supermemory

name: supermemory description: > Long-term memory that persists across conversations. Save facts, decisions, and preferences to a knowledge graph that builds a living profile of the user over time. Replaces static user docs with dynamic context that grows and self-corrects. compatibility: Created for Zo Computer metadata: author: skeletorjs category: Community

Setup

1. Sign up at supermemory.ai and get an API key
2. Add SUPERMEMORY_API_KEY to your Zo secrets at Settings > Advanced
3. Install this skill to Skills/supermemory/
4. Important: Always run memory.py through the Zo shell (run_bash_command tool), not the local shell. The API key is only available in the Zo environment.

CLI

python3 /home/workspace/Skills/supermemory/scripts/memory.py <command> [options]

Multiple Containers

The script supports multiple memory containers via the --as flag. This is useful for separating the user's context from the AI's own self-knowledge:

# Save to the default (user) container
python3 memory.py save --content "User prefers dark mode"

# Save to a custom container
python3 memory.py save --as myai --content "I noticed a pattern in how I approach debugging"

# Search a specific container
python3 memory.py search --as myai --query "debugging patterns"

# Get profile for a specific container
python3 memory.py profile --as myai

To customize container names, edit the CONTAINER_DEFAULT and CONTAINER_SELF variables at the top of memory.py.

How Supermemory Works

Supermemory is a knowledge graph, not a key-value store. When you save content, it:

1. Extracts facts: AI analyzes the content and pulls out discrete memories
2. Builds relationships: Each memory connects to existing ones via three relationship types:
   • Updates: New fact contradicts old one. Old gets marked isLatest=false. Searches return current info.
   • Extends: New fact enriches existing one. Both remain valid and searchable.
   • Derives: System infers new facts from patterns across memories.
3. Maintains profiles: The /v4/profile endpoint auto-generates a static/dynamic profile from all accumulated memories.

Automatic Forgetting

Supermemory handles memory decay on its own:

• Time-based: Temporary facts ("meeting at 3pm today") are automatically forgotten after they expire.
• Contradiction resolution: When new facts update old ones, searches return current info. History is preserved but deprioritized.
• Noise filtering: Casual, non-meaningful content doesn't become permanent memories.

You do NOT need to manually delete stale temporal memories.

How to Use It

RECALL (search when you need context)

Run memory.py search or memory.py profile whenever context would help:

• Conversation start: Profile for general context, search for topic-relevant memories.
• Mid-conversation: When uncertain about prior decisions, past context, or preferences. Don't guess when you can check.
• Meeting prep: Before calendar events, search for context about attendees, company, or topic.
• "Do you remember": Any time the user asks about past context.

CAPTURE (save when something worth remembering happens)

Run memory.py save as things happen, not just at conversation end.

Write content as entity-centric facts, not raw notes:

• Good: "User decided to use DuckDB for task management"
• Bad: "We talked about databases and decided on DuckDB"

What to save:

• Decisions the moment they're made
• Preferences about tools, workflow, communication
• Key facts about people, companies, projects
• Meeting takeaways after processing transcripts
• Significant project/architecture context

What NOT to save:

• Trivial exchanges
• Temporary/session-specific context (debugging steps, draft iterations)
• Raw data that belongs in files

Save Quality

Saves should be useful cold, readable by a future version of you with zero context about the current conversation.

Good saves:

• "User decided to use DataForSEO over SEMrush for client SEO work because of pay-as-you-go pricing and API flexibility."
• "User prefers Bun over Node for new TypeScript projects. Confirmed across multiple sessions."

Bad saves:

• "User decided on DataForSEO." (no context on why or what it replaced)
• "User likes Bun." (preferences need specificity)

Minimum bar: Every save should include at least: who or what entity it's about, what the fact/decision/preference is, and enough context that the "why" is inferrable.

Correction Saves

When the user changes a previous decision or preference, save the correction with explicit supersession language:

• "User changed the pricing model from flat fee to hybrid. Previous model was $X/month flat."
• "User no longer wants to use Redis for caching. Switched to in-memory caching for simplicity."

The knowledge graph uses this language to mark old memories as stale.

Tags

Tags go in --tags as comma-separated values. Keep them simple:

Using customId

customId is for document-level identity, not memory-level dedup. Use it when:

• You have a growing document you'll update over time (e.g., a conversation transcript)
• You want to fully replace a previous version of the same content
• You're ingesting something with a natural external ID (meeting ID, ticket ID)

You do NOT need customId for every individual fact you save. The graph handles dedup through its relationship system.

Conversation Ingestion

For long or important conversations, use the conversation command:

python3 memory.py conversation --content "..." --id "conv-2026-02-16-topic"

Supermemory extracts multiple connected memories from a single conversation.

Profile as Living Context

Supermemory automatically builds the user's profile from all accumulated memories:

• Static: Long-term stable facts (role, location, preferences)
• Dynamic: Recent context and temporary states (current projects, active discussions)

The profile is fully automatic. Run memory.py profile to check current state.