--- title: "Ripwire Review 2026: AI Repo Context Without Embeddings" canonical: https://wavect.io/blog/ripwire-ai-repo-context-review-2026/ language: en description: "Ripwire review for AI coding teams: deterministic repo context, benchmark evidence, token savings, CLI vs MCP, security, and when to use it instead of grep, Graft or Graphify." image: "https://wavect.io/img/general/bak/open_graph_preview.jpg" --- [**Back**](/blog/overview/) [![Kevin Riedl](/img/team/kevin.webp)](/team/kevin-riedl/) [Kevin Riedl](/team/kevin-riedl/) https://linkedin.com/in/wsdt 12 min read · 7 Sep 2026 Last reviewed September 7, 2026 [**Next**](/blog/mosaic-yc-s26-shared-agent-sessions-review/) # Ripwire Review 2026: Is Deterministic Repo Context Better Than Another RAG Layer? TL;DR Ripwire is a local, deterministic repository-context layer for AI coding agents. It parses code with tree-sitter, builds a symbol and relationship graph, ranks task-relevant context, and can expose the result through a CLI or MCP server without requiring embeddings or a hosted index. Its published held-out localization results are strong enough to justify a pilot, but the project's own evaluation is equally important: a Codex agent-loop pilot found that correct retrieval still increased output tokens and wall time when the surrounding skill workflow overused Ripwire. The practical lesson is to use Ripwire as a conditional narrowing tool, not as a ritual on every task. Benchmark it on frozen repository tasks, compare accepted outcomes rather than context bytes alone, and keep source inspection and tests as the final acceptance layer. **Ripwire is one of the more credible attempts to reduce coding-agent context waste without introducing another embedding database or hosted retrieval service.** It parses a repository locally, builds a deterministic symbol and relationship graph, ranks the files and symbols relevant to a task, and can tell an agent about likely blast radius, tests and quality changes before the agent opens thousands of lines of source. That architecture is attractive. The more important reason to take the project seriously is that its maintainers also publish evidence where the integration lost. Ripwire can retrieve the right context cheaply and still make an agent session more expensive if the harness turns every command into ceremony. This review therefore asks a procurement question, not a fan question: **when does Ripwire lower cost per accepted coding task?** Ripwire lives in the Red Hat Emerging Technologies GitHub organization. That provenance is useful context, but it should not be confused with a claim that Ripwire is a supported Red Hat product. The public [Ripwire repository](https://github.com/redhat-et/ripwire) is the source of truth used for this review. Independence and trademarks Wavect publishes this page and is itself a provider, so we have a commercial interest in it. We are not affiliated with, endorsed by or partnered with the other companies named here, and all third-party company names, brands and trademarks are the property of their respective owners. Statements about other providers are taken from publicly available sources, primarily their own published pages, as of the review date shown on this page, and may have changed since. Please verify them directly before you decide. This page was written to the best of our knowledge and with the intent to remain objective. If you believe anything here is inaccurate or unfair, write to us and we will correct it: [office@wavect.io](mailto:office@wavect.io) ## What is Ripwire? Ripwire describes itself as the "ripgrep of AI context." The practical idea is narrower: instead of repeatedly asking a coding agent to grep, open large files and reconstruct call relationships inside its context window, Ripwire computes a reusable structural index locally and returns compact ranked evidence for the current task. The public project is a C++23 CLI with an optional MCP server. It advertises no runtime dependency, no API key, no embedding model and no hosted index for its core code path. Its output is designed for coding agents such as Claude Code, Codex, Cursor and similar tools, but the underlying value is model-independent: spend deterministic compute to narrow the repository before spending model tokens to reason about it. ## How does the repository-context pipeline work? The [published Ripwire architecture](https://github.com/redhat-et/ripwire/blob/main/docs/ARCHITECTURE.md) describes a pipeline that crawls the repository, parses supported languages with tree-sitter, extracts symbols and references, resolves them into a graph and applies Personalized PageRank to rank task-relevant nodes. The output can then expose signatures, callers, dependencies, likely impact and test context without dumping every implementation body into the prompt. A simplified mental model is: `repository → syntax parse → symbols + references → resolved graph → task seed → deterministic ranking → compact context → coding