bkmr

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  "build_dependencies": [
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    "onnxruntime",
    "openssl@3"
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  "desc": "Unified CLI Tool for Bookmark, Snippet, and Knowledge Management",
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  "enrichment_fetched_at": "2026-06-20T23:35:53+00:00",
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  "full_name": "bkmr",
  "github_default_branch": "main",
  "github_last_commit_at": "2026-06-14T09:30:44Z",
  "github_readme_excerpt": "\u003cimg src=\"docs/bkmr-logo.png\" alt=\"rsenv logo\" width=\"240\"\u003e\n\n![Crates.io](https://img.shields.io/crates/v/bkmr)\n![Crates.io](https://img.shields.io/crates/d/bkmr)\n[![Docs.rs](https://docs.rs/bkmr/badge.svg)](https://docs.rs/bkmr)\n[![Build Status][build-image]][build-url]\n\n\u003e Store anything, find it by meaning, act on it instantly.\n\n# Beyond Bookmarks and Snippets: Knowledge Management for Humans and Agents\n\n`bkmr` - [crate of the week 482](https://this-week-in-rust.org/blog/2023/02/15/this-week-in-rust-482/) - memories, bookmarks, snippets, text - search it, invoke it!\n\n**Organize**, **find**, and **apply** various content types:\n\n- Web URLs with automatic metadata extraction\n- Code snippets for quick access and reuse\n- Shell commands with immediate execution capabilities\n- Markdown documents with live rendering, incl. TOC\n- Plain text with Jinja template interpolation\n- Local files and directories integration\n\n## Why bkmr?\n\n- **Developer- and agent-focused**: Integrates seamlessly with workflow and toolchain\n- **Agent-friendly**: JSON output, non-interactive mode, and `_mem_` system tag for AI agent memory\n- **Multifunctional**: Handles many content types with context-aware actions\n- **Intelligent**: Full-text and semantic search capabilities\n- **Privacy-focused**: Fully local \u2014 database, embeddings, and search all run offline\n- **Fast**: 20x faster than similar Python tools\n- **Automation-ready**: Programmatic CLI with `--json`, `--np`, `--stdout` for pipelines and integrations\n- **[Editor Integration](https://github.com/sysid/bkmr/wiki/Editor-Integration)**: Built-in LSP server\n\n## Agent Memory and Skill\n\nPersistent long-term memory for AI agents. The `_mem_` system tag and `hsearch` (hybrid FTS + semantic search) create a complete read/write memory interface:\n\n```bash\n# Agent stores memory:\nbkmr add \"Prod DB is PostgreSQL 15 on port 5433\" fact,database \\\n  --title \"Production database config\" -t mem --no-web\n\n# Agent queries memories with natural language (hybrid",
  "github_repo": "sysid/bkmr",
  "github_stars": 257,
  "github_topics": [
    "ai-agent",
    "bookmark-manager",
    "cli",
    "knowledge-management",
    "local-first",
    "lsp",
    "onnx",
    "rust",
    "semantic-search",
    "sqlite"
  ],
  "homepage": "https://github.com/sysid/bkmr",
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  "last_seen": "2026-06-20T23:34:18+00:00",
  "license": "BSD-3-Clause",
  "llm_generated_at": null,
  "llm_model": null,
  "name": "bkmr",
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  "optional_dependencies": [],
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  "raw_hash": "6295820b97035e0e",
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  "revision": 0,
  "ruby_source_path": "Formula/b/bkmr.rb",
  "tap": "homebrew/core",
  "test_dependencies": [],
  "uses_from_macos": [
    "python"
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  "version_head": "HEAD",
  "version_stable": "7.6.5",
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  "why_use_this": null
}