EverOS

Vision

Build an open-source Python memory framework where AI agents' long-term memory is plain Markdown files on the user's disk, not opaque rows in a hosted database.

Scope

In scope (v1):

• Local deployment for personal agents or small teams
• Conversation, workflow, agent-trace, file-knowledge → structured memory
• Hybrid retrieval (BM25 + vector + scalar filter)
• Cascade index sync (md edit → LanceDB sub-second)
• Dual-track memory (user-track / agent-track)
• Offline memory evolution (Foresight / AtomicFact / Profile / Skill)
• CLI + HTTP API

Out of scope (v1, future v2):

• Multi-tenant / group / community deployment (10K+ users)
• End-to-cloud sync (planned for v2)
• Distributed deployment / sharding

Design philosophy

1. Markdown as Source of Truth

delete all LanceDB / SQLite files → can rebuild from md
delete any md file               → memory is gone

User trust comes from physical visibility — the user can cat / vim / grep their own memory at any time.

2. Three-piece storage with clear job boundaries

Component	Role	Does NOT do
Markdown files	Truth source — entries, frontmatter	Search (grep is degraded fallback only)
SQLite	Queue, cascade audit log, sensitive data isolation	Vector / full-text
LanceDB	Vector ANN + BM25 + scalar filter, single-query hybrid	Be the source of truth (loss = rebuild from md)

3. Algorithm-orchestration separation

everalgo (a set of separate PyPI packages — everalgo-core / -boundary / -user-memory / -agent-memory / -rank, plus the optional -parser extra) holds the extraction algorithms (MemCell extraction, Episode generation, Profile evolution). EverOS calls everalgo via the PromptSlot interface; everalgo knows nothing about storage.

This boundary lets the same algorithm power both this open-source lightweight version and other product forms.

4. DDD layered architecture

entrypoints  →  service  →  memory  →  infra
                              ↓
                        component / core / config

Strict single-direction dependency, enforced by import-linter in CI.

Why src layout (src/everos/)

• Standard PyPA project structure used when shipping to PyPI
• Avoid namespace collision with system packages named memory, infra, etc.
• Avoid accidental import of working-tree code in dev (PyPA recommendation)

Comparable projects (where EverOS differs)

Project	Position	Difference
mem0	API-first memory service	mem0 stores in vector DB; we store in md files
Letta	Agent OS w/ Core/Recall/Archival	Letta uses Postgres; we use markdown filesystem
MemOS	Multi-classification memory	MemOS targets enterprise; we target lightweight (single-user / small team)
memsearch	md-first search engine	Closest to us; we add memory extraction (not just search)

Roadmap

• v0.1 (MVP) — Phase 1 core loop: markdown + lancedb + cascade + episode extraction
• v0.2 — Full extraction pipeline (workspace / agent / knowledge), evolution framework
• v0.3 — Production hardening, full CLI, HTTP API, Obsidian demo
• v1.0 — Stable API, PyPI release, comprehensive docs
• v2 (future) — Edge-to-cloud sync via EverMe (separate project)

Status

Stable (v1.0.1) — Released on PyPI; the v1 API is stable. Development continues on dev toward v1.1.