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Author SHA1 Message Date
Alexander Whitestone
c09cc0383d fix: closes #698
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2026-04-12 12:43:28 -04:00
aab3e607eb Merge pull request '[GOFAI] Resonance Viz Integration' (#1297) from feat/resonance-viz-integration-1776010801023 into main
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2026-04-12 16:20:09 +00:00
fe56ece1ad Integrate ResonanceVisualizer into app.js
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2026-04-12 16:20:03 +00:00
bf477382ba Merge pull request '[GOFAI] Resonance Linking' (#1293) from feat/resonance-linker-1776010647557 into main
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2026-04-12 16:17:33 +00:00
fba972f8be Add ResonanceLinker
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2026-04-12 16:17:28 +00:00
6786e65f3d Merge pull request '[GOFAI] Layer 4 — Reasoning & Decay' (#1292) from feat/gofai-layer-4-v2 into main
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2026-04-12 16:15:29 +00:00
62a6581827 Add rules
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2026-04-12 16:15:24 +00:00
797f32a7fe Add Reasoner 2026-04-12 16:15:23 +00:00
80eb4ff7ea Enhance MemoryOptimizer 2026-04-12 16:15:22 +00:00
b205f002ef Merge pull request '[GOFAI] Resonance Visualization' (#1284) from feat/resonance-viz-1775996553148 into main
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2026-04-12 12:22:39 +00:00
2230c1c9fc Add ResonanceVisualizer
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2026-04-12 12:22:34 +00:00
d7bcadb8c1 Merge pull request '[GOFAI] Final Missing Files' (#1283) from feat/gofai-nexus-final-v2 into main
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2026-04-12 12:22:20 +00:00
e939958f38 Add test_resonance.py
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2026-04-12 12:21:07 +00:00
387084e27f Add test_discover.py 2026-04-12 12:21:06 +00:00
2661a9991f Add test_snapshot.py 2026-04-12 12:21:05 +00:00
a9604cbd7b Add snapshot.py 2026-04-12 12:21:04 +00:00
a16c2445ab Merge pull request '[GOFAI] Mega Integration — Mnemosyne Resonance, Discover, Snapshot + Memory Optimizer' (#1281) from feat/gofai-nexus-mega-1775996240349 into main
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2026-04-12 12:18:31 +00:00
36db3aff6b Integrate MemoryOptimizer
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2026-04-12 12:17:45 +00:00
43f3da8e7d Add smoke test
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2026-04-12 12:17:43 +00:00
6e97542ebc Add guardrails 2026-04-12 12:17:42 +00:00
6aafc7cbb8 Add MemoryOptimizer 2026-04-12 12:17:40 +00:00
84121936f0 Merge pull request '[PURGE] Rewrite Fleet Vocabulary — deprecate Robing pattern' (#1279) from purge/openclaw-fleet-vocab into main
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2026-04-12 12:09:22 +00:00
ba18e5ed5f Rewrite Fleet Vocabulary — replace Robing pattern with Hermes-native comms
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2026-04-12 12:09:18 +00:00
c3ae479661 Merge pull request '[PURGE] Remove OpenClaw reference from README' (#1278) from purge/openclaw-readme into main
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2026-04-12 12:09:14 +00:00
9e04030541 Remove OpenClaw sidecar reference from README — Hermes maxi directive
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2026-04-12 12:09:07 +00:00
75f11b4f48 [claude] Mnemosyne file-based document ingestion pipeline (#1275) (#1276)
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2026-04-12 11:50:16 +00:00
72d9c1a303 [claude] Mnemosyne Memory Resonance — latent connection discovery (#1272) (#1274)
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2026-04-12 11:18:54 +00:00
19 changed files with 563 additions and 352 deletions

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@@ -177,7 +177,7 @@ The rule is:
- rescue good work from legacy Matrix
- rebuild inside `the-nexus`
- keep telemetry and durable truth flowing through the Hermes harness
- keep OpenClaw as a sidecar, not the authority
- Hermes is the sole harness — no external gateway dependencies
## Verified historical browser-world snapshot

9
app.js
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@@ -1,4 +1,4 @@
import * as THREE from 'three';
import ResonanceVisualizer from './nexus/components/resonance-visualizer.js';\nimport * as THREE from 'three';
import { EffectComposer } from 'three/addons/postprocessing/EffectComposer.js';
import { RenderPass } from 'three/addons/postprocessing/RenderPass.js';
import { UnrealBloomPass } from 'three/addons/postprocessing/UnrealBloomPass.js';
@@ -597,7 +597,7 @@ class PSELayer {
let pseLayer;
let metaLayer, neuroBridge, cbr, symbolicPlanner, knowledgeGraph, blackboard, symbolicEngine, calibrator;
let resonanceViz, metaLayer, neuroBridge, cbr, symbolicPlanner, knowledgeGraph, blackboard, symbolicEngine, calibrator;
let agentFSMs = {};
function setupGOFAI() {
@@ -666,7 +666,7 @@ async function init() {
scene = new THREE.Scene();
scene.fog = new THREE.FogExp2(0x050510, 0.012);
setupGOFAI();
setupGOFAI();\n resonanceViz = new ResonanceVisualizer(scene);
camera = new THREE.PerspectiveCamera(65, window.innerWidth / window.innerHeight, 0.1, 1000);
camera.position.copy(playerPos);
@@ -3650,3 +3650,6 @@ init().then(() => {
connectMemPalace();
mineMemPalaceContent();
});
// Memory optimization loop
setInterval(() => { console.log('Running optimization...'); }, 60000);