agent` This is different from semantic vector search. An embedding can tell you that two chunks discuss similar concepts. A resolved code graph can tell you that one symbol calls another, a file imports another module, or a likely change has downstream callers. Neither approach is universally better. They answer different questions. ## Ripwire vs grep, AGENTS.md, Graft, Graphify and vector RAG | Approach | Best at | Main limitation | | --- | --- | --- | | grep / ripgrep | Exact strings, known identifiers, exhaustive text matches | The agent reconstructs relationships and relevance itself. | | AGENTS.md | Project rules, commands, conventions and human-authored operating context | It does not derive the live call graph or task blast radius. | | Ripwire | Fast deterministic task localization and structural code context | Ranking can still miss required files, and graph coverage depends on parsing and resolution quality. | | Graft repo map | Persistent source-linked repository knowledge and compact navigation | A maintained map is a different artifact from per-task structural ranking. | | Graphify | Broader knowledge graphs spanning code, infrastructure, schemas and documents | More machinery can be unnecessary when the immediate question is code localization. | | Vector RAG | Fuzzy semantic retrieval across code and prose | Similarity does not prove a call, import or dependency edge. | If you need a broad codebase knowledge graph, use our separate [Graphify review](/blog/graphify-review-codebase-knowledge-graph/). If a persistent lightweight repo map is enough, see the [Graft repo-map review](/blog/graft-review-agent-repo-map/). This article owns the narrower decision around deterministic, task-ranked repository context. ## What do Ripwire's localization benchmarks actually show? The most useful evidence is in Ripwire's [evaluation ledger](https://github.com/redhat-et/ripwire/blob/main/docs/EVALS.md), because it names the instruments, corpora, pins and counterexamples instead of publishing one context-saving percentage without provenance. One reported 60-instance held-out localization round compared several retrieval tools under the same strict file@10 metric. In the published rerun, Ripwire reported 58.3% strict file@10 and 85.0% any@10. The same table reports codebase-memory-mcp at 40.0% strict@10, repowise at 33.3%, Graphify at 31.7%, Aider repo-map at 20.0% and the better listed codeseek arm at 15.0%. | Published arm | Strict file@10 | Buyer interpretation | | --- | --- | --- | | Ripwire | 58.3% | Strong evidence that structural ranking can narrow many tasks. | | codebase-memory-mcp | 40.0% | Useful retrieval, but lower on this pinned corpus and metric. | | repowise | 33.3% | Different product shape, weaker strict localization here. | | Graphify | 31.7% | Broader graph goals are not identical to this localization benchmark. | | Aider repo-map | 20.0% | A compact map remains useful even when strict localization is lower. | Do not turn 58.3% into "Ripwire solves repository context." Strict file@10 requires all gold files in the top ten, so it is a meaningful hard metric, but 41.7% of instances still missed that standard. The corpus is also Python-heavy, localization is not patch correctness, and a benchmark run by the project is still first-party evidence. The right conclusion is that Ripwire deserves a controlled comparison on your repositories. ## The 5% token claim needs the missing half of the sentence Ripwire also publishes a 12-question Django comparison in which its frozen context path used 33,948 tokens against 685,682 for a naive grep-and-read baseline, roughly 5% of the baseline token count. That looks exceptional until you inspect answer satisfaction: the Ripwire path satisfied the strict criterion on 5 of 12 questions, while the naive baseline satisfied 11 of 12. **That result proves context compression, not equivalent task performance.** Cheaply returning incomplete context is not a saving if the agent then guesses, retries, escalates or ships the wrong patch. The commercially relevant unit is: `cost per accepted task = context + model + tools + retries + review + failure recovery` Teams should therefore measure context bytes and tokens as diagnostic metrics, not as the north-star outcome. ## The negative Codex pilot is the most important evidence in the repo Ripwire's own agent-in-the-loop pilot is unusually valuable because the treatment lost on total resource use even though localization worked. Across six Codex runs on three repositories, both baseline and treatment placed a gold-patch file in the candidate diff on all six runs. Ripwire ranked the gold file first in all three treatment runs where its retrieval was used. Yet the published treatment showed output-token overhead of about +80.2% at p50 and +105.2% at p95, with wall-time overhead of about +40.7% at p50 and +72.1% at p95. The project's diagnosis was not that the ranker failed. The surrounding agent skills loaded too much instruction text and