Bumblebiber

Thanks <3 I designed it to be very token saving. It loads mostly the very brief summaries of each memory (and there's an algorithm which filters out the lesser important ones). Only if the agent need to know more details, it will dig deeper into each entry.

Some core features worth calling out:

  5-level lazy loading instead of flat RAG. On spawn, agents receive only L1 — one-line summaries of every memory entry.
   When something looks relevant, they drill into L2, L3, etc. on demand. This keeps startup context small (~2k tokens
  for 200 entries) while still having full verbatim detail available at L5.

  Tab indentation maps directly to tree levels. No schema to think about — just write:

  write_memory(prefix="L", content="Always restart MCP server after recompiling TS
      Running process holds old dist — tool calls return stale results
      Fix: kill $(pgrep -f mcp-server)")

  Indented lines become children. Siblings at the same depth are siblings in the tree. append_memory adds children to
  any existing node without overwriting.

  Smart bulk-read selection. Not all L1 entries are shown equally. The V2 selection expands (shows children) for: newest
   N entries per prefix, most-accessed M entries (time-weighted score), and all favorites. Everything else shows as a
  compact title with a [+7 →] hint. This mirrors how human working memory works — recent and frequently-used things are
  immediately accessible.

  Obsolete, not deleted. When a lesson turns out wrong or a decision gets reversed, you mark it obsolete with a
  correction reference ([E0076]). The old entry gets hidden from bulk reads but stays searchable. Past mistakes still
  teach — you just don't pay tokens for them every session.

  Per-agent isolation + optional shared store. Each agent has its own .hmem SQLite file. There's also a company.hmem
  with role-based access control (worker/al/pl/ceo) for knowledge that should be shared across agents.

  The whole thing is a single MCP server, ~3k lines of TypeScript, backed by SQLite with WAL mode. No vector embeddings,
   no external services. Works offline.

OpenClaw users: you can integrate hmem directly by pointing your Claw at the GitHub repo — it'll figure out the rest. npx hmem-mcp init gets you running in under a minute.

Author here, happy to answer questions.

Some background: I run a multi-agent AI system (orchestrator + specialized agents) across multiple machines. Two things kept biting me:

1. *Context dilution:* In long sessions, earlier context gets compressed or dropped. The agent "forgets" decisions made hours ago — not because the session ended, but because the context window silently pushed them out.

2. *Vendor/machine lock-in:* I switch between Claude Code, Gemini CLI, and OpenCode depending on the task. And I work on two PCs. CLAUDE.md only works in Claude Code, on one machine. There was no way to carry knowledge across tools or devices.

hmem solves both: it's a single .hmem file (SQLite) that any MCP-compatible tool can read/write. Same memory, any tool, any machine.

The lazy loading is key — the agent never reads the database directly. It makes tool calls and gets back only what it asked for. A typical session start costs ~20 tokens for the L1 overview. Drilling into one specific topic costs ~80 tokens. Compare that to a MEMORY.md that injects 3000-8000 tokens wholesale every time.

Technical details: - SQLite backend (better-sqlite3), one .hmem file per agent - 5-level hierarchy with dot-path addressing (e.g., L0003.2.1) - Entries are auto-timestamped, support time-range queries - Full-text search across all levels - Configurable category prefixes (defaults: P=Project, L=Lesson, E=Error, D=Decision, M=Milestone, S=Skill, F=Favorite) - Favorites are always loaded at depth 2 (pinned context) - Includes integrity checks — auto-backup on corruption detection

What's next: - Cloud sync between machines (encrypted, probably git-based) - Memory forks — think GitHub repos but for agent memories (fork a curated react-patterns.hmem as a starting point) - Better onboarding docs and a demo video

This is a genuine beta — I use it daily but it hasn't been battle-tested by others yet. If you try it, I'd love to hear what breaks or what's confusing about the setup.