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
supersedesedge 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: (Repo in comment)
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.
Top comments (1)
Repo : github.com/NodeDex/NodeDex-v0.1