EverOS 1.1.0

EverOS 1.1.0

EverOS 1.1.0 expands the memory system beyond user episodes with first-class
knowledge management, reflection, and stronger operational guarantees around
search, persistence, and API contracts.

Highlights

• Added Knowledge APIs and storage for document creation, listing, patching,
  deletion, taxonomy handling, and knowledge-topic search.
• Added Reflection orchestration for periodically merging and refining episode
  clusters.
• Expanded OME runtime support with event IDs, run-record storage migration,
  configuration reload behavior, and testing harness improvements.
• Improved search and get behavior for agent-owned memory, including agent
  cases, agent skills, and owner-type isolation.
• Reworked API error handling around typed exception handlers and consistent
  error envelopes.
• Updated docs, OpenAPI schema, configuration examples, and test coverage for
  the 1.1.0 surface area.

Compatibility Notes

• Existing local TUI demo registration remains available.
• Existing DashScope rerank support is preserved; the DashScope provider file
  is not replaced by the 1.1.0 update.
• The release intentionally leaves directories outside the 1.1.0 update scope,
  including existing use-case and iOS demo material, untouched.
• Knowledge search requires configured embedding and rerank providers. Missing
  providers now fail explicitly with configuration errors rather than silently
  returning degraded results.

Upgrade Notes

• Regenerate or review docs/openapi.json after route or DTO changes.
• Run uv sync --frozen against the updated uv.lock.
• Review config.example.toml, src/everos/config/default.toml, and
  src/everos/config/default_ome.toml for new Knowledge and OME settings.
• If running e2e tests without live provider credentials, use dummy provider
  environment variables for startup-only checks; live vector and rerank paths
  remain behind slow/live markers.

Verification

This release was checked with:

• uv run ruff check .
• uv run pytest tests/unit
• uv run pytest tests/integration
• uv run lint-imports
• uv run pytest tests/e2e with dummy LLM and embedding environment variables
• Targeted search/get regression tests for agent and deprecated-filter handling

EverOS 1.0.1

Security

• 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.

Documentation

• 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.

EverOS 1.0.0

Superseded: EverOS 1.0.1 is available with security hardening for caller-supplied identifiers and markdown write containment. See EverOS 1.0.1.

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

Added

• 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, SQLite, and LanceDB. No external services required.
• Hybrid retrieval: BM25, vector, and scalar filtering in a single LanceDB query.
• Cascade index sync: editing a .md file triggers file-watcher based index sync.
• Dual-track memory for user memories and agent memories.
• CLI and FastAPI HTTP API, async-first throughout.
• Pluggable OpenAI-compatible providers for LLM, embedding, and rerank.
• Decoupled memory extraction algorithms via the standalone everalgo-* libraries.