# memory

Vaibhav Srivastav's Codex prompt is interesting because it describes an agent maintenance loop: look back at recent work, find repeated workflows, and package only high-confidence patterns into Skills, automations, or subagents. It is agent dreaming: turning busy work into capability.

Mem0 breaks down Codex CLI memory: not a vector database, but local Markdown, background summaries, credential scrubbing, and grep search. This post looks at when local notes are enough, and when a semantic memory layer makes sense.

Voice agents cannot reuse text-agent memory architectures as-is. Manthan Gupta breaks down why latency budgets, noisy transcripts, and cold-start identity make voice memory a different problem.

A context window is a model's day: how many lessons, messages, tool results, and task events Ryland can experience before sleep, compression, or collapse.

Anthropic's Claude Dreams is not just summarization. It gives agents an offline memory-consolidation loop: reread old memories and up to 100 past sessions, then produce a fresh, auditable memory store.

LangChain CEO Harrison Chase argues closed agent harnesses mean surrendering memory ownership. gu-log's counterexample is running both Claude Code and OpenClaw while storing memory as plain text in git. The lock-in is memory format, not harness licensing.

Claude Code shipped Auto-Memory — AI can finally take its own notes. But we've been doing this with OpenClaw for months. A hands-on comparison of two memory architectures: design philosophy, real pitfalls, and why memory is a trust problem, not just a tech one.

Indie hacker Ramya's OpenClaw agent kept losing its memory. She spent 5 days debugging — from compaction amnesia, garbage search results, retrieval not triggering, long session context loss, to a system prompt that bloated by 28%. Here are her 10 hard-won lessons.

How OpenClaw remembers things, learns new skills, and runs tasks on autopilot. From Embeddings to Cron Jobs — explained for Python developers.

Junde Wu built OneContext after getting fed up with coding agents forgetting between sessions. It uses filesystem, Git, and knowledge graphs to work across sessions, devices, Claude Code, and Codex; the GCC paper hits 48% on SWE-Bench-Lite.

Deep dive into Clawdbot's two-layer memory system: Daily Logs (stream of consciousness) + Long-term Memory (knowledge base) + Hybrid Search (semantic + keyword) + Lifecycle Management (Flush, Compaction, Pruning).

We added Supermemory to Claude Code. Now it's ridiculously powerful. Claude Code should know you — not just this one session, but forever. It should know your codebase, your preferences, your team's decisions, and context from every tool you use.