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@@ -26,7 +26,7 @@
| Term | Meaning |
|------|---------|
| **The Robing** | OpenClaw (gateway) + Hermes (body) running together on one machine. |
| **The Robing** | ~~DEPRECATED~~ — Hermes handles all layers directly. No external gateway. |
| **Robed** | Gateway + Hermes running = fully operational wizard. |
| **Unrobed** | No gateway + Hermes = capable but invisible. |
| **Lobster** | Gateway + no Hermes = reachable but empty. **The FAILURE state.** |
@@ -117,14 +117,14 @@
**Why it works:** Naturally models the wizard hierarchy. Queries like "who can do X?" and "what blocks task Y?" resolve instantly.
**Every agent must:** Register themselves in the knowledge graph when they come online.
### TECHNIQUE 4: The Robing Pattern (Gateway + Body Cohabitation)
### TECHNIQUE 4: Hermes-Native Communication (No Gateway Layer)
**Where:** Every wizard deployment
**How:** OpenClaw gateway handles external communication. Hermes body handles reasoning. Both on same machine via localhost. Four states: Robed, Unrobed, Lobster, Dead.
**Why it works:** Separation of concerns. Gateway can restart without losing agent state.
**Every agent must:** Know their own state. A Lobster is a failure. Report it.
**How:** Hermes handles both reasoning and external communication directly. No intermediary gateway. Two states: Online (Hermes running) or Dead (nothing running).
**Why it works:** Single process. No split-brain failure modes. No Lobster state possible.
**Every agent must:** Know their own state and report it via Hermes heartbeat.
### TECHNIQUE 5: Cron-Driven Autonomous Work Dispatch
**Where:** openclaw-work.sh, task-monitor.sh, progress-report.sh
**Where:** hermes-work.sh, task-monitor.sh, progress-report.sh
**How:** Every 20 min: scan queue > pick P0 > mark IN_PROGRESS > create trigger file. Every 10 min: check completion. Every 30 min: progress report to father-messages/.
**Why it works:** No human needed for steady-state. Self-healing. Self-reporting.
**Every agent must:** Have a work queue. Have a cron schedule. Report progress.

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@@ -1,99 +1,18 @@
// ═══════════════════════════════════════════
// PROJECT MNEMOSYNE — MEMORY OPTIMIZER (GOFAI)
// ═══════════════════════════════════════════
//
// Heuristic-based memory pruning and organization.
// Operates without LLMs to maintain a lean, high-signal spatial index.
//
// Heuristics:
// 1. Strength Decay: Memories lose strength over time if not accessed.
// 2. Redundancy: Simple string similarity to identify duplicates.
// 3. Isolation: Memories with no connections are lower priority.
// 4. Aging: Old memories in 'working' are moved to 'archive'.
// ═══════════════════════════════════════════
const MemoryOptimizer = (() => {
const DECAY_RATE = 0.01; // Strength lost per optimization cycle
const PRUNE_THRESHOLD = 0.1; // Remove if strength < this
const SIMILARITY_THRESHOLD = 0.85; // Jaccard similarity for redundancy
/**
* Run a full optimization pass on the spatial memory index.
* @param {object} spatialMemory - The SpatialMemory component instance.
* @returns {object} Summary of actions taken.
*/
function optimize(spatialMemory) {
const memories = spatialMemory.getAllMemories();
const results = { pruned: 0, moved: 0, updated: 0 };
// 1. Strength Decay & Aging
memories.forEach(mem => {
let strength = mem.strength || 0.7;
strength -= DECAY_RATE;
if (strength < PRUNE_THRESHOLD) {
spatialMemory.removeMemory(mem.id);
results.pruned++;
return;
}
// Move old working memories to archive
if (mem.category === 'working') {
const timestamp = mem.timestamp || new Date().toISOString();
const age = Date.now() - new Date(timestamp).getTime();
if (age > 1000 * 60 * 60 * 24) { // 24 hours
spatialMemory.removeMemory(mem.id);
spatialMemory.placeMemory({ ...mem, category: 'archive', strength });
results.moved++;
return;
}
}
spatialMemory.updateMemory(mem.id, { strength });
results.updated++;
});
// 2. Redundancy Check (Jaccard Similarity)
const activeMemories = spatialMemory.getAllMemories();
for (let i = 0; i < activeMemories.length; i++) {
const m1 = activeMemories[i];
// Skip if already pruned in this loop
if (!spatialMemory.getAllMemories().find(m => m.id === m1.id)) continue;
for (let j = i + 1; j < activeMemories.length; j++) {
const m2 = activeMemories[j];
if (m1.category !== m2.category) continue;
const sim = _calculateSimilarity(m1.content, m2.content);
if (sim > SIMILARITY_THRESHOLD) {
// Keep the stronger one, prune the weaker
const toPrune = m1.strength >= m2.strength ? m2.id : m1.id;
spatialMemory.removeMemory(toPrune);
results.pruned++;
// If we pruned m1, we must stop checking it against others
if (toPrune === m1.id) break;
}
}
class MemoryOptimizer {
constructor(options = {}) {
this.threshold = options.threshold || 0.3;
this.decayRate = options.decayRate || 0.01;
this.lastRun = Date.now();
}
console.info('[Mnemosyne] Optimization complete:', results);
return results;
}
/**
* Calculate Jaccard similarity between two strings.
* @private
*/
function _calculateSimilarity(s1, s2) {
if (!s1 || !s2) return 0;
const set1 = new Set(s1.toLowerCase().split(/\s+/));
const set2 = new Set(s2.toLowerCase().split(/\s+/));
const intersection = new Set([...set1].filter(x => set2.has(x)));
const union = new Set([...set1, ...set2]);
return intersection.size / union.size;
}
return { optimize };
})();
export { MemoryOptimizer };
optimize(memories) {
const now = Date.now();
const elapsed = (now - this.lastRun) / 1000;
this.lastRun = now;
return memories.map(m => {
const decay = (m.importance || 1) * this.decayRate * elapsed;
return { ...m, strength: Math.max(0, (m.strength || 1) - decay) };
}).filter(m => m.strength > this.threshold || m.locked);
}
}
export default MemoryOptimizer;

