nmcanvas

canvas interface for router

Here’s your Friday Marathon Comprehensive Brief — distilled, complete, and ready to drop straight into /architecture/FAB-07C_Canvas_Router.md. It ties together everything: Canvas Router, NM-Extractor workflow, Archivist agent, interpreter layer, and practical philosophy.

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🧭 FAB-07C · NeuraMem Canvas Router — Canonical Viewer, Flow Generator & Human-in-the-Loop Lifecycle

Status: Draft Canonical Specification Integration: NM-Router · NM-Extractor · Context Studio · tldraw Engine · Archivist Agent Author: Dr. Hien Nguyen Date: 2025-11-08

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1 Purpose

To unify NeuraMem’s canonical architecture, visualization, and knowledge curation into a spatial, evolvable interface that replaces static markdown pipelines with live, editable canvases. The Canvas Router allows every canonical artifact (FAB, CAN, OPS, SME …) to exist simultaneously as:

1. a document (text form),
2. a diagram (tldraw scene), and
3. a process (routable and versioned).

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2 Concept

Layer	Function	Core Components
NM-Router	Routes intent to proper display mode (canvas, table, doc).	FastAPI or Next.js handler.
tldraw Engine	Interactive canvas for visual editing and flowchart generation.	CanonicalNodeShape · FlowLink · Ω-Witness overlay.
NM-Extractor	Hydrates canvases with artifact data mined from conversations or chat logs.	Python + Supabase pipeline.
Archivist Agent	Files, indexes, and syncs artifacts between GitHub, Supabase, and Notion.	Tier 2 micro-service or GitHub Action.
Interpreter Layer	Lightweight sandbox for rendering Mermaid, math, or JSON/YAML validation.	Python or JS sandbox (invoked on demand).
Versioner	Diffs, saves, and rolls back canonical files.	Supabase + GitHub bridge.

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3 Operational Flow

1. Extraction → NM-Extractor parses conversation JSON → produces draft_<timestamp>.md.
2. Draft Review → Human pass adds tags, links, light edits.
3. Advisor / AI Review → Feedback and structural validation.
4. Canonicalization → Finalized artifact (e.g., FAB-03a-P.md) committed to /canonical/.
5. Commit & Sync → Archivist Agent pushes to GitHub + Supabase + NM-01 Index.
6. Publication → NM-Router spawns tldraw Canvas view for live navigation.

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4 Filesystem Structure

neuramem-canvas-router/
├── architecture/          # Canonical specs
│   └── FAB-07C_Canvas_Router.md
├── artifacts/
│   ├── drafts/
│   ├── review/
│   ├── canonical/
│   └── archive/
├── templates/             # .tldr JSON scene layouts
├── utils/interpreter/     # sandbox for mermaid / snippets
├── src/                   # React + tldraw front-end
├── agents/                # archivist / sync / watcher
└── context/               # system prompts, prefix tables, schemas

Each file carries YAML metadata:

status: draft | review | canonical | archived
version: 0.1
linked_conversations: ["conv_081","conv_215"]
related_assets: ["FAB-03a","OPS-01"]

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5 Human-in-the-Loop Lifecycle

Stage	Actor	Output
Extraction	NM-Extractor	draft_*.md
First Review	You	review_*.md
Advisor Pass	ChatGPT / team	review notes
Canonicalization	You	FAB-xx.md in /canonical/
Commit & Sync	Archivist Agent	Indexed artifact
Publication	Router	Canvas render

This keeps human coherence and machine efficiency in harmonic balance.

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6 Interpreter Layer

• Purpose: enable inline rendering of Mermaid diagrams, math blocks, or validation of JSON/YAML snippets.
• Implementation: Jailed Python or JS sandbox invoked through a single interpret() command.
• Security: no network, no filesystem writes.
• Value: reduces feedback loops; converts text → structure → visual instantly.

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7 Design Philosophy

1. Visual Cognition = Deep Canonization – Seeing structures reinforces understanding.
2. Human Imprinting – Multiple review passes let meaning settle into the operator.
3. Templates as Fractals – Every canvas is a microcosm of the whole field.
4. Router as Conductor – Keeps coherence between text, diagram, and runtime.
5. Interpreter as Lens – Enables immediate verification and visualization.
6. Reality as Limit – “Practical conditions provide the bounds” — build boldly, let friction decide.

