Claude Code 에이전트가 항상 구стар 컨텍스트에서 작업합니다. 저는 이를 되돌림, 재연산, 모든 수정에 앞서서 유지할 수 있도록

Every long Claude Code session has the same hidden failure mode: the agent is always working from stale context.

It re-reads the same 12 files across three sessions to "remind itself" of an interface you already showed it. It refactors getUserById without checking who calls it. It edits a config with no memory of why the previous version was that way. It's not the context window. The window is fine. There's no persistent, time-aware representation of your codebase for the agent to re-query. So it guesses. And you pay tokens for every re-read.

I built Memtrace to fix exactly this.

Two things it does that no other memory tool does:

(1) Always-fresh state. Every edit you make triggers a 42ms incremental snapshot of the changes applied by the coding agent. The agent's memory is never one-session-old. After a refactor it knows the blast radius before you do: every caller, every test, every consumer of the function you just touched. Your agent stops asking "what does getUserById return?" 30 seconds after seeing it.

(2) Rewind and replay. This is the part nobody else has. Your codebase is stored bi-temporally so every change becomes a recallable episode. When the agent debugs a regression, it can replay how the broken function got to its current state.

• What worked before.
• What changed when.
• Which commit introduced the bug

Not just "guess from current state.", instead replay.

My architectural bet that makes both possible: zero LLM inference during indexing. Tree-sitter parses your code into an AST, and the AST IS the structural representation. You don't pay an LLM to re-derive what your compiler already knows.

Retrieval is hybrid. Tantivy BM25 for lexical recall (the "find getUserById" query). Jina-code 768-dim embeddings indexed in HNSW for semantic recall (the "find anything that authenticates a user" query). Two ranked lists, fused with Reciprocal Rank Fusion at k=60. One signal alone misses, together they hit. The embedding model matters here: Jina-code is trained on code, not generic prose, so the semantic side actually understands "this is an auth handler" instead of pattern-matching on the word "auth."

The bi-temporal layer is what makes rewind possible. Every node and edge carries valid_time AND transaction_time, so "what did this function look like Monday" is a real query, not a git-blame heuristic. It's also what gives the agent the blast radius before a refactor: typed edges (CALLS, IMPORTS, IMPLEMENTS, EXTENDS, CONTAINS, TYPE_REFERENCES, INSTANTIATES) traversed in graph time, not text time.

Speed only matters because freshness has to be cheap. If snapshotting after every edit is expensive, you can't afford to do it on every edit. So the indexing path is bottlenecked by I/O, not LLM tokens.

I built it using Claude Code. Mid-build, Claude Code lost the plot on Memtrace's own architecture and it started contradicting decisions from 50 turns earlier. It re-read the same files. It forgot which retrieval weights I'd already tuned. I was experiencing the exact pain I was building Memtrace to solve, while building Memtrace.

When the beta binary was ready, I pointed it at Memtrace's own codebase. The session-loss stopped. The blind refactor suggestions stopped.

It's free, but the binary currently requires an approval key, just so you are warned.

Not gatekeeping. Not marketing. The indexer keeps tripping on patterns I didn't anticipate: mixed pnpm/npm lockfiles, Rust proc-macros, Python Python TYPE_CHECKING blocks. Every one of these came from real beta users in the last two weeks, not from my test corpus. When that happens I want to ship you a fix in 24 hours, not lose you to a flaky first impression. So I'm pacing approvals to my own feedback bandwidth, not your patience. I'd rather have 500 users for whom this is magic than 50,000 for whom it's broken.

I'm trying to keep approval under 24h, but capping at 50 per week right now. The benchmark harness is fully open and runnable without the key, if