decider

Strands Decider is a small decision model for agentic AI workflows, developed for use with the Strands Agents SDK. It selects among options, answers yes/no questions, or scores inputs against an ordered rubric, returning a calibrated confidence value for each decision. These capabilities support tasks such as model routing, tool selection, argument checking, triage, guardrail checks, and evaluation. The model uses a pretrained decoder LLM torso with its language-modeling head replaced by a pointer head. The head compares hidden states at the answer position and at each option's final token, scoring all options in one forward pass without text generation or a decoding loop. The same masked-softmax design supports its choice, noul, and score question types. The repository includes a command-line interface, a local HTTP server, an example that gates a Strands agent's tool call, and optional image inputs through a vision mode. It can run on CUDA, Apple silicon through MLX or MPS, and CPU. The project is distributed under the Apache License 2.0 and can be installed with pip.

Ollaya is an open-source system for running decision models locally. It accepts a message, email, ticket, or other JSON state together with typed questions and returns calibrated probabilities for choices, scores, or binary decisions rather than generated text. The Ollaya daemon pulls models by name and serves them through a CLI and HTTP APIs, including TypeSafe-compatible `/v1/systemone`, `/v1/decisions`, and `/v1/models` endpoints. Its native API adds routing information and timings, while model definitions can embed reusable question sets. It supports ONNX Runtime on CPU and NVIDIA GPUs, and llama.cpp for GGUF models on CPU, CUDA, and Apple silicon. Ollaya also provides an MCP server for clients such as Claude Code, Claude Desktop, and Cursor. It publishes small ONNX graphs while obtaining the original model weights from their authors' Hugging Face repositories, pinned and verified by commit and SHA-256; it does not re-host those weights. The project is distributed under Apache-2.0, while individual models retain their own licenses, and it is available as a CLI, daemon, desktop application, and Docker image.

kev is an open-source, local Jev-inspired decision model from Jared Palmer. It runs on a laptop and accepts a document or other state plus typed questions, returning calibrated probabilities rather than generated text. It supports yes/no (noul), multiple-choice, and ordered-score questions. The model uses a LoRA adapter and a readout head on a Qwen base model. It packs the state and all questions into one sequence, applies a block-causal mask so each question can see the state but not sibling questions, and performs one prefill pass without decoding. A pointer head scores each option against the question's decision token and applies softmax; the head is trained with cross-entropy on labelled outcomes. The local server exposes a TypeSafe System One-compatible API at POST /v1/systemone, allowing the official TypeSafe SDK to connect by changing its base URL. It also provides model information, option-order permutation, and separate-pass comparison endpoints, plus a local playground. The repository includes 0.5B, 0.6B, 4B, and 8B model checkpoints and recipes for local or Modal-based training and evaluation. Serving is tested on Apple Silicon, with larger checkpoints requiring bf16 on a 32 GB Mac. The server has no authentication and is intended for local use. The project is licensed under Apache-2.0; base models and datasets have separate licenses.

Jevmind is a Python decision layer for software agents. It turns agent decisions into typed answers such as yes/no results, choices, and scores with confidence values, then applies a code-defined gate: uncertain answers escalate, trusted positive answers may act, and trusted negative answers are held. Its decisions are recorded in an append-only, hash-chained ledger, graded against later outcomes, and optionally recalibrated with Platt scaling. It includes nine skills for compacting tool output, navigating repositories, reviewing diffs, routing tasks to model tiers, checking completion claims, curating training data, walking linked markdown vaults, guarding commands, and running a structured-state control-loop demo. The default local brain uses readable rules, reflexes, and BM25 retrieval without a language model; an optional Jev HTTP brain can answer the same typed questions, while replay mode uses recorded answers. Jevmind runs as a command-line tool and MCP server, includes a Claude Code hook for shell-command screening, supports Python 3.10+, has no runtime dependencies, and is distributed under the MIT license.

Laya is an Apache-2.0 Python package and encoder-model decision engine developed by Convai Innovations. It accepts text or JSON state and evaluates typed questions in a single forward pass, returning categorical choices with probabilities and confidence, ordinal scores, or calibrated Boolean probabilities rather than generated text. Its stated uses include routing, ticket triage, prompt-injection detection, moderation, phishing detection, and churn-risk classification. Laya includes English, multilingual, and typed-decisions checkpoints, plus a router that selects a checkpoint using explicit settings, detected script, and best-effort language detection. The checkpoints use reinforcement learning against strictly proper scoring rules (RLCD); the project also provides fine-tuning and temperature-calibration workflows. The multilingual checkpoint is intended for more than 100 languages, while the repository cautions that the base models can perform near chance on some zero-shot typed-decision tasks and that large choice sets are constrained by the option-token budget.