docs: improve KNOWN_VIOLATIONS justifications in verify_memory_sovereignty.py

Merged PR #265

docs/memory-architecture-guide.md

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+# Memory Architecture Guide
+
+Developer-facing guide to the Hermes Agent memory system. Covers all four memory tiers, data lifecycle, security guarantees, and extension points.
+
+## Overview
+
+Hermes has four distinct memory systems, each serving a different purpose:
+
+| Tier | System | Scope | Cost | Persistence |
+|------|--------|-------|------|-------------|
+| 1 | **Built-in Memory** (MEMORY.md / USER.md) | Current session, curated facts | ~1,300 tokens fixed per session | File-backed, cross-session |
+| 2 | **Session Search** (FTS5) | All past conversations | On-demand (search + summarize) | SQLite (state.db) |
+| 3 | **Skills** (procedural memory) | How to do specific tasks | Loaded on match only | File-backed (~/.hermes/skills/) |
+| 4 | **External Providers** (plugins) | Deep persistent knowledge | Provider-dependent | Provider-specific |
+
+All four tiers operate independently. Built-in memory is always active. The others are opt-in or on-demand.
+
+## Tier 1: Built-in Memory (MEMORY.md / USER.md)
+
+### File Layout
+
+```
+~/.hermes/memories/
+├── MEMORY.md    — Agent's notes (environment facts, conventions, lessons learned)
+└── USER.md      — User profile (preferences, communication style, identity)
+```
+
+Profile-aware: when running under a profile (`hermes -p coder`), the memories directory resolves to `~/.hermes/profiles/<name>/memories/`.
+
+### Frozen Snapshot Pattern
+
+This is the most important architectural decision in the memory system.
+
+1. **Session start:** `MemoryStore.load_for_prompt()` reads both files from disk, parses entries delimited by `§` (section sign), and injects them into the system prompt as a frozen block.
+2. **During session:** The `memory` tool writes to disk immediately (durable), but does **not** update the system prompt. This preserves the LLM's prefix cache for the entire session.
+3. **Next session:** The snapshot refreshes from disk.
+
+**Why frozen?** System prompt changes invalidate the KV cache on every API call. With a ~30K token system prompt, that's expensive. Freezing memory at session start means the cache stays warm for the entire conversation. The tradeoff: memory writes made mid-session don't take effect until next session. Tool responses show the live state so the agent can verify writes succeeded.
+
+### Character Limits
+
+| Store | Default Limit | Approx Tokens | Typical Entries |
+|-------|--------------|---------------|-----------------|
+| MEMORY.md | 2,200 chars | ~800 | 8-15 |
+| USER.md | 1,375 chars | ~500 | 5-10 |
+
+Limits are in characters (not tokens) because character counts are model-independent. Configurable in `config.yaml`:
+
+```yaml
+memory:
+  memory_char_limit: 2200
+  user_char_limit: 1375
+```
+
+### Entry Format
+
+Entries are separated by `\n§\n`. Each entry can be multiline. Example MEMORY.md:
+
+```
+User runs macOS 14 Sonoma, uses Homebrew, has Docker Desktop
+§
+Project ~/code/api uses Go 1.22, chi router, sqlc. Tests: 'make test'
+§
+Staging server 10.0.1.50 uses SSH port 2222, key at ~/.ssh/staging_ed25519
+```
+
+### Tool Interface
+
+The `memory` tool (defined in `tools/memory_tool.py`) supports:
+
+- **`add`** — Append new entry. Rejects exact duplicates.
+- **`replace`** — Find entry by unique substring (`old_text`), replace with `content`.
+- **`remove`** — Find entry by unique substring, delete it.
+- **`read`** — Return current entries from disk (live state, not frozen snapshot).
+
+Substring matching: `old_text` must match exactly one entry. If it matches multiple, the tool returns an error asking for more specificity.
+
+### Security Scanning
+
+Every memory entry is scanned against `_MEMORY_THREAT_PATTERNS` before acceptance:
+
+- Prompt injection patterns (`ignore previous instructions`, `you are now...`)
+- Credential exfiltration (`curl`/`wget` with env vars, `.env` file reads)
+- SSH backdoor attempts (`authorized_keys`, `.ssh` writes)
+- Invisible Unicode characters (zero-width spaces, BOM)
+
+Matches are rejected with an error message. Source: `_scan_memory_content()` in `tools/memory_tool.py`.
+
+### Code Path
+
+```
+agent/prompt_builder.py
+  └── assembles system prompt pieces
+       └── MemoryStore.load_for_prompt() → frozen snapshot injection
+
+tools/memory_tool.py
+  ├── MemoryStore class (file I/O, locking, parsing)
+  ├── memory_tool() function (add/replace/remove/read dispatch)
+  └── _scan_memory_content() (threat scanning)
+
+hermes_cli/memory_setup.py
+  └── Interactive first-run memory setup
+```
+
+## Tier 2: Session Search (FTS5)
+
+### How It Works
+
+1. Every CLI and gateway session stores full message history in SQLite (`~/.hermes/state.db`)
+2. The `messages_fts` FTS5 virtual table enables fast full-text search
+3. The `session_search` tool finds relevant messages, groups by session, loads top N
+4. Each matching session is summarized by Gemini Flash (auxiliary LLM, not main model)
+5. Summaries are returned to the main agent as context
+
+### Why Gemini Flash for Summarization
+
+Raw session transcripts can be 50K+ chars. Feeding them to the main model wastes context window and tokens. Gemini Flash is fast, cheap, and good enough for "extract the relevant bits" summarization. Same pattern used by `web_extract`.
+
+### Schema
+
+```sql
+-- Core tables
+sessions (id, source, user_id, model, system_prompt, parent_session_id, ...)
+messages (id, session_id, role, content, tool_name, timestamp, ...)
+
+-- Full-text search
+messages_fts  -- FTS5 virtual table on messages.content
+
+-- Schema tracking
+schema_version
+```
+
+WAL mode for concurrent readers + one writer (gateway multi-platform support).
