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Mem0 Dream

Free

Optimize your stored memories for clarity and relevance.

by mem0ai63k stars on mem0ai/mem0
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Updated Aug 7, 2026
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Free · Opens the source repo

What Mem0 Dream does

Mem0 Dream is a memory consolidation tool designed to enhance the quality of stored project memories by merging duplicates, resolving contradictions, and pruning stale entries. It operates through a systematic process that begins with loading retention policies, which dictate how long different types of memories should be kept. By analyzing all project memories, it identifies near-duplicates and contradictions, allowing users to manage their memory database effectively. This is particularly useful when dealing with large sets of memories that can become noisy or repetitive over time.

The skill works in a stepwise manner, ensuring that each phase builds upon the previous one. After loading the retention policies, it fetches all project memories and analyzes them for issues. Near-duplicates are identified based on a defined similarity threshold, while contradictions are flagged for user review. Additionally, memories that exceed their retention period or lack unique project-specific information are marked for pruning. This structured approach ensures that users are fully informed of potential changes before any modifications are made, maintaining control over their memory database.

Mem0 Dream is particularly suited for developers and designers who rely heavily on project memories for decision-making and collaboration. As projects evolve, the number of stored memories can grow significantly, leading to confusion and inefficiencies. By consolidating these memories, users can ensure that their memory database remains relevant and easy to navigate, ultimately improving productivity and decision-making processes.

However, this skill may not be ideal for users who prefer a more hands-off approach to memory management, as it requires user input for resolving contradictions and applying changes. Additionally, if a project has a very low number of memories, the benefit of using this tool may be minimal, as there would be fewer duplicates or stale entries to address.

When to use it

Use this skill when your project has accumulated a large number of memories, and you need to clean up duplicates and contradictions.

When not to use it

This tool may not be necessary for smaller projects with few memories or for users who do not wish to manually review proposed changes.

What you can build with it

Cleaning Up a Large Memory Database

When your project has accumulated thousands of memories, use Mem0 Dream to streamline and optimize the data.

Resolving Conflicting Memories

If you encounter contradictory memories that could impact project decisions, this skill helps clarify which to retain.

Maintaining Memory Quality Over Time

Periodically run Mem0 Dream to ensure your project memories remain relevant and useful as the project evolves.

How to install Mem0 Dream

View source

1. Install with the skills CLI

npx skills add mem0ai/mem0/dream --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by mem0ai

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

Determine the active retention policy by running the parser script. Use the appropriate PLUGIN_ROOT variable for the current platform (${CLAUDE_PLUGIN_ROOT}, ${CODEX_PLUGIN_ROOT}, or ${CURSOR_PLUGIN_ROOT}):

python3 "<PLUGIN_ROOT>/scripts/parse_mem0_config.py" "<cwd>"

Parse the JSON output (a dict of category → days | null). If the script fails or returns {}, fall back to these built-in defaults:

metadata.typeDefault retention
session_state90 days
compact_summary90 days
all othersno pruning

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).
  2. 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>
    
  4. 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:
    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:health --deep — quick quality scan without applying changes

Frequently asked questions about Mem0 Dream

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