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jevXagent

jevXagent is a localhost proxy that sits in front of Claude Code, Codex, Kilo Code and Cline. It asks Jev to classify the prompt first, then injects that verdict into the agent's system context so the main model skips re-deriving the same classification.

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2
Language
Python
License
MIT
Published by
Community
Author
j1s4nn
Last verified
Oct 2026

j1s4nn/jevXagent

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When Jev meets LLM — Transparent proxy that makes AI coding agents 3× faster and 80% cheaper. Cut response times from 2.4s to 890ms with Line J architecture.

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1
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Updated Sep 2026

Repo stats from the GitHub API, cached Oct 2026.

About this project

Cut AI Agent Response Time by 70% & Save 80% on Tokens. The transparent proxy that makes your AI coding agents faster, cheaper, and smarter. Quick Start • Features • How It Works • Stats • Docs. jevXagent is a transparent proxy that supercharges your AI coding agents (Claude Code, Codex, Kilo, Cline) by routing prompts through Jev's blazing-fast decision engine before the main LLM generates a response. Your AI agent wastes time and tokens doing the same classification work over and over:

Quoted from the project's README. Read the full README.

Highlights

  • The injected system message carries category, urgency and action, so the agent generates instead of spending output tokens working out what kind of task it is.
  • Fail-open by design: if Jev times out the request still goes through, and images and PDFs skip Jev automatically.
  • Per-request metrics print Jev latency, token counts and agent time-to-first-token, and every request is appended to a JSONL statistics file.
  • A jev_enabled toggle in ~/.jevxagent/config.json switches Jev off so you can measure the difference on your own prompts.

Watch out

The numbers are the project's own, taken from a chart in its statistics folder and a table in the README: response time 2,400 ms to 890 ms, output tokens 240 to 48, cost per request $0.0072 to $0.0018, with no prompt set, sample size or methodology given. The whole premise is that classification is wasted work in your agent, which holds up for repetitive, clearly-typed prompts and much less for open-ended ones. Setup is manual: clone, pip install -e ., then answer prompts in an interactive run, and the proxy binds to localhost on a fixed port. The 19 passing tests cover adapter detection, injection, end-to-end flows, fail-open handling and statistics persistence; a web dashboard, Docker packaging, model-specific pricing and Prometheus export are still unchecked roadmap items. Copy reviewed against the repository README on 2026-10-04, not from running it.

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