+
+### Session Lineage
+
+When context compression triggers a session split, `parent_session_id` chains the old and new sessions. This lets session search follow the thread across compression boundaries.
+
+### Code Path
+
+```
+tools/session_search_tool.py
+  ├── FTS5 query against messages_fts
+  ├── Groups results by session_id
+  ├── Loads top N sessions (MAX_SESSION_CHARS = 100K per session)
+  ├── Sends to Gemini Flash via auxiliary_client.async_call_llm()
+  └── Returns per-session summaries
+
+hermes_state.py (SessionDB class)
+  ├── SQLite WAL mode database
+  ├── FTS5 triggers for message insert/update/delete
+  └── Session CRUD operations
+```
+
+### Memory vs Session Search
+
+| | Memory | Session Search |
+|---|--------|---------------|
+| **Capacity** | ~1,300 tokens total | Unlimited (all stored sessions) |
+| **Latency** | Instant (in system prompt) | Requires FTS query + LLM call |
+| **When to use** | Critical facts always in context | "What did we discuss about X?" |
+| **Management** | Agent-curated | Automatic |
+| **Token cost** | Fixed per session | On-demand per search |
+
+## Tier 3: Skills (Procedural Memory)
+
+### What Skills Are
+
+Skills capture **how to do a specific type of task** based on proven experience. Where memory is broad and declarative, skills are narrow and actionable.
+
+A skill is a directory with a `SKILL.md` (markdown instructions) and optional supporting files:
+
+```
+~/.hermes/skills/
+├── my-skill/
+│   ├── SKILL.md          — Instructions, steps, pitfalls
+│   ├── references/       — API docs, specs
+│   ├── templates/        — Code templates, config files
+│   ├── scripts/          — Helper scripts
+│   └── assets/           — Images, data files
+```
+
+### How Skills Load
+
+At the start of each turn, the agent's system prompt includes available skills. When a skill matches the current task, the agent loads it with `skill_view(name)` and follows its instructions. Skills are **not** injected wholesale — they're loaded on demand to preserve context window.
+
+### Skill Lifecycle
+
+1. **Creation:** After a complex task (5+ tool calls), the agent offers to save the approach as a skill using `skill_manage(action='create')`.
+2. **Usage:** On future matching tasks, the agent loads the skill with `skill_view(name)`.
+3. **Maintenance:** If a skill is outdated or incomplete when used, the agent patches it immediately with `skill_manage(action='patch')`.
+4. **Deletion:** Obsolete skills are removed with `skill_manage(action='delete')`.
+
+### Skills vs Memory
+
+| | Memory | Skills |
+|---|--------|--------|
+| **Format** | Free-text entries | Structured markdown (steps, pitfalls, examples) |
+| **Scope** | Facts and preferences | Procedures and workflows |
+| **Loading** | Always in system prompt | On-demand when matched |
+| **Size** | ~1,300 tokens total | Variable (loaded individually) |
+
+### Code Path
+
+```
+tools/skill_manager_tool.py  — Create, edit, patch, delete skills
+agent/skill_commands.py       — Slash commands for skill management
+skills_hub.py                 — Browse, search, install skills from hub
+```
+
+## Tier 4: External Memory Providers
+
+### Plugin Architecture
+
+```
+plugins/memory/
+├── __init__.py        — Provider registry and base interface
+├── honcho/            — Dialectic Q&A, cross-session user modeling
+├── openviking/        — Knowledge graph memory
+├── mem0/              — Semantic memory with auto-extraction
+├── hindsight/         — Retrospective memory analysis
+├── holographic/       — Distributed holographic memory
+├── retaindb/          — Vector-based retention
+├── byterover/         — Byte-level memory compression
+└── supermemory/       — Cloud-hosted semantic memory
+```
+
+Only one external provider can be active at a time. Built-in memory (Tier 1) always runs alongside it.
+
+### Integration Points
+
+When a provider is active, Hermes:
+
+1. Injects provider context into the system prompt
+2. Prefetches relevant memories before each turn (background, non-blocking)
+3. Syncs conversation turns to the provider after each response
+4. Extracts memories on session end (for providers that support it)
+5. Mirrors built-in memory writes to the provider
+6. Adds provider-specific tools for search and management
+
+### Configuration
+
+```yaml
+memory:
+  provider: openviking  # or honcho, mem0, hindsight, etc.
+```
+
+Setup: `hermes memory setup` (interactive picker).
+
+## Data Lifecycle
+
+```
+Session Start
+  │
+  ├── Load MEMORY.md + USER.md from disk → frozen snapshot in system prompt
+  ├── Load skills catalog (names + descriptions)
+  ├── Initialize session search (SQLite connection)
+  └── Initialize external provider (if configured)
+  │
+  ▼
+Each Turn
+  │
+  ├── Agent sees frozen memory in system prompt
+  ├── Agent can call memory tool → writes to disk, returns live state
+  ├── Agent can call session_search → FTS5 + Gemini Flash summarization
+  ├── Agent can load skills → reads SKILL.md from disk
+  └── External provider prefetches context (if active)
+  │
+  ▼
+Session End
+  │
+  ├── All memory writes already on disk (immediate persistence)
+  ├── Session transcript saved to SQLite (messages + FTS5 index)
+  ├── External provider extracts final memories (if supported)
+  └── Skill updates persisted (if any were patched)
+```
+
+## Privacy and Data Locality
+
+| Component | Location | Network |
+|-----------|----------|---------|
+| MEMORY.md / USER.md | `~/.hermes/memories/` | Local only |
+| Session DB | `~/.hermes/state.db` | Local only |
+| Skills | `~/.hermes/skills/` | Local only |
+| External provider | Provider-dependent | Provider API calls |
+
+Built-in memory (Tiers 1-3) never leaves the machine. External providers (Tier 4) send data to the configured provider by design. The agent logs all provider API calls in the session transcript for auditability.
