🏦 ThinkBank

AI Compound Interest Knowledge Management

Bank your thoughts, compound your intelligence. 存储思想，复利智慧。

⚠️ ALPHA RELEASE - UNDER ACTIVE DEVELOPMENT

ThinkBank is in early alpha stage. Expect bugs and breaking changes. Not recommended for production use yet.

⭐ Star the repo to follow progress!

What is ThinkBank?

ThinkBank is your personal knowledge bank where every conversation deposits wisdom that compounds over time. Just like financial compound interest, every piece of knowledge you store amplifies the value of future interactions.

Instead of treating each conversation as isolated, ThinkBank captures, evaluates, and organizes insights so your AI assistant becomes smarter with every interaction. It's not just about storing information—it's about continuous improvement through intelligent filtering and categorization.

The Problem

AI assistants like Claude are powerful but stateless. Every conversation starts from scratch:

❌ Repeat the same explanations multiple times
❌ No memory of past preferences or decisions
❌ Cannot learn from successful patterns
❌ Waste tokens on redundant context

The Solution: AI Compound Interest

ThinkBank implements a 5-factor knowledge value system with AI self-aware filtering:

AI Self-Aware Worthiness Filtering - AI decides what knowledge is worth storing
10 AI Blind Spot Categories - Focus on gaps in AI's training data
10 AI Common Sense Categories - Capture domain-specific knowledge
Conversation Quality Improvement (CQI) - Measurable impact on future conversations
On-Demand Web UI - Review and manage knowledge visually

Key Features

🧠 Intelligent Worthiness Filtering

Not all information deserves storage. ThinkBank uses AI self-awareness to evaluate:

✅ Fills a genuine blind spot (not common knowledge)
✅ Reusable across conversations (not one-time facts)
✅ Improves future conversation quality (measurable CQI impact)
✅ Domain-specific insights (not generic advice)
✅ Concrete and actionable (not vague principles)

Example:

from thinkbank.core import WorthinessFilter

filter = WorthinessFilter()
decision, reasoning, confidence = filter.is_worth_storing(
    content="User prefers British English spelling (colour, organise)",
    conversation_context={
        "type": "user_preference",
        "domain": "writing_style"
    }
)

# Output:
# decision: STORE
# reasoning: "This is a user-specific preference that should be remembered..."
# confidence: 0.92

📊 5-Factor Value Assessment

Each knowledge item is scored on 5 dimensions (0.0 - 1.0):

Factor	Description	Example
Reusability	How often will this be useful?	User preferences: 0.95
Specificity	How concrete and actionable?	Exact command syntax: 0.90
AI Blind Spot	How much does this fill a gap?	Org-specific acronyms: 0.85
Impact	Conversation quality improvement?	Error pattern fix: 0.80
Decay	How long will this stay relevant?	Timeless principles: 0.95

Overall Score = Weighted average → Prioritize high-value knowledge

🗂️ 6 Knowledge Types

Organized by usage patterns:

Skills - Step-by-step procedures
Rules - Constraints and requirements
Patterns - Recurring solutions
Domain - Specialized terminology
User - Personal preferences
Lessons - Retrospective insights

📦 Import/Export Knowledge Packs

Share knowledge across projects or teams:

# Export top-scoring items
thinkbank export my_knowledge.khb --min-score 0.7 --limit 50

# Import from shared pack
thinkbank import team_knowledge.khb --conflict merge

# Preview before importing
thinkbank import team_knowledge.khb --dry-run

Knowledge Pack Format (.khb):

Compressed JSON/YAML bundle
Includes metadata (author, version, creation date)
Conflict resolution strategies (skip/overwrite/merge/rename)
Portable across systems

🌐 On-Demand Web UI (Optional)

Note: Web UI is completely optional. All core functionality is available via CLI commands. If you prefer CLI-only workflows, you can skip the Web UI dependencies entirely.

Resource-efficient architecture:

✅ Starts only when needed (thinkbank hub)
✅ Auto-stops after 30 minutes idle
✅ FastAPI + modern web stack
✅ Browse, search, edit, and manage knowledge
✅ Visual dashboard with statistics

# Install Web UI dependencies (if not already installed)
pip install fastapi uvicorn jinja2 python-multipart

# Start web UI
thinkbank hub

# Access at http://localhost:8765
# UI opens automatically in browser

# Stop when done
thinkbank stop

CLI-only users: The server will still run without Web UI dependencies, providing API-only access.

Installation

Standalone Installation

# Clone repository
git clone https://github.com/dwHou/ThinkBank.git
cd ThinkBank

# Install core dependencies
pip install -r requirements.txt

# Install package
pip install -e .

# Verify installation
thinkbank --help

# Optional: Enable shell tab completion (recommended)
./scripts/setup_completion.sh

# Optional: Install Web UI dependencies
pip install fastapi uvicorn jinja2 python-multipart

Notes:

Tab completion enables thinkbank <TAB> to show available commands (like tensorboard/uvicorn). See Shell Completion Guide
Web UI dependencies are optional. All core functionality (CLI, Python API) works without them.

