spikuit 0.2.0

A knowledge base that gets smarter the more you use it

Spikuit

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A knowledge base that gets smarter the more you use it.

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What is Spikuit?

Spikuit (spike + circuit, pronounced /spaɪ.kɪt/) is a personal knowledge system built around one idea: your knowledge base should learn from you, not just store things.

Every time you search, review, or ask a question, Spikuit quietly adapts — boosting what's useful, connecting related ideas, and surfacing what you're about to forget.

It works as:

• A self-improving knowledge base — search quality gets better through usage, not re-indexing
• A study partner — an AI tutor that teaches, quizzes, and coaches based on what you actually know
• An agent's brain — a knowledge graph that AI agents can read, write, and learn from

What makes it different?

	Anki	Obsidian + SRS	Spikuit
Scheduling	Per-card	Per-note	Per-concept, connected
Knowledge structure	Flat deck	Manual links	Auto-growing graph
Search	Keyword	Keyword + tags	Semantic + graph-weighted
Retrieval quality	Static	Static	Improves with usage
AI integration	Limited	Plugins	Built-in (agent-native)

Spikuit doesn't replace these tools — it explores what becomes possible when you combine a knowledge graph, spaced repetition, and AI agents into one system.

Use Cases

"I want to remember what I learn"

You: /tutor

Tutor: You have 5 concepts due. Let's start with PageRank —
       it's a prerequisite for APPNP which is also due.
       [teaches the concept, then quizzes you]

You:   [answers]

Tutor: Good, but you mixed up the convergence condition.
       Here's what actually happens: ...
       [re-explains, then tries a different question angle]

Review concepts with an AI tutor that adapts to your understanding level. It doesn't just quiz you — it teaches weak areas, gives feedback on mistakes, and adjusts difficulty based on how well you know each concept.

"I want to build a knowledge base that's actually searchable"

# Add knowledge
spkt add "# Functor\n\nA mapping between categories." -t concept -d math
spkt add "# Monad\n\nA monoid in endofunctors." -t concept -d math

# Search — results ranked by relevance, how well you know each concept,
# and how central it is in your knowledge graph
spkt retrieve "category theory"

The more you use it, the better search gets. Concepts you review often rank higher. Related concepts surface together. Unhelpful results get pushed down automatically.

"I want an AI that knows what I know"

from spikuit_core import QABotSession

session = QABotSession(circuit, persist=True)
results = await session.ask("What is a functor?")

# Results get better over time:
# - Follow-up questions auto-penalize unhelpful prior results
# - Accepting results boosts them for future queries
# - The graph remembers what's useful across sessions
await session.accept([results[0].neuron_id])

Give an AI agent a Spikuit brain, and it can search your knowledge, add new concepts from conversations, and track what you've mastered vs. what needs review.

How It Works (in brief)

Spikuit organizes knowledge as a graph — concepts are nodes, relationships are edges. When you interact with the graph, three things happen automatically:

1. Smart scheduling — each concept has its own review timing based on how well you know it (powered by FSRS)
2. Activation spreading — reviewing one concept nudges related concepts closer to their review time. Connections that are used together get stronger.
3. Search optimization — results are ranked by relevance × how well you know each concept × how central it is in your graph. Feedback from conversations continuously improves ranking.

For the technical details behind these mechanisms, see Appendix: Algorithms.

Quick Start

# Install
git clone https://github.com/takyone/spikuit.git
cd spikuit
uv sync --package spikuit-cli

# Initialize a brain (interactive wizard)
spkt init

# Add knowledge
spkt add "# Functor\n\nA mapping between categories." -t concept -d math

# Review what's due
spkt due
spkt quiz

# Search
spkt retrieve "functor"

# Visualize your knowledge graph
spkt visualize

Documentation

• Getting Started — install, init, first commands
• How to Use — use cases, agent skills, Python API
• Concepts — brain, graph model, how things connect
• CLI Reference — all spkt commands
• Appendix: Algorithms — FSRS, graph propagation, technical details
• API Reference — Python API docs

Architecture

spikuit-core/     # Pure engine (no LLM dependency)
spikuit-cli/      # spkt command
spikuit-agents/   # Agent skills and adapters

The core engine is LLM-independent — spkt commands work standalone. Agent skills (/tutor, /learn, /qabot) add LLM-powered interactions on top, designed for tools like Claude Code.

Development

uv sync --package spikuit-core --extra dev
uv run --package spikuit-core pytest spikuit-core/tests/ -v

License

Apache-2.0