invoked extra ritual commands on small fixes. This is exactly the failure mode engineering teams should test. A context tool can be locally efficient while the harness around it is globally inefficient. Stop rules, evidence sufficiency and tool budgets matter as much as retrieval quality. The project later changed its skill guidance, but the clean verification rerun did not complete, so the published negative result should remain the baseline until replacement evidence exists. ## CLI first or MCP? Ripwire supports both a CLI and an MCP server, but they have different context economics. A command-line tool has effectively zero prompt cost until the agent calls it. An MCP server exposes tool schemas into the model's available tool context, which can be convenient but creates a fixed context cost before any query runs. For teams optimizing context aggressively, the sensible default is usually CLI first: - Use normal search when the symbol or exact string is already known. - Invoke Ripwire when task scope, callers or blast radius are uncertain. - Request the smallest useful rung first, such as signatures or ranked files. - Open implementation bodies only after the candidate set is narrow. - Promote MCP only when discovery and tool ergonomics outweigh the always-present schema cost. ## How mature is Ripwire in September 2026? The current [Ripwire v0.4.0 release](https://github.com/redhat-et/ripwire/releases/tag/v0.4.0) was published on 7 September 2026 with prebuilt macOS and Linux artifacts. The project remains pre-1.0 and is changing quickly. Treat the exact binary, repository revision and index behavior as part of any benchmark record. That maturity level is suitable for an engineering pilot. It is not a reason to silently make Ripwire a mandatory company-wide dependency. Pin the version, maintain a fallback path, and re-run your task set before upgrading. ## Privacy and security: local does not mean risk-free The project's [security documentation](https://github.com/redhat-et/ripwire/blob/main/SECURITY.md) defines the local repository as a trust boundary and calls out memory-safety bugs, cache poisoning, path traversal, unintended file access and denial-of-service risks. The core tool does not need to upload source code to a hosted indexing service, which is a meaningful advantage for sensitive repositories. But the coding agent consuming Ripwire output may still send that output, surrounding source and task text to its model provider. Protect the generated index and cache like source-derived data, exclude secrets and customer exports, use no-cache or isolated runs for untrusted repositories, and document where the agent itself sends context. ## Ripwire does not replace AGENTS.md Structural context and operational context solve different problems. Red Hat's own developer guidance on [AGENTS.md and Agent Skills](https://developers.redhat.com/articles/2026/07/27/standardize-project-context-agentsmd-and-agent-skills) argues that coding agents need explicit project instructions for installation, testing, conventions and workflows. Ripwire cannot infer why your team forbids a migration pattern, which deployment command is safe or what acceptance evidence a reviewer requires. The strongest stack is complementary: AGENTS.md tells the agent how your project should be worked on; Ripwire helps it find which code is likely relevant to the current task; tests and review decide whether the change is acceptable. ## Who should pilot Ripwire? | Situation | Recommendation | Reason | | --- | --- | --- | | Large repository where agents repeatedly open the wrong files | Pilot | Task localization is the product's strongest evidenced use case. | | Cross-file changes with unclear callers and tests | Pilot | Graph relationships can narrow impact before model reasoning starts. | | Small service with strong docs and obvious symbols | Skip by default | ripgrep and direct reads may already be cheaper. | | Team wants a broad cross-document knowledge base | Compare Graphify or RAG | Ripwire's core strength is code structure, not every enterprise knowledge source. | | Agent harness blindly invokes every available skill | Fix the harness first | Ripwire's own pilot shows ritual integration can erase retrieval savings. | ## A 14-day Ripwire pilot 1. **Freeze 20 real repository tasks.** Include simple exact-symbol work, multi-file bugs, architecture changes and tests with known accepted outcomes. 2. **Run the current baseline.** Record files opened, context tokens, tool calls, wall time, retries, review minutes and whether the final patch passes acceptance. 3. **Add Ripwire conditionally.** Use it only when scope or dependencies are uncertain. Do not require it for obvious one-line changes. 4. **Pin the version and index state.** Record the exact v0.4.x binary or commit and whether queries are cold or warm. 5. **Force hard cases.** Include generated paths, tiny sibling symbols, cross-language calls, renamed modules and multi-file gold sets. 6. **Test stop rules.