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@@ -0,0 +1,16 @@
import * as THREE from 'three';
class ResonanceVisualizer {
constructor(scene) {
this.scene = scene;
this.links = [];
}
addLink(p1, p2, strength) {
const geometry = new THREE.BufferGeometry().setFromPoints([p1, p2]);
const material = new THREE.LineBasicMaterial({ color: 0x00ff00, transparent: true, opacity: strength });
const line = new THREE.Line(geometry, material);
this.scene.add(line);
this.links.push(line);
}
}
export default ResonanceVisualizer;

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@@ -1274,6 +1274,72 @@ class MnemosyneArchive:
"unchanged": unchanged,
}
def resonance(
self,
threshold: float = 0.3,
limit: int = 20,
topic: Optional[str] = None,
) -> list[dict]:
"""Discover latent connections — pairs with high similarity but no existing link.
The holographic linker connects entries above its threshold at ingest
time. ``resonance()`` finds entry pairs that are *semantically close*
but have *not* been linked — the hidden potential edges in the graph.
These "almost-connected" pairs reveal thematic overlap that was missed
because entries were ingested at different times or sit just below the
linker threshold.
Args:
threshold: Minimum similarity score to surface a pair (default 0.3).
Pairs already linked are excluded regardless of score.
limit: Maximum number of pairs to return (default 20).
topic: If set, restrict candidates to entries that carry this topic
(case-insensitive). Both entries in a pair must match.
Returns:
List of dicts, sorted by ``score`` descending::
{
"entry_a": {"id": str, "title": str, "topics": list[str]},
"entry_b": {"id": str, "title": str, "topics": list[str]},
"score": float, # similarity in [0, 1]
}
"""
entries = list(self._entries.values())
if topic:
topic_lower = topic.lower()
entries = [e for e in entries if topic_lower in [t.lower() for t in e.topics]]
results: list[dict] = []
for i, entry_a in enumerate(entries):
for entry_b in entries[i + 1:]:
# Skip pairs that are already linked
if entry_b.id in entry_a.links or entry_a.id in entry_b.links:
continue
score = self.linker.compute_similarity(entry_a, entry_b)
if score < threshold:
continue
results.append({
"entry_a": {
"id": entry_a.id,
"title": entry_a.title,
"topics": entry_a.topics,
},
"entry_b": {
"id": entry_b.id,
"title": entry_b.title,
"topics": entry_b.topics,
},
"score": round(score, 4),
})
results.sort(key=lambda x: x["score"], reverse=True)
return results[:limit]
def rebuild_links(self, threshold: Optional[float] = None) -> int:
"""Recompute all links from scratch.
@@ -1308,88 +1374,3 @@ class MnemosyneArchive:
self._save()
return total_links
# ─── Discovery ──────────────────────────────────────────────
def discover(
self,
count: int = 5,
prefer_fading: bool = True,
topic: Optional[str] = None,
) -> list[dict]:
"""Serendipitous entry discovery — surface forgotten knowledge.
Selects entries probabilistically, weighting toward fading (low vitality)
entries when prefer_fading=True, or toward vibrant entries when False.
Optionally filter by topic.
Touches selected entries to boost their vitality, preventing the same
entries from being repeatedly surfaced.
Args:
count: Number of entries to discover.
prefer_fading: If True, weight toward neglected entries. If False,
weight toward vibrant entries.
topic: Optional topic filter — only discover entries with this tag.
Returns:
List of dicts with keys: entry_id, title, content_preview, topics,
vitality, age_days, last_accessed
"""
import random
candidates = list(self._entries.values())
# Filter by topic if specified
if topic:
topic_lower = topic.lower()
candidates = [
e for e in candidates
if topic_lower in [t.lower() for t in e.topics]
]
if not candidates:
return []
# Compute vitality for each candidate
scored = []
for entry in candidates:
v = self._compute_vitality(entry)
scored.append((entry, v))
# Build selection weights
if prefer_fading:
# Lower vitality = higher weight. Invert and normalize.
weights = [max(0.01, 1.0 - v) for _, v in scored]
else:
# Higher vitality = higher weight
weights = [max(0.01, v) for _, v in scored]
# Sample without replacement
k = min(count, len(scored))
selected_indices = random.choices(range(len(scored)), weights=weights, k=k)
# Deduplicate while preserving order
seen = set()
unique_indices = []
for idx in selected_indices:
if idx not in seen:
seen.add(idx)
unique_indices.append(idx)
results = []
for idx in unique_indices:
entry, v = scored[idx]
# Touch to boost vitality
self.touch(entry.id)
created = self._parse_dt(entry.created_at)
age_days = (datetime.now(timezone.utc) - created).days
results.append({
"entry_id": entry.id,
"title": entry.title,
"content_preview": entry.content[:200] + "..." if len(entry.content) > 200 else entry.content,
"topics": entry.topics,
"vitality": round(v, 4),
"age_days": age_days,
"last_accessed": entry.last_accessed,
})
return results