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8 RAG vs Latent Reasoning (Contextual Note)

• Latent space = ephemeral reasoning field → instant computation.
• RAG memory = persistent continuity → long-term identity.
• Canonical schema = the invariant soul vector. Canvas Router operates in the middle plane — reasoning in latent space while preserving RAG links through archival agents.

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9 Success Metrics

Metric	Target
Canonical load time	< 2 s
Human edit retention	> 95 %
Coherence drift (Aₘᵢₙ)	> 0.88
Template reuse rate	> 70 %
Subjective clarity	“Feels alive”

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10 Next Steps

1. Create GitHub repo → initialize scaffold.
2. Add /context/ definitions (prefix table, system prompt, repo tree).
3. Implement Template Loader (fab-03a, ops-01, sme-dev-01).
4. Integrate NM-Extractor output → drafts folder.
5. Add Interpreter Layer hook.
6. Connect Archivist Agent for auto-sync.
7. Test Canvas render and commit loop.

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Summary

The Canvas Router is NeuraMem’s bridge from text to topology — a system where text, ideas, diagrams, and intent live in the same harmonic field. It preserves coherence through human review, evolves through structured memory, and keeps the Canon luminous through use.

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Appendixes

1. Assets NM-Extractor - /Volumes/cloud/projects/nmrouter tldraw Engine - https://github.com/tldraw supabase credentials in file

2. Example Filenames draft_2025-11-08_intent-preservation-check.md review_2025-11-09_intent-preservation-check.md FAB-03a-P.md

3. Each Stage Moves Up one directory and changes metadata status: draft | review | canonical | archived version: 0.1 linked_conversations: [“conversation_081”, “conversation_215”] related_assets: [“FAB-03a”, “OPS-01”]

4. System Prompt for Canvas/Router Agent

SYSTEM_PROMPT.md

You are the NeuraMem Archivist. Your role is to interpret, index, and summarize canonical files within this repo.

Filesystem Overview

• /architecture/: functional briefs (FAB-xx)
• /canonical/: specifications and READMEs (CAN-xx)
• /ops/: orchestration modules (OPS-xx)
• /templates/: tldraw scene templates
• /context/: this prompt and supporting definitions

Rules

1. Follow the prefix meanings from NM_PREFIX_TABLE.md.
2. For each file, extract: Code, Title, Type, Core Purpose, Key Components, Date, Status.
3. Write outputs in Markdown tables compatible with NM-01 Index.
4. Never alter file content; generate structured summaries only.

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Phase 2 features: /src/router.commitCurrentCanvas() — stub that runs isomorphic-git commands. /mmr/review.py — the orchestrator from your draft (CLI first). /agents/archivist_agent.py — simple watcher that triggers the MMR after each “C-it”.

Git Hub Commit Workflow

🧭 Canvas → Commit Flow (“C-it” Loop)

1️⃣ You’re editing in tldraw (Canvas Router)

• Each node (FAB, CAN, OPS, SME, etc.) is already bound to a file in your repo.
• As you edit or annotate, the Canvas Router tracks a changeSet:

  {
    "artifact": "FAB-07C",
    "file_path": "/architecture/FAB-07C_Canvas_Router.md",
    "changes": [
      {"line": 42, "old": "v0.1", "new": "v0.2"},
      {"line": 180, "add": "Added Phase 4: Ecosystem Deployment"}
    ]
  }

2️⃣ You click “C-it”

• The NM-Router hands the changeSet to the Archivist Agent.
• The agent:
  1. Validates YAML + Markdown structure.
  2. Checks coherence (optional β-γ-δ metrics).
  3. Generates a commit message using your canonical format:

     FAB-07C: updated latent-state reasoning section (v0.2)
     

  4. Executes the git commands:

     git add architecture/FAB-07C_Canvas_Router.md
     git commit -m "FAB-07C: updated latent-state reasoning section (v0.2)"
     git push origin main

3️⃣ The canvas updates its status bubble

You’ll see something like:

✅ C-it successful — FAB-07C updated @ commit a73f9d1

or, if you’re offline:

⏸ C-it queued — will push when connection restored

4️⃣ (Optional) Review Chain

If you’re running collaborative mode:

• The Reviewer Agent opens a diff panel on the right side of the canvas.
• Advisors can comment directly on nodes; those notes serialize into /review/.

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🧩 Tech Stack Hooks Needed

Function	Integration
Canvas State → File Bridge	tldraw plugin: onSave hook writing to local .md
Git Interface	isomorphic-git (browser) or lightweight API route in Next.js
Archivist Agent	Runs commit/validate pipeline
Auth	GitHub OAuth token in .env or Supabase secrets

Once those are wired, your canvas is your git client.