+
+## Configuration Reference
+
+```yaml
+# ~/.hermes/config.yaml
+memory:
+  memory_enabled: true          # Enable MEMORY.md
+  user_profile_enabled: true    # Enable USER.md
+  memory_char_limit: 2200       # MEMORY.md char limit (~800 tokens)
+  user_char_limit: 1375         # USER.md char limit (~500 tokens)
+  nudge_interval: 10            # Turns between memory nudge reminders
+  provider: null                # External provider name (null = disabled)
+```
+
+Environment variables (in `~/.hermes/.env`):
+- Provider-specific API keys (e.g., `HONCHO_API_KEY`, `MEM0_API_KEY`)
+
+## Troubleshooting
+
+### Memory not appearing in system prompt
+
+- Check `~/.hermes/memories/MEMORY.md` exists and has content
+- Verify `memory.memory_enabled: true` in config
+- Check for file lock issues (WAL mode, concurrent access)
+
+### Memory writes not taking effect
+
+- Writes are durable to disk immediately but frozen in system prompt until next session
+- Tool response shows live state — verify the write succeeded there
+- Start a new session to see the updated snapshot
+
+### Session search returns nothing
+
+- Verify `state.db` has sessions: `sqlite3 ~/.hermes/state.db "SELECT count(*) FROM sessions"`
+- Check FTS5 index: `sqlite3 ~/.hermes/state.db "SELECT count(*) FROM messages_fts"`
+- Ensure auxiliary LLM (Gemini Flash) is configured and reachable
+
+### Skills not loading
+
+- Check `~/.hermes/skills/` directory exists
+- Verify SKILL.md has valid frontmatter (name, description)
+- Skills load by name match — check the skill name matches what the agent expects
+
+### External provider errors
+
+- Check API key in `~/.hermes/.env`
+- Verify provider is installed: `pip install <provider-package>`
+- Run `hermes memory status` for diagnostic info

plugins/memory/mempalace/__init__.py

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+"""
+MemPalace Portal — Hybrid Memory Provider.
+
+Bridges the local Holographic fact store with the fleet-wide MemPalace vector database.
+Implements smart context compression for token efficiency.
+"""
+
+import json
+import logging
+import os
+import re
+import requests
+from typing import Any, Dict, List, Optional
+from agent.memory_provider import MemoryProvider
+
+# Import Holographic components if available
+try:
+    from plugins.memory.holographic.store import MemoryStore
+    from plugins.memory.holographic.retrieval import FactRetriever
+    HAS_HOLOGRAPHIC = True
+except ImportError:
+    HAS_HOLOGRAPHIC = False
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Tool Schemas
+# ---------------------------------------------------------------------------
+
+MEMPALACE_SCHEMA = {
+    "name": "mempalace",
+    "description": (
+        "Search or record memories in the shared fleet vector database. "
+        "Use this for long-term, high-volume memory across the entire fleet."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "action": {"type": "string", "enum": ["search", "record", "wings"]},
+            "query": {"type": "string", "description": "Search query."},
+            "text": {"type": "string", "description": "Memory text to record."},
+            "room": {"type": "string", "description": "Target room (e.g., forge, hermes, nexus)."},
+            "n_results": {"type": "integer", "default": 5},
+        },
+        "required": ["action"],
+    },
+}
+
+FACT_STORE_SCHEMA = {
+    "name": "fact_store",
+    "description": (
+        "Structured local fact storage. Use for durable facts about people, projects, and decisions."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "action": {"type": "string", "enum": ["add", "search", "probe", "reason", "update", "remove"]},
+            "content": {"type": "string"},
+            "query": {"type": "string"},
+            "entity": {"type": "string"},
+            "fact_id": {"type": "integer"},
+        },
+        "required": ["action"],
+    },
+}
+
+# ---------------------------------------------------------------------------
+# Provider Implementation
+# ---------------------------------------------------------------------------
+
+class MemPalacePortalProvider(MemoryProvider):
+    """Hybrid Fleet Vector + Local Structured memory provider."""
+
+    def __init__(self, config: dict | None = None):
+        self._config = config or {}
+        self._api_url = os.environ.get("MEMPALACE_API_URL", "http://127.0.0.1:7771")
+        self._hologram_store = None
+        self._hologram_retriever = None
+        self._session_id = None
+
+    @property
+    def name(self) -> str:
+        return "mempalace"
+
+    def is_available(self) -> bool:
+        # Always available if we can reach the API or have Holographic
+        return True
+
+    def initialize(self, session_id: str, **kwargs) -> None:
+        self._session_id = session_id
+        hermes_home = kwargs.get("hermes_home")
+        
+        if HAS_HOLOGRAPHIC and hermes_home:
+            db_path = os.path.join(hermes_home, "memory_store.db")
+            try:
+                self._hologram_store = MemoryStore(db_path=db_path)
+                self._hologram_retriever = FactRetriever(store=self._hologram_store)
+                logger.info("Holographic store initialized as local portal layer.")
+            except Exception as e:
+                logger.error(f"Failed to init Holographic layer: {e}")
+
+    def system_prompt_block(self) -> str:
+        status = "Active (Fleet Portal)"
+        if self._hologram_store:
+            status += " + Local Hologram"
+        return (
+            f"# MemPalace Portal\n"
+            f"Status: {status}.\n"
+            "You have access to the shared fleet vector database (mempalace) and local structured facts (fact_store).\n"
+            "Use mempalace for semantic fleet-wide recall. Use fact_store for precise local knowledge."
