`Memtrace`: rewindable, always-fresh memory for coding agents

Code-agent memory is being rebuilt around codebase state, not chat history. If this works, it cuts re-reads, surfaces refactor blast radius fast, and makes agent debugging more trustworthy.

[ KEY POINTS ]

Every edit triggers a claimed 42ms incremental snapshot, so the agent queries current code state instead of stale session memory.
The standout feature is bi-temporal rewind: valid_time plus transaction_time lets an agent replay when a bug was introduced, not just inspect the latest file.
Indexing skips LLM inference entirely and uses Tree-sitter ASTs, which keeps freshness tied to I/O cost instead of token cost.
Retrieval combines BM25 via Tantivy with jina-code 768-dim embeddings in HNSW, then merges results with RRF k=60 for code-aware search.
The stronger signal is the pain point itself: long-running agents still lose architectural context, so persistent code memory looks like a real product wedge.

Originalwww.reddit.com/r/ClaudeAI/comments/1t3du61/your_claude_code_agent_is_always_working_from/Read original →

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`Memtrace`: rewindable, always-fresh memory for coding agents

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