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Alternatives to IWE

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    Related Servers

    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI agents to capture, structure, remember, and retrieve source-backed memory as local Markdown files, with reviewable writes and no cloud dependency.
      190
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Gives AI agents a long-term memory layer over plain Markdown notes, letting them run ranked, citable searches, record new facts, and inspect conflicts and stale entries. Results carry freshness labels, supersession history, and audit fields like search timestamp and no-match status so agents can prove a search actually ran.
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      TheWeave is a markdown-native memory architecture for Claude and any MCP-aware agent. Your agent's memory lives as plain .md files you own: a 5-verb MCP core over the vault, query-driven PageRank retrieval, bi-temporal facts (valid_from / valid_until), and a persona-as-vault model. No database and no embeddings server. The files are the memory, inspectable in your editor and versionable in git.
      2
      Apache 2.0
    • A
      license
      B
      quality
      F
      maintenance
      Provides an agent-first note-taking system designed from the ground up for AI collaboration. Organizes your notes as a local vault of ordinary markdown files with semantic note types.
      28
      14 npm
      10
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Persistent graph-based memory for AI agents, stored as plain markdown — no vector DB. Typed nodes and 11 relation types via 5 MCP tools (search, get, create, link, related), stdio and HTTP/SSE transports.
      3
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to search, read, and traverse a local knowledge base of Markdown files using full-text search and relationship graph, reducing token usage.
      MIT

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