Changelog

All notable changes to EverOS are documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

Unreleased

Unreleased changes on dev will be listed here.

Path-traversal hardening for caller-supplied identifiers. sender_id now carries the same path-safety guard as app_id / project_id: a character whitelist plus rejection of the . / .. tokens. The whitelist admits @ and + so email-style ids and plus-addressing still pass.
Defense-in-depth write containment. MarkdownWriter now rejects any write target that resolves outside the configured memory root before reading, creating parent directories, or writing files. The API layer maps this backstop error to HTTP 400.

Add a multimodal usage guide and correct the multimodal error semantics after end-to-end verification.
Document the upcoming Knowledge Wiki and idle/offline Reflection/Dreaming roadmap in the README and documentation set.
Rename outdated algorithm-library references to everalgo across docs and code comments; no code identifiers changed.
Fix accuracy drift found in a documentation audit; reflect the everalgo packages being published and the v1.0.0 stable status.

First public release of EverOS — a Markdown-first memory extraction framework for AI agents.

Markdown as source of truth — all memory persists as plain .md files you can open, edit, grep, and version with Git.
Lightweight three-piece storage — Markdown (truth) + SQLite (state / queue / audit) + LanceDB (vector + BM25 + scalar index). No external services required.
Hybrid retrieval — BM25, vector, and scalar filtering in a single LanceDB query.
Cascade index sync — editing a .md file triggers a file watcher → entry-level diff → sub-second LanceDB sync.
Dual-track memory — user-track (Episodes / Profiles) and agent-track (Cases / Skills).
Multi-source extraction — conversations, workflows, agent traces, and file knowledge.
CLI + HTTP API — the everos command-line tool and a FastAPI server, async-first throughout.
Pluggable providers — LLM / embedding / rerank via the OpenAI-compatible protocol (works with OpenAI, OpenRouter, vLLM, Ollama, …).
Decoupled algorithms — memory extraction algorithms live in the standalone everalgo-* libraries published on PyPI.