# 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 that agent harnesses are tied to memory, and using a closed harness means surrendering memory ownership to a third party. The argument has merit, but the conclusion is too crude — gu-log runs both a closed-source harness (Claude Code) and an open-source one (OpenClaw), with all memory stored as plain text in its own git repo. The real lock-in isn't about harness licensing — it's about memory format.

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 from Oxford + NUS got fed up with coding agents forgetting everything between sessions. So he built OneContext — a Git-inspired context management system using file system + Git + knowledge graphs. Works across sessions, devices, and different agents (Claude Code / Codex). The underlying GCC paper achieves 48% on SWE-Bench-Lite, beating 26 systems. Backed by an ACL 2025 main conference long paper.

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.