0Latency uses recursive CTEs in Postgres to map entity relationships efficiently — available on Scale ($89/mo) and Enterprise.
Set up webhooks to get real-time notifications when your AI agent stores memories, detects contradictions, or hits decay thresholds. Complete guide with code examples.
Build multi-agent AI systems with shared memory. 0Latency provides agent-scoped and shared memory pools, session handoff, and native integrations with CrewAI, AutoGen, and OpenClaw.
Tutorial: Give your Paperclip AI company persistent memory with 0Latency. Stop losing context between sessions and let your AI agents coordinate through shared memory.
Learn how temporal memory decay and access reinforcement make AI agents smarter. 0Latency's half-life model keeps relevant memories fresh while fading stale ones — just like the human brain.
How to detect and resolve contradictions in AI agent memory. Learn algorithms for flagging conflicting information and strategies for maintaining memory consistency.
Learn how negative recall lets AI agents suppress outdated, incorrect, or retracted memories. Handle corrections, privacy deletions, and preference changes gracefully.
Learn the 6 essential memory types for AI agents: facts, preferences, events, relationships, procedures, and emotional context. Code examples for storing and querying each type.
A fair, detailed comparison between Mem0 and 0Latency. Features, pricing, architecture, and what each platform does best for AI agent memory.
After months running a production AI agent, we discovered 36% of critical memories were lost during context compaction. Here's what we measured, why existing solutions fail, and the memory architecture that took recall from 65% to 95%.