clear-mind 0.1.4

An AI companion that grows alongside you through your Obsidian notes

Clear Mind

中文文档

You and an AI co-manage one Obsidian vault — with crystal-clear boundaries.

Clear Mind reads your notes, understands who you are, and grows alongside you. It remembers your patterns, tracks your growth, and suggests entropy reduction. But it never touches your notes — all its writing is confined to its own folder, enforced at the code level.

This is not another AI that dumps files into your vault. Clear Mind is a disciplined co-manager that knows its place.

Core Philosophy: Co-manage, Don't Pollute

Most AI tools write files everywhere, cluttering your carefully curated vault. Clear Mind is different:

Your Obsidian Vault
├── daily-notes/       ← Your notes. Sacred. Read-only for the agent.
├── projects/          ← The agent observes, understands, but NEVER modifies.
├── ideas/             ← Your creative space remains yours.
│
└── _clear_mind/       ← Agent's space. Only here it writes.
    ├── about_user.md      Growing understanding of you
    ├── entropy_log.md     Entropy reduction observations
    ├── reflections/       Daily reflections on what changed
    └── ...

The boundary is not just a prompt — it's enforced in code:

• write_agent_note and append_agent_note validate the path starts with _clear_mind/
• Path traversal attempts are blocked (../../etc/passwd → rejected)
• The agent has zero write tools that operate outside _clear_mind/
• Even if the LLM "decides" to write elsewhere, the tool layer refuses

What the agent DOES do:

• Read your notes to understand your thinking, projects, and patterns
• Build a growing profile in _clear_mind/about_user.md — so it remembers you across sessions
• Write daily reflections in _clear_mind/reflections/ — tracking what changed and what it means
• Log entropy reduction opportunities in _clear_mind/entropy_log.md — suggestions, never actions

What the agent NEVER does:

• Modify, delete, or rearrange your notes
• Create files outside _clear_mind/
• Act on suggestions without your explicit consent

Features

• Local-first — runs on LM Studio, Ollama, or any OpenAI-compatible API. Zero cloud dependency.
• Obsidian CLI integration — uses the official Obsidian CLI (v1.12+) for all vault operations
• Hard boundary enforcement — the agent physically cannot write outside _clear_mind/, enforced at the tool level, not just the prompt
• Heartbeat monitoring — daily vault change detection with zero token cost when nothing changed
• Incremental growth — about_user.md grows with every interaction, building real institutional memory

Quick Start

Prerequisites

• Python 3.12+
• Obsidian desktop app running (v1.12+ with CLI enabled)
• A local LLM server (e.g. LM Studio, Ollama)

Install

pip install clear-mind

Setup

clear-mind init

Interactive setup will ask for:

1. Your Obsidian vault path
2. LLM API base URL (default: http://localhost:1234/v1)
3. API key (default: lm-studio)
4. Model name (default: qwen3.5-9b)

This creates a .env file and initializes the _clear_mind/ folder in your vault.

Example: LM Studio with Qwen 3.5

1. Download LM Studio, search and load unsloth/qwen3.5-35b-a3b
2. Start the local server (default port 1234)
3. Run:

clear-mind init

When prompted:

Obsidian vault path: /Users/you/MyVault
LLM base URL [http://localhost:1234/v1]:       ← press Enter
API key [lm-studio]:                            ← press Enter
Model name [qwen3.5-9b]: unsloth/qwen3.5-35b-a3b

Or configure via .env directly:

CLEAR_MIND_VAULT_PATH=/Users/you/MyVault
CLEAR_MIND_BASE_URL=http://localhost:1234/v1
CLEAR_MIND_API_KEY=lm-studio
CLEAR_MIND_MODEL_NAME=unsloth/qwen3.5-35b-a3b

Then start chatting:

clear-mind chat

Chat

clear-mind chat

Start an interactive conversation. The agent can read your notes, search your vault, and write reflections — but only within its own folder.

Heartbeat

Single run (e.g. via cron):

clear-mind heartbeat

Long-running daemon:

clear-mind serve

The heartbeat scans for vault changes since the last run. If nothing changed, it exits immediately (zero token cost). If changes are detected, the agent reads the changed notes and updates its understanding.

Diagnose

clear-mind doctor

Checks your configuration, vault structure, Obsidian CLI availability, and model connection.

