Beyond Bookmarks and Snippets: Knowledge Management for Humans and Agents

bkmr - crate of the week 482 - memories, bookmarks, snippets, text - search it, invoke it!

Organize, find, and apply various content types:

Web URLs with automatic metadata extraction
Code snippets for quick access and reuse
Shell commands with immediate execution capabilities
Markdown documents with live rendering, incl. TOC
Plain text with Jinja template interpolation
Local files and directories integration

Why bkmr?

Developer- and agent-focused: Integrates seamlessly with workflow and toolchain
Agent-friendly: JSON output, non-interactive mode, and _mem_ system tag for AI agent memory
Multifunctional: Handles many content types with context-aware actions
Intelligent: Full-text and semantic search capabilities
Privacy-focused: Fully local — database, embeddings, and search all run offline
Fast: 20x faster than similar Python tools
Automation-ready: Programmatic CLI with --json, --np, --stdout for pipelines and integrations
Editor Integration: Built-in LSP server

Agent Memory and Skill

Persistent long-term memory for AI agents. The _mem_ system tag and hsearch (hybrid FTS + semantic search) create a complete read/write memory interface:

# Agent stores memory:
bkmr add "Prod DB is PostgreSQL 15 on port 5433" fact,database \
  --title "Production database config" -t mem --no-web

# Agent queries memories with natural language (hybrid search)
bkmr hsearch "database configuration" -t _mem_ --json --np

# All output is structured JSON — designed for programmatic consumption

Use skill/bkmr-memory. It defines a comprehensive memory protocol with taxonomy, deduplication, and session workflows.

See Agent Integration.

Quick Examples

# Quick fuzzy search with interactive selection
bkmr search --fzf

# Add URL with automatic metadata extraction
bkmr add https://example.com tag1,tag2

# Store code snippet
bkmr add "SELECT * FROM users" sql,_snip_ --title "User Query"

# Shell script with interactive execution
bkmr add "#!/bin/bash\necho 'Hello'" utils,_shell_ --title "Greeting"

# Render markdown in browser with TOC
bkmr add "# Notes\n## Section 1" docs,_md_ --title "Project Notes"

# Import files with frontmatter
bkmr import-files ~/scripts/ --base-path SCRIPTS_HOME

# Local semantic search (no API keys needed)
bkmr sem-search "containerized application security"

# Agent memory: store and retrieve knowledge
bkmr add "Prod DB on port 5433" fact,database --title "Prod DB config" -t mem --no-web
bkmr hsearch "database config" -t _mem_ --json --np

Screenshots

General Usage:

bkmr demo

Fuzzy Search with FZF:

fzf demo

Agent Memory:

agent demo

Detailed walkthroughs: Overview | Getting Started | Search and Filter | Edit and Update | Tag Management

Getting Started

Installation

# Via cargo
cargo install bkmr

# Via pip/pipx/uv
pip install bkmr

# Via brew
brew install bkmr
export ORT_DYLIB_PATH=/opt/homebrew/lib/libonnxruntime.dylib

Bkmr