slm-remember

slm-remember — Capture Durable Facts

Store atomic, durable facts into SuperLocalMemory for retrieval in future sessions. One fact per call. Recall before you remember.

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What to store (and what not to)

Store:

• Architectural decisions ("Decided to use Postgres not MySQL — reason: JSONB support")
• Project conventions ("All API routes follow /api/v1/resource/{id} pattern")
• Hard constraints ("Never expose raw SQL errors to the HTTP response")
• Resolved gotchas ("Ollama needs keep_alive=-1 or it unloads the model between calls")
• Security rules ("Rate limit all public endpoints at 100 req/min")

Do not store:

• Transient context that is only relevant within this conversation
• Large blobs of code or full file contents (those belong in the project, not memory)
• Facts the project README already captures

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Recall-before-remember (mandatory discipline)

Before calling remember, always call recall first with the core terms of what you are about to store. If a near-duplicate exists:

• Use update_memory(fact_id, content) to refine the existing fact instead of creating a new one.
• Only call remember when no sufficiently similar fact is found.

Duplicates degrade retrieval quality for every future session.

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MCP-first workflow

1. Check for duplicates first

recall(query="JWT token expiry auth", limit=5, session_id="<sid>")

If a near-duplicate is returned:

update_memory(
  fact_id="f8a2bc91",
  content="JWT tokens use 1h expiry for API access tokens; refresh tokens 30d (updated 2026-06-16)",
)

update_memory returns {"success": true, "fact_id": "f8a2bc91", "content": "..."}.

2. Store a new fact

remember(
  content="Decided to use JWT with 1h expiry for API auth; refresh tokens persist 30 days",
  tags="auth,security,decision",
  project="superlocalmemory",
  importance=8,
  session_id="<sid>",
)

Real response shape:

{
  "success": true,
  "fact_ids": ["c9d4e112"],
  "count": 1,
  "pending": false,
  "message": "Stored (recallable now; enriching async)."
}

When pending: true, the daemon was offline at save time; the fact enters a pending queue and becomes recallable once the daemon is back. Do not re-save.

Never claim "saved" unless success: true is in the response.

3. Parameter reference

remember(
  content: str,       # required — the atomic fact to store
  tags: str = "",     # comma-separated tags, e.g. "auth,security,gotcha"
  project: str = "",  # project scope, e.g. "superlocalmemory"
  importance: int = 5,# 1–10; see scale below
  session_id: str = "",# from session_init; attributes the write to this session
)

importance scale:

• 1–3: Low — passing notes, ideas, soft preferences
• 4–6: Normal — patterns, conventions, standard decisions (default: 5)
• 7–8: High — architectural decisions, integration contracts, known gotchas
• 9–10: Critical — security rules, blockers, irreversible decisions

Use 7–10 only for facts that would cause real damage if forgotten.

4. One fact per call

Store one atomic fact per remember call. Do not concatenate multiple unrelated points into a single content string — they will be hard to update individually and harder to retrieve cleanly. If you have three separate decisions, make three calls.

5. Always set tags and project

Untagged, unscoped facts are harder to retrieve and harder to manage. Minimum: set tags to one or two relevant terms and project to the repo/product name.

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Deleting stale facts via CLI

For deletion, the CLI is the authoritative surface. The MCP forget tool in v3.6.14 runs an Ebbinghaus decay cycle — it does NOT delete by query. For targeted deletion, use the CLI:

# Preview what would be deleted (always do this first)
slm forget "<query>" --dry-run [--json]

# Execute deletion after confirming the preview
slm forget "<query>" --yes [--json]

# Delete a specific fact by exact ID (use when you have the fact_id)
slm delete <fact_id> --yes [--json]

Flags verified in source (main.py):

• slm forget: positional query, --dry-run, --yes / -y, --json
• slm delete: positional fact_id, --yes / -y, --json

Always run --dry-run first and review the preview before passing --yes.

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CLI fallback (when MCP is unavailable)

# Store a fact
slm remember "<content>" [--tags a,b,c] [--json]

# Flags verified in source (main.py): --tags, --json, --sync
# --sync: wait for full enrichment before returning (default is async)

Flags that do NOT exist on slm remember: --importance, --project, --format — these are MCP-only params or fabricated.

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Update vs forget discipline

Scenario	Action
Fact is still true but needs refinement	update_memory(fact_id, new_content)
Fact is superseded or wrong	slm forget "<query>" --dry-run then --yes
Duplicate found that matches recall result	update_memory on the existing one
Fact has a known ID and is clearly obsolete	slm delete <fact_id> --yes

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SuperLocalMemory v3.6.14 · Qualixar · AGPL-3.0-or-later