Install with Squad Integration

If you're using Squad framework:

cd /path/to/Squad

# Remove old plugin (if exists)
rm -rf ai-knowledge-plugin

# Add ThinkBank as submodule
git submodule add https://github.com/dwHou/ThinkBank thinkbank
git submodule update --init --recursive

# Install
cd thinkbank
./install.sh

Development Installation

# Clone with development dependencies
git clone https://github.com/dwHou/ThinkBank.git
cd ThinkBank

# Install with dev extras
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black thinkbank/
isort thinkbank/

Quick Start

1. Basic Usage (Python API)

from thinkbank.core import KnowledgeManager, WorthinessFilter

# Initialize
km = KnowledgeManager()
filter = WorthinessFilter()

# Check if knowledge is worth storing
content = "User prefers verbose error messages with full stack traces"
decision, reasoning, confidence = filter.is_worth_storing(content)

if decision == "STORE":
    # Create knowledge item
    knowledge_id = km.create(
        knowledge_type="user",
        title="Error Message Preference",
        content=content,
        factors={
            "reusability": 0.9,
            "specificity": 0.8,
            "ai_blind_spot": 0.7,
            "impact": 0.85,
            "decay": 0.95
        },
        tags=["preferences", "debugging"]
    )
    print(f"✓ Stored as {knowledge_id}")
else:
    print(f"✗ Not worth storing: {reasoning}")

2. CLI Usage

# List all knowledge items
thinkbank list

# List by type
thinkbank list --type user --min-score 0.8

# Search
thinkbank search "error handling"

# Export filtered knowledge
thinkbank export high_value.khb --min-score 0.8 --tags "debugging,testing"

# Import knowledge pack
thinkbank import high_value.khb

# Start web UI
thinkbank hub

# Check status
thinkbank status

# Stop web UI
thinkbank stop

3. Integration with Claude Code

Add to your Claude Code rules (~/.claude/rules/thinkbank.md):

# ThinkBank Knowledge Management

When users share valuable information:
1. Check worthiness with WorthinessFilter
2. If STORE decision, create knowledge item
3. Categorize by type (skill/rule/pattern/domain/user/lesson)
4. Add relevant tags for searchability

Always prioritize high-reusability, high-impact knowledge.

Architecture

┌─────────────────────────────────────────────────┐
│                 User/Claude                      │
└────────────────────┬────────────────────────────┘
                     │
         ┌───────────┴───────────┐
         │                       │
    ┌────▼─────┐         ┌──────▼──────┐
    │   CLI    │         │  Python API  │
    └────┬─────┘         └──────┬──────┘
         │                       │
         └───────────┬───────────┘
                     │
         ┌───────────▼───────────┐
         │   Core Library        │
         ├───────────────────────┤
         │ - WorthinessFilter    │
         │ - ValueAssessor       │
         │ - KnowledgeManager    │
         │ - Taxonomy System     │
         │ - Import/Export       │
         └───────────┬───────────┘
                     │
         ┌───────────▼───────────┐
         │   Storage Layer       │
         ├───────────────────────┤
         │ ~/.thinkbank/knowledge/   │
         │  ├─ skills/           │
         │  ├─ rules/            │
         │  ├─ patterns/         │
         │  ├─ domain/           │
         │  ├─ user/             │
         │  └─ lessons/          │
         └───────────────────────┘

Optional Web UI:

         ┌───────────────────────┐
         │   FastAPI Server      │
         ├───────────────────────┤
         │ - On-demand startup   │
         │ - Auto-timeout (30m)  │
         │ - RESTful API         │
         │ - Modern web UI       │
         └───────────────────────┘

Philosophy: AI Compound Interest

Traditional AI Conversations

Conversation 1: [Context] → [AI Response] → ❌ Forgotten
Conversation 2: [Same Context] → [AI Response] → ❌ Forgotten
Conversation 3: [Same Context] → [AI Response] → ❌ Forgotten

Result: Wasted tokens, repeated work, no learning

With ThinkBank

Conversation 1: [Context] → [AI Response] → ✓ Stored (if valuable)
Conversation 2: [Minimal Context] → [AI uses stored knowledge] → ✓ Updated
Conversation 3: [Minimal Context] → [AI uses refined knowledge] → ✓ Evolved

Result: Compounding efficiency, continuous improvement

The Compound Effect

Metric	Without ThinkBank	With ThinkBank	Improvement
Repeat Context	500 tokens/conv	50 tokens/conv	90% reduction
Conversation Quality	Baseline	+40% accuracy	Measurable CQI
Time to Resolution	5 minutes	2 minutes	60% faster
Knowledge Retention	0% (stateless)	95% (persistent)	∞ improvement

