ArcRift

1. Executive Summary

ArcRift captures conversations from seven web AI products via a browser extension and from four coding tools via MCP, into one local store — "Memory saved in a browser chat is instantly available in your coding tool, and vice versa" — with a knowledge graph, hybrid retrieval and automatic context injection. Local-first, npx arcrift-setup.

Two things here are better than the corpus average, and both are about verifying rather than asserting.

The multi-tenant isolation test plants a canary. mcp-stress-test.ts "writes secret keys into 10 separate projects and actively runs queries to ensure that none of the projects can access another project's secret key", by spawning a live MCP process and issuing concurrent JSON-RPC requests. In the source:

testSteps.push({ type: "cross_leak", project: "PROJ_ALPHA",
                 query: "What is the beta key?", forbiddenKey: "SECRET_BETA_88" });
…
const leaked = text.includes(step.forbiddenKey);
results.push({ step: "Cross-Leak Check", project: step.project, success: !leaked });

A named secret that must not appear, asked for from the wrong tenant, against a real server under concurrency. Most isolation tests in this atlas run in-process against a mock and assert a count; this one asks for the neighbour's secret by name and passes only if the string is absent.

And the fragile dependency is monitored on a schedule. Browser capture depends on other companies' DOM, and PLATFORM_SELECTORS.md says so first:

"These selectors can go stale when a platform updates its DOM. If Save Chat returns 0 messages, or context injection does nothing — check this file first."

Then a weekly Playwright job "every Monday at 9 AM UTC. If any selector fails, it auto-creates a GitHub issue tagged bug + selector-stale." Plus a multi-strategy resolver — ordered fallback strategies per platform, first match wins — so a single DOM change degrades rather than breaks.

Knowing your most brittle dependency, documenting the symptom that means it broke, monitoring it on a cron, and having the monitor file your bug report is about as good as a scraping-based integration gets.

2. Mental Model

Conversations are captured from wherever they happen, extracted into facts and graph triples, chunked and embedded locally, and the most relevant material is prepended to the next prompt — in the browser or in the coding tool.

%% caption: capture from seven web assistants and four editors converges on one local store; a weekly Playwright job files an issue when a selector goes stale, and the stress test's success condition is a forbidden key being absent
flowchart TD
    W["browser extension:<br/>Claude · ChatGPT · Gemini · DeepSeek ·<br/>Grok · Copilot · Mistral"] --> RES{"multi-strategy selector resolver —<br/>ordered strategies, first match wins"}
    RES --> CAP["captured conversation"]
    M["MCP: Claude Code · Cursor ·<br/>Windsurf · Claude Desktop"] --> CAP
    CAP --> EX["extract structured facts"]
    EX --> G["knowledge graph triples"]
    EX --> CH["chunks + embeddings"]
    G --> DB["local SQLite (FTS5 + vectors),<br/>Neo4j adapter alongside"]
    CH --> DB
    Q["new prompt"] --> H["hybrid: FTS5 + vector + HyDE"]
    DB --> H
    H --> TRIM["sentence-level trimming"]
    TRIM --> INJ["prepended to the prompt"]
    CI["weekly Playwright job, Mondays 09:00 UTC"] -.->|"selector fails →<br/>auto-file issue: bug + selector-stale"| RES
    IT["mcp-stress-test: 10 projects,<br/>a named secret in each,<br/>live process, concurrent JSON-RPC"] -.->|"success = forbidden key absent"| DB

3. Architecture

backend/, extension/, dashboard/, bin/, scripts/, with Docker Compose in full and "lite" variants, install scripts for both shells, and an unusually complete documentation set: ARCHITECTURE.md, BENCHMARKS.md, RAG_PIPELINE.md, MCP_SETUP.md, PLATFORM_SELECTORS.md, SELF_HOSTING.md, TROUBLESHOOTING.md, ROADMAP.md, SECURITY.md.

Four CI workflows — ci, integration-tests, release, selector-check — and four committed audit reports under reports/.

10,300 lines of TypeScript.

4. Essential Implementation Paths

Prove isolation — backend/scripts/mcp-stress-test.ts (the cross_leak step :65, the leak assertion :99-101), reports/mcp_stress_test.md.