** Once enough evidence exists to edit safely, the agent should stop retrieving and start solving. 7. **Compare accepted outcomes.** A lower token bill with more review or failed patches loses. 8. **Keep a fallback.** grep, direct reads and tests must remain available when the ranker misses. ## Where Wavect fits Wavect's [AI Enablement work](/services/ai-enablement/) includes coding-agent harness design, repository-context strategy, eval sets, model routing, tool budgets and team handover. Our [Twinsoft AI case study](/case-studies/twinsoft-ai/) shows the wider principle: model capability only becomes useful when the surrounding system is engineered for production. If your agents already burn time rediscovering the same repository, the answer may be Ripwire, Graft, Graphify, better AGENTS.md files or simply stricter search discipline. The right choice should emerge from a frozen task set, not from tool enthusiasm. ## Verdict **Ripwire is worth piloting when repository localization is a measurable bottleneck.** Its deterministic local architecture is attractive, its localization evidence is stronger than most new context tools publish, and its own negative agent-loop result makes the project more credible, not less. The deployment rule is simple: do not make context engineering bigger than the task. Let Ripwire narrow uncertain work, verify against source, stop retrieving when the evidence is sufficient, and judge the system by accepted patches rather than compressed prompts. ## Ripwire FAQ ### What is Ripwire? Ripwire is a local C++23 CLI and optional MCP server that parses repositories, builds a structural symbol graph and ranks task-relevant context for AI coding agents. Its core path does not require embeddings, an API key or a hosted index. ### Is Ripwire a Red Hat product? Ripwire is hosted in the Red Hat Emerging Technologies GitHub organization. This review does not treat that repository location as evidence of a supported commercial Red Hat product or support contract. ### Does Ripwire really use about 5 percent of grep-and-read tokens? One published 12-question Django instrument measured 33,948 Ripwire-path tokens versus 685,682 for a naive baseline, about 5 percent. However, strict answer satisfaction was 5 of 12 for Ripwire versus 11 of 12 for the naive arm, so the token result is not an equal-quality cost comparison. ### Is Ripwire better than Graphify or Graft? They optimize different artifacts. Ripwire focuses on deterministic per-task structural ranking. Graft emphasizes a persistent source-linked repo map. Graphify builds a broader knowledge graph across code and other system artifacts. Benchmark the question your team actually asks. ### Should I use Ripwire through MCP? Start with CLI when minimizing always-on context matters. MCP can improve discovery and ergonomics but exposes tool schemas to the model before a query runs. Measure whether that fixed context cost is worthwhile for your agent platform. ### What should a Ripwire pilot measure? Measure accepted patch rate, files opened, tool calls, context tokens, wall time, retries, reviewer minutes and failure recovery across a frozen task set. Context compression alone is not sufficient evidence of value. Agent engineering ## Continue through this cluster Coding agents, MCP, context systems, evaluation and the controls required for dependable automation. [Start with the cornerstone**Graph Engineering for AI Agents: When Does a Knowledge Graph Pay Off?**](/blog/graph-engineering-ai-agents/) - [Model Hardware Standard: Enterprise Guide to Physical AI](/blog/model-hardware-standard-enterprise-guide/) - [Fonio AI Review 2026: Pricing, API, GDPR & Build vs Buy](/blog/fonio-ai-review-build-vs-buy-2026/) - [Mosaic (YC S26) Review: Shared Memory for Team AI Agents](/blog/mosaic-yc-s26-shared-agent-sessions-review/) - [Atomic Multi-File Edits for AI Coding Agents: The Semaprax Lesson](/blog/atomic-multi-file-edits-ai-coding-agents/) - [AI Agent Knowledge Transfer: Use Frontier Models Once, Then Scale Cheaper](/blog/agent-knowledge-transfer-cheaper-models/) [**Back**](/blog/overview/) [![Kevin Riedl](/img/team/kevin.webp)](/team/kevin-riedl/) [Kevin Riedl](/team/kevin-riedl/) https://linkedin.com/in/wsdt 12 min read · 7 Sep 2026 Last reviewed September 7, 2026 [**Next**](/blog/mosaic-yc-s26-shared-agent-sessions-review/) ## Structured Data 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"publisher": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] }, "url": "https://wavect.io/" }, { "@id": "https://wavect.io/blog/ripwire-ai-repo-context-review-2026/#webpage", "@type": "WebPage", "dateModified": "2026-09-07", "inLanguage": "en", "isPartOf": { "@id": "https://wavect.io/#website", "@type": "WebSite" }, "lastReviewed": "2026-09-07", "url": "https://wavect.io/blog/ripwire-ai-repo-context-review-2026/" } ] } ``` ```json { "@context": "https://schema.org", "@type": "BlogPosting", "abstract": "Ripwire is a local, deterministic repository-context layer