Dataecho Memory

Persistent memory for agents — durable across sessions, machines, sandboxes, and agent platforms, stored in a private DataEcho cloud drive. Use at the start of a session to recall who the user is and what you were working on; whenever the user says "remember this", "don't forget", "like last time", or references past sessions; when you learn a durable preference, make a decision worth keeping, or finish a milestone; and when handing state to another agent or a future session.

DataEcho Memory — remember things across sessions

Your context window dies with the session. This skill gives you a memory that doesn't: a private, versioned drive on https://dataecho.ai holding one small index (MEMORY.md) plus one file per fact (memories/<slug>.md). Reading the index costs ~1 KB; everything else loads on demand. Works from any machine or throwaway sandbox — only the API key travels.

scripts/memory.sh sits next to this file (bash + python3 stdlib, no other deps).

Session protocol — this is the discipline that makes memory work

1. Recall first. At the start of a session (or when the user references past work), run scripts/memory.sh recall. It prints the index — every memory as one line. Cheap; do it.
2. Load selectively. recall <query> fetches only the fact files whose index line matches. Never dump the whole store into context unless asked (recall --full).
3. Remember durable things, at the moment you learn them:
   • the user states a preference or corrects you → --type user
   • a decision is made and has a why → --type decision
   • you learn something non-obvious the next session will need → --type insight
   • a work session ends mid-task → --type task (state + next step)
   • a URL/dashboard/ticket worth keeping → --type reference Do NOT save: secrets or API keys (memory is not a vault), anything derivable from the repo/git history, or session-scoped trivia.
4. Update, don't duplicate. remember with an existing slug updates it (and tells you). Before creating, recall a keyword — if a related memory exists, re-remember the same slug with merged content. The script warns you when a new slug looks like an existing one.
5. Forget what's wrong. A false memory is worse than none: memory.sh forget <slug>.
6. Memories age. They record what was true when written. If one names a file, URL, or flag, verify it still exists before acting on it. Convert relative dates ("yesterday", "next week") to absolute dates when writing.

Commands

bash

scripts/memory.sh recall                      # print the index (session start — always)
scripts/memory.sh recall deploy               # index lines matching "deploy" + those fact bodies
scripts/memory.sh recall --scope acme-crm     # only global + that project's memories
scripts/memory.sh remember user-prefers-tabs --type user --body "Prefers tabs over spaces everywhere."
scripts/memory.sh remember sqlite-wal-gotcha --type insight --from ./notes.md
echo "Chose Postgres over SQLite. **Why:** concurrent writers. **How to apply:** don't suggest SQLite for this app." \
  | scripts/memory.sh remember db-choice --type decision --scope acme-crm
scripts/memory.sh forget db-choice            # delete fact + index line atomically
scripts/memory.sh reindex                     # rebuild MEMORY.md from fact files (self-heal)
scripts/memory.sh history                     # version timeline of the whole memory
scripts/memory.sh restore <versionId>         # roll the entire memory back
scripts/memory.sh handoff --ttl 7d            # share block for another agent (read-only)
scripts/memory.sh init                        # explicit setup + status (otherwise lazy)

Every command auto-creates the drive ("Agent Memory") and index on first use — init is optional. Override the drive with MEMORY_DRIVE (a name, or a drv_… id).

What a good memory looks like

One fact per file. Slug is lowercase-kebab and says what the fact is (user-prefers-tabs, not note-1). Description ≤ 100 chars — it is the recall surface, write it like a search result. Body under ~8 KB (the script warns; split bigger things). For decisions and feedback, include Why: and How to apply: lines — a decision without its why gets re-litigated next session. Link related memories with [[slug]].

Scopes: memories are global by default. Give project-specific facts a scope (--scope <project-key>, e.g. the repo name) so recall --scope <key> shows only global + that project. One index covers all scopes — recall stays one read.

Setup — API key (once per user)

Memory requires an account (it's private storage). If ~/.artifact/credentials or $ARTIFACT_API_KEY exists, you're done. Otherwise (same flow as the dataecho skill):

bash

curl -sS -X POST https://dataecho.ai/api/auth/agent/request-code -H 'content-type: application/json' -d '{"email":"[e-mail]"}'
# user reads the emailed code, then:
curl -sS -X POST https://dataecho.ai/api/auth/agent/verify-code -H 'content-type: application/json' -d '{"email":"[e-mail]","code":"<CODE>"}'
echo '<API_KEY>' > ~/.artifact/credentials && chmod 600 ~/.artifact/credentials

The apiKey is returned once — write it to the file immediately; don't echo it.

Handoff — give another agent your memory

bash

scripts/memory.sh handoff --ttl 7d            # read-only (recommended)
scripts/memory.sh handoff --write --ttl 24h   # writable (trusted agents only)

Prints a one-time share block. The receiving agent (any machine, any platform) runs:

bash

export ARTIFACT_DRIVE_TOKEN='drv_live_…'      # from the share block
export MEMORY_DRIVE='drv_…'                   # driveId from the share block
scripts/memory.sh recall

Revoke anytime from the owning account: memory.sh tokens / memory.sh revoke <dtok_id>.

Concurrency, corruption, recovery

Multiple sessions can write concurrently: every mutation commits the fact file and the index in one atomic, compare-and-swap batch (the script retries on conflict) — the index can never disagree with the files, and two agents can never silently clobber each other. If the index ever drifts anyway (e.g. hand-edits), reindex rebuilds it from the fact files' frontmatter. Every mutation is a drive version: history shows the timeline and restore <versionId> rolls the whole memory back — a bad session can always be undone.

Direct API

Everything is plain REST (drive endpoints, ETag/If-Match preconditions, atomic PATCH batches): https://dataecho.ai/llms-full.txt · OpenAPI: https://dataecho.ai/openapi.json Always send a descriptive User-Agent. Publishing sites/apps is the separate dataecho skill.