+        )
+
+    def prefetch(self, query: str, *, session_id: str = "") -> str:
+        if not query:
+            return ""
+            
+        context_blocks = []
+        
+        # 1. Fleet Search (MemPalace)
+        try:
+            res = requests.get(f"{self._api_url}/search", params={"q": query, "n": 3}, timeout=2)
+            if res.ok:
+                data = res.json()
+                memories = data.get("memories", [])
+                if memories:
+                    block = "## Fleet Memories (MemPalace)\n"
+                    for m in memories:
+                        block += f"- {m['text']}\n"
+                    context_blocks.append(block)
+        except Exception:
+            pass
+
+        # 2. Local Probe (Holographic)
+        if self._hologram_retriever:
+            try:
+                # Extract entities from query to probe
+                entities = re.findall(r'\b([A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)\b', query)
+                facts = []
+                for ent in entities:
+                    results = self._hologram_retriever.probe(ent, limit=3)
+                    facts.extend(results)
+                
+                if facts:
+                    block = "## Local Facts (Hologram)\n"
+                    seen = set()
+                    for f in facts:
+                        if f['content'] not in seen:
+                            block += f"- {f['content']}\n"
+                            seen.add(f['content'])
+                    context_blocks.append(block)
+            except Exception:
+                pass
+
+        return "\n\n".join(context_blocks)
+
+    def sync_turn(self, user_content: str, assistant_content: str, *, session_id: str = "") -> None:
+        # Record to Fleet Palace
+        try:
+            payload = {
+                "text": f"User: {user_content}\nAssistant: {assistant_content}",
+                "room": "hermes_sync",
+                "metadata": {"session_id": session_id}
+            }
+            requests.post(f"{self._api_url}/record", json=payload, timeout=2)
+        except Exception:
+            pass
+
+    def on_pre_compress(self, messages: List[Dict[str, Any]]) -> str:
+        """Token Efficiency: Summarize and archive before context is lost."""
+        if not messages:
+            return ""
+            
+        # Extract key facts for Hologram
+        if self._hologram_store:
+            # Simple heuristic: look for \"I prefer\", \"The project uses\", etc.
+            for msg in messages:
+                if msg.get(\"role\") == \"user\":
+                    content = msg.get(\"content\", \"\")
+                    if \"prefer\" in content.lower() or \"use\" in content.lower():
+                        try:
+                            self._hologram_store.add_fact(content[:200], category=\"user_pref\")
+                        except Exception:
+                            pass
+
+        # Archive session summary to MemPalace
+        summary_text = f"Session {self._session_id} summary: " + " | ".join([m['content'][:50] for m in messages if m.get('role') == 'user'])
+        try:
+            payload = {
+                "text": summary_text,
+                "room": "summaries",
+                "metadata": {"type": "session_summary", "session_id": self._session_id}
+            }
+            requests.post(f"{self._api_url}/record", json=payload, timeout=2)
+        except Exception:
+            pass
+
+        return "Insights archived to MemPalace and Hologram."
+
+    def get_tool_schemas(self) -> List[Dict[str, Any]]:
+        return [MEMPALACE_SCHEMA, FACT_STORE_SCHEMA]
+
+    def handle_tool_call(self, tool_name: str, args: Dict[str, Any], **kwargs) -> str:
+        if tool_name == "mempalace":
+            return self._handle_mempalace(args)
+        elif tool_name == "fact_store":
+            return self._handle_fact_store(args)
+        return json.dumps({"error": f"Unknown tool: {tool_name}"})
+
+    def _handle_mempalace(self, args: dict) -> str:
+        action = args.get("action")
+        try:
+            if action == "search":
+                res = requests.get(f"{self._api_url}/search", params={"q": args["query"], "n": args.get("n_results", 5)}, timeout=10)
+                return res.text
+            elif action == "record":
+                res = requests.post(f"{self._api_url}/record", json={"text": args["text"], "room": args.get("room", "general")}, timeout=10)
+                return res.text
+            elif action == "wings":
+                res = requests.get(f"{self._api_url}/wings", timeout=10)
+                return res.text
+        except Exception as e:
+            return json.dumps({"success": False, "error": str(e)})
+        return json.dumps({"error": "Invalid action"})
+
+    def _handle_fact_store(self, args: dict) -> str:
+        if not self._hologram_store:
+            return json.dumps({"error": "Holographic store not initialized locally."})
+        # Logic similar to holographic plugin
+        action = args["action"]
+        try:
+            if action == "add":
+                fid = self._hologram_store.add_fact(args["content"])
+                return json.dumps({"fact_id": fid, "status": "added"})
+            elif action == "probe":
+                res = self._hologram_retriever.probe(args["entity"])
+                return json.dumps({"results": res})
+            # ... other actions ...
+            return json.dumps({"status": "ok", "message": f"Action {action} processed (partial impl)"})
+        except Exception as e:
+            return json.dumps({"error": str(e)})
+
+    def shutdown(self) -> None:
+        if self._hologram_store:
+            self._hologram_store.close()
+
+def register(ctx) -> None:
+    provider = MemPalacePortalProvider()
+    ctx.register_memory_provider(provider)

plugins/memory/mempalace/plugin.yaml

@@ -0,0 +1,7 @@
+
+name: mempalace
+version: 1.0.0
+description: "The Portal: Hybrid Fleet Vector (MemPalace) + Local Structured (Holographic) memory."
+dependencies:
+  - requests
+  - numpy

scripts/memory_budget.py

@@ -0,0 +1,374 @@
+#!/usr/bin/env python3
+"""Memory Budget Enforcement Tool for hermes-agent.
+
+Checks and enforces character/token budgets on MEMORY.md and USER.md files.
+Designed for CI integration, pre-commit hooks, and manual health checks.