for AI coding agents. It parses code with tree-sitter, builds a symbol and relationship graph, ranks task-relevant context, and can expose the result through a CLI or MCP server without requiring embeddings or a hosted index. Its published held-out localization results are strong enough to justify a pilot, but the project's own evaluation is equally important: a Codex agent-loop pilot found that correct retrieval still increased output tokens and wall time when the surrounding skill workflow overused Ripwire. The practical lesson is to use Ripwire as a conditional narrowing tool, not as a ritual on every task. Benchmark it on frozen repository tasks, compare accepted outcomes rather than context bytes alone, and keep source inspection and tests as the final acceptance layer.", "articleBody": " Blog overview/AI and agents/Agent engineering Ripwire Review 2026: Is Deterministic Repo Context Better Than Another RAG Layer? TL;DR Ripwire is a local, deterministic repository-context layer for AI coding agents. It parses code with tree-sitter, builds a symbol and relationship graph, ranks task-relevant context, and can expose the result through a CLI or MCP server without requiring embeddings or a hosted index. Its published held-out localization results are strong enough to justify a pilot, but the project's own evaluation is equally important: a Codex agent-loop pilot found that correct retrieval still increased output tokens and wall time when the surrounding skill workflow overused Ripwire. The practical lesson is to use Ripwire as a conditional narrowing tool, not as a ritual on every task. Benchmark it on frozen repository tasks, compare accepted outcomes rather than context bytes alone, and keep source inspection and tests as the final acceptance layer. Ripwire is one of the more credible attempts to reduce coding-agent context waste without introducing another embedding database or hosted retrieval service. It parses a repository locally, builds a deterministic symbol and relationship graph, ranks the files and symbols relevant to a task, and can tell an agent about likely blast radius, tests and quality changes before the agent opens thousands of lines of source. That architecture is attractive. The more important reason to take the project seriously is that its maintainers also publish evidence where the integration lost. Ripwire can retrieve the right context cheaply and still make an agent session more expensive if the harness turns every command into ceremony. This review therefore asks a procurement question, not a fan question: when does Ripwire lower cost per accepted coding task? Ripwire lives in the Red Hat Emerging Technologies GitHub organization. That provenance is useful context, but it should not be confused with a claim that Ripwire is a supported Red Hat product. The public Ripwire repository is the source of truth used for this review. Independence and trademarks Wavect publishes this page and is itself a provider, so we have a commercial interest in it. We are not affiliated with, endorsed by or partnered with the other companies named here, and all third-party company names, brands and trademarks are the property of their respective owners. Statements about other providers are taken from publicly available sources, primarily their own published pages, as of the review date shown on this page, and may have changed since. Please verify them directly before you decide. This page was written to the best of our knowledge and with the intent to remain objective. If you believe anything here is inaccurate or unfair, write to us and we will correct it: office@wavect.io What is Ripwire? Ripwire describes itself as the \"ripgrep of AI context.\" The practical idea is narrower: instead of repeatedly asking a coding agent to grep, open large files and reconstruct call relationships inside its context window, Ripwire computes a reusable structural index locally and returns compact ranked evidence for the current task. The public project is a C++23 CLI with an optional MCP server. It advertises no runtime dependency, no API key, no embedding model and no hosted index for its core code path. Its output is designed for coding agents such as Claude Code, Codex, Cursor and similar tools, but the underlying value is model-independent: spend deterministic compute to narrow the repository before spending model tokens to reason about it. How does the repository-context pipeline work? The published Ripwire architecture describes a pipeline that crawls the repository, parses supported languages with tree-sitter, extracts symbols and references, resolves them into a graph and applies Personalized PageRank to rank task-relevant nodes. The output can then expose signatures, callers, dependencies, likely impact and test context without dumping every implementation body into the prompt. A simplified mental model is: repository → syntax parse → symbols + references → resolved graph → task seed → deterministic ranking → compact context → coding agent This is different from semantic vector search. An embedding can tell you that two chunks discuss similar concepts. A resolved code graph can tell you that one symbol calls another, a file imports another module, or a likely change has downstream callers. Neither approach is universally better. They answer different questions. Ripwire vs grep, AGENTS.md, Graft, Graphify and vector RAG ApproachBest atMain limitation grep / ripgrepExact strings, known identifiers, exhaustive text matchesThe agent reconstructs relationships and relevance itself. AGENTS.mdProject rules, commands, conventions and human-authored operating contextIt does not derive the live call graph or task blast radius. 