Jevgrep is an MIT-licensed CLI (`jg`) that answers a natural-language question about a repository with relevant files, reading leads, and verbatim source excerpts in one stdout response, using Jev to judge relevance across folders, files, and declarations. **Jevgrep is the no-index counter-bet in this category: instead of building and maintaining an embedding or graph index, every query pays a model to judge relevance on the spot, and the author's own ten-task run prices that trade at roughly 29 percent lower agent cost at equal task success.** ## What it is **You ask what the code does, and `jg` returns where it lives.** A query like `jg "How are telemetry events recorded and sent?"` explores the repository hierarchy, selects files using content previews, identifies useful source units, and prints a summary, a compact file list, and source excerpts with line references to stdout. It uses [Jev](../../hybrid-execution/jev/index.md) to judge relevance with boolean questions over folders, files, and declarations, and requires Node.js 22+ with a key for Vercel AI Gateway, TypeSafe, OpenRouter, OpenCode Zen, or any TypeSafe-compatible endpoint. Declaration parsing covers Python, TypeScript/JavaScript, Go, and Rust, with a content-preview fallback for other text. The MIT-licensed CLI ships from David Zhang (dzhng), the engineer behind deep-research, alongside an agent-skill installer (`jg skill`) that wires Claude Code, Codex, OpenCode, and other detected agents to reach for it. ## Status **Eleven days old and the fastest start in this category, with a thin discussion footprint so far.** 2,355 stars, 171 forks, and 25 open issues and pull requests since the repository appeared on 2026-09-26, pushed 2026-10-02, all as of 2026-10-07 (GitHub API). Star History Chart The npm package `@dzhng/jevgrep` sits at v0.8.0 (published 2026-10-01) across 16 releases in its first week, with 5,869 trailing-month downloads (window 2026-09-05 to 2026-10-04). Its Show HN (2026-09-28) drew 5 points and zero comments, so adoption is running on the author's reputation and the skill distribution rather than discussion. The earlier run that rejected this repository as a harness candidate recorded it three days into its life; the star count and a proper category fit resolved it as a note this run. ## Strengths - **No index to build, update, or trust**: the search runs against the working tree, so there is no stale-index failure mode and nothing to wire into CI. - The agent skill is unusually disciplined: it tells the agent to start behavioral discovery with `jg` but to use grep and direct reads for exact symbols, which is the right division of labor and a discipline few skill descriptions attempt. - The benchmark reporting is the category's most candid: single-run caveats in its own text, a separate total-cost rerun including the Jev bill (25.8 percent lower), and an explicit statement that the runs establish no statistical equivalence. - It composes with anything that reads stdout, so it needs no MCP server, no daemon, and no per-agent integration work beyond the skill. ## Cautions - **Every query sends eligible source content to a model provider**: the docs say default filtering excludes credentials and dependencies but is not a guarantee, so the search root is a privacy decision. - Every query costs provider tokens, so a team that searches constantly may pay more than an index would charge after its one-time build, and the vendor-run benchmark covers ten tuned Python tasks from one repository. - Pre-1.0 with a v0.x CLI, one maintainer, and breaking changes already recorded (0.3.0 replaced environment-based credentials with `jg auth`). - Declaration-aware output is limited to four language families, and queries fall back to content previews elsewhere. - The star count is eleven days old; the same velocity that looks like momentum is also the easiest number to fake with a launch push, so the footprint deserves a re-read before this note's framing ages. ## Pricing **Free and MIT-licensed; the meter is your provider's model bill.** The CLI has no account and no paid tier, but each query runs Jev judgments under your own key (Vercel AI Gateway, TypeSafe, OpenRouter, OpenCode Zen, or a custom TypeSafe-compatible endpoint), so the running cost is the provider's usage at its published rates. The author's own total-cost measurement puts the Jev share at roughly a quarter to a third of the savings it produces. ## Compared to - [Semble](../semble/index.md): the local-index counterpart, free queries after an index build; choose Semble for repeated searching on one machine, Jevgrep when you do not want the index at all or the question is behavioral rather than similarity-based. - [Sourcegraph code context platform](../sourcegraph-code-context/index.md): indexed cross-repository search at enterprise price; Jevgrep is one repo per query, but starts at zero dollars and zero setup. - [Repomix](../repomix/index.md): the inclusion-first alternative that packs the whole repo; Jevgrep selects the slice instead, paying per query rather than per token packed. ## Bottom line **Recommended for agents starting unfamiliar multi-file work in repositories where nobody maintains an index, and for teams already paying metered model bills who will measure savings on their own tasks.** Not for exact-symbol lookups (grep wins), teams that cannot send source to a model provider, or anyone who needs proven savings beyond one author-run benchmark. My disagreeable claim: every index in this category is a cache standing in front of model judgment, and Jevgrep's numbers hint that the cache no longer pays for itself in the start-of-task discovery case, which would make the maintained index the legacy option here within a year. ## Changes - 2026-10-07 - Created from the 2026-10-07 entrant scan (the GitHub created-after search), superseding the 2026-09-29 harnesses-routed rejection after the repository reached 2,355 stars, with the no-index framing and the vendor-run-benchmark caveat recorded. ## See also - [Context Engines Feature Matrix](../context-engines-feature-matrix/index.md) - the category comparison this note joins as the eleventh column - [Semble](../semble/index.md) - the local-index counterpart answering the same where-do-I-start question - [Jev](../../hybrid-execution/jev/index.md) - the model doing the relevance judging under every query - [Semantic code search in coding tools](../../retrieval/semantic-code-search/index.md) - the indexed-retrieval pattern this tool skips - [Agentic Coding Tools Landscape](../../agentic-coding-tools-landscape/index.md) - where the context-engine layer sits in the four-layer map ## References - https://api.github.com/repos/dzhng/jevgrep - repository stats: 2,355 stars, 171 forks, created 2026-09-26, pushed 2026-10-02, as of 2026-10-07 - https://raw.githubusercontent.com/dzhng/jevgrep/main/README.md - architecture, benchmark tables with per-run caveats, privacy and caching notes - https://raw.githubusercontent.com/dzhng/jevgrep/main/skills/jevgrep/SKILL.md - the agent skill surface and the grep-for-exact-symbols division of labor - https://raw.githubusercontent.com/dzhng/jevgrep/main/apps/cli/README.md - auth flow, supported providers, `jg doctor`, cache controls - https://registry.npmjs.org/@dzhng%2Fjevgrep - the package, v0.8.0 latest, 16 versions since 2026-09-26 - https://api.npmjs.org/downloads/point/last-month/@dzhng/jevgrep - 5,869 downloads, window 2026-09-05 to 2026-10-04, fetched 2026-10-07 - https://github.com/dzhng/jevgrep/releases - the release train from v0.4.4 to v0.8.0 (2026-09-28 through 2026-10-01) - https://news.ycombinator.com/item?id=49880146 - the Show HN: 5 points, zero comments, 2026-09-28 (verified via the Algolia items API) - https://api.github.com/repos/dzhng/deep-research - the author's track record: 19,764 stars on deep-research as of 2026-10-07 - https://api.github.com/users/dzhng - the author's profile: David Zhang