fast-jev-compaction

Jev Review is a local-first MCP plugin for continuous software-quality review by AI coding agents, powered by Jev. It runs as a local Node.js server over MCP stdio and exposes a focused `jev_review` tool: an agent submits a task, focused diff, selected files, or repository context, and Jev returns structured per-dimension scores and confidence values for areas such as correctness, complexity, readability, modularity, changeability, testing, reliability, security, and documentation. When a previous evaluation is supplied, the plugin reports metric deltas, improvements, regressions, and weak dimensions rather than producing a blended overall score or prose diagnosis; the coding agent remains responsible for identifying causes and changing the code. It supports Claude Code, Codex, Cursor, and OpenCode, and is distributed directly from its GitHub repository rather than npm. The process keeps the API key locally and sends only explicitly supplied review context directly to the configured Jev API; it has no hosted backend, database, telemetry service, or author-operated proxy. The repository is licensed under MIT.

Jevgrep is a command-line tool for coding agents that finds relevant files and source context in unfamiliar repositories from natural-language questions. It uses Jev to judge relevance across folders, files, and declarations, explores qualifying branches of the repository hierarchy, selects files through content previews, identifies useful source units and surrounding context, and returns a summary followed by file locations, reading leads, and verbatim excerpts with line references. Python and TypeScript/JavaScript support declaration parsing, while other text uses a fallback. It also includes an agent skill that teaches supported coding agents when to invoke the CLI and how to use its results. The CLI is distributed through npm as @dzhng/jevgrep, requires Node.js 22 or later on macOS or Linux, and requires authentication with a supported AI provider. Searches send eligible source content to Jev through the selected provider; local filtering excludes ignored, hidden, dependency/build, binary, and obvious credential files but is not a guarantee that sensitive data is removed. The project is MIT-licensed.

jcode is an open-source, Rust-based terminal harness for AI coding agents and LLM workflows, designed for interactive development across multiple sessions. It supports resumable sessions, session search, multi-agent swarm and helper-agent workflows, compatible model-provider integrations, MCP, browser automation, and a self-development mode; installation scripts target macOS, Linux, and Windows. For automatic memory, jcode embeds turns and responses as semantic vectors, searches a graph of stored memories using cosine similarity, and feeds relevant results into the conversation. A memory sideagent can verify and expand retrieval, while periodic extraction stores new memories and ambient consolidation reorganizes entries and checks for staleness and conflicts. Explicit memory tools and traditional retrieval over previous sessions are also available. Its terminal UI includes side panels, diff views, inline Mermaid diagrams, information widgets, custom scrolling, and real-time rendering. The project supplies a Mermaid renderer without browser or TypeScript dependencies and publishes benchmarks covering RAM consumption, startup and input-readiness times, and memory scaling across multiple active sessions.

Jive is an open-source terminal coding agent that plans work as executable graphs. It replaces sequential tool calls with graph calls: the planner produces a directed acyclic graph containing tool calls and Jev calls, allowing the agent to perform multi-step profiling, dataset analysis, and repetitive tasks with fast decisions inserted between harder reasoning steps. The project emphasizes a lightweight, token-efficient agent scaffold with eager execution, flexible graphs, and support for system-one and system-two models. It can be installed with the project's shell installer and is distributed under the MIT license.

Simple Jev is an open-model classifier and scoring server from Featherless AI. It accepts shared context or chat messages plus questions, then reads the model's next-token logits for predefined answer labels instead of generating a prose or JSON completion. The server constructs JSON responses for choice classification, ordered rubric scores, and truth or support judgments, including confidence and label distributions. Its Hugging Face Transformers implementation identifies the exact common token prefix across questions, evaluates that prefix once, reuses its KV cache for question-specific suffixes, and batches the suffixes before scoring the allowed answer tokens. The response-scoring code normalizes the selected logits; shared context is counted once for usage purposes, and no output tokens are generated. The API provides non-streaming /v1/classifier and /v1/systemone endpoints, with local execution supported through Python, PyTorch, and Transformers. A public demo API is available, while production deployments can use Featherless paid plans or run the server themselves. The repository also includes RFDT, which fine-tunes models directly on allowed answer-token logits using the same prompt structure.