Configuration

All settings are loaded from a .env file or environment variables with the CLEAR_MIND_ prefix:

Variable	Default	Description
CLEAR_MIND_VAULT_PATH	(required)	Path to your Obsidian vault
CLEAR_MIND_BASE_URL	http://localhost:1234/v1	LLM API base URL
CLEAR_MIND_API_KEY	lm-studio	API key
CLEAR_MIND_MODEL_NAME	qwen3.5-9b	Model to use
CLEAR_MIND_HEARTBEAT_CRON	0 9 * * *	Heartbeat schedule (daily at 9am)
CLEAR_MIND_CHECKPOINTER_PATH	~/_clear_mind_state/checkpoints.db	State persistence path

Real-World Example

The following is actual output from Clear Mind running with LM Studio + unsloth/qwen3.5-35b-a3b on a test vault with 4 notes.

Heartbeat: Automatic Reflection

Your notes in the vault:

daily/2026-03-28.md — "今天开始研究 gRPC，感觉流式传输比 REST 优雅很多。下周做 POC。
                      读了《思考快与慢》第 12 章，关于过度自信的偏见。"
daily/2026-03-29.md — "决定在项目里用 gRPC 替换 REST。流式 API 正好解决了实时推送的需求。
                      发现 Obsidian 标签命名不一致，需要统一。"

projects/distributed-system.md  — notes on CAP theorem, Raft/Paxos, message queues
projects/decision-framework.md  — methodology: boundaries → options → score → POC → rollback

After clear-mind heartbeat, the agent reads all changed notes and writes:

_clear_mind/about_user.md — Evolving user profile:

## Technical Focus
- Studying distributed systems: CAP theorem, consistency models, consensus algorithms
- Currently focused on gRPC for microservices, particularly streaming RPC
- Decided to replace REST with gRPC due to real-time push requirements
- Uses a structured decision framework: define boundaries → list options → score → POC → rollback

## Learning Habits
- Self-reflective about cognitive biases in coding ("this is simple" leading to debugging hell)
- Plans POCs before full adoption

_clear_mind/reflections/2026-03-29.md — Daily reflection:

## What Changed
The user made a concrete decision to migrate from REST to gRPC.
They didn't just switch because gRPC is "cooler" — they followed their
own methodology: identified the need (streaming), evaluated tradeoffs
(learning cost acceptable), and planned a POC before full migration.

There's a nice parallel between their technical work and personal growth:
studying overconfidence bias while simultaneously being disciplined enough
to do a POC rather than diving in headfirst.

## No Major Entropy Issues
The tag naming inconsistency is already on their TODO list — user is
aware and will address when ready.

_clear_mind/entropy_log.md — Entropy tracking:

## 2026-03-29: Tag Naming Inconsistency (Already Logged)
User noted tag system has mixed Chinese/English naming conventions.
Already on their TODO list. No action needed from agent.

Chat: Interactive Q&A

You: 请读一下 daily/2026-03-29.md 然后告诉我你了解到了什么

Clear Mind: 从这篇日记中我了解到：
  • 决定在项目里用 gRPC 替换 REST，因为流式 API 能解决实时推送的需求
  • 在整理 Obsidian 的标签系统，发现了中英文混用的问题
  • 计划读《系统之美》第三章

  看起来你今天做了两个重要的决定：技术架构升级和知识管理系统优化。
  这两个都是熵减的好方向！

Clear Mind vs Claude Code + Obsidian

	Clear Mind	Claude Code + Obsidian
Runtime	Standalone CLI agent, runs locally or as a daemon	IDE extension, requires editor session
LLM	Local-first (LM Studio, Ollama)	Cloud API (Anthropic, OpenAI)
Obsidian integration	Purpose-built Obsidian CLI tools (14 tools)	Generic file read/write
Write boundary	Hard enforced: agent can only write to _clear_mind/	No boundary: can write anywhere
Heartbeat	Built-in: detects vault changes, runs automatically	No automatic scanning
State	Persistent across sessions (SQLite checkpointer)	Per-session, no cross-session memory
Cost	Zero after setup (local model)	Per-token API cost

Architecture

clear_mind/
├── cli.py          Typer CLI (init, chat, heartbeat, serve, doctor)
├── agent.py        DeepAgents SDK agent assembly
├── obsidian.py     Obsidian CLI tools (14 tools: read, search, write...)
├── config.py       pydantic-settings configuration
├── heartbeat.py    Vault change scanning + scheduling
└── prompts.py      System prompts (identity, boundaries, heartbeat)

The agent is assembled with DeepAgents SDK on top of LangGraph, using a SQLite checkpointer for state persistence across sessions.

License

MIT