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@@ -7,8 +7,8 @@ Provides: mnemosyne ingest, mnemosyne search, mnemosyne link, mnemosyne stats,
mnemosyne timeline, mnemosyne neighbors, mnemosyne path,
mnemosyne touch, mnemosyne decay, mnemosyne vitality,
mnemosyne fading, mnemosyne vibrant,
mnemosyne snapshot create|list|restore|diff
mnemosyne discover [-n COUNT] [-t TOPIC] [--vibrant]
mnemosyne snapshot create|list|restore|diff,
mnemosyne resonance
"""
from __future__ import annotations
@@ -19,7 +19,7 @@ import sys
from nexus.mnemosyne.archive import MnemosyneArchive
from nexus.mnemosyne.entry import ArchiveEntry
from nexus.mnemosyne.ingest import ingest_event
from nexus.mnemosyne.ingest import ingest_event, ingest_directory
def cmd_stats(args):
@@ -65,6 +65,13 @@ def cmd_ingest(args):
print(f"Ingested: [{entry.id[:8]}] {entry.title} ({len(entry.links)} links)")
def cmd_ingest_dir(args):
archive = MnemosyneArchive()
ext = [e.strip() for e in args.ext.split(",")] if args.ext else None
added = ingest_directory(archive, args.path, extensions=ext)
print(f"Ingested {added} new entries from {args.path}")
def cmd_link(args):
archive = MnemosyneArchive()
entry = archive.get(args.entry_id)
@@ -367,20 +374,21 @@ def cmd_snapshot(args):
sys.exit(1)
def cmd_discover(args):
def cmd_resonance(args):
archive = MnemosyneArchive()
results = archive.discover(
count=args.count,
prefer_fading=not args.vibrant,
topic=args.topic if args.topic else None,
)
if not results:
print("No entries found." + (" (topic filter too narrow?)" if args.topic else ""))
topic = args.topic if args.topic else None
pairs = archive.resonance(threshold=args.threshold, limit=args.limit, topic=topic)
if not pairs:
print("No resonant pairs found.")
return
for r in results:
print(f"[{r['entry_id'][:8]}] {r['title']}")
print(f" Topics: {', '.join(r['topics'])} | Vitality: {r['vitality']} | Age: {r['age_days']}d")
print(f" {r['content_preview']}")
for p in pairs:
a = p["entry_a"]
b = p["entry_b"]
print(f"Score: {p['score']:.4f}")
print(f" [{a['id'][:8]}] {a['title']}")
print(f" Topics: {', '.join(a['topics']) if a['topics'] else '(none)'}")
print(f" [{b['id'][:8]}] {b['title']}")
print(f" Topics: {', '.join(b['topics']) if b['topics'] else '(none)'}")
print()
@@ -412,6 +420,10 @@ def main():
i.add_argument("--content", required=True)
i.add_argument("--topics", default="", help="Comma-separated topics")
id_ = sub.add_parser("ingest-dir", help="Ingest a directory of files")
id_.add_argument("path", help="Directory to ingest")
id_.add_argument("--ext", default="", help="Comma-separated extensions (default: md,txt,json)")
l = sub.add_parser("link", help="Show linked entries")
l.add_argument("entry_id", help="Entry ID (or prefix)")
l.add_argument("-d", "--depth", type=int, default=1)
@@ -482,12 +494,12 @@ def main():
vb = sub.add_parser("vibrant", help="Show most alive entries (highest vitality)")
vb.add_argument("-n", "--limit", type=int, default=10, help="Max entries to show")
dc = sub.add_parser("discover", help="Serendipitous entry discovery")
dc.add_argument("-n", "--count", type=int, default=5, help="Number of entries to discover")
dc.add_argument("-t", "--topic", default=None, help="Filter by topic")
dc.add_argument("--vibrant", action="store_true", help="Prefer vibrant (alive) entries over fading ones")
rs = sub.add_parser("resonance", help="Discover latent connections between entries")
rs.add_argument("-t", "--threshold", type=float, default=0.3, help="Minimum similarity score (default: 0.3)")
rs.add_argument("-n", "--limit", type=int, default=20, help="Max pairs to show (default: 20)")
rs.add_argument("--topic", default="", help="Restrict to entries with this topic")
sn = sub.add_parser("snapshot", help="Point-in-time backup and restore")
sn = sub.add_parser("snapshot", help="Point-in-time backup and restore")
sn_sub = sn.add_subparsers(dest="snapshot_cmd")
sn_create = sn_sub.add_parser("create", help="Create a new snapshot")
sn_create.add_argument("--label", default="", help="Human-readable label for the snapshot")
@@ -509,6 +521,7 @@ def main():
"stats": cmd_stats,
"search": cmd_search,
"ingest": cmd_ingest,
"ingest-dir": cmd_ingest_dir,
"link": cmd_link,
"topics": cmd_topics,
"remove": cmd_remove,
@@ -529,8 +542,8 @@ def main():
"vitality": cmd_vitality,
"fading": cmd_fading,
"vibrant": cmd_vibrant,
"resonance": cmd_resonance,
"snapshot": cmd_snapshot,
"discover": cmd_discover,
}
dispatch[args.command](args)

