maomao-2001/whispera/mem0-dream

Mem0 Dream — Memory Consolidation

This skill performs a memory consolidation pass: it fetches all project memories, identifies near-duplicates, flags contradictions, and prunes stale entries based on configured retention policies. All proposed changes are shown as a diff for user approval before anything is modified.

IMPORTANT: Execute steps strictly in order (1 → 2 → 3 → 4 → 5 → 6). Each step depends on the previous one. Do NOT run steps in parallel or skip ahead.

Step 1: Load Retention Policies

Check for a project config file in the project root (current working directory):

1. Look for .mem0.json first. If it exists, parse it as JSON and read the

retention field (a dict of category → days | null).

1. If .mem0.json is not present, look for .mem0.md. If it exists, scan it

for a retention: section or YAML front matter with retention settings and parse what you find.

1. If neither file exists, skip config loading entirely.

If no config is found or the config contains no retention settings, fall back to these built-in defaults:

Store the resolved policies for use in Step 3.

Step 2: Fetch ALL Project Memories

Call get_memories to retrieve every memory for the active project:

get_memories(
    filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}]},
    page_size=200,
)

If the response indicates more pages exist, paginate until all memories are fetched. Collect the full list before proceeding. If zero memories are found, print:

No memories found for project <project_id>. Nothing to consolidate.

…and stop.

Step 3: Analyze — Find Issues

Work entirely in-memory; do not modify anything yet.

Group memories by metadata.type (use "unknown" when the field is absent). For each group, identify the following:

3a. Near-duplicate pairs (merge candidates)

Two memories are near-duplicates when they express the same fact or decision but phrased differently (e.g., "Use PostgreSQL for auth" and "Auth DB is PostgreSQL").

Heuristics — two memories are near-duplicates if all of these hold:

• Similarity threshold: estimated cosine similarity > 0.9 (use noun/keyword overlap as proxy — if >60% of significant nouns overlap, treat as >0.9 similarity).
• Same metadata.type.
• Neither memory is pinned (metadata.pinned != true).

For each qualifying pair, draft a merged version that is more complete and specific than either original.

3b. Contradictions

Two memories contradict when they assert opposing facts about the same topic (e.g., "Deploy to ECS" vs. "Deploy to Vercel").

Identify the likely winner: the more recent memory with higher confidence wins. Store both IDs and their content for user review.

3c. Prune candidates

A memory is a prune candidate when any of the following is true:

1. Its metadata.type has a retention policy and the memory is older than the

configured number of days (compare created_at to today).

1. Its confidence score is below 0.3 AND it contains no information unique to

this project (no file paths, identifiers, or domain-specific nouns).

Always skip memories where `metadata.pinned == true`, regardless of age or confidence.

Step 4: Print Diff Report

Print a structured diff to the terminal before making any changes. Use exactly this format:

## dream — consolidation report
 
Merges (<N>):
  [mem0:<id1>] + [mem0:<id2>] → "<merged content, 100 chars>"
 
Conflicts (<N>):
  [mem0:<idA>] vs [mem0:<idB>] — "<topic>" [A/B/skip]
 
Prune (<N>):
  [mem0:<id>] — <type>, <age>d old
 
Proposed: <N> merges, <N> prunes, <N> conflicts. Apply? [Y/n]

If there are zero items in any category, omit that section entirely.

If there are zero total proposals (no merges, no prunes, no conflicts), print:

Dream complete. No duplicate, contradictory, or stale memories found.

…and stop.

Step 5: Wait for User Input and Apply

5a. Contradictions

For each CONFLICT pair in the report, wait for the user to type A, B, or skip (case-insensitive). If they enter nothing (empty), treat as skip.

Record the winner for each pair before proceeding to the final apply confirmation.

5b. Final confirmation

After all conflict resolutions are collected, prompt:

Apply? [Y/n]

If the user types n or no (case-insensitive), print Cancelled. No changes made. and stop.

If the user confirms (Y, yes, or empty / Enter), apply all changes in this order:

Merges

For each approved merge pair:

1. delete_memory(<id1>)
2. delete_memory(<id2>)
3. add_memory with:

• text="<merged content>"
• user_id=<active_user_id>
• app_id=<active_project_id> (top-level, not in metadata)
• metadata={"type": "<original type>", "branch": "<active_branch>", "confidence": <higher of the two original scores>, "source": "mem0-dream"}
• infer=False

Contradictions (resolved)

For each resolved conflict where the user chose A or B:

• Delete the loser (the non-chosen memory): delete_memory(memory_id=<loser_id>)

Contradictions where the user chose skip are left untouched.

Prunes

For each prune candidate:

• delete_memory(<memory_id>)

Step 6: Print Summary

After all changes are applied, print:

Dream complete — merged: <N>, pruned: <N>, conflicts resolved: <N>, skipped: <N>

Auto mode

When invoked with --auto (e.g., /mem0-dream --auto), run non-interactively:

• Merges: applied automatically (no contradiction, both are compatible).
• Prunes: applied automatically (age/confidence-based, no ambiguity).
• Contradictions: skipped — they require human judgment.

Concurrency guard

Before doing any work, check for a lock file at /tmp/mem0_dream_auto.lock:

• If the lock file exists and is less than 10 minutes old, print [mem0-dream --auto] Another run in progress — skipping. and stop.
• Otherwise, create the lock file (write the current timestamp). Delete it when done (in all exit paths).

Execution

In auto mode:

1. Load policies and fetch memories (Steps 1–3) as normal.
2. Apply merges and prunes silently without printing the diff or prompting.
3. Print a compact summary:

`` [mem0-dream --auto] project=<id> merged=<N> pruned=<N> conflicts_skipped=<N> ``

1. If contradictions were detected but skipped, check if a mem0-dream-auto reminder already exists before storing one:

• Search for existing reminders: search_memories(query="mem0-dream contradictions manual review", filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}, {"metadata": {"source": "mem0-dream-auto"}}]}, top_k=1)
• If a result exists with similarity > 0.9, skip storing the reminder (one already exists).
• If no match, store the reminder:

``python add_memory( text="mem0-dream detected <N> contradiction(s) requiring manual review. Run /mem0-dream to resolve them interactively.", user_id="<active_user_id>", app_id="<active_project_id>", metadata={"type": "task_learning", "source": "mem0-dream-auto", "branch": "<active_branch>"}, infer=False, ) ``

See also

• /mem0-forget — targeted deletion of specific memories (search + confirm + delete)
• /mem0-status --deep — quick quality scan without applying changes

Output formatting

IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like bold, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.