I taught AI the 13 thinking tools that Einstein and Picasso used — it independently discovered laws I spent months extracting manually

What it is

An open-source framework where AI uses the same 13 cognitive tools that history's greatest minds used (from the book "Sparks of Genius" by Root-Bernstein, 1999): observe, imagine, abstract, find patterns, analogize, empathize, play, transform, synthesize, etc.

You give it a goal + data. It thinks through the data using all 13 tools and extracts core principles.

GitHub: https://github.com/PROVE1352/cognitive-sparks

Why I built it

Every AI agent framework (LangGraph, CrewAI, AutoGPT) teaches agents what to do — call tools, manage state, follow workflows.

Nobody teaches them how to think.

I wanted to see: if the 13 thinking tools are truly universal (used by scientists, artists, and engineers identically), can we implement them as AI primitives?

The weird part: it has a nervous system

Most frameworks use a "CEO pattern" — one orchestrator tells tools what to run in what order. That's how corporations work, not how intelligence works.

Sparks has an actual neural circuit (~30 neuron populations, ~80 learned connections). Tools don't run in a fixed order. The execution sequence emerges from neural dynamics:

• Empty state → "observation hunger" signal drives the observe tool to fire first
• After observations → pattern recognition neurons activate highest
• After patterns → abstraction neurons win
• No code says "observe then patterns then abstract." It just happens.

The connections learn via STDP (spike-timing dependent plasticity) and evolve across sessions. The framework literally gets smarter with every use.

The validation that convinced me

I had 15 months of densely analyzed market data. Over those months, I manually extracted 3 "core laws" governing market behavior. Took months of work.

I fed the raw data to Sparks: "find the fundamental laws."

It found 12 principles. The top 3 matched my manually-extracted laws. Plus 9 additional principles I hadn't formalized.

	Standard (7 tools)	Deep (13 tools)
Principles	7	12
Avg confidence	80%	91%
Coverage	68%	85%
Cost	$6	$9

The 6 "creative" tools (imagine, body-think, empathize, play, shift-dimension, transform) contributed 5 principles that the analytical-only pipeline missed.

What makes it different

LangGraph/CrewAI:  Conductor tells musicians what to play and when
Sparks:            No conductor. Musicians hear each other. Order emerges.

• 13 cognitive primitives (not just "call this API")
• Neural circuit drives execution (not if-else rules)
• Self-optimization: it analyzes its own output quality and fixes its own prompts
• Full loop: extract → validate → evolve → predict → feedback
• Multi-model: Claude, GPT-4o, Gemini, Ollama — any LLM backend
• Cross-session learning: connection weights persist and evolve

Try it

pip install -e .
sparks run --goal "Find the core principles" --data ./your-data/ --depth standard

Works with Claude Code CLI (free with subscription), OpenAI, Google Gemini, or any OpenAI-compatible API (Ollama, Groq).

What's next

• Google Colab notebook (try without installing)
• Benchmark against GPT-Researcher, STORM
• Embedding-based convergence detection

Built solo with Claude Code over a long weekend. Happy to answer any questions about the architecture or results.