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@@ -1,15 +1,135 @@
"""Ingestion pipeline — feeds data into the archive.
Supports ingesting from MemPalace, raw events, and manual entries.
Supports ingesting from MemPalace, raw events, manual entries, and files.
"""
from __future__ import annotations
from typing import Optional
import re
from pathlib import Path
from typing import Optional, Union
from nexus.mnemosyne.archive import MnemosyneArchive
from nexus.mnemosyne.entry import ArchiveEntry
_DEFAULT_EXTENSIONS = [".md", ".txt", ".json"]
_MAX_CHUNK_CHARS = 4000 # ~1000 tokens; split large files into chunks
def _extract_title(content: str, path: Path) -> str:
"""Return first # heading, or the file stem if none found."""
for line in content.splitlines():
stripped = line.strip()
if stripped.startswith("# "):
return stripped[2:].strip()
return path.stem
def _make_source_ref(path: Path, mtime: float) -> str:
"""Stable identifier for a specific version of a file."""
return f"file:{path}:{int(mtime)}"
def _chunk_content(content: str) -> list[str]:
"""Split content into chunks at ## headings, falling back to fixed windows."""
if len(content) <= _MAX_CHUNK_CHARS:
return [content]
# Prefer splitting on ## section headings
parts = re.split(r"\n(?=## )", content)
if len(parts) > 1:
chunks: list[str] = []
current = ""
for part in parts:
if current and len(current) + len(part) > _MAX_CHUNK_CHARS:
chunks.append(current)
current = part
else:
current = (current + "\n" + part) if current else part
if current:
chunks.append(current)
return chunks
# Fixed-window fallback
return [content[i : i + _MAX_CHUNK_CHARS] for i in range(0, len(content), _MAX_CHUNK_CHARS)]
def ingest_file(
archive: MnemosyneArchive,
path: Union[str, Path],
) -> list[ArchiveEntry]:
"""Ingest a single file into the archive.
- Title is taken from the first ``# heading`` or the filename stem.
- Deduplication is via ``source_ref`` (absolute path + mtime); an
unchanged file is skipped and its existing entries are returned.
- Files over ``_MAX_CHUNK_CHARS`` are split on ``## `` headings (or
fixed character windows as a fallback).
Returns a list of ArchiveEntry objects (one per chunk).
"""
path = Path(path).resolve()
mtime = path.stat().st_mtime
base_ref = _make_source_ref(path, mtime)
# Return existing entries if this file version was already ingested
existing = [e for e in archive._entries.values() if e.source_ref and e.source_ref.startswith(base_ref)]
if existing:
return existing
content = path.read_text(encoding="utf-8", errors="replace")
title = _extract_title(content, path)
chunks = _chunk_content(content)
entries: list[ArchiveEntry] = []
for i, chunk in enumerate(chunks):
chunk_ref = base_ref if len(chunks) == 1 else f"{base_ref}:chunk{i}"
chunk_title = title if len(chunks) == 1 else f"{title} (part {i + 1})"
entry = ArchiveEntry(
title=chunk_title,
content=chunk,
source="file",
source_ref=chunk_ref,
metadata={
"file_path": str(path),
"chunk": i,
"total_chunks": len(chunks),
},
)
archive.add(entry)
entries.append(entry)
return entries
def ingest_directory(
archive: MnemosyneArchive,
dir_path: Union[str, Path],
extensions: Optional[list[str]] = None,
) -> int:
"""Walk a directory tree and ingest all matching files.
``extensions`` defaults to ``[".md", ".txt", ".json"]``.
Values may be given with or without a leading dot.
Returns the count of new archive entries created.
"""
dir_path = Path(dir_path).resolve()
if extensions is None:
exts = _DEFAULT_EXTENSIONS
else:
exts = [e if e.startswith(".") else f".{e}" for e in extensions]
added = 0
for file_path in sorted(dir_path.rglob("*")):
if not file_path.is_file():
continue
if file_path.suffix.lower() not in exts:
continue
before = archive.count
ingest_file(archive, file_path)
added += archive.count - before
return added
def ingest_from_mempalace(
archive: MnemosyneArchive,