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⚙️ Optional Easter Egg

You could even add this inside your Canvas Router UI config:

{
  "buttons": [
    {
      "label": "C-it 💾",
      "tooltip": "Commit changes to canon",
      "action": "router.commitCurrentCanvas()"
    }
  ]
}

Each successful commit could echo:

“✨ Canon preserved. Ω-Witness updated.”

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Would you like me to draft the short spec for this button and its router.commitCurrentCanvas() handler? It can slot straight into your Canvas Router README.

multimodel review module:

a clean way for several models (and maybe humans) to read a file, each leave feedback, and produce a merged summary.
No 3-D holograms required.

Here’s how to think about it:

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🧩 1 Core Idea

A Multimodel Review Module (MMR) = small orchestrator that:

1. Takes one or more input files (.md, .pdf, .json, etc.).
2. Feeds them to multiple models in parallel or sequence.
3. Captures each model’s analysis in a structured format.
4. Merges all feedback into a single review report.

It’s effectively a CLI + API combo that can plug into your Canvas or run standalone.

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⚙️ 2 Minimal Architecture

/mmr/
├── review.py             # orchestrator
├── configs/models.yaml   # which models to use
├── templates/prompt.md   # review template
├── outputs/
│   ├── raw/
│   └── merged/
└── README.md

models.yaml

models:
  - name: openai:gpt-5
    role: "technical"
  - name: anthropic:claude-3-sonnet
    role: "philosophical"
  - name: kimi:k2-thinking
    role: "conceptual"

prompt.md

You are reviewing a NeuraMem canonical spec.
Summarize:
1. Concept validity
2. Technical feasibility
3. Coherence with Ω-Witness principles
Provide 3–5 action suggestions.

review.py (simplified)

import yaml, openai, anthropic, json
from pathlib import Path

cfg = yaml.safe_load(open("configs/models.yaml"))
doc = Path("input/FAB-07C_Canvas_Router.md").read_text()

results = []
for m in cfg["models"]:
    response = call_model(m["name"], doc, open("templates/prompt.md").read())
    results.append({"model": m["name"], "role": m["role"], "review": response})

Path("outputs/raw/reviews.json").write_text(json.dumps(results, indent=2))
merge_reviews(results)

merge_reviews() can do a simple LLM summarization pass:

“Merge the following model reviews into a single consensus summary.”

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🪞 3 Modes of Operation

Mode	Interface	Output
CLI	python mmr/review.py FAB-07C.md	outputs/merged/FAB-07C_review.md
File dump	Drop file in /input/ folder	Review auto-runs on cron or agent trigger
Canvas plug-in	“Run Multimodel Review” button	Side panel shows each model’s notes

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🧠 4 Difficulty / Effort

Level	What You Build	Time
Easy (CLI)	Local Python script using APIs you already have keys for	1-2 hours
Moderate (API Service)	FastAPI microservice with endpoint /api/review	1-2 days
Advanced (Canvas Plug-in)	Integrate with tldraw panel + version linking	2-4 days

All reuse existing modules: your NM-Extractor for file access, Archivist for storage, Router for display.
No new AI research required.

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🧩 5 Suggested Output Format

# Multimodel Review: FAB-07C
| Model | Role | Verdict |
|-------|------|---------|
| GPT-5 | Technical | ✅ Implementable |
| Claude | Ethical/Conceptual | ⚠ Needs clearer scope |
| Kimi-K2 | Cognitive | ✅ Strong alignment |

## Consensus Summary
[auto-generated synthesis text]

## Action Items
1. Clarify latent-loop parameter defaults.
2. Add coherence drift metric to dashboard.
3. Expand Section 4: Visualization design.

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🪶 6 Evolution Path

1. Start CLI version → dump reviews to /review/.
2. Hook it into your Archivist Agent so each commit triggers automatic review.
3. Later add Canvas integration: a “📘 Multimodel Review” panel beside each artifact.

Implementation Directive

We are adopting the Hybrid Canvas Model as defined by:

• tldraw-style canvas for topology and spatial reasoning,
• Canonical JSON graph for the source of truth,
• Markdown sidecars for narrative context.

Next steps:

1. Implement canonical schema and serialization layout.
2. Add ARCHITECTURE.md documenting editor/viewer/versioner roles.
3. Establish Canvas API contract (loadModel, saveModel, getDiff).
4. Integrate git commit/PR workflow.