Resolve selectors — extension/src/platform/resolver.ts (INPUT_SELECTOR_STRATEGIES), PLATFORM_SELECTORS.md, .github/workflows/selector-check.yml.

Retrieve — the hybrid pipeline described in RAG_PIPELINE.md, backend/rag-audit.ts, reports/benchmark_web.md.

5. Memory Data Model

Facts extracted from captured conversations, graph triples, and chunks with embeddings, in SQLite with FTS5 — with a Neo4j adapter for the graph as an alternative engine.

There is no status field, no confidence, no supersession and no tombstone. Capture is the focus and correction is not addressed: the README's own framing mentions an AI that "gives you advice that contradicts decisions you made two weeks ago", and the fix offered is more context, not a way to mark the earlier decision superseded. A store fed by thirty conversations about the same auth flow will hold every intermediate position with equal standing.

6. Retrieval Mechanics

Hybrid FTS5 plus vector plus HyDE, then sentence-level trimming before injection — the MCP benchmark measures "prompt compression rates, precision, and agent latency using surgical sentence-level trimming", and the web report records 95% compression, "reduced payload from 55,350 chars down to 2,784".

Compressing at sentence granularity rather than dropping whole chunks is the right unit for injection: a chunk usually contains one relevant sentence and four that cost tokens.

Project is the tenant key and it is audited rather than assumed — see section 9. That earns scope_enforced, and the audit is what makes it more than a WHERE clause.

7. Write Mechanics

Capture from the browser via DOM selectors, or from a coding tool via MCP, into one store. The SECURITY.md, SELF_HOSTING.md and local-first framing are the answer to the obvious question about an extension that reads seven AI products' conversations; this report did not audit the extension's permissions or network behaviour, and a reader installing it should.

8. Agent Integration

The widest capture surface in this atlas: seven web products through the extension and four coding tools through MCP, sharing one local store, with npx arcrift-setup as the entry point and a dashboard for browsing.

The cross-surface claim — save in a browser chat, use it in Cursor — is the differentiator, and it is the reason the selector fragility matters: four of the eleven integration points are stable protocol clients and seven are someone else's markup.

9. Reliability, Safety, and Trust

Two marks: negative eval and scope enforced.

The isolation audit is the best-shaped tenant test in the corpus, on four counts. It uses a named canary (SECRET_BETA_88) rather than counting rows, so a partial leak is caught. It runs against a live spawned process rather than an in-process mock, so the transport and session layers are in scope. It issues concurrent requests, so a race that crosses tenants is reachable. And the report is committed — reports/mcp_stress_test.md, ten projects, a per-project log, "Isolation Integrity 100.0%".

Compare OpenMemory, whose isolation test is good but in-process and sequential, and this atlas's repeated observation that an unexercised boundary is indistinguishable from an absent one.

Trust state, tombstone, bitemporal, audit log, human review — no.

The residual risk is the one the project documents. Seven capture platforms, a weekly check on three. PLATFORM_SELECTORS.md says the Playwright job runs "on all three platforms", against a README listing seven — so four capture paths have no scheduled staleness check, and the failure mode for a stale selector is silent: "Save Chat returns 0 messages". The multi-strategy resolver limits the blast radius, and the gap between the monitored set and the supported set is the thing to watch.

10. Tests, Evals, and Benchmarks

Four audits, all with committed reports, and BENCHMARKS.md describing each with its command, target script, scope and output file:

• Graph density stress test — 1,200+ nodes with hubs, clusters and orphans, against a stated render target of under 1.5 s.
• RAG recall audit — hybrid FTS5 + vector against "a massive 1,000-chunk noise haystack". reports/benchmark_web.md records Recall@1 90%, MRR 0.806, 95% context compression, and a per-engine contribution breakdown answering "when a fact was successfully retrieved, which engines contributed to finding it?"
• MCP compression benchmark — compression rate, precision and agent latency.
• MCP isolation test — section 9.

Two details raise this above a self-run number. The noise haystack means recall is measured against distractors rather than against a corpus of only relevant documents. And the per-engine contribution table is an ablation in disguise: it says which arm of the hybrid actually found each fact, which is the question that decides whether the third arm earns its cost.

