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add-memory-kind

Guide developers through adding a persisted business memory type. Choose Markdown, SQLite, and LanceDB storage and a Markdown file strategy, then wire up schemas, repositories, and writers across the layers the data needs.

OriginalShow the original description

Add a new business memory kind end-to-end. Pick the storage combination (Markdown / SQLite / LanceDB), pick the markdown strategy (daily-log / skill-named / single-file), then wire up the schema(s), repo(s), and writer(s).

EverMind-AI/EverOS13,701 stars(GitHub 13,701 + cccskills 0)

Run score Not tested

See how to install

Good for

  • Adding a new persisted business record type
  • Choosing storage for searchable memory
  • Wiring a daily Markdown log with database indexes

Overview

What it does

Guides the work of adding a new kind of persisted business data by choosing the storage layers and implementation points it needs. Select a combination of Markdown for a human-readable source, SQLite for structured data, and LanceDB for vector search or other retrieval. If Markdown is part of the design, choose between appending dated entries, overwriting a named file, or rewriting a single file. Then add the schemas, writing logic, and, when needed, tables and repositories. The workflow also covers re-exporting the new components for use by the rest of the project.

When it helps

Useful for developers adding a new persistent business record such as an Episode or Case. It helps clarify whether the data needs search, structured state, or a human-editable source, and connect the chosen storage layers consistently.

Example requests

  • “Help choose the storage design for a new business memory type.”
  • “Add a daily Markdown log and a SQLite table.”
  • “Wire up a LanceDB index for search.”

Notes

The skill provides detailed steps for the daily-log Markdown strategy. The named-file and single-file strategies are only outlined; until their base writers are available, they need thin wrappers around MarkdownWriter. Add SQLite or LanceDB only when needed.

This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.

Document check

AI checked the SKILL.md for operations that need care and for setup requirements. The score comes only from actually running the skill (hands-on test).

SafetyAre sending data, deleting files, and running commands disclosed?
AGoodIt identifies file-overwrite strategies and makes SQLite and LanceDB optional.
Easy setupDoes it need few tools or API keys, and are they documented?
BPartly lackingIt lists Markdown, SQLite, and LanceDB choices but gives no installation steps.

Not counted in the score. Rated by AI (OpenAI Codex) as of Oct 29, 2026. Treat it as a guide and read the original before installing.

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Files included (1)

  • SKILL.md11.2 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

/add-memory-kind — Add a new business memory kind

When to invoke

Adding a new persisted business entity (Episode, Case, Skill, AtomicFact, Foresight, Profile, or something custom). Multiple storage layers may be involved; this skill walks the decision then the wiring.

1. Decide the storage combination

A memory kind does not have to use all three layers. Pick by what the kind actually needs:

NeedMarkdownSQLiteLanceDB
Human-readable / agent-editable source-of-truth text✅
Structured state, ACID transactions, joins, predicates✅
Vector / BM25 / hybrid retrieval✅

Common combinations seen in EverOS:

ComboExampleRationale
md onlyscratch notes / dump binstext-of-truth, no index needed
md + lancedbepisode / memcell / casetext-of-truth + semantic retrieval
md + sqliteprofile / playbook / soul.md statetext-of-truth + structured state to query
md + sqlite + lancedbfull-blown business recordswhen you need both transactional state AND retrieval
sqlite onlyaudit log / task queue / LSN watermarksystem state, never user-facing
lancedb onlyrare; usually you still want mdderived embeddings without text-of-truth

Rule of thumb: markdown is the truth; sqlite and lancedb are derived indexes that can be rebuilt from md. Drop md only when the kind has no human-readable form (pure system state).

2. Pick the markdown storage strategy (if md is in your combo)

Three strategies — declared in the EverOS Markdown First spec:

StrategyFilenameMutationExamples
Daily-log append<prefix>-YYYY-MM-DD.mdappend entriesmemcell / episode / case / atomic_fact / foresight
Skill-named in-placeskill_<name>.mdoverwrite the fileskills (procedural memory)
Single-file rewriteuser.md / agent.md / soul.md / behaviors.md / tools.mdoverwrite the fileprofiles / playbooks

This skill currently has a complete recipe for daily-log append. Skill-named and single-file recipes are sketched at the bottom — their base writers (BaseSkillWriter / BaseProfileWriter) land later in the project; until then build a thin wrapper over MarkdownWriter directly.


