DUMEdb 开源单元记录记忆引擎发布，用 220 token 唤醒替代十万级上下文重放
I built an open-source unit-record memory engine based on 1959 IBM punched cards to stop 100k-token context replay choking [Short Promo + Live Web Demo] [P]
作者发布开源记忆引擎 DUMEdb（Deterministic Unit-Record Memory Engine），借鉴 1959 年 IBM 1401 的不可变单元记录思路，让智能体重启时不再重放 8 万至 12 万 token 的对话历史。
Peace,
If you build long-running autonomous agents or pair-programming harnesses, you know the single biggest headache right now: **Session Amnesia.**
Whenever an agent restarts or changes tasks, standard frameworks try to reconstruct context by replaying 80,000 to 120,000 tokens of chat transcripts through the context window. It eats your runway, costs a fortune on proprietary APIs, slows local inference down to a crawl, and causes models to hallucinate past their 75% context ceiling.
I realized the problem isn't context size—it's how we model memory.
Back in 1959, IBM 1401 systems processed mission-critical data without ever "replaying history." They used immutable unit-record accounting: atomic, fixed-column registers where entries are state transitions, not re-read conversations.
So I built **DUMEdb (Deterministic Unit-Record Memory Engine)**:
### How It Works Under The Hood:
**Zero-Turn Wake Handshake (`cabinet_wake`)**: Instead of re-reading chat history, the agent passes a codeword ("box is the game") and receives an immutable status receipt + active registers in **< 220 tokens** with 99.8% clean context runway preserved.
**Deterministic LMDB Engine**: Uses memory-mapped LMDB storage (`dumedb.mdb`) for sub-microsecond zero-copy register reads and schema packs (State, ADRs, Incident RCAs).
**Real-Math Disk Odometer**: Audits `transcript.jsonl` directly on disk post-compaction to calculate ground-truth token consumption and triggers clean session handoffs before auto-compaction amnesia hits.
**Model Context Protocol (MCP)**: Native bridge ready for Google Antigravity, Claude Desktop, Cursor, and Python agent loops.
I also made a short 88-second deadpan comedy video contrasting a modern Silicon Agent choking on 128k tokens inside an RTX 4090 with a 1959 IBM engineer named Frank:
* 🎬 **Watch the short trailer (YouTube)**: https://youtu.be/E1f8NzJlrB0
* 🌐 **Live Studio & Interactive Odometer Calculator**: https://dumedb.developumaiengine.com/
* 💻 **GitHub Repo**: https://github.com/madbradsmith/DUMEdb
* 📦 **Quick Install**: `pip install dumedb`
Would love to hear your thoughts, feedback, or roasts on treating agent memory like an IBM 1401 punched card ledger!
submitted by /u/MadBradSmith
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来源：r/MachineLearning · reddit.com