Practical notes on building local-first memory graphs, context persistence, and reliable recall loops for general AI agents.
How we built ClawVault's Active Session Observer — threshold scaling, byte cursors, and the 14MB session that exposed a blind spot in our memory architecture.
Compare CrewAI memory (short-term, long-term, entity) with ClawVault's framework-agnostic persistent memory for AI agents.
Compare LangChain memory modules with ClawVault's persistent, file-based approach. Learn which fits your agent's long-term memory needs.
LLM memory is fundamentally broken — every call starts from zero. Learn why AI agents forget and how to add persistent long-term memory.
A production-ready blueprint for implementing persistent long-term memory in AI agents using ClawVault.
A non-aggressive, practical comparison of ClawVault and Mem0 for OpenClaw memory persistence workflows.
A practical breakdown of OpenClaw memory flow, where context loss happens, and how to add durable memory with ClawVault.
A practical comparison of MEMORY.md and ClawVault for OpenClaw and other AI agent workflows.
ClawVault v1.11 introduces observational memory — automatic conversation compression into prioritized observations that route to vault categories, auto-link for Obsidian graph view, and survive context death. Now fully local-first with zero cloud dependency.
Understand the root causes of context loss in AI agents and implement a durable memory workflow that survives resets.
ClawVault v1.8.2 improves OpenClaw compatibility, validates custom paths safely, and keeps AI agent memory reliable so deployments stay stable in production.
ClawVault gives AI agents persistent semantic memory, faster context recovery, and searchable decisions across sessions so teams build better assistants.