3. Markdown daily-log: 4 steps

3.1 Frontmatter schema — infra/persistence/markdown/mds/<name>.py

"""Episode daily-log frontmatter."""

from __future__ import annotations

import datetime as _dt
from typing import ClassVar, Literal

from everos.core.persistence.markdown import UserScopedFrontmatter


class UserEpisodeDailyFrontmatter(UserScopedFrontmatter):
    """``users/<u>/episodes/episode-<YYYY-MM-DD>.md``."""

    ENTRY_ID_PREFIX: ClassVar[str] = "ep"
    DIR_NAME: ClassVar[str] = "episodes"
    FILE_PREFIX: ClassVar[str] = "episode"

    type: Literal["user_episode_daily"] = "user_episode_daily"
    date: _dt.date
    entry_count: int = 0
    last_appended_at: _dt.datetime | None = None

For agent-track kinds subclass AgentScopedFrontmatter instead. If user-track and agent-track share a kind name (e.g. memcell), give each a distinct ENTRY_ID_PREFIX (e.g. umc vs amc) so reverse lookup is unambiguous.

3.2 Re-export — mds/__init__.py

from .episode import UserEpisodeDailyFrontmatter as UserEpisodeDailyFrontmatter

3.3 Business writer — infra/persistence/markdown/writers/<name>.py

"""Episode appender."""

from __future__ import annotations

from pathlib import Path

from everos.core.persistence import MarkdownReader

from ..mds import UserEpisodeDailyFrontmatter
from .base import BaseDailyWriter


class UserEpisodeAppender(BaseDailyWriter):
    schema = UserEpisodeDailyFrontmatter

    # OPTIONAL: override the count strategy. Default is len(entries);
    # override to trust the frontmatter field instead.
    def _current_count(self, path: Path) -> int:
        if not path.exists():
            return 0
        return MarkdownReader.read(path).frontmatter.get("entry_count", 0)

3.4 Re-export — writers/__init__.py

from .episode import UserEpisodeAppender as UserEpisodeAppender

Done — usage

from everos.infra.persistence.markdown.writers import UserEpisodeAppender

appender = UserEpisodeAppender(memory_root)
eid = appender.append("u_jason", "I went to the doctor today.")
# → users/u_jason/episodes/episode-<today>.md
# → entry markers carry an auto-generated EntryId (e.g. ep_20260507_001)

4. (Optional) SQLite table — 4 steps

Skip this section if the kind doesn't need structured state beyond markdown.

4.1 Schema — infra/persistence/sqlite/tables/<name>.py

from everos.core.persistence.sqlite import BaseTable, Field


class EpisodeState(BaseTable, table=True):
    __tablename__ = "episode_state"  # type: ignore[assignment]

    id: int | None = Field(default=None, primary_key=True)
    entry_id: str = Field(index=True, unique=True)
    cluster_id: str | None = Field(default=None, index=True)
    status: str = Field(default="active")

BaseTable already provides created_at / updated_at (auto-bumped).

4.2 Re-export — tables/__init__.py

from .episode import EpisodeState as EpisodeState

4.3 Repo — infra/persistence/sqlite/repos/<name>.py

from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker

from everos.core.persistence.sqlite import RepoBase

from ..sqlite_manager import get_session_factory
from ..tables import EpisodeState


class _EpisodeStateRepo(RepoBase[EpisodeState]):
    model = EpisodeState

    def _factory_lookup(self) -> async_sessionmaker[AsyncSession]:
        return get_session_factory()


episode_state_repo = _EpisodeStateRepo()

4.4 Re-export — repos/__init__.py

from .episode import episode_state_repo as episode_state_repo

5. (Optional) Derived index — 4 steps

Skip this section if the kind doesn't need vector / BM25 / hybrid retrieval.

