Jev Filter

[Jev Filter: give your agent the signal. Collect once, decide with context, return the proof.]

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简体中文 · Quick start · Documentation · Agent setup · Benchmarks

Jev Filter

Semantic filtering for AI agent tool results. Turn large tool outputs into compact, typed decisions before they enter your agent's context. Keep source records inside your program, return relevant evidence and unresolved IDs, and recover originals by ID.

• Filter with context. Screen search results, complete code symbols and correlated logs against your task and supplied facts.
• Keep decisions traceable. Return typed answers, source IDs and review items; preserve missing facts and partial failures.
• Verify actions in your program. For short browser and desktop goals, Jev selects observed controls; the program owns execution, safety gates and final-state checks.

An independent MIT project maintained by Apixly. Official npm package: @apixly/jev-filter. What it does and when to use it.

Discover and connect: View the MCP Servers listing ↗ · Set up a client and four read-only tools →

Quick start

Node.js 22+ · macOS / Linux · Python included in the npm distribution. Windows: use WSL or install Python natively. Installation and doctor are offline; inference needs a TypeSafe Jev API key.

npm install -g @apixly/jev-filterjev-filter doctor

Run this from any directory:

export TYPESAFE_API_KEY='your-key'
jev-filter query --input - --mode choose \  --task 'Choose the CURRENT unresolved DNS failure' <<'JSON'[  {"id":"a","text":"DNS recovered; requests now succeed."},  {"id":"b","text":"DNS lookup still fails before connection."}]JSON

Expected selection, with diagnostics omitted:

{"selected_ids":["b"],"review_ids":[],"complete":true}

For real batches, collect and filter in one call, before raw output reaches the conversation:

jev-filter exec --task 'Find unresolved network failures' \  --analysis analysis.json -- your-collector --json

Start with the copyable analysis contract. Parse the packet even on exit 2: complete=false and review_ids need attention. Small examples teach the interface; exact paths, IDs, selectors, calculations and short outputs usually belong in native tools. Installation, keys and result handling →

Practical tutorials

• Filter search results for RAG and AI agents: select passages against explicit evidence requirements before they enter the main model's context.
• Search code by behavior: combine complete code symbols, optional bounded recall and semantic judgment.
• Triage correlated logs: preserve request history and separate current failures from recovered events.

Copyable synthetic inputs and analysis contracts are included. Read the tutorials on the documentation site · FAQ.

Measured advantages

Paid live evidence from 2026-10-04, using synthetic inputs. Source hashes, per-run usage, timing and failures are public; each result belongs to its stated scope.

• Returned tool context: 91.6–97.2% less across 24 real agent runs. Filtered completion was 12/12, raw completion 11/12, and all 24/24 ID sets were correct.
• Same-output selection: Jev's batched API path screened 96 records in a median 1.313 s, versus 13.421 s for Luna and 14.543 s for Astra through signed-in Codex CLI. All 9/9 runs selected the same 48 IDs; CLI startup is included, and no primary-agent continuation follows Jev.
• Jev-stage batching: 2 requests instead of 96, 56.5% fewer input tokens and 2.58× filtering speed versus single-record parallel Jev calls. This measures the Jev stage, not a whole-agent speedup.

Latency and cost vary. Five of six whole-operation cells were slower by 0.1–18.4%; cold API-equivalent cost ranged from 23.2% lower to 1.1% higher. Subscription billing remains unknown. Smaller context does not guarantee lower latency or billing.

Methods, negative results and reproduction · Per-run whole-operation data

Watch the program turn a goal into a verified result

[Recorded synthetic shop run: Jev selects observed controls, the program pauses before placing an order, and verification follows confirmation]

Each step observes the page or window, lets Jev choose from program-enumerated operations and controls, rechecks the target, executes and verifies. Model output never becomes a selector, coordinate, command or unvetted text. Irreversible actions pause for confirmation.

jev-filter browse --url https://shop.example/ \  --goal 'Find in-stock red shoes in size 42' \  --value query='red shoes' --verify-text 'Search results'

Replace the example URL with your permitted test page and meaningful verifier. For browse and desktop, require status: done and an independent final-state check; extract and survey require ok and complete. A selected action or model-reported DONE alone is not proof of completion. A separate verified primary-planner reference reached 6/6 expected outcomes after strict review; it adds substantial model time and is not an automatic fallback shipped in the product.

Interactive replay: browser, desktop, survey · Recording method · Safety gates and limitations

One core, your existing agent

CLI · Python · JavaScript / TypeScript · MCP. Use the shell, embed jev_filter.batch.run, import createClient from @apixly/jev-filter, or expose four read-only MCP tools. Each route keeps evidence and partial results in the same core. JavaScript and MCP examples →

Install the supplied skill for Codex, Claude Code or another compatible harness:

# Codex; use ~/.claude/skills for Claude Code.mkdir -p ~/.codex/skillscp -R "$(npm root -g)/@apixly/jev-filter/skills/jev-filter" ~/.codex/skills/

Give the agent one routing rule:

Use jev-filter for many records that need a clear semantic judgment.Pass the task, scope, exclusions, success criteria and sourced facts.Keep collection → analysis → compact output inside one tool call.Inspect unresolved IDs; use native tools for exact or short work.Do not repeat settled judgments or wrap an existing Jev workflow again.

Jev does not inherit the agent's chat. Put shared facts in context, history on each record, and declare required fields. Keep authorization and execution verification in your host program. Five-minute setup → · Full integration guide →

Inspectable from input to release

• Bounded, uncached inference. Automatic batching and at most 30 requests in flight; no result cache.
• Local evidence and numeric telemetry. Private archives support original-by-ID recovery. An optional local dashboard shows usage, unknowns and negative net values; input-equivalent estimates are not invoice savings.
• Explicit trust boundaries. Inference inputs go to TypeSafe. exec runs your trusted command and is not a sandbox. Hosted execution keeps freshness, origin and desktop safety gates. Security policy
• Portable, verifiable releases. Python core, bundled npm platform packages, checksums and build provenance. Distribution and recovery

The legacy jev-context command and Python imports remain compatible. Jev Filter is independently maintained by Apixly / JIA-ss and is not affiliated with TypeSafe.

Contribute

git clone https://github.com/apixly-ai/jev-filter.gitcd jev-filterpython -m venv .venv && . .venv/bin/activatepython -m pip install -e '.[code,dev]'sh scripts/check.shnpm test

Contributing · Development and releases · Changelog · Report a bug · MIT license