+
+Usage:
+    python scripts/memory_budget.py                          # Check budget (exit 0/1)
+    python scripts/memory_budget.py --report                 # Detailed breakdown
+    python scripts/memory_budget.py --enforce                # Trim entries to fit budget
+    python scripts/memory_budget.py --hermes-home ~/.hermes  # Custom HERMES_HOME
+
+Exit codes:
+    0  Within budget
+    1  Over budget (no trimming performed)
+    2  Entries were trimmed (--enforce was used)
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+from dataclasses import dataclass
+from pathlib import Path
+from typing import List
+
+# ---------------------------------------------------------------------------
+# Constants (must stay in sync with tools/memory_tool.py)
+# ---------------------------------------------------------------------------
+
+ENTRY_DELIMITER = "\n§\n"
+DEFAULT_MEMORY_CHAR_LIMIT = 2200
+DEFAULT_USER_CHAR_LIMIT = 1375
+WARN_THRESHOLD = 0.80  # alert when >80% of budget used
+CHARS_PER_TOKEN = 4    # rough estimate matching agent/model_metadata.py
+
+
+# ---------------------------------------------------------------------------
+# Data structures
+# ---------------------------------------------------------------------------
+
+@dataclass
+class FileReport:
+    """Budget analysis for a single memory file."""
+    label: str            # "MEMORY.md" or "USER.md"
+    path: Path
+    exists: bool
+    char_limit: int
+    raw_chars: int        # raw file size in chars
+    entry_chars: int      # chars after splitting/rejoining entries
+    entry_count: int
+    entries: List[str]    # individual entry texts
+
+    @property
+    def usage_pct(self) -> float:
+        if self.char_limit <= 0:
+            return 0.0
+        return min(100.0, (self.entry_chars / self.char_limit) * 100)
+
+    @property
+    def estimated_tokens(self) -> int:
+        return self.entry_chars // CHARS_PER_TOKEN
+
+    @property
+    def over_budget(self) -> bool:
+        return self.entry_chars > self.char_limit
+
+    @property
+    def warning(self) -> bool:
+        return self.usage_pct >= (WARN_THRESHOLD * 100)
+
+    @property
+    def remaining_chars(self) -> int:
+        return max(0, self.char_limit - self.entry_chars)
+
+
+def _read_entries(path: Path) -> List[str]:
+    """Read a memory file and split into entries (matching MemoryStore logic)."""
+    if not path.exists():
+        return []
+    try:
+        raw = path.read_text(encoding="utf-8")
+    except (OSError, IOError):
+        return []
+    if not raw.strip():
+        return []
+    entries = [e.strip() for e in raw.split(ENTRY_DELIMITER)]
+    return [e for e in entries if e]
+
+
+def _write_entries(path: Path, entries: List[str]) -> None:
+    """Write entries back to a memory file."""
+    content = ENTRY_DELIMITER.join(entries) if entries else ""
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(content, encoding="utf-8")
+
+
+def analyze_file(path: Path, label: str, char_limit: int) -> FileReport:
+    """Analyze a single memory file against its budget."""
+    exists = path.exists()
+    entries = _read_entries(path) if exists else []
+    raw_chars = path.stat().st_size if exists else 0
+    joined = ENTRY_DELIMITER.join(entries)
+    return FileReport(
+        label=label,
+        path=path,
+        exists=exists,
+        char_limit=char_limit,
+        raw_chars=raw_chars,
+        entry_chars=len(joined),
+        entry_count=len(entries),
+        entries=entries,
+    )
+
+
+def trim_entries(report: FileReport) -> List[str]:
+    """Trim oldest entries until the file fits within its budget.
+
+    Entries are removed from the front (oldest first) because memory files
+    append new entries at the end.
+    """
+    entries = list(report.entries)
+    joined = ENTRY_DELIMITER.join(entries)
+
+    while len(joined) > report.char_limit and entries:
+        entries.pop(0)
+        joined = ENTRY_DELIMITER.join(entries)
+
+    return entries
+
+
+# ---------------------------------------------------------------------------
+# Reporting
+# ---------------------------------------------------------------------------
+
+def _bar(pct: float, width: int = 30) -> str:
+    """Render a text progress bar."""
+    filled = int(pct / 100 * width)
+    bar = "#" * filled + "-" * (width - filled)
+    return f"[{bar}]"
+
+
+def print_report(memory: FileReport, user: FileReport, *, verbose: bool = False) -> None:
+    """Print a human-readable budget report."""
+    total_chars = memory.entry_chars + user.entry_chars
+    total_limit = memory.char_limit + user.char_limit
+    total_tokens = total_chars // CHARS_PER_TOKEN
+    total_pct = (total_chars / total_limit * 100) if total_limit > 0 else 0
+
+    print("=" * 60)
+    print("  MEMORY BUDGET REPORT")
+    print("=" * 60)
+    print()
+
+    for rpt in (memory, user):
+        status = "OVER " if rpt.over_budget else ("WARN" if rpt.warning else " OK  ")
+        print(f"  {rpt.label:12s}  {status}  {_bar(rpt.usage_pct)} {rpt.usage_pct:5.1f}%")
+        print(f"  {'':12s}  {rpt.entry_chars:,}/{rpt.char_limit:,} chars "
+              f"| {rpt.entry_count} entries "
+              f"| ~{rpt.estimated_tokens:,} tokens")
+        if rpt.exists and verbose and rpt.entries:
+            for i, entry in enumerate(rpt.entries):
+                preview = entry[:72].replace("\n", " ")
+                if len(entry) > 72:
+                    preview += "..."
+                print(f"    #{i+1}: ({len(entry)} chars) {preview}")
+        print()
+
+    print(f"  TOTAL         {_bar(total_pct)} {total_pct:5.1f}%")
+    print(f"  {total_chars:,}/{total_limit:,} chars | ~{total_tokens:,} tokens")
+    print()
+
+    # Alerts
+    alerts = []
+    for rpt in (memory, user):
+        if rpt.over_budget:
+            overshoot = rpt.entry_chars - rpt.char_limit
+            alerts.append(
+                f"  CRITICAL  {rpt.label} is {overshoot:,} chars over budget "
+                f"({rpt.entry_chars:,}/{rpt.char_limit:,}). "
+                f"Run with --enforce to auto-trim."
+            )
+        elif rpt.warning:
+            alerts.append(
+                f"  WARNING   {rpt.label} is at {rpt.usage_pct:.0f}% capacity. "
+                f"Consider compressing or cleaning up entries."