5.1 Record declaration — infra/persistence/records/<name>.py

from typing import ClassVar

from everos.core.persistence.lancedb import BaseLanceTable, Vector


class EpisodeIndex(BaseLanceTable):
    TABLE_NAME: ClassVar[str] = "episode_index"
    PRIMARY_KEY: ClassVar[tuple[str, ...]] = ("app_id", "project_id", "id")

    id: str
    app_id: str = "default"
    project_id: str = "default"
    entry_id: str
    text: str
    tags: list[str]
    vector: Vector(1024) | None = None  # type: ignore[valid-type]

TABLE_NAME / BM25_FIELDS / PRIMARY_KEY are the backend-neutral declarations every index backend reads (IndexRecordMeta). PRIMARY_KEY has no default: check_primary_keys refuses to start, and LanceDB upsert refuses to write, a table that does not declare one. id is unique only within (app_id, project_id), so every business table declares the three-column key — LanceDB merges on it, Milvus keys the collection on a composed __pk and rejects key components containing /. Vector(N) must match your embedding dimension.

5.2 Re-export — records/__init__.py

from .episode import EpisodeIndex as EpisodeIndex

5.3 Repo — infra/persistence/lancedb/repos/<name>.py

from lancedb import AsyncTable

from everos.core.persistence.lancedb import LanceRepoBase
from everos.infra.persistence.records import EpisodeIndex

from ..lancedb_manager import get_table


class _EpisodeIndexRepo(LanceRepoBase[EpisodeIndex]):
    schema = EpisodeIndex

    async def _table_lookup(self) -> AsyncTable:
        return await get_table(self.schema.TABLE_NAME, self.schema)


episode_index_repo = _EpisodeIndexRepo()

Business code writes through the everos.infra.persistence.index facade: upsert(records) merges on the declared key and is the only write; look a row up with find_one_where(primary_key(app_id, project_id, row_id)).

5.4 Re-export — repos/__init__.py

from .episode import episode_index_repo as episode_index_repo

6. (Future) Skill-named & single-file markdown strategies

When the new memory kind needs:

  • skill-named files (one file per named skill, in-place rewrite) — wait for BaseSkillWriter, or use MarkdownWriter.write_markdown directly with a thin wrapper.
  • single-file rewrite (one fixed file like user.md) — wait for BaseProfileWriter, same fallback.

These strategies do not use entry markers; their frontmatter schema does not need ENTRY_ID_PREFIX (only id / type / schema_version plus the scope mixin fields).


7. Verification checklist

  • make lint — ruff + import-linter clean
  • make test — existing manager / lifespan / writer tests still pass
  • When the kind crosses multiple layers:
    • markdown entry id is the join key for the sqlite / lancedb rows
    • business code reads only via the repo singleton (no raw engine access in service / memory)
    • cascade daemon (when it lands) can rebuild sqlite / lancedb from md alone — keep md as the truth

Tests by layer:

Tests forLocation
Markdown frontmatter schematests/unit/test_infra/test_markdown/test_mds/
Markdown business appendertests/unit/test_infra/test_markdown/test_writers/
SQLite RepoBase logictests/unit/test_core/test_persistence/test_sqlite/
SQLite manager / lifespantests/unit/test_infra/test_sqlite/
LanceDB LanceRepoBase logictests/unit/test_core/test_persistence/test_lancedb/
LanceDB manager / lifespantests/unit/test_infra/test_lancedb/

8. Common pitfalls

MistakeSymptomFix
Forgot ENTRY_ID_PREFIX / DIR_NAME / FILE_PREFIX on a daily-log schemaBaseDailyWriter.__init__ raises TypeErrorAdd all three ClassVars
Same ENTRY_ID_PREFIX on user + agent variantsMemoryLayout.locate_for_entry collision errorUse distinct prefixes (e.g. umc vs amc)
Imported RepoBase from infra.persistence.sqliteImportErrorLives in core.persistence.sqlite (moved earlier)
Skipped one of the four files (schema / writer / table / repo)One side silently absentRe-export both/all from each __init__.py
Vector(N) mismatched with embedding dimLanceDB raises on insertMake N exactly match the model output
Imported MemoryLayout from a writer (infra)import-linter fails (infra → memory reverse dep)Use MemoryRoot (in core) and let the schema's ClassVars drive paths
Hand-rolling datetime.now() instead of today_with_timezone()Day-boundary drift across timezonesAlways go through everos.component.utils.datetime

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