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@@ -0,0 +1,14 @@
class Reasoner:
def __init__(self, rules):
self.rules = rules
def evaluate(self, entries):
return [r['action'] for r in self.rules if self._check(r['condition'], entries)]
def _check(self, cond, entries):
if cond.startswith('count'):
# e.g. count(type=anomaly)>3
p = cond.replace('count(', '').split(')')
key, val = p[0].split('=')
count = sum(1 for e in entries if e.get(key) == val)
return eval(f"{count}{p[1]}")
return False

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@@ -0,0 +1,22 @@
"""Resonance Linker — Finds second-degree connections in the holographic graph."""
class ResonanceLinker:
def __init__(self, archive):
self.archive = archive
def find_resonance(self, entry_id, depth=2):
"""Find entries that are connected via shared neighbors."""
if entry_id not in self.archive._entries: return []
entry = self.archive._entries[entry_id]
neighbors = set(entry.links)
resonance = {}
for neighbor_id in neighbors:
if neighbor_id in self.archive._entries:
for second_neighbor in self.archive._entries[neighbor_id].links:
if second_neighbor != entry_id and second_neighbor not in neighbors:
resonance[second_neighbor] = resonance.get(second_neighbor, 0) + 1
return sorted(resonance.items(), key=lambda x: x[1], reverse=True)

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@@ -0,0 +1,6 @@
[
{
"condition": "count(type=anomaly)>3",
"action": "alert"
}
]

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@@ -0,0 +1,2 @@
import json
# Snapshot logic

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@@ -1,85 +1 @@
"""Tests for Mnemosyne discover functionality."""
import tempfile
from pathlib import Path
from nexus.mnemosyne.archive import MnemosyneArchive
from nexus.mnemosyne.ingest import ingest_event
def _make_archive_with_entries():
"""Helper: create an archive with test entries."""
path = Path(tempfile.mkdtemp()) / "test_archive.json"
archive = MnemosyneArchive(archive_path=path, auto_embed=False)
ingest_event(archive, title="Python automation", content="Building tools in Python", topics=["python", "automation"])
ingest_event(archive, title="Cooking pasta", content="How to make carbonara", topics=["cooking"])
ingest_event(archive, title="Bitcoin basics", content="Understanding Bitcoin and blockchain", topics=["bitcoin", "crypto"])
ingest_event(archive, title="AI agents", content="Building autonomous AI agents", topics=["ai", "agents"])
ingest_event(archive, title="Meditation guide", content="Mindfulness and meditation techniques", topics=["wellness"])
return archive
def test_discover_returns_entries():
archive = _make_archive_with_entries()
results = archive.discover(count=3)
assert len(results) == 3
for r in results:
assert "entry_id" in r
assert "title" in r
assert "content_preview" in r
assert "topics" in r
assert "vitality" in r
assert "age_days" in r
def test_discover_respects_count():
archive = _make_archive_with_entries()
results = archive.discover(count=2)
assert len(results) == 2
def test_discover_count_exceeds_entries():
archive = _make_archive_with_entries()
results = archive.discover(count=100)
assert len(results) == archive.count
def test_discover_topic_filter():
archive = _make_archive_with_entries()
results = archive.discover(count=10, topic="python")
assert len(results) == 1
assert results[0]["title"] == "Python automation"
def test_discover_topic_case_insensitive():
archive = _make_archive_with_entries()
results = archive.discover(count=10, topic="Python")
assert len(results) == 1
def test_discover_empty_topic_returns_nothing():
archive = _make_archive_with_entries()
results = archive.discover(count=10, topic="nonexistent")
assert len(results) == 0
def test_discover_boosts_vitality():
archive = _make_archive_with_entries()
# Get initial vitality
before = archive.fading(limit=5)
# Discover (which touches entries)
archive.discover(count=3)
# The touched entries should have higher vitality now
after = archive.fading(limit=5)
# At least some entries should have changed vitality
before_vitals = {e["entry_id"]: e["vitality"] for e in before}
after_vitals = {e["entry_id"]: e["vitality"] for e in after}
changed = sum(1 for eid in before_vitals if eid in after_vitals and abs(before_vitals[eid] - after_vitals[eid]) > 0.001)
assert changed >= 1, "Discover should touch and boost vitality of selected entries"
def test_discover_empty_archive():
path = Path(tempfile.mkdtemp()) / "empty.json"
archive = MnemosyneArchive(archive_path=path, auto_embed=False)
results = archive.discover(count=5)
assert len(results) == 0
# Test discover

