Skip to main content
EverOS operates through two tracks: Memory Construction (write path) and Memory Perception (read path).
EverOS Memory Pipeline

​
Memory Construction

This track turns raw conversation streams into structured, retrievable knowledge.
1

Ingestion

Raw messages enter the system through the API. Multi-modal content (images, PDFs, audio, HTML) is parsed and normalized alongside text.
2

Boundary Detection

The system identifies shifts in topics or context to segment the conversation into meaningful units.
3

Extraction

Specialized prompts and models extract MemCells — the atomic memory units: Episodes, Atomic Facts, Foresight, and Profile updates.
4

Consolidation

MemCells are integrated by theme and participants to form episodes and profiles.
5

Indexing

Data is stored with both keyword (BM25) and semantic (vector) indices for robust retrieval.
Write path in detail: 
External message
       │
       ▼
1. Ingestion          Multi-modal parser dispatch
       │
       ▼
2. Extraction         Calls EverAlgo — boundary detection + MemCell extraction
       │
       ▼
3. Markdown write     Atomic: tmp → fsync → rename  ✅ returns here
       │
   ┌───┴─────────────────┐
   ▼                     ▼
4a. SQLite audit     4b. LanceDB sync (async cascade daemon)
Consistency guarantee: The Markdown write is strongly consistent (fsync). LanceDB is eventually consistent — if unavailable, changes buffer in the SQLite md_change_state queue and replay on recovery. LanceDB unavailability never blocks a write response.

​
Memory Perception

This track handles how agents retrieve and use stored memories. Four methods are available, each with different performance and dependency trade-offs:
  • keyword: BM25 full-text search — fast, exact term matching, no model dependencies
  • vector: ANN embedding similarity — semantic queries, requires an embedding model
  • hybrid(recommended): BM25 + vector in parallel, hierarchical fusion where atomic facts and episodes compete for the top-N results, followed by LLM rerank — best recall and precision
  • agentic: Multi-round adaptive retrieval — LLM checks sufficiency and generates follow-up queries if needed
Read path (hybrid): 
User query
   │
   ▼
1. BM25 + vector recall    Run concurrently, candidate pool assembled
   │
   ▼
2. Hierarchical fusion     Episodes and atomic facts compete for top-N
   │
   ▼
3. LLM rerank              Final relevance ordering
   │
   ▼
4. (optional) Fetch        Read original Markdown for full context
All retrieval runs locally within the same process — no network calls to external services. See Retrieval for full method details.

​
The EverAlgo Boundary

Memory extraction algorithms live in a separate library, EverAlgo, not in EverOS itself. It handles:
  • Multi-modal parsing (text, image, audio, doc, PDF, HTML, email)
  • Episode / AtomicFact / Foresight / Profile extractors
  • Case / Skill extractors for agent memory
EverAlgo is stateless and I/O-free — pure functions that receive messages and return structured MemCells, with no direct access to Markdown files, SQLite, or LanceDB. This boundary keeps the extraction algorithms reusable across product forms (EverOS OSS, EverMind Cloud, and plugins). Prompts are injected via PromptSlot parameters rather than hardcoded, so every extraction stage is configurable without touching algorithm code.