sift-light

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sift-light — Evidence-first search for AI agents.

A general-purpose local search plugin that helps agents find files, documents, notes, logs and other text material.

Think of a patient librarian: describe what you need, and it helps locate the shelf, open the relevant page and follow the next lead. A small search brings the passages straight to you. A broad search starts with a map so you can decide where to look first.

How it helps

Find a passage without opening every folder

Looking for an error message, a sentence or a name is like giving a librarian a keyword. When there are only a few matches, the plugin returns their text and locations directly, saving repeated file opening and scrolling.

Start with a map when the collection is large

“Which documents mention refunds?” can produce a lot of material. The plugin first presents matching files and snippets, like marking promising stops on a map. The agent can choose what to open before filling the conversation with entire documents.

Keep your bookmark for the next question

“Continue from where we stopped” can follow the existing result to its next page. The agent can also open the surrounding text of a match, like returning to a bookmarked passage to read what came before and after it.

Recover when the remembered wording is not exact

Use mode: "hybrid" with one natural-language query when a sentence may have been remembered with different wording. Hybrid always runs an exact literal search and the installed local Concept model under one owned request. Exact evidence appears first; semantic candidates are clearly labeled, ranked only by similarity and removed when they overlap an exact match. The initial page shares counts, coverage, source references, one inspection cursor and a compact preview instead of concatenating two complete responses. conceptLimit changes only the non-overlapping semantic supplement (default 3, maximum 20); it never displaces literal evidence. The returned matches request opens the same snapshot's complete exact-first pagination without rerunning either search.

Concept ranking covers every UTF-8 passage admitted by the request's documented source budget; it no longer samples a fixed prefix of the scope. Concept and hybrid searches automatically admit up to 2,000 files and process them sequentially in bounded 200-file batches, then merge every batch into one global ranking and one coverage result. Batches share the request's 32 MiB read budget, so raising the file ceiling does not multiply the content budget. Users do not need to plan or continue batches themselves. maxFilesToParse remains an optional advanced hard ceiling when a smaller scope is intentional. Passages that exceed the model token window are ranked through overlapping token-safe windows, so later text is not silently discarded. Offline embeddings are cached by content, model revision and chunking revision in a bounded 512 MiB local cache. Repeated content is reused, changed content misses naturally, and cache write or cleanup failures remain visible in the result.

Slow Concept and hybrid requests return within the default five-second wait window with status: "waiting" or "running", an operationId, progress and an exact nextRequest such as { "mode": "await", "operationId": "..." }. Copy that request unchanged: it resumes the same computation and never restarts the query or downgrades to a literal-only result. A final result remains available for stable re-fetch for 10 minutes, with up to 32 terminal results retained per service session, and mode: "cancel" stops the owned work and waits for cleanup. Each service session admits at most eight pending operations; an operation has one total deadline controlled by SIFT_LIGHT_CONCEPT_TIMEOUT_MS (integer milliseconds from 1000 through 3600000; default 600000) and a 120-second idle continuation lease. A real model, source or resource failure is returned as a failure with its diagnostic. Source generation is re-enumerated and re-verified before publication, so changes refresh the operation and mixed versions are never marked complete. Admission planning counts (filesEnumerated, filesAdmitted, filesSkippedEmpty, filesUnavailable, passagesQueued, batchesPlanned, batchesCompleted) stay visible. Empty files are a normal skip and do not mark the result partial.

Give several search conditions together

“Find files mentioning both the customer and a refund” works like selecting documents with two labels. “Any of these words will do” works like handing over a shortlist. Multiple conditions can be expressed together to reduce repeated searches.

Choose the drawer you want searched

Ask the agent to restrict a search to one folder when that is the scope you need. If you remember only part of a filename, start by finding the file and then inspect its contents—like narrowing a cabinet down to a shelf and then a document.

Multi-word files queries require each word literally in the path; a single abbreviation still supports fuzzy matching. Use hybrid or concept for business intent. Ordinary content searches retain their default zero-result expansion; use scope: "strict" to stay within an explicit path. Expansion is announced before returned evidence.

Know what has been shown

Long results arrive in pages with a way to continue. When the original material changes, the plugin asks for a fresh check. Like a careful research assistant, it distinguishes the passages already shown from the pages still to come.

A complete snapshot describes match retention, not complete source text. Truncated matching-line excerpts show their limit and an executable inspectRequest, which remains usable after the final match page. Follow pagination cursors instead of repeating the query with a different limit. In Pi and OMP, the passive session status includes the loaded package version, counts returned new queries, distinguishes complete, partial and unfinished results, and reports non-cancelled failed calls. Cursor and operation continuations do not inflate the new-query count.

Ordinary searches keep repository ignore rules, but now report policy-filtered filesystem coverage instead of calling an absence exhaustive when ignored files exist. Use mode: "audit" with named literal patterns for one bounded receipt covering declared scope, enumerated/searched/skipped files, ignored-file policy, per-pattern present/absent_with_complete_coverage/unknown findings, and start/end source stability. Set ignorePolicy: "include" when an audit must include ignored configuration and generated files; .git internals and protected paths remain excluded.

Discover language capabilities before loading a provider

Use mode: "capabilities" with the project root for a compact, names-only inventory. JavaScript, TypeScript and TSX support AST structure, roles, outline, static imports and related-test candidates. Go supports AST structure and roles. Python supports bounded indentation-based outline. Swift and other languages remain available to ordinary content search, filename discovery and source inspection. Capability inventory does not start parsers or the Concept model.

Language-service navigation is out of scope. Asking for definitions, references, implementations, callers, callees, dependencies, dependents, trace or impact fails explicitly, because a text search dressed up as precise navigation would be a worse answer than a clear refusal. No language server runs while you work.

Check saved source evidence

Use mode: "validate" with a saved ordinary-search or analysis cursor, optionally selecting a matchIndex. Validation compares retained sources with the current worktree or their pinned Git objects, reports current, stale or unknown, and preserves incomplete search coverage. Structured metadata is under details.validation and details.analysis.validation; the removed graph fields and trace cursors are no longer supported. Validation checks sources on demand and does not run background watchers. It checks saved evidence, not whether new matching files have appeared since the search.

Narrow by file age or inspect code structure

Worktree searches can use modifiedAfter and modifiedBefore as Unix millisecond bounds. The lower bound is inclusive and the upper bound is exclusive, so a time window can be expressed without changing the search pattern. The same filter applies to content and filename searches; unavailable file metadata is reported as incomplete evidence rather than silently treated as a match.

Use mode: "outline" with a concrete JS/TS/TSX or Python file to see bounded symbol ranges. JS/TS/TSX use ast-grep; Python uses indentation-based class, function and method evidence. These ranges do not prove compiler bindings, runtime calls or test coverage. mode: "tests" provides JS/TS/TSX related-test candidates; unsupported language operations fail explicitly. Use ordinary search and inspect for Swift source.

The readable result keeps the main evidence compact. Per-item ranges, counts, coverage and continuation requests remain in structured details, so a client can use the structured fields without requiring a second search.

Common uses

Tell your agent what you need, for example:

• “Which documents mention the refund deadline?”
• “Find this error in the logs and show the surrounding messages.”
• “Find files containing both the customer name and the order number.”
• “List matches for any of these keywords.”
• “I remember part of the meeting-notes filename. Help me find it.”
• “Search only this folder; do not expand the scope.”
• “Show me which files contain relevant text, then open two of them.”
• “Continue from the previous page and show the remaining passages.”
• “Search files modified since this Unix millisecond timestamp.”
• “I may remember this sentence