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supermemory

Stores and retrieves long-term agent memory via Supermemory's knowledge-graph API.

Updated May 6, 2026
Search Tags:#API integration#knowledge graph#retrieval#agent memory#document ingestion#long-term context#conversation ingestion
One-click install
npx skills add https://github.com/Uniquecrete/ThinkFasterv1 --skill supermemory-uniquecrete
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: supermemory
Source: https://github.com/Uniquecrete/ThinkFasterv1/tree/main/Skills/supermemory
Command: npx skills add https://github.com/Uniquecrete/ThinkFasterv1 --skill supermemory-uniquecrete

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, and includes scripts (resource) components.

What problem does it solve?

It helps AI agents retain long-term facts and conversation-derived knowledge so they can search and recall relevant information across sessions instead of relearning the same context repeatedly.

Core Features & Use Cases

  • Long-term memory via Supermemory knowledge graph: Save, search, update, and soft-delete memories with versioning and contradiction handling.
  • Two ingestion modes: Capture single facts instantly with remember, or ingest larger content/URLs via add document pipeline.
  • Conversation ingestion for relationship-aware recall: Store structured role-attributed message histories (user/assistant/system/tool) incrementally using the companion script.
  • Use Case: After answering a question with a verified explanation, ingest that Q&A as a conversation memory so future searches can retrieve the decision, rationale, and corrected details.

Quick Start

Ask your AI agent to save a key preference as a memory by running npx supermemory remember "The user prefers concise answers with bullet points for checklists." --tag your-container-tag.

Frequently Asked Questions about supermemory

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I give my AI agent long-term memory across conversations?▼

Agent memory is retained across sessions by storing and retrieving long-term context using a knowledge-graph API. This allows agents to search and recall information across conversations instead of relearning context repeatedly.

How do I ingest documents and URLs into an AI knowledge graph for retrieval?▼

You can ingest larger content via an add document pipeline or capture single facts instantly. The system supports direct document and URL ingestion to build a searchable knowledge base for agent recall.

Can I save structured conversation histories for relationship-aware recall?▼

Yes, you can store structured role-attributed message histories incrementally using a companion script. This enables conversation ingestion for relationship-aware recall of past decisions and rationales.

Do I need an API key to store agent memories with Supermemory?▼

Yes, you need a SUPERMEMORY_API_KEY configured in your runtime environment. The skill uses the requests dependency and npx supermemory commands to interact with the knowledge-graph API for memory storage.

How does the knowledge graph handle memory updates and contradictions?▼

The knowledge graph supports saving, searching, updating, and soft-deleting memories with versioning and contradiction handling. This ensures durable context remains accurate when facts change over time.

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