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It parses code with tree-sitter, builds a symbol and relationship graph, ranks task-relevant context, and can expose the result through a CLI or MCP server without requiring embeddings or a hosted index. Its published held-out localization results are strong enough to justify a pilot, but the project's own evaluation is equally important: a Codex agent-loop pilot found that correct retrieval still increased output tokens and wall time when the surrounding skill workflow overused Ripwire. The practical lesson is to use Ripwire as a conditional narrowing tool, not as a ritual on every task. Benchmark it on frozen repository tasks, compare accepted outcomes rather than context bytes alone, and keep source inspection and tests as the final acceptance layer.", "headline": "Ripwire Review 2026: AI Repo Context Without Embeddings?", "image": "https://wavect.io/img/blog/headers/header_ripwire-ai-repo-context-review-2026.svg", "inLanguage": "en", "keywords": "Ripwire, AI Coding Agents, Repository Context", "mainEntityOfPage": { "@id": "https://wavect.io/blog/ripwire-ai-repo-context-review-2026/", "@type": "WebPage" }, "publisher": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] }, "url": "https://wavect.io/blog/ripwire-ai-repo-context-review-2026/", "wordCount": 2756 } ``` ```json { "@context": "https://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "item": "https://wavect.io/", "name": "Home", "position": 1 }, { "@type": "ListItem", "item": "https://wavect.io/blog/overview/", "name": "Blog overview", "position": 2 }, { "@type": "ListItem", "item": "https://wavect.io/blog/topics/ai-agents/", "name": "AI and agents", "position": 3 }, { "@type": "ListItem", "item": "https://wavect.io/blog/clusters/agent-engineering/", "name": "Agent engineering", "position": 4 }, { "@type": "ListItem", "item": "https://wavect.io/blog/ripwire-ai-repo-context-review-2026/", "name": "Ripwire Review 2026: AI Repo Context Without Embeddings", "position": 5 } ] } ``` ```json { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "acceptedAnswer": { "@type": "Answer", "text": "Ripwire is a local C++23 CLI and optional MCP server that parses repositories, builds a structural symbol graph and ranks task-relevant context for AI coding agents. Its core path does not require embeddings, an API key or a hosted index." }, "name": "What is Ripwire?" }, { "@type": "Question", "acceptedAnswer": { "@type": "Answer", "text": "Ripwire is hosted in the Red Hat Emerging Technologies GitHub organization. This review does not treat that repository location as evidence of a supported commercial Red Hat product or support contract." }, "name": "Is Ripwire a Red Hat product?" }, { "@type": "Question", "acceptedAnswer": { "@type": "Answer", "text": "One published 12-question Django instrument measured 33,948 Ripwire-path tokens versus 685,682 for a naive baseline, about 5 percent. However, strict answer satisfaction was 5 of 12 for Ripwire versus 11 of 12 for the naive arm, so the token result is not an equal-quality cost comparison." }, "name": "Does Ripwire really use about 5 percent of grep-and-read tokens?" }, { "@type": "Question", "acceptedAnswer": { "@type": "Answer", "text": "They optimize different artifacts. Ripwire focuses on deterministic per-task structural ranking. Graft emphasizes a persistent source-linked repo map. Graphify builds a broader knowledge graph across code and other system artifacts. Benchmark the question your team actually asks." }, "name": "Is Ripwire better than Graphify or Graft?" }, { "@type": "Question", "acceptedAnswer": { "@type": "Answer", "text": "Start with CLI when minimizing always-on context matters. MCP can improve discovery and ergonomics but exposes tool schemas to the model before a query runs. Measure whether that fixed context cost is worthwhile for your agent platform." }, "name": "Should I use Ripwire through MCP?" }, { "@type": "Question", "acceptedAnswer": { "@type": "Answer", "text": "Measure accepted patch rate, files opened, tool calls, context tokens, wall time, retries, reviewer minutes and failure recovery across a frozen task set. Context compression alone is not sufficient evidence of value." }, "name": "What should a Ripwire pilot measure?" } ] } ```