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Fable Jev

Dual-brain routing that pairs Jev reflex judgments with get-fable deterministic invariants, plus lossless context compaction and shadow-mode telemetry.

See highlightsOpen github.com
Stars
10
Language
TypeScript
License
MIT
Published by
Community
Author
imMamdouhaboammar
Last verified
Oct 2026

About this project

Where TypeSafe AI's System One (Jev) meets get-fable's Deterministic Lifecycle Engine. Architecture • Fast Compaction • Awesome Jev • Playbook • Benchmarks • Roadmap • Parent Repo. The dirty secret of modern AI coding agents is that they are cognitively imbalanced: They are sluggish and token-hungry: Asking a 70B+ reasoning LLM to generate paragraphs of Chain-of-Thought just to pick which tool or skill to trigger next wastes 2 to 5 seconds and thousands of tokens per step.

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

Tagscommunityawesomejevcatalog-import
Works forJev for ClassificationJev for Tool calling
Used inJev for Software & DevTools
Languages
TypeScript

Highlights

  • Choice, Score, and Noul primitives return typed probabilities without prose generation.
  • Route fusion lets deterministic invariants override Jev on safety-critical matches.
  • Two-stage disambiguation shortlists candidates then checks semantic fitness before commit.
  • Shadow mode, 1200ms circuit breaker, and verbatim fast compaction with telemetry logging.

Watch out

Latency, cost, and token-reduction figures come from the repo docs and benchmark pages, so confirm them in your own runs before sizing a system on them. Copy reviewed against the repository README on 2026-10-04, not from running it.

Questions about this entry

Short answers drawn from the linked project or page.

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