Greppy – A drop-in grep with code-nav subcommands for AI agents

Greppy, a drop-in grep replacement with code-navigation subcommands for AI agents, improves agent accuracy on structural code questions from ~53% to ~87% while using fewer tokens. The Rust binary includes built-in symbol graph and semantic search, requiring no external indexing. Early benchmarks show 2.3× faster resolution and ~9× fewer input tokens compared to plain grep.

Standard grep, plus a few commands your coding agent uses to navigate code — who-calls, impact, semantic-search, brief. On structural code-navigation questions the agent answers correctly ~87% of the time instead of ~53% with plain grep — using fewer tokens. One native Rust binary. greppy is a drop-in grep — every flag works exactly as before — that also answers the questions an agent normally burns rounds on: who calls this function, what breaks if I change it, where is the code that does X. One line in your agent's config below tells it the extra commands exist, and it stops looping through text matches. Standard grep — every command works, unchanged: greppy -rn "TODO" src/ greppy -i "connection refused" server.log A few extra commands, on the same binary: greppy who-calls parse config who calls this function greppy impact User --direction incoming what breaks if I change User greppy semantic-search "restrict a value to a range" find code by meaning greppy brief split blueprint path definition + callers + callees The same coding agent MiniMax-M3, driven by Pi Code answers one who-calls question on a real repo — left with plain grep, right with greppy. greppy resolves the callers in a single greppy who-calls call instead of a grep-and-read spiral: 2.3× faster, 14 → 5 tool calls, ~9× fewer input tokens. Counters are live from the recorded run. 1. Install the binary. cargo build --release --bin greppy --features embedded-model sudo install -m 0755 target/release/greppy /usr/local/bin/greppy Everything is automatic — the code graph and the semantic model are built into the binary and build themselves on first use. Nothing to index, nothing to download, no flags to configure. Prebuilt binaries for macOS / Linux / Windows are on the Releases /metric-space-ai/greppy/releases page. Want it as a transparent grep drop-in too? Install it a second time as grep . 2. Tell your agent the extra commands exist. Delegate it — in your agent's chat, say install https://github.com/metric-space-ai/greppy/ — or paste the snippet below into the file your agent reads for project instructions CLAUDE.md , AGENTS.md , .cursor/rules , .windsurfrules , or the system prompt . This project has greppy — standard grep plus a few code-navigation commands over a prebuilt symbol graph and an on-device semantic index. Every normal grep invocation and flag works exactly as usual. CODE-NAVIGATION COMMANDS. SYMBOL is a function / method / class / type name. They return resolved results as qualified name file:line , not text matches: greppy who-calls SYMBOL the callers of SYMBOL incoming calls greppy callees SYMBOL the functions SYMBOL calls outgoing calls greppy find-usages SYMBOL every reference to SYMBOL calls, uses, imports greppy brief SYMBOL SYMBOL's definition plus its callers and callees, in one call greppy impact SYMBOL the transitive set of code a change to SYMBOL reaches greppy search-symbols NAME definitions whose name matches NAME a name or fragment greppy path --from A --to B a call chain from symbol A to symbol B, if one exists SEMANTIC SEARCH — use when you do NOT know the symbol's name: greppy semantic-search "PLAIN-ENGLISH DESCRIPTION" Describe the behaviour or code you are looking for in plain English e.g. "restrict a value to a range", "retry a failed HTTP request" . Returns the closest-matching definitions by meaning signature + file:line . EXPAND — get the full source in one call instead of opening files by hand: greppy expand ID who-calls / callees / impact / semantic-search may end their output with a line Expand: greppy expand <id . Run it to print the prepared evidence pack — the full source of the top matches, bundled — in a single call, instead of reading each file:line yourself. FLAGS append to any command above : --code include each result's source lines so no separate read is needed --all return every result turn off the default truncation --json machine-readable output with exact counts --root DIR run against a repo other than the current directory --kind KIND search-symbols restrict to function|method|class|struct|enum|trait --direction incoming|outgoing, --depth N impact which way and how far to walk --from A --to B path the two endpoint symbols Prefer these over grepping a symbol name and reading every hit: who-calls / callees / impact answer relationship questions directly, and semantic-search finds code you cannot name. What an agent actually pays for is billed tokens and wall-clock time. The benchmark: 14 coding-agent models Claude