nouse 0.2.0

The Cognitive Substrate Framework for Model-Agnostic AI — the missing link to AGI

Nouse

Persistent domain memory for LLMs. Works with any model.

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The result that motivated this

Model                               Score   Questions
─────────────────────────────────────────────────────
llama3.1-8b  (no memory)            46%     60
llama-3.3-70b  (no memory)          47%     60
llama3.1-8b  + Nouse memory  →      96%     60

An 8B model with Nouse outperforms a 70B model without it.

The effect is not about retrieval. It is about disambiguation — a small, precise knowledge signal redirects the model's existing priors onto the correct frame. We call this the Intent Disambiguation Effect.

→ Full benchmark: eval/RESULTS.md

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What Nouse is

Nouse (νοῦς, Gk. mind) is a persistent, self-growing knowledge graph that attaches to any LLM as a memory substrate.

Your documents, conversations, research
           ↓
    Nouse knowledge graph
    (KuzuDB + Hebbian learning)
           ↓
    brain.query("your question")
           ↓
    Structured context injected into any LLM prompt

It is not a RAG system. RAG retrieves chunks. Nouse extracts relations — typed, weighted, evidence-scored connections between concepts — and injects a compact, structured context block.

It learns continuously. Every interaction strengthens or weakens connections (Hebbian plasticity). There is no retraining. No gradient descent. The graph grows.

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Quick start

pip install nouse

# Attach to your knowledge graph
import nouse
brain = nouse.attach()

# Query and inject context
result = brain.query("transformer attention mechanism")
print(result.context_block())   # inject this into your LLM prompt
print(result.confidence)        # 0.0 – 1.0
print(result.strong_axioms())   # verified high-evidence relations

Works with any provider — OpenAI, Anthropic, Groq, Cerebras, Ollama:

# You handle the LLM call. Nouse handles the memory.
context = brain.query(user_question).context_block()
response = openai.chat(messages=[
    {"role": "system", "content": context},
    {"role": "user",   "content": user_question},
])

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Run the benchmark yourself

git clone https://github.com/base76-research-lab/NoUse
cd NoUse
pip install -e .

# Generate questions from your own graph
python eval/generate_questions.py --n 60

# Run benchmark (requires Cerebras or Groq API key, or use Ollama)
python eval/run_eval.py \
  --small cerebras/llama3.1-8b \
  --large groq/llama-3.3-70b-versatile \
  --n 60 --no-judge

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How the graph grows

Read a document / have a conversation
           ↓
    nouse daemon (background)
           ↓
    DeepDive: extract concepts + relations
           ↓
    Hebbian update: strengthen confirmed paths
           ↓
    NightRun: consolidate, prune weak edges
           ↓
    Ghost Q (nightly): ask LLM about weak nodes → enrich graph

The daemon runs as a systemd service. It watches your files, chat history, browser bookmarks — anything you configure. You never manually curate the graph.

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Architecture

nouse/
├── inject.py          # Public API: attach(), NouseBrain, Axiom, QueryResult
├── field/
│   └── surface.py     # KuzuDB graph interface
├── daemon/
│   ├── main.py        # Autonomous learning loop
│   ├── nightrun.py    # Nightly consolidation (9 phases)
│   ├── node_deepdive.py  # 5-step concept extraction
│   └── ghost_q.py     # LLM-driven graph enrichment
└── search/
    └── escalator.py   # 3-level knowledge escalation

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The hypothesis (work in progress)

small model + Nouse[domain]  >  large model without Nouse

We have evidence for this in our benchmark. The next step is to test across more domains, more models, and with an LLM judge instead of keyword scoring.

Contributions welcome — especially domain-specific question banks.

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Install & run daemon

pip install -e ".[dev]"

# Start the learning daemon
nouse daemon start

# Interactive REPL with memory
nouse run

# Check graph stats
nouse status

Requires Python 3.11+. Graph stored in ~/.local/share/nouse/field.kuzu.

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License

MIT — Björn Wikström / Base76 Research Lab