ripwire

Rip'n Fast. Fewer Tokens. Better Code.

The ripgrep of AI context. A map before your agent reads the repo — and a check on what it writes.

Ranked, deterministic call graph: what to touch, what it breaks, which tests to run. On the edit: blast radius, tests that reach it, twelve quality kinds reporting only what got worse, forgotten co-changes, fields read and written, names that resolve more than one way.

Just want to use it? Install it with the one line below, then start each coding session by telling your agent to use it, for example: "Use ripwire on this repo." That is all most people need: the install also teaches your agent when to reach for each command.

Want every detail? The reference guide near the bottom covers install, commands, output format, exit codes and limits. You do not need it to get started.

For a single developer, this tool is a good lookup accelerator. For an orchestrated fleet, it's load-bearing: it halved the research spend, twice redirected tasks before wasted work, prevented at least one silent-divergence shipped bug, and turned code-quality hygiene from a hope into a per-task mechanical gate. Whole-workflow ~2×; per-lookup 10–25×; and two moments where one call was worth more than the rest of the session's tooling combined.

— the report's bottom line

Field report: what ripwire contributed to a large multi-agent coding engagement

Context, genericized: one orchestrating session directing ~20 sequential/parallel coding agents over a ~1,500-file C++/Metal codebase across two days — a deep architecture audit, then a 14-task feature wave (new subsystems, measurement infrastructure, a search-archive migration), ending in a verified all-on release flip. Every agent was instructed to lead with ripwire for orientation and to close with its quality gates.

The headline numbers

• Roughly half the total token spend of the audit/research phase, saved. This is the operator's whole-phase estimate — it includes everything the research agents did, ordinary file reads and all, which makes it conservative rather than cherry-picked. The per-call factors underneath are much steeper: a single doc-recall call served the relevant sections of a 164KB planning document in ~6K tokens (~25×), and agents that led with the tool ran ~30–40% leaner on tool-call counts than agents doing raw read fan-outs over comparable questions.
• Two tasks had their direction changed by a single call. One task was chartered to activate a steering behavior the team believed was in production; one --callers query returned zero production callers, redirecting the task to the real gap (the data provider that behavior needed had never been wired anywhere). Another task refactored a widely-shared computation; --edit-check flagged 5 of 6 call sites as incompatible — sites a text search had missed — that would otherwise have silently diverged from the canonical path the day the feature was enabled, surfacing months later as an unexplainable visual bug.
• A dozen-plus real code-quality defects fixed, not waived, across ~15 implementing agents — all caught by --quality-delta at each agent's "I think I'm done" moment: a 480-token duplicated routine, a fourth private copy of a shared RNG utility, a hand-duplicated cost function, a pair of near-identical functions with a flipped sign (correct at one boundary, quietly wrong at the other), test-fixture duplication across sibling suites, and functions that had quietly absorbed a second job. None of these would have failed a test; all of them are the sediment that rots a codebase under high-velocity multi-agent development. The gate made removing them routine instead of heroic.

Where the value concentrated

1. Orientation and recall (the token win). Ranked-signature task orientation and section-granular document recall meant agents started from answers, not file dumps. Recall over planning docs was the orchestrator's single most-used verb — the audit's entire framing came from two calls.
2. The contract checker (the shipped-bug preventions). Beyond the 5-of-6 catch above, --edit-check gave near-free proof-of-contract on every public-symbol change across the wave — the kind of verification that otherwise simply doesn't happen at agent speed.
3. The quality gate as a protocol. The measurable effect wasn't any single catch — it was that fifteen different agents, none sharing context, all converged on the same "fix or justify with a written reason" discipline, with an acknowledgments ledger that survived across tasks. Unattended orchestration usually leaks quality; here the leak-check was mechanized.
4. Persistent notes. Agents left gotcha notes pinned to symbols mid-wave (a caller-wiring law, an arming-rule invariant), which later agents' lookups surfaced automatically — cheap institutional memory between contexts that never met.

The honest boundary

• The engagement's deepest findings did not come from the tool. A structural capacity ceiling, a mis-derived constant, a floating-point re-association drift of 1 ulp, and a seed-keying bug were all found by bespoke measurement the agents built — baseline worktree diffs over full data fills, multi-arm attribution sweeps, funnels. The tool is a floor for honesty and orientation, not a substitute for measurement design.
• Known false-positive mode, handled by its own documentation: the contract checker's arity heuristic over-counts on defaulted trailing parameters (one task saw "incompatible=18" that were all fine). The tool's own trust-calibration notes say to verify by compiling in exactly this case, and agents that followed them lost nothing.
• For broad, common-word conceptual queries, plain grep-and-read still occasionally won — consistent with the tool's own guidance that it shines on specific technical asks.

Bottom line

For a single developer, this tool is a good lookup accelerator. For an orchestrated fleet, it's load-bearing: it halved the research spend, twice redirected tasks before wasted work, prevented at least one silent-divergence shipped bug, and turned code-quality hygiene from a hope into a per-task mechanical gate. Whole-workflow ~2×; per-lookup 10–25×; and two moments where one call was worth more than the rest of the session's tooling combined.

The model's own report of one engagement, on a version before 0.5; not a controlled measurement. Controlled measurements are in docs/EVALS.md.

Beside those sits a labelled survey of 237 tools that contributed nothing and says so. The two sets are disjoint by construction, so they add rather than nest — a tool that gave a lesson is never counted twice.

Both halves are load-bearing, and they are doing different jobs. The settled results are what make the quality lens trustworthy: McCabe on complexity (1976), Halstead on volume (1977), Spärck Jones on term specificity (1972), Nagappan & Ball on churn. Fifty years of replication means those are not opinions, and a tool that measures your code should be built on the ones that survived.

The recent work is what makes it current: seventeen of the folded papers are from 2026, seven published in the last two months and three in the last thirty days (dates as of 2026-09-08; every row carries its arXiv id, so the claim is checkable rather than atmospheric). Retrieval for coding agents, context-compression cost, placebo-controlled localization — that literature is months old, not decades, and several rows were folded within weeks of the paper appearing.

Neither half alone would be enough. A tool built only on the classics would not know what an agent needs; one built only on last month's preprints would have nothing underneath it. And the newest row is a result that failed when it was tested here — which is the point of writing them down. All three counts are re-derived from that document's own tables by test/readmedriftcheck.sh on every run, which fails if this page and those tables disagree, so the claim cannot quietly drift. The row-by-row ledger is docs/LINEAGE.md.

Languages: Rust · C++ · Objective-C/C++ · C · Metal · CUDA · Python · Go · Swift · TypeScript · JavaScript · Java · Ruby · PHP · Lua · Elixir · Dart · Kotlin · GDScript · Clojure · Bash · C# · JSON · TOML · YAML · Markdown — see language support and limits.

Latest: 0.6.7 — more complete answers, faster calls, fewer bytes. Release notes · the presentation · the changelog — with thanks to the contributors named there; this release is largely theirs.

Measured on 48 matched questions across django, webpack and this repository. Across all three, ripwire indexes in 0.25–0.45 s and 6.6–16.5 MB against that server's 23–52 s and 391–623 MB. The full method, the wins named one by one and the losses included, is in Against the leading graph-database code-context MCP server and docs/EVALS.md.