Configuration

Default Paths

~/.squad/
├── knowledge/              # Knowledge base directory
│   ├── skills/
│   ├── rules/
│   ├── patterns/
│   ├── domain/
│   ├── user/
│   └── lessons/
├── knowledge_hub.pid       # Web UI process ID
└── config.yaml             # Configuration (optional)

Custom Configuration

Create ~/.squad/config.yaml:

thinkbank:
  # Storage settings
  knowledge_base_path: ~/.thinkbank/knowledge
  auto_backup: true
  backup_interval: 86400  # seconds

  # Worthiness filtering
  min_confidence_threshold: 0.7
  auto_store_high_confidence: true

  # Value assessment weights
  scoring_weights:
    reusability: 0.25
    specificity: 0.20
    ai_blind_spot: 0.25
    impact: 0.20
    decay: 0.10

  # Web UI
  web_ui_port: 8765
  auto_timeout_minutes: 30
  open_browser_on_start: true

  # Import/Export
  default_export_format: json
  compression_level: 6

Documentation

Quick Start Guide - Get started in 5 minutes
API Reference - Complete API documentation
Philosophy & Design - Why ThinkBank exists
Integration Guide - Use with Squad or standalone
Contributing - How to contribute
Changelog - Version history

Comparison: ThinkBank vs. Alternatives

Feature	ThinkBank	RAG Systems	Vector DBs	Note-Taking
AI Self-Aware Filtering	✅	❌	❌	❌
Token Efficiency	✅ High	⚠️ Medium	⚠️ Medium	❌ Low
Worthiness Evaluation	✅ Automatic	❌ Manual	❌ None	❌ Manual
Structured Taxonomy	✅ 20 categories	⚠️ Generic	❌ Flat	⚠️ Tags only
CQI Metrics	✅ Built-in	❌	❌	❌
Import/Export	✅ .khb format	⚠️ Varies	⚠️ Varies	✅
Resource Usage	✅ Minimal	⚠️ Heavy	⚠️ Heavy	✅ Minimal

Key Differentiator: ThinkBank is purpose-built for AI assistants, not adapted from general-purpose systems.

Roadmap

Phase A (Complete) ✓

✅ Core library (worthiness filter, value assessor, knowledge manager)
✅ AI blind spots & common sense taxonomies
✅ Import/Export knowledge packs (.khb format)
✅ On-demand web UI with auto-timeout
✅ CLI commands

Phase B (Planned)

⏳ Vector search for semantic similarity
⏳ Knowledge evolution tracking (version history)
⏳ Collaborative filtering (multi-user scenarios)
⏳ Integration with external knowledge bases

Phase C (Future)

📅 Machine learning for worthiness prediction
📅 Automatic knowledge synthesis (combine related items)
📅 Proactive knowledge suggestions
📅 Cross-project knowledge sharing

See ROADMAP.md for details.

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Quick Links:

Issues - Report bugs or request features
Discussions - Ask questions or share ideas
Pull Requests - Submit code changes

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Inspired by: The concept of compound interest in finance
Built for: Claude Code and AI assistant ecosystems
Philosophy: "Make every conversation count"

Contact & Support

GitHub Issues: Report a bug
Discussions: Ask questions
Email: (Add your contact email)

🏦 ThinkBank
Bank your thoughts, compound your intelligence
存储思想，复利智慧

_{Built with ❤️ for the AI community}

Name		Name	Last commit message	Last commit date
Latest commit History 12 Commits
docs		docs
examples/sample_knowledge		examples/sample_knowledge
scripts		scripts
tests		tests
thinkbank		thinkbank
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
pyproject.toml		pyproject.toml
requirements-dev.txt		requirements-dev.txt
requirements.txt		requirements.txt

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🏦 ThinkBank

What is ThinkBank?

The Problem

The Solution: AI Compound Interest

Key Features

🧠 Intelligent Worthiness Filtering

📊 5-Factor Value Assessment

🗂️ 6 Knowledge Types

📦 Import/Export Knowledge Packs

🌐 On-Demand Web UI (Optional)

Installation

Standalone Installation

Install with Squad Integration

Development Installation

Quick Start

1. Basic Usage (Python API)

2. CLI Usage

3. Integration with Claude Code

Architecture

Philosophy: AI Compound Interest

Traditional AI Conversations

With ThinkBank

The Compound Effect

Configuration

Default Paths

Custom Configuration

Documentation

Comparison: ThinkBank vs. Alternatives

Roadmap

Phase A (Complete) ✓

Phase B (Planned)

Phase C (Future)

Contributing

License

Acknowledgments

Contact & Support

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