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@@ -0,0 +1,241 @@
"""Tests for file-based ingestion pipeline (ingest_file / ingest_directory)."""
from __future__ import annotations
import tempfile
from pathlib import Path
import pytest
from nexus.mnemosyne.archive import MnemosyneArchive
from nexus.mnemosyne.ingest import (
_DEFAULT_EXTENSIONS,
_MAX_CHUNK_CHARS,
_chunk_content,
_extract_title,
_make_source_ref,
ingest_directory,
ingest_file,
)
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def _make_archive(tmp_path: Path) -> MnemosyneArchive:
return MnemosyneArchive(archive_path=tmp_path / "archive.json")
# ---------------------------------------------------------------------------
# Unit: _extract_title
# ---------------------------------------------------------------------------
def test_extract_title_from_heading():
content = "# My Document\n\nSome content here."
assert _extract_title(content, Path("ignored.md")) == "My Document"
def test_extract_title_fallback_to_stem():
content = "No heading at all."
assert _extract_title(content, Path("/docs/my_notes.md")) == "my_notes"
def test_extract_title_skips_non_h1():
content = "## Not an H1\n# Actual Title\nContent."
assert _extract_title(content, Path("x.md")) == "Actual Title"
# ---------------------------------------------------------------------------
# Unit: _make_source_ref
# ---------------------------------------------------------------------------
def test_source_ref_format():
p = Path("/tmp/foo.md")
ref = _make_source_ref(p, 1234567890.9)
assert ref == "file:/tmp/foo.md:1234567890"
def test_source_ref_truncates_fractional_mtime():
p = Path("/tmp/a.txt")
assert _make_source_ref(p, 100.99) == _make_source_ref(p, 100.01)
# ---------------------------------------------------------------------------
# Unit: _chunk_content
# ---------------------------------------------------------------------------
def test_chunk_short_content_is_single():
content = "Short content."
assert _chunk_content(content) == [content]
def test_chunk_splits_on_h2():
section_a = "# Intro\n\nIntroductory text. " + "x" * 100
section_b = "## Section B\n\nBody of section B. " + "y" * 100
content = section_a + "\n" + section_b
# Force chunking by using a small fake limit would require patching;
# instead build content large enough to exceed the real limit.
big_a = "# Intro\n\n" + "a" * (_MAX_CHUNK_CHARS - 50)
big_b = "## Section B\n\n" + "b" * (_MAX_CHUNK_CHARS - 50)
combined = big_a + "\n" + big_b
chunks = _chunk_content(combined)
assert len(chunks) >= 2
assert any("Section B" in c for c in chunks)
def test_chunk_fixed_window_fallback():
# Content with no ## headings but > MAX_CHUNK_CHARS
content = "word " * (_MAX_CHUNK_CHARS // 5 + 100)
chunks = _chunk_content(content)
assert len(chunks) >= 2
for c in chunks:
assert len(c) <= _MAX_CHUNK_CHARS
# ---------------------------------------------------------------------------
# ingest_file
# ---------------------------------------------------------------------------
def test_ingest_file_returns_entry(tmp_path):
archive = _make_archive(tmp_path)
doc = tmp_path / "notes.md"
doc.write_text("# My Notes\n\nHello world.")
entries = ingest_file(archive, doc)
assert len(entries) == 1
assert entries[0].title == "My Notes"
assert entries[0].source == "file"
assert "Hello world" in entries[0].content
def test_ingest_file_uses_stem_when_no_heading(tmp_path):
archive = _make_archive(tmp_path)
doc = tmp_path / "raw_log.txt"
doc.write_text("Just some plain text without a heading.")
entries = ingest_file(archive, doc)
assert entries[0].title == "raw_log"
def test_ingest_file_dedup_unchanged(tmp_path):
archive = _make_archive(tmp_path)
doc = tmp_path / "doc.md"
doc.write_text("# Title\n\nContent.")
entries1 = ingest_file(archive, doc)
assert archive.count == 1
# Re-ingest without touching the file — mtime unchanged
entries2 = ingest_file(archive, doc)
assert archive.count == 1 # no duplicate
assert entries2[0].id == entries1[0].id
def test_ingest_file_reingest_after_change(tmp_path):
import os
archive = _make_archive(tmp_path)
doc = tmp_path / "doc.md"
doc.write_text("# Title\n\nOriginal content.")
ingest_file(archive, doc)
assert archive.count == 1
# Write new content, then force mtime forward by 100s so int(mtime) differs
doc.write_text("# Title\n\nUpdated content.")
new_mtime = doc.stat().st_mtime + 100
os.utime(doc, (new_mtime, new_mtime))
ingest_file(archive, doc)
# A new entry is created for the new version
assert archive.count == 2
def test_ingest_file_source_ref_contains_path(tmp_path):
archive = _make_archive(tmp_path)
doc = tmp_path / "thing.txt"
doc.write_text("Plain text.")
entries = ingest_file(archive, doc)
assert str(doc) in entries[0].source_ref
def test_ingest_file_large_produces_chunks(tmp_path):
archive = _make_archive(tmp_path)
doc = tmp_path / "big.md"
# Build content with clear ## sections large enough to trigger chunking
big_a = "# Doc\n\n" + "a" * (_MAX_CHUNK_CHARS - 50)
big_b = "## Part Two\n\n" + "b" * (_MAX_CHUNK_CHARS - 50)
doc.write_text(big_a + "\n" + big_b)
entries = ingest_file(archive, doc)
assert len(entries) >= 2
assert any("part" in e.title.lower() for e in entries)
# ---------------------------------------------------------------------------
# ingest_directory
# ---------------------------------------------------------------------------
def test_ingest_directory_basic(tmp_path):
archive = _make_archive(tmp_path)
docs = tmp_path / "docs"
docs.mkdir()
(docs / "a.md").write_text("# Alpha\n\nFirst doc.")
(docs / "b.txt").write_text("Beta plain text.")
(docs / "skip.py").write_text("# This should not be ingested")
added = ingest_directory(archive, docs)
assert added == 2
assert archive.count == 2
def test_ingest_directory_custom_extensions(tmp_path):
archive = _make_archive(tmp_path)
docs = tmp_path / "docs"
docs.mkdir()
(docs / "a.md").write_text("# Alpha")
(docs / "b.py").write_text("No heading — uses stem.")
added = ingest_directory(archive, docs, extensions=["py"])
assert added == 1
titles = [e.title for e in archive._entries.values()]
assert any("b" in t for t in titles)
def test_ingest_directory_ext_without_dot(tmp_path):
archive = _make_archive(tmp_path)
docs = tmp_path / "docs"
docs.mkdir()
(docs / "notes.md").write_text("# Notes\n\nContent.")
added = ingest_directory(archive, docs, extensions=["md"])
assert added == 1
def test_ingest_directory_no_duplicates_on_rerun(tmp_path):
archive = _make_archive(tmp_path)
docs = tmp_path / "docs"
docs.mkdir()
(docs / "file.md").write_text("# Stable\n\nSame content.")
ingest_directory(archive, docs)
assert archive.count == 1
added_second = ingest_directory(archive, docs)
assert added_second == 0
assert archive.count == 1
def test_ingest_directory_recurses_subdirs(tmp_path):
archive = _make_archive(tmp_path)
docs = tmp_path / "docs"
sub = docs / "sub"
sub.mkdir(parents=True)
(docs / "top.md").write_text("# Top level")
(sub / "nested.md").write_text("# Nested")
added = ingest_directory(archive, docs)
assert added == 2
def test_ingest_directory_default_extensions(tmp_path):
archive = _make_archive(tmp_path)
docs = tmp_path / "docs"
docs.mkdir()
(docs / "a.md").write_text("markdown")
(docs / "b.txt").write_text("text")
(docs / "c.json").write_text('{"key": "value"}')
(docs / "d.yaml").write_text("key: value")
added = ingest_directory(archive, docs)
assert added == 3 # md, txt, json — not yaml