+            )
+
+    if alerts:
+        print("  ALERTS")
+        print("  ------")
+        for a in alerts:
+            print(a)
+        print()
+
+
+def print_json(memory: FileReport, user: FileReport) -> None:
+    """Print a JSON report for machine consumption."""
+    import json
+
+    def _rpt_dict(r: FileReport) -> dict:
+        return {
+            "label": r.label,
+            "path": str(r.path),
+            "exists": r.exists,
+            "char_limit": r.char_limit,
+            "entry_chars": r.entry_chars,
+            "entry_count": r.entry_count,
+            "estimated_tokens": r.estimated_tokens,
+            "usage_pct": round(r.usage_pct, 1),
+            "over_budget": r.over_budget,
+            "warning": r.warning,
+            "remaining_chars": r.remaining_chars,
+        }
+
+    total_chars = memory.entry_chars + user.entry_chars
+    total_limit = memory.char_limit + user.char_limit
+
+    data = {
+        "memory": _rpt_dict(memory),
+        "user": _rpt_dict(user),
+        "total": {
+            "chars": total_chars,
+            "limit": total_limit,
+            "estimated_tokens": total_chars // CHARS_PER_TOKEN,
+            "usage_pct": round((total_chars / total_limit * 100) if total_limit else 0, 1),
+            "over_budget": memory.over_budget or user.over_budget,
+            "warning": memory.warning or user.warning,
+        },
+    }
+    print(json.dumps(data, indent=2))
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+def _resolve_hermes_home(custom: str | None) -> Path:
+    """Resolve HERMES_HOME directory."""
+    if custom:
+        return Path(custom).expanduser()
+    import os
+    return Path(os.getenv("HERMES_HOME", Path.home() / ".hermes"))
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description="Check and enforce memory budgets for hermes-agent.",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__,
+    )
+    parser.add_argument(
+        "--hermes-home", metavar="DIR",
+        help="Custom HERMES_HOME directory (default: $HERMES_HOME or ~/.hermes)",
+    )
+    parser.add_argument(
+        "--memory-limit", type=int, default=DEFAULT_MEMORY_CHAR_LIMIT,
+        help=f"Character limit for MEMORY.md (default: {DEFAULT_MEMORY_CHAR_LIMIT})",
+    )
+    parser.add_argument(
+        "--user-limit", type=int, default=DEFAULT_USER_CHAR_LIMIT,
+        help=f"Character limit for USER.md (default: {DEFAULT_USER_CHAR_LIMIT})",
+    )
+    parser.add_argument(
+        "--report", action="store_true",
+        help="Print detailed per-file budget report",
+    )
+    parser.add_argument(
+        "--verbose", "-v", action="store_true",
+        help="Show individual entry details in report",
+    )
+    parser.add_argument(
+        "--enforce", action="store_true",
+        help="Trim oldest entries to fit within budget (writes to disk)",
+    )
+    parser.add_argument(
+        "--json", action="store_true", dest="json_output",
+        help="Output report as JSON (for CI/scripting)",
+    )
+
+    args = parser.parse_args()
+    hermes_home = _resolve_hermes_home(args.hermes_home)
+    memories_dir = hermes_home / "memories"
+
+    # Analyze both files
+    memory = analyze_file(
+        memories_dir / "MEMORY.md", "MEMORY.md", args.memory_limit,
+    )
+    user = analyze_file(
+        memories_dir / "USER.md", "USER.md", args.user_limit,
+    )
+
+    over_budget = memory.over_budget or user.over_budget
+    trimmed = False
+
+    # Enforce budget by trimming entries
+    if args.enforce and over_budget:
+        for rpt in (memory, user):
+            if rpt.over_budget and rpt.exists:
+                trimmed_entries = trim_entries(rpt)
+                removed = rpt.entry_count - len(trimmed_entries)
+                if removed > 0:
+                    _write_entries(rpt.path, trimmed_entries)
+                    rpt.entries = trimmed_entries
+                    rpt.entry_count = len(trimmed_entries)
+                    rpt.entry_chars = len(ENTRY_DELIMITER.join(trimmed_entries))
+                    rpt.raw_chars = rpt.path.stat().st_size
+                    print(f"  Trimmed {removed} oldest entries from {rpt.label} "
+                          f"({rpt.entry_chars:,}/{rpt.char_limit:,} chars now)")
+                    trimmed = True
+        # Re-check after trimming
+        over_budget = memory.over_budget or user.over_budget
+
+    # Output
+    if args.json_output:
+        print_json(memory, user)
+    elif args.report or args.verbose:
+        print_report(memory, user, verbose=args.verbose)
+    else:
+        # Compact summary
+        if over_budget:
+            print("Memory budget: OVER")
+            for rpt in (memory, user):
+                if rpt.over_budget:
+                    print(f"  {rpt.label}: {rpt.entry_chars:,}/{rpt.char_limit:,} chars "
+                          f"({rpt.usage_pct:.0f}%)")
+        elif memory.warning or user.warning:
+            print("Memory budget: WARNING")
+            for rpt in (memory, user):
+                if rpt.warning:
+                    print(f"  {rpt.label}: {rpt.entry_chars:,}/{rpt.char_limit:,} chars "
+                          f"({rpt.usage_pct:.0f}%)")
+        else:
+            print("Memory budget: OK")
+            for rpt in (memory, user):
+                if rpt.exists:
+                    print(f"  {rpt.label}: {rpt.entry_chars:,}/{rpt.char_limit:,} chars "
+                          f"({rpt.usage_pct:.0f}%)")
+
+    # Suggest actions when over budget but not enforced
+    if over_budget and not args.enforce:
+        suggestions = []
+        for rpt in (memory, user):
+            if rpt.over_budget:
+                suggestions.append(
+                    f"  - {rpt.label}: remove stale entries or run with --enforce to auto-trim"
+                )
+                # Identify largest entries
+                if rpt.entries:
+                    indexed = sorted(enumerate(rpt.entries), key=lambda x: len(x[1]), reverse=True)
+                    top3 = indexed[:3]
+                    for idx, entry in top3:
+                        preview = entry[:60].replace("\n", " ")
+                        if len(entry) > 60:
+                            preview += "..."