Opus/Sonnet/Fable, GPT-5.5, Gemini, Grok, DeepSeek, Qwen, GLM, Kimi, MiniMax-M3, … , each driven by Pi Code https://pi.dev , answer 35 code-navigation questions across 7 real repositories Rust serde + tokio , Python flask + django , Java gson , TypeScript zod , Go hugo . Every task runs twice — once with plain grep , once with greppy , same agent, same prompt. Answers are floor-graded : a pass must name the ground-truth symbol/file each anchor rg-verified at generation time . The harness is in bench/agent efficiency/ /metric-space-ai/greppy/blob/main/bench/agent efficiency . Correctness is the headline. On structural navigation — who-calls , callees , impact/blast-radius , find-symbol — the agent answers correctly 87% of the time with greppy vs 53% with plain grep graded by the repo's own grade answers.py /metric-space-ai/greppy/blob/main/bench/agent efficiency/grade answers.py . Across all 35 questions: 90% vs 63%. Plain grep is cheap but frequently confidently wrong; greppy resolves the relationship in one call. | On structural navigation questions | grep | greppy | |---|---|---| Answered correctly floor-graded | 53% | 87% | Input tokens median · mean saving | 1× | 1.2× · 2.3× fewer | Search-context tokens median · mean | 1× | 1.7× · 5.1× less | Output tokens median | 1× | 1.2× fewer | So it is not a cost-for-accuracy trade: on structural questions greppy is both more correct and cheaper. Where plain grep keeps up: open-ended "how does this subsystem work" questions. Both tools reach the answer there ~98% correct either way , but greppy's precise locator makes the agent read more to explain the mechanism , so it costs a little more . greppy's edge is pinpoint / structural questions — the semantic path is being tuned to also lead the agent to the answer in one step. The gain depends on the model. Priced at each model's real OpenRouter list rate, the actual dollar cost of the structural tasks drops with greppy for most models — a median of ~16% cheaper — but it swings widely from +54% on MiMo to −84% on Grok 4.3, which spirals and does not track a model's general agentic-benchmark score. Benchmark your own model — most come out ahead, and every model gets the correctness lift. Real cost = each model's OpenRouter list price × tokens, summed over the structural tasks. The scatter shows the efficiency gain is real for most models but genuinely model-dependent, not something a model's agentic rank predicts. Standard grep. Any invocation that isn't one of the extra commands runs real grep and returns its output and exit code unchanged. A precomputed code graph. An indexed, typed symbol graph CALLS / USES / TYPE REF / IMPORTS answers who-calls / callees / find-usages / impact / path directly — resolved relationships with file:line , not text matches — collapsing several grep+read rounds into one call. Native semantic search. For a natural-language query that shares no words with the code, semantic-search embeds the query with Google's EmbeddingGemma on greppy's own native Rust inference CPU / Apple Metal / NVIDIA CUDA, auto-detected — no llama.cpp, no Python, no HTTP and returns the nearest code spans by meaning. A small warm daemon keeps the model resident between calls and drops it after idle, so it never holds GPU memory while you're not searching. One native Rust binary. The EmbeddingGemma model is baked into the binary; tree-sitter parsers and SQLite are compiled in statically. Early and evolving — the drop-in grep core is solid; the intelligence layers around it are beta. Solid: the grep drop-in and the code-graph commands who-calls / callees / find-usages / impact / path / brief on supported languages. Supported languages 107 : python , csharp , go , cpp , php , rust , swift , scala , c , java , javascript , typescript , ruby , bash , kotlin , fsharp , julia , ocaml , d , gdscript , zig , elm , erlang , crystal , gleam , objc , solidity , prisma , protobuf , css , dockerfile , json , groovy , lua , sql , make , nix , cmake , dart , fortran , elixir , scheme , vue , astro , svelte , verilog , glsl , hcl , matlab , r , purescript , racket , clojure , haskell , cuda , tcl , graphql , pascal , powershell , html , yaml , hlsl , cobol , fish , ini , vhdl , json5 , awk , cairo , ada , hare , kdl , jsonnet , llvm , janet , jinja2 , bicep , gotemplate , just , devicetree , liquid , assembly , hyprlang , gn , blade , cfml , cfscript , csv , bibtex , beancount , gitattributes , markdown , toml , xml , scss , perl , fennel , starlark , ron , dotenv , properties , po , diff , rst , mermaid , regex , linkerscript . More land in each release. Beta: semantic-search — the on-device EmbeddingGemma inference is solid and the model ships inside the binary. Newer than the graph commands, so still labelled beta. Not yet production-ready — use it as a fast code-navigation aid, not a system of record. MIT — see LICENSE /metric-space-ai/greppy/blob/main/LICENSE . Third-party notices: THIRD PARTY.md /metric-space-ai/greppy/blob/main/THIRD PARTY.md .