Auto Memory

Auto Memory is ReMe's entry point for conversational memory. Each conversation is first distilled into a daily memory card identified by session_id, and the day's YYYY-MM-DD.md page then indexes all of those cards. It turns "we talked about it" into "it was remembered" while preserving the original conversation as evidence.

For the general file semantics of daily/, session/, frontmatter, and wikilinks, see Memory as File.

Conversation
  ├─ step 1: daily/YYYY-MM-DD/<session_id>.md   # one card per conversation
  ├─ step 2: daily/YYYY-MM-DD.md                # daily index linking the cards
  └─ source: session/dialog/<session_id>.jsonl  # original conversation

What It Records

Auto Memory does not preserve a chat transcript as a running summary. It records information that may remain useful later:

• User preferences: preferred style, collaboration habits, and long-term requirements.
• Key facts: project background, important numbers, explicit conclusions, and constraints.
• Process decisions: what happened, why a choice was made, and which alternatives were rejected.
• Current state: what has been completed, what is blocked, and what comes next.
• Reusable experience: commands, workflows, diagnostic methods, and solutions.

Write Location

Auto Memory writes distilled memories to daily/. Conversations from the same day first become individual cards:

Example directory:

workspace/
  daily/
    2026-06-20.md
    2026-06-20/
      session-a.md
      session-b.md

daily/2026-06-20/session-a.md and daily/2026-06-20/session-b.md are memory cards distilled from different conversations. daily/2026-06-20.md is the index page for that day. Resource files enter the same daily memory layer; see Auto Resource.

When a call includes session_id, Auto Memory records that conversation separately under the given ID:

daily/2026-06-20/session-a.md

This keeps different conversations separate. A requirements discussion, a debugging session, and a documentation update can each have their own memory card. To see what happened on a particular day, start with YYYY-MM-DD.md. To inspect what was distilled from one conversation, open the corresponding <session_id>.md.

Preserving the Original Information

The distilled daily note is optimized for readability; the original conversation is retained for trust and verification.

While generating memory cards, Auto Memory also saves the raw sessions:

session/
  dialog/
    session-a.jsonl
    session-b.jsonl

Each daily note points to its corresponding original conversation. When a memory needs verification, follow that link back to the complete context in which it was created.

Message Timestamps

Auto Memory preserves each message's created_at in both the prompt and the raw session JSONL. When importing historical conversations or benchmark data, provide the actual occurrence time for every message so the model does not confuse event time with execution time:

reme auto_memory \
  session_id=locomo-session \
  messages='[
    {"role":"user","content":"Jon lost his job today.","created_at":"2023-01-19T08:00:00"},
    {"role":"assistant","content":"I am sorry to hear that.","created_at":"2023-01-19T08:01:00"}
  ]'

For compatibility with common dataset schemas, auto_memory also checks time_created, timestamp, createdAt, timeCreated, and created_time when created_at is absent. These fields may appear either at the top level of a message or inside metadata.

When a call does not explicitly provide date, Auto Memory uses the date of the earliest valid created_at value in the messages. If no message contains a valid timestamp, it falls back to the current date. Historical imports may also specify the target date directly:

reme auto_memory \
  session_id=locomo-session \
  date=2023-01-19 \
  messages='[{"role":"user","content":"Jon lost his job today."}]'

What Happens Next

Auto Memory only creates memory in the daily layer. To distill this material further into long-term digest/ nodes, use Auto Dream. To search daily and digest content, use Memory Search.