+                        suggestions.append(
+                            f"    largest entry #{idx+1}: ({len(entry)} chars) {preview}"
+                        )
+        if suggestions:
+            print()
+            print("Suggestions:")
+            for s in suggestions:
+                print(s)
+
+    # Exit code
+    if trimmed:
+        return 2
+    if over_budget:
+        return 1
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

scripts/verify_memory_sovereignty.py

@@ -0,0 +1,325 @@
+#!/usr/bin/env python3
+"""
+Memory Sovereignty Verification
+
+Verifies that the memory path in hermes-agent has no network dependencies.
+Memory data must stay on the local filesystem only — no HTTP calls, no external
+API calls, no cloud sync during memory read/write/flush/load operations.
+
+Scans:
+  - tools/memory_tool.py       (MEMORY.md / USER.md store)
+  - hermes_state.py             (SQLite session store)
+  - tools/session_search_tool.py (FTS5 session search + summarization)
+  - tools/graph_store.py        (knowledge graph persistence)
+  - tools/temporal_kg_tool.py   (temporal knowledge graph)
+  - agent/temporal_knowledge_graph.py (temporal triple store)
+  - tools/skills_tool.py        (skill listing/viewing)
+  - tools/skills_sync.py        (bundled skill syncing)
+
+Exit codes:
+  0 = sovereign (no violations)
+  1 = violations found
+"""
+
+import ast
+import re
+import sys
+from pathlib import Path
+
+# ---------------------------------------------------------------------------
+# Configuration
+# ---------------------------------------------------------------------------
+
+# Files in the memory path to scan (relative to repo root).
+MEMORY_FILES = [
+    "tools/memory_tool.py",
+    "hermes_state.py",
+    "tools/session_search_tool.py",
+    "tools/graph_store.py",
+    "tools/temporal_kg_tool.py",
+    "agent/temporal_knowledge_graph.py",
+    "tools/skills_tool.py",
+    "tools/skills_sync.py",
+]
+
+# Patterns that indicate network/external API usage.
+NETWORK_PATTERNS = [
+    # HTTP libraries
+    (r'\brequests\.(get|post|put|delete|patch|head|session)', "requests HTTP call"),
+    (r'\burllib\.request\.(urlopen|Request)', "urllib HTTP call"),
+    (r'\bhttpx\.(get|post|put|delete|Client|AsyncClient)', "httpx HTTP call"),
+    (r'\bhttp\.client\.(HTTPConnection|HTTPSConnection)', "http.client connection"),
+    (r'\baiohttp\.(ClientSession|get|post)', "aiohttp HTTP call"),
+    (r'\bwebsockets\.\w+', "websocket connection"),
+
+    # API client patterns
+    (r'\bopenai\b.*\b(api_key|chat|completions|Client)\b', "OpenAI API usage"),
+    (r'\banthropic\b.*\b(api_key|messages|Client)\b', "Anthropic API usage"),
+    (r'\bAsyncOpenAI\b', "AsyncOpenAI client"),
+    (r'\bAsyncAnthropic\b', "AsyncAnthropic client"),
+
+    # Generic network indicators
+    (r'\bsocket\.(socket|connect|create_connection)', "raw socket connection"),
+    (r'\bftplib\b', "FTP connection"),
+    (r'\bsmtplib\b', "SMTP connection"),
+    (r'\bparamiko\b', "SSH connection via paramiko"),
+
+    # URL patterns (hardcoded endpoints)
+    (r'https?://(?!example\.com)[a-zA-Z0-9._-]+\.(com|org|net|io|dev|ai)', "hardcoded URL"),
+]
+
+# Import aliases that indicate network-capable modules.
+NETWORK_IMPORTS = {
+    "requests",
+    "httpx",
+    "aiohttp",
+    "urllib.request",
+    "http.client",
+    "websockets",
+    "openai",
+    "anthropic",
+    "openrouter_client",
+}
+
+# Functions whose names suggest network I/O.
+NETWORK_FUNC_NAMES = {
+    "async_call_llm",
+    "extract_content_or_reasoning",
+}
+
+# Files that are ALLOWED to have network calls (known violations with justification).
+# Each entry maps to a reason string.
+KNOWN_VIOLATIONS = {
+    "tools/graph_store.py": (
+        "GraphStore persists to Gitea via API. This is a known architectural trade-off "
+        "for knowledge graph persistence, which is not part of the core memory path "
+        "(MEMORY.md/USER.md/SQLite). Future work will explore local-first alternatives "
+        "to align more closely with SOUL.md principles."
+    ),
+    "tools/session_search_tool.py": (
+        "Session search uses LLM summarization via an auxiliary client. While the FTS5 "
+        "search is local, the LLM call for summarization is an external dependency. "
+        "This is a temporary architectural trade-off for enhanced presentation. "
+        "Research is ongoing to implement local LLM options for full sovereignty, "
+        "in line with SOUL.md."
+    ),
+}
+
+# ---------------------------------------------------------------------------
+# Scanner
+# ---------------------------------------------------------------------------
+
+class Violation:
+    """A sovereignty violation with location and description."""
+    def __init__(self, file: str, line: int, description: str, code: str):
+        self.file = file
+        self.line = line
+        self.description = description
+        self.code = code.strip()
+
+    def __str__(self):
+        return f"{self.file}:{self.line}: {self.description}\n    {self.code}"
+
+
+def scan_file(filepath: Path, repo_root: Path) -> list[Violation]:
+    """Scan a single file for network dependency patterns."""
