Compare
How Memophant compares.
Most teams give their AI a memory one of three ways. Here's how each holds up against a structured, repo-resident, drift-aware system.
| Memophant | CLAUDE.md files | Cursor rules | Cloud memory tools | |
|---|---|---|---|---|
| Lives in your repo as plain markdown | ||||
| Structured, searchable knowledge graph | ||||
| Flags notes as stale when code drifts | ||||
| Doesn't bloat the context window | ||||
| Wiki, design, code, tasks + more tiers | ||||
| Distills durable memory from your sessions | ||||
| Keychain-backed credential registry | ||||
| Two-tier secret-scan before publish | ||||
| Works across every agent (MCP) | ||||
| Your data never leaves your machine | ||||
| No subscription required |
The honest take
Each approach has its place. None of them maintain themselves.
vs. a long CLAUDE.md
A single growing file is easy to start and impossible to maintain. It bloats the context window, gets truncated, and rots silently. Memophant keeps CLAUDE.md thin — a pointer — and puts the real knowledge in structured, drift-aware tiers.
vs. Cursor rules
Rules files capture conventions but not decisions, architecture, or history — and they're per-tool. Memophant's memory is structured, searchable, and shared across every agent that opens the repo.
vs. cloud memory tools
Hosted memory means your project's knowledge lives on someone else's server, behind a subscription, in a format you can't diff. Memophant's memory is markdown in your repo — versioned, reviewable, and yours if the vendor disappears.
What that costs you, job by job.
Every approach above leaves the same work to you or your agent. Here's what each of those jobs actually takes.
| The job | Doing it by hand | With Memophant | Measured |
|---|---|---|---|
| Find the decision behind some code | grep hands back filenames — then you open four of them to find the answer | One ranked search returns the passage, plus the notes it links to | 61 ms |
| Get an agent up to speed on a topic | Read a note, parse its links, open each one: five to eight round trips | One call returns the note and everything connected to it | 146 ms |
| Write down a decision | Append to a file and hope the shape is right | Validated, stamped with the commit it was true at, secret-scanned, indexed — and told which existing notes it should link to | under 90 ms |
| Stop a credential from being written down | Nothing catches it until code review. Sometimes nobody does. | The write is refused before it touches disk, and the safe place to put it is named | blocked in 53 ms |
| Rename or move a note | mv leaves every link in the repo pointing at nothing | Every inbound link is rewritten for you | 41 ms |
| Know whether a note is still true | Read it and hope the code hasn't moved since | Reads warn you when the anchored code drifted — with the one call that clears it | 1 ms per read |
| Audit the whole corpus | Hours of reading. Realistically, you skip it. | Counts by cause and the worst offenders, in one call | one call |
Timings from one real working session against a 218-note repo, measured by the server's own query log. The honest caveat: plain grep is still faster at finding a literal string. This matters when the answer needs structure, judgment, or a guard.
What it costs your agent, job by job.
Time isn't the only budget. Everything your agent reads to answer a question eats the context it needs to do the work.
| The job | Your agent alone | Agent tokens | With Memophant | Context saved |
|---|---|---|---|---|
| Learn how your repo actually works | Read the source it's buried in. One subsystem here is 9 files and 5,700 lines — and the reasoning still isn't written down anywhere in it | ~100k | One build_context call returns the decision note plus every note it links to — the what and the why, in ~3.4k tokens | 97% less |
| Find out why it's built that way | Archaeology: replay a single file's history and infer intent from the diffs | ~53k | The decision note says it in prose — the constraint, the trade-off, and what was rejected | 96% less |
| Audit what's gone stale | Read all 219 notes, then diff every file each one is anchored to | ~339k | One health call returns counts by cause and the worst offenders, in ~400 tokens | 99.9% less |
| Find the passage that answers a question | grep gives you paths, so you pull whole files into context to read them — four candidates, here | ~6.2k | Ranked hits arrive with the matching passage inline — usually you never open the file | ~3× less |
| Check one note against the code | Diff all nine files the note is anchored to, since the commit it was last verified at | ~1.5k | The note arrives already carrying the verdict — no diffing, no extra calls, ~30 tokens | 98% less |
| Pick up where the last session left off | Re-explore the tree, re-read the same files, re-derive the same conclusions | ~100k, again | Every session opens with the corpus map — areas, recent decisions, open tasks — already loaded | 0 calls |
Every figure is a byte/4 estimate measured on this repo — 219 notes, 1.36 MB of memory — and the top two rows use one note's own declared source_paths: exactly the files an agent would otherwise have to read. The honest caveat: a note is a distillation, not a replacement for source. That's precisely why Memophant drift-checks them — when the code moves, the note tells you.
The memory layer that keeps itself honest.
Trade a rotting CLAUDE.md for structured memory you can trust.