feat: NouseAgent — single-model sub-agent architecture

[base76-research-lab]

What this does

Replaces the fragmented multi-model stack with a single capable local model using role-specific system prompts.

Before: deepseek-r1:1.5b (extraction, timing out) + external APIs (Groq, Cerebras, OpenRouter) + ad-hoc httpx calls in domain_bootstrap

After: gemma4:e2b handles every workload via NouseAgent — one model, six roles, no external dependencies

Changes

src/nouse/llm/agent.py — NouseAgent

agent = NouseAgent("gemma4:e2b")

relations = await agent.extract(text)           # extractor role
answer    = await agent.synthesize(query, ctx)  # synthesizer role
questions = await agent.curiosity(topic)        # curiosity role
facts     = await agent.bootstrap(topic)        # bootstrap role
verdict   = await agent.validate(claim)         # validator role
reply     = await agent.chat(message, context)  # chat role

Each role uses the same model with a different system prompt. Zero routing overhead. Consistent quality across all workloads.

src/nouse/cli/commands/run.py + nouse run command

nouse run                              # interactive REPL with graph R/W
nouse run "What is epistemic memory?"  # single query
nouse run --model gemma4:26b           # upgrade model

Flow: user query → graph.query() → inject context → NouseAgent.chat() → brain.learn()

examples/Modelfile

ollama create NoUse -f examples/Modelfile
nouse run --model NoUse

Creates a named Ollama model with NoUse's epistemic identity baked into the system prompt. Distributable as ollama create NoUse.

inject.py — domain_bootstrap refactored

domain_bootstrap() now uses NouseAgent.bootstrap() directly instead of raw httpx calls with hardcoded deepseek-r1:1.5b.

Tested

NouseAgent("gemma4:e2b")
  extract:    3 relations from 2-sentence text ✓
  bootstrap:  10 relations for "Hebbian plasticity" ✓
  curiosity:  3 high-quality questions for "epistemic memory" ✓
  synthesize: grounded answer from graph nodes ✓

Minimum hardware

Component	Model	Size
Reasoning (all roles)	gemma4:e2b	7.2 GB
Embeddings	qwen3-embedding:4b	3.9 GB

Fits on any machine with 8 GB VRAM. No cloud API keys needed.

Closes #18

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