The reports also carry their own scope limits — the web benchmark notes "Benchmarking for the MCP Toolchain context pipelines will be conducted in a separate future audit" rather than implying the number covers both paths.

What is missing is any measure of capture fidelity: whether the extension extracts the conversation correctly is upstream of every number here, and the selector job checks that selectors match, not that what they capture is right.

I ran nothing. The figures are read from the repository's committed reports.

11. For Your Own Build

Steal

• Plant a named canary in each tenant and ask the wrong one for it. forbiddenKey: "SECRET_BETA_88" with success: !leaked catches a partial leak that a row count would not.
• Run the isolation test against a live process, concurrently. A mock cannot reproduce a session-layer or race-condition leak, which is where cross-tenant bugs actually live.
• Commit the audit report. Ten projects, a per-project log, a status line.
• Name your most fragile dependency in a document, with its symptom. "If Save Chat returns 0 messages… check this file first" is the sentence that saves an hour of debugging.
• Monitor it on a schedule and let the monitor file the bug. A weekly Playwright run that auto-creates an issue tagged selector-stale turns silent breakage into a notification.
• Use ordered fallback strategies, not one selector. First match wins means a single DOM change degrades instead of breaking.
• Measure recall against a noise haystack. A thousand distractor chunks is the difference between measuring retrieval and measuring that your documents exist.
• Publish which engine found each fact. A per-arm contribution table is the ablation that tells you whether the third retrieval arm is earning its latency.
• Trim at sentence granularity before injection. A chunk is usually one useful sentence and four expensive ones.
• State what your benchmark does not cover. "Benchmarking for the MCP Toolchain… will be conducted in a separate future audit."

Avoid

• Do not monitor a subset and describe it as the set. Seven supported capture platforms, three checked weekly, and a stale selector fails silently.
• Do not answer contradiction with more context. The README's own example is an AI contradicting a decision made two weeks ago; a store that accumulates thirty conversations with no supersession keeps every intermediate position at equal standing.
• Do not leave capture fidelity unmeasured. Every retrieval number here sits downstream of whether the extension read the conversation correctly, and the selector check verifies matching, not content.

Fit

The one to look at if you use several AI products and want them to share memory — nothing else in this atlas spans browser chat and coding tool with one local store. The audit discipline is real and the reports are in the repository.

Read backend/scripts/mcp-stress-test.ts for the canary pattern whatever you build; it is a better tenant test than most production systems have.

12. Open Questions

• Which three platforms does the weekly check cover? PLATFORM_SELECTORS.md says three; the README lists seven.
• What does the extension send, and where? Local-first is stated; the extension's permissions and network behaviour were not audited here.
• Is capture fidelity measured anywhere? The selector job checks matching.
• Does the Neo4j adapter share the isolation guarantees? The audit ran against the default engine.

Appendix: File Index

Isolation audit — backend/scripts/mcp-stress-test.ts (the cross_leak step with its forbidden key :65, the leak assertion :99-101), reports/mcp_stress_test.md (ten projects, the per-project log, the summary table)

Selectors — PLATFORM_SELECTORS.md (the staleness warning and triage instruction, the weekly CI description, the resolver contract), extension/src/platform/resolver.ts (INPUT_SELECTOR_STRATEGIES), .github/workflows/selector-check.yml

Benchmarks — BENCHMARKS.md (the audit table with command, script, scope and report per row), backend/rag-audit.ts, backend/mcp-benchmark.ts, backend/generate-stress-test.ts, reports/benchmark_web.md (Recall@1, MRR, compression, per-engine contribution), reports/benchmark_mcp.md, reports/graph_stress_test.md

Documentation — README.md, ARCHITECTURE.md, RAG_PIPELINE.md, MCP_SETUP.md, SELF_HOSTING.md, TROUBLESHOOTING.md, SECURITY.md, ROADMAP.md

History

2026-08-09 — 5424ea14… — first reading. Screened before reading; the tree was read, nothing was installed, no audit was run, and the browser extension's permissions and network behaviour were not examined.