hermitcrab-ai 0.1.0a1

A lightweight, memory-first, Nostr-primary personal AI assistant

🦀 HermitCrab

Your local, private AI companion that actually remembers — and gets better over time

What is HermitCrab, really?

HermitCrab is a personal AI agent you run on your own machine.
It’s not another cloud wrapper, not a bloated framework, not yet another SaaS subscription trap.

It’s small (under 7,000 lines of core code), readable, auditable, and built around one simple idea:
Your AI should remember what matters to you — forever — without turning into a black box.

Think of it as a second brain you can carry in your pocket (or copy to a new laptop/VPS in seconds).
Just move the workspace/ folder and you’re back in business — same memories, same personality, same progress.

Why people may be drawn to it

• Runs fully offline with local models (Ollama default)
• Remembers things in plain, human-readable Markdown files (Obsidian compatible, git-friendly)
• Automatically distills conversations into facts, tasks, decisions, goals, reflections
• Reflects on itself — spots patterns, mistakes, contradictions, and suggests improvements
• Talks via Nostr (primary), Telegram, email, or plain CLI — your choice
• Stays tiny, fast, and cheap — no 100k+ line monolith

Same crab, new shell.
Move your workspace anywhere. The agent picks up exactly where it left off.

Quick Start (3 commands)

1. Install

   pip install hermitcrab-ai
   

2. Set up your workspace & config

   hermitcrab onboard
   

   (creates ~/.hermitcrab/ with config and empty workspace)

3. Pick a model & run
   Edit ~/.hermitcrab/config.json to point to your favorite model (local or cloud).
   Then just:

   hermitcrab agent
   

You’re now talking to your own persistent, memory-aware agent.

How the agent actually thinks & remembers

HermitCrab is not a stateless chat loop.
Every session follows a clean lifecycle:

1. You talk → agent responds → tools run if needed
2. Session ends (you exit, or 30 min of silence)
3. Journal synthesis — narrative summary of what happened (cheap model)
4. Distillation — extracts new facts, tasks, goals, decisions (cheap model)
5. Reflection — looks for mistakes, contradictions, patterns (smarter model)

All extracted knowledge lands as tiny, atomic Markdown notes in workspace/memory/:

workspace/
├── memory/
│   ├── facts/          # preferences, hard truths
│   ├── decisions/      # choices & reasoning
│   ├── goals/          # long-term objectives
│   ├── tasks/          # things to do (with deadlines & status)
│   └── reflections/    # self-analysis, cleanups
├── journal/            # narrative session summaries
└── sessions/           # raw chat logs (for debugging)

Everything is:

• Human-readable & editable (open in Obsidian, Vim, Notepad)
• Structured with YAML frontmatter
• Wikilink-friendly
• Deterministic — Python, not the LLM, writes the files

No vector databases. No silent embeddings. No hidden state corruption.

Channels — where you talk to your crab

• Nostr (default / primary) — encrypted DMs (NIP-04 + NIP-17 groups coming)
• Telegram — classic bot
• Email — IMAP/SMTP
• CLI — quick local chats

All channels feed into the same memory & reflection engine.

Tools — what the agent can actually do

Tool	What it does
read_file	Peek at files in workspace
write_file	Create / overwrite files
edit_file	Precise replacements
list_dir	Browse directories
exec	Run safe shell commands
web_search	DuckDuckGo search (no API key needed)
message	Reply to you
spawn	Launch sub-agents
cron	Schedule recurring jobs

Execution is always gated by Python — the LLM can only propose.

Self-Improvement — the part that actually matters

HermitCrab gets smarter over time by:

• Distilling conversations → new facts/tasks/goals/reflections
• Reflecting on patterns → mistakes, contradictions, model misbehavior
• Routing jobs to the right model:
  • Interactive replies → strong model (Claude, GPT-4o, etc.)
  • Journal + distillation → cheap local (Llama 3.2 3B, Phi-3-mini)
  • Reflection → medium model

This keeps costs low while letting the agent learn without constant supervision.

Architecture at a glance

Total core agent code: 6,927 lines (run ./core_agent_lines.sh to verify).

hermitcrab/
├── agent/         # loop, tools, memory handling
├── channels/      # Nostr, Telegram, email, CLI
├── providers/     # LLM abstraction (litellm + fallbacks)
├── config/        # typed config loading
├── cli/           # typer-based interface
└── utils/         # helpers

Design rules we live by:

• Python is the source of truth — LLM is untrusted
• Memory is deterministic & auditable
• Local-first by default
• Small enough to read in a weekend
• Forkable, hackable, understandable

Comparison — why this feels different

Aspect	HermitCrab	Typical AI Framework / Chatbot
Core code size	~7k lines	50k–300k+ lines
Memory	Atomic Markdown	Vector DB or forgotten
Portability	Copy workspace → works	Cloud account locked
Transparency	Fully auditable	Opaque internals
Cost	Local models cheap	API calls add up fast
Self-improvement	Built-in distillation & reflection	Rare or manual

Roadmap (where we're going)

Done

• Atomic memory system
• Journal + distillation
• Reflection basics
• Nostr integration
• Local-first deployment

In progress

• Observability / metrics
• Full integration tests

Planned

• Journal search
• Backup & migration helpers
• Optional health-check endpoint
• Web chat companion (static HTML + Nostr)

Why I built this

Most AI tools today are:

• Tied to someone else’s cloud
• Forget everything after 4k tokens
• Impossible to truly understand or audit
• Expensive to run 24/7

HermitCrab exists to prove a quieter truth:

A personal AI can be small, local, private, deterministic, and still grow with you — without turning into a 200k-line monster or a subscription bill.

Keep it yours. Keep it local. Keep it simple. 🦀

Get started

pip install hermitcrab-ai
hermitcrab onboard
hermitcrab gateway

Welcome to your own second brain.
Let’s make it remember everything that matters.