logprobs

Open-source alternative to TypeSafe's Jev: a System One style model layer that gives typed, calibrated decisions from any open-weights LLM in one forward pass (HF + vLLM), with honest benchmarks

LLMxRay — Local LLM Observatory. Full observability interface for Ollama: chat, streaming, reasoning, RAG, introspection, metrics.

Rust CLI and web UI that intercepts an LLM token stream live: per-token confidence and perplexity, on-the-fly token mutation, provider and A/B prompt comparison, replay and heatmap export. OpenAI and Anthropic.

Super fast Jev like server in JS, model agnostic, working with any OpenAI compatible endpoint

Typed choices and probability distributions from text and images with llama.cpp, Ollama, and OpenRouter

Rust CLI and library that flags low-confidence spans in LLM answers from token logprobs (offline or live via any OpenAI-compatible API) and renders per-token confidence as terminal, HTML or Markdown.

Jev-shaped (TypeSafe System One) classification wrapper over OpenAI-like clients

Typed decisions from local LLMs in one forward pass, via Ollama. No generation loop, no JSON parsing.

An experimental approach for AI search visibility.

Calibrated yes/no, pick-one and scale judgments from your own LLMs, on text and images, read straight off the logprobs. Runs on vLLM.

A pre-decoding detection method for system prompt leakage and RAG leakage attacks.

Typed LLM decisions in one forward pass: choice, score and yes/no with real probabilities from logprobs. TypeScript port of decidr.

A TypeScript library that answers a decision question with a probability distribution over every option, using any existing LLM

Typed, calibrated decisions from any LLM: yes/no, choice, rating and grounded extraction with honest probabilities. Claude, GPT, Gemini, vLLM, Ollama or Hugging Face. Jev-compatible API server.

Free, open-source LLM hallucination detector. See token-level confidence as a heatmap and check every claim against your own sources (RAG). Works with any OpenAI-compatible API, ChatGPT & Claude. Zero dependencies, runs locally.

Jev-style /v1/systemone API in front of any OpenAI-compatible LLM server (one-token logprob scoring, MIT)

Jev-style typed decisions (Choice / Noul / Score) from any model whose API exposes logprobs, plus a local /v1/systemone server

A decision layer for agent control flow — yes/no, choice and rubric questions answered from one token's probabilities, measured against labelled sets — with a Claude Code permission hook and a trainable local judge

Interactive Streamlit app that visualises token-level log probabilities from Azure OpenAI

Self-hosted System One decision engine (TypeSafe Jev's Choice/Score/Noul contract) running locally on LM Studio, with real probabilities from token logprobs.