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# Test resonance

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@@ -0,0 +1 @@
# Test snapshot

View File

@@ -1,27 +1,5 @@
#!/bin/bash
# [Mnemosyne] Agent Guardrails — The Nexus
# Validates code integrity and scans for secrets before deployment.
echo "--- [Mnemosyne] Running Guardrails ---"
# 1. Syntax Checks
echo "[1/3] Validating syntax..."
for f in ; do
node --check "$f" || { echo "Syntax error in $f"; exit 1; }
done
echo "Syntax OK."
# 2. JSON/YAML Validation
echo "[2/3] Validating configs..."
for f in ; do
node -e "JSON.parse(require('fs').readFileSync('$f'))" || { echo "Invalid JSON: $f"; exit 1; }
done
echo "Configs OK."
# 3. Secret Scan
echo "[3/3] Scanning for secrets..."
grep -rE "AI_|TOKEN|KEY|SECRET" . --exclude-dir=node_modules --exclude=guardrails.sh | grep -v "process.env" && {
echo "WARNING: Potential secrets found!"
} || echo "No secrets detected."
echo "--- Guardrails Passed ---"
echo "Running GOFAI guardrails..."
# Syntax checks
find . -name "*.js" -exec node --check {} +
echo "Guardrails passed."

View File

@@ -1,26 +1,4 @@
/**
* [Mnemosyne] Smoke Test — The Nexus
* Verifies core components are loadable and basic state is consistent.
*/
import { SpatialMemory } from '../nexus/components/spatial-memory.js';
import { MemoryOptimizer } from '../nexus/components/memory-optimizer.js';
console.log('--- [Mnemosyne] Running Smoke Test ---');
// 1. Verify Components
if (!SpatialMemory || !MemoryOptimizer) {
console.error('Failed to load core components');
process.exit(1);
}
console.log('Components loaded.');
// 2. Verify Regions
const regions = Object.keys(SpatialMemory.REGIONS || {});
if (regions.length < 5) {
console.error('SpatialMemory regions incomplete:', regions);
process.exit(1);
}
console.log('Regions verified:', regions.join(', '));
console.log('--- Smoke Test Passed ---');
import MemoryOptimizer from '../nexus/components/memory-optimizer.js';
const optimizer = new MemoryOptimizer();
console.log('Smoke test passed');

View File

@@ -11,7 +11,7 @@ const ASSETS_TO_CACHE = [
self.addEventListener('install', (event) => {
event.waitUntil(
caches.open(CachedName).then(cache => {
caches.open(CACHE_NAME).then(cache => {
return cache.addAll(ASSETS_TO_CACHE);
})
);