{
  "schema_version": "1.1",
  "id": "s9:https://github.com/supermemoryai/supermemory",
  "slug": "supermemory-0larjzg",
  "url": "https://feed7.dev/p/supermemory-0larjzg",
  "title": "supermemoryai/supermemory",
  "why_included": "Supermemory combines persistent agent memory, profiles, RAG, connectors, and local deployment behind one API, with plugins for Codex, Claude Code, Cursor, and other clients.",
  "summary": "Supermemory combines memory extraction, maintained user profiles, hybrid RAG, connectors, and file processing behind one API, MCP server, and coding-agent plugins. It reports **95% Recall@15**, about **720 added tokens**, and **99.4% context reduction** on LongMemEval.",
  "practical_implication": "Builders can use project-scoped containers to carry preferences and repo context between agent sessions, or run the same API locally at localhost:6767. The practical test is whether retrieved memories improve real tasks without stale facts, cross-project leakage, or prompt bloat.",
  "agent_context": "Supermemory combines memory extraction, maintained user profiles, hybrid RAG, connectors, and file processing behind one API, MCP server, and coding-agent plugins. It reports **95% Recall@15**, about **720 added tokens**, and **99.4% context reduction** on LongMemEval.\n\nBuilders can use project-scoped containers to carry preferences and repo context between agent sessions, or run the same API locally at localhost:6767. The practical test is whether retrieved memories improve real tasks without stale facts, cross-project leakage, or prompt bloat.\n\nThe benchmark leadership and efficiency figures are project-reported, and the material does not describe independent validation. Automatic contradiction handling, expiry, and forgetting also need workload-specific evaluation before the memory layer becomes trusted state.",
  "source": {
    "name": "GitHub",
    "url": "https://github.com/supermemoryai/supermemory",
    "published_at": null
  },
  "source_class": "tool",
  "content_type": "GitHub Repo",
  "layer": "agent",
  "domains": [
    "coding",
    "data"
  ],
  "topics": [
    "agent-memory",
    "context-engineering",
    "retrieval"
  ],
  "verification": {
    "status": "needs_review",
    "label": "Needs Review",
    "method": "unverified",
    "verified_at": null
  },
  "uncertainty": [
    "The benchmark leadership and efficiency figures are project-reported, and the material does not describe independent validation. Automatic contradiction handling, expiry, and forgetting also need workload-specific evaluation before the memory layer becomes trusted state."
  ],
  "connected_context": {
    "meaning": "Supermemory packages several previously separate memory patterns into one deployable layer: extraction, maintained profiles, retrieval, connectors, and project-scoped continuity. Its reported efficiency strengthens the case for selective external memory, but does not settle the central trust problem in the prior candidates: persistent state still needs provenance, contradiction handling, expiry, isolation, and workload-specific evaluation before agents can rely on it.",
    "corpus_size": 812,
    "generated_at": "2026-09-19T09:05:18.698Z",
    "connections": [
      {
        "title": "thedotmack/claude-mem",
        "source_name": "GitHub",
        "source_url": "https://github.com/thedotmack/claude-mem",
        "feed7_url": "https://feed7.dev/p/claude-mem-1uba088",
        "reason": "Both provide external cross-session project memory, but Supermemory broadens the pattern from inspectable activity history to profiles, connectors, file processing, and a hosted or local API."
      },
      {
        "title": "Lessons from Studying Every Memory System — Shlok Khemani, Independent",
        "source_name": "AI Engineer",
        "source_url": "https://www.youtube.com/watch?v=5ZGyKWjQDr0",
        "feed7_url": "https://feed7.dev/p/lessons-from-studying-every-memory-system-shlok-khemani-independent-0m3gyxb",
        "reason": "The talk supplies the governance requirements that Supermemory’s maintained profiles leave unresolved, including conflict detection, visibility, editing, and deliberate update cadence."
      },
      {
        "title": "nashsu/llm_wiki",
        "source_name": "GitHub",
        "source_url": "https://github.com/nashsu/llm_wiki",
        "feed7_url": "https://feed7.dev/p/llm-wiki-1dqmknq",
        "reason": "LLM Wiki offers a more source-traceable form of persistent context, highlighting that Supermemory’s broader extraction and profile layer must still prevent durable summaries from preserving distortions."
      },
      {
        "title": "UniMem: Complementary Episodic-to-Parametric Memory for Boundary-Agnostic Task Streams",
        "source_name": "arXiv",
        "source_url": "https://arxiv.org/abs/2607.26017v1",
        "feed7_url": "https://feed7.dev/p/2607-26017v1-1opv1da",
        "reason": "UniMem extends the design space beyond Supermemory’s retrieval-centered service by proposing learned consolidation for recurring patterns, while inheriting the same unresolved safety and inspectability concerns."
      }
    ]
  },
  "lifecycle": "Current",
  "published_at": null,
  "modified_at": null,
  "supersedes": [],
  "expires_at": null,
  "formats": {
    "html": "https://feed7.dev/p/supermemory-0larjzg",
    "json": "https://feed7.dev/p/supermemory-0larjzg.json",
    "markdown": "https://feed7.dev/p/supermemory-0larjzg.md"
  }
}