Thought my Agent was doing fine until i found out its down a rabbit hole , a decision we changed few weeks ago, it was confidently wrong about the decision. So i built a Memory myself, open source tell me what you guys think

The thing that finally broke me wasn't my agent forgetting stuff. Forgetting is annoying but it announces itself the agent asks again, you sigh, you re-explain. What broke me was the silent version: my agent confidently re-proposed an approach we'd tried and abandoned a month earlier. Another time it planned against a decision we'd replaced two weeks before the old decision was still sitting in its notes, looking exactly as authoritative as the new one. Nothing failed loudly. It just quietly burned the hours again. Generic memory fixes forgetting. Nothing I tried fixes being confidently wrong about the past because that's not a recall problem, it's a status problem. "We ruled this out," "this was replaced," "this is still unverified" that's not what a similarity search returns. So I spent 3 months building the other half: NodeDex a local graph of your project's reasoning, built automatically from your agent's conversations by a background pipeline (the agent never has to remember to save): - dead-ends are first-class: an enumerable list of what was tried and abandoned, with the why the agent is taught to check it BEFORE proposing - decisions carry their why + the alternatives that lost - when something gets replaced, nothing is deleted — a `supersedes` edge points old truth → current truth, so the agent can't mistake stale for current To be clear about what it's NOT: it doesn't replace Claude's native memory or your fact store those remember *notes and preferences*, and they're good at it. This is a different job (the project's decision history). Run both. You can poke it in 60 seconds, no API key: ``` npx nodedex demo ``` That serves a small sample project graph over MCP. Point Claude (or any MCP agent) at it and ask: *"Is 'keep the counters in Redis' still the current decision?"* then watch it follow the supersede edge and answer with the replacement instead of the stale one. That moment is the whole product. Honest limits, before you find them: - the dead-end check is a strong nudge (server instructions + a skill), not a hard block a pre generation hook gate is on the roadmap - extraction needs a smart, big context model (Gemini Flash-Lite ≈ half a cent per session; my 12B local test *understood* everything but failed the strict structured passes floor is ~27-30B local with real 16k+ context) - it's early and solo-built (1196 tests pass, but it's been on npm for three days) Local SQLite, AGPL, graph never leaves your machine. Repo: [link] I'd love for people to break it especially: does your agent actually check the dead ends unprompted in your setup, or does it need the nudge? That's the question I most need real world answers to.