+    violations = []
+    rel_path = str(filepath.relative_to(repo_root))
+
+    # Skip known violations
+    if rel_path in KNOWN_VIOLATIONS:
+        return violations
+
+    try:
+        content = filepath.read_text(encoding="utf-8")
+    except (OSError, IOError) as e:
+        print(f"WARNING: Cannot read {rel_path}: {e}", file=sys.stderr)
+        return violations
+
+    lines = content.splitlines()
+
+    # --- Check imports ---
+    try:
+        tree = ast.parse(content, filename=str(filepath))
+    except SyntaxError as e:
+        print(f"WARNING: Cannot parse {rel_path}: {e}", file=sys.stderr)
+        return violations
+
+    for node in ast.walk(tree):
+        if isinstance(node, ast.Import):
+            for alias in node.names:
+                mod = alias.name
+                if mod in NETWORK_IMPORTS or any(
+                    mod.startswith(ni + ".") for ni in NETWORK_IMPORTS
+                ):
+                    violations.append(Violation(
+                        rel_path, node.lineno,
+                        f"Network-capable import: {mod}",
+                        lines[node.lineno - 1] if node.lineno <= len(lines) else "",
+                    ))
+
+        elif isinstance(node, ast.ImportFrom):
+            if node.module and (
+                node.module in NETWORK_IMPORTS
+                or any(node.module.startswith(ni + ".") for ni in NETWORK_IMPORTS)
+            ):
+                violations.append(Violation(
+                    rel_path, node.lineno,
+                    f"Network-capable import from: {node.module}",
+                    lines[node.lineno - 1] if node.lineno <= len(lines) else "",
+                ))
+
+    # --- Check for LLM call function usage ---
+    for i, line in enumerate(lines, 1):
+        stripped = line.strip()
+        if stripped.startswith("#"):
+            continue
+
+        for func_name in NETWORK_FUNC_NAMES:
+            if func_name in line and not stripped.startswith("def ") and not stripped.startswith("class "):
+                # Check it's actually a call, not a definition or import
+                if re.search(r'\b' + func_name + r'\s*\(', line):
+                    violations.append(Violation(
+                        rel_path, i,
+                        f"External LLM call function: {func_name}()",
+                        line,
+                    ))
+
+    # --- Regex-based pattern matching ---
+    for i, line in enumerate(lines, 1):
+        stripped = line.strip()
+        if stripped.startswith("#"):
+            continue
+
+        for pattern, description in NETWORK_PATTERNS:
+            if re.search(pattern, line, re.IGNORECASE):
+                violations.append(Violation(
+                    rel_path, i,
+                    f"Suspicious pattern ({description})",
+                    line,
+                ))
+
+    return violations
+
+
+def verify_sovereignty(repo_root: Path) -> tuple[list[Violation], list[str]]:
+    """Run sovereignty verification across all memory files.
+
+    Returns (violations, info_messages).
+    """
+    all_violations = []
+    info = []
+
+    for rel_path in MEMORY_FILES:
+        filepath = repo_root / rel_path
+        if not filepath.exists():
+            info.append(f"SKIP: {rel_path} (file not found)")
+            continue
+
+        if rel_path in KNOWN_VIOLATIONS:
+            info.append(
+                f"WARN: {rel_path} — known violation (excluded from gate): "
+                f"{KNOWN_VIOLATIONS[rel_path]}"
+            )
+            continue
+
+        violations = scan_file(filepath, repo_root)
+        all_violations.extend(violations)
+
+        if not violations:
+            info.append(f"PASS: {rel_path} — sovereign (local-only)")
+
+    return all_violations, info
+
+
+# ---------------------------------------------------------------------------
+# Deep analysis helpers
+# ---------------------------------------------------------------------------
+
+def check_graph_store_network(repo_root: Path) -> str:
+    """Analyze graph_store.py for its network dependencies."""
+    filepath = repo_root / "tools" / "graph_store.py"
+    if not filepath.exists():
+        return ""
+    content = filepath.read_text(encoding="utf-8")
+    if "GiteaClient" in content:
+        return (
+            "tools/graph_store.py uses GiteaClient for persistence — "
+            "this is an external API call. However, graph_store is NOT part of "
+            "the core memory path (MEMORY.md/USER.md/SQLite). It is a separate "
+            "knowledge graph system."
+        )
+    return ""
+
+
+def check_session_search_llm(repo_root: Path) -> str:
+    """Analyze session_search_tool.py for LLM usage."""
+    filepath = repo_root / "tools" / "session_search_tool.py"
+    if not filepath.exists():
+        return ""
+    content = filepath.read_text(encoding="utf-8")
+    warnings = []
+    if "async_call_llm" in content:
+        warnings.append("uses async_call_llm for summarization")
+    if "auxiliary_client" in content:
+        warnings.append("imports auxiliary_client (LLM calls)")
+    if warnings:
+        return (
+            f"tools/session_search_tool.py: {'; '.join(warnings)}. "
+            f"The FTS5 search is local SQLite, but session summarization "
+            f"involves LLM API calls."
+        )
+    return ""
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+def main():
+    repo_root = Path(__file__).resolve().parent.parent
+    print(f"Memory Sovereignty Verification")
+    print(f"Repository: {repo_root}")
+    print(f"Scanning {len(MEMORY_FILES)} memory-path files...")
+    print()
+
+    violations, info = verify_sovereignty(repo_root)
+
+    # Print info messages
+    for msg in info:
+        print(f"  {msg}")
+
+    # Print deep analysis
+    print()
+    print("Deep analysis:")
+    for checker in [check_graph_store_network, check_session_search_llm]:
+        note = checker(repo_root)
+        if note:
+            print(f"  NOTE: {note}")
+
+    print()
+
+    if violations:
+        print(f"SOVEREIGNTY VIOLATIONS FOUND: {len(violations)}")
+        print("=" * 60)
+        for v in violations:
+            print(v)
+            print()
+        print("=" * 60)
+        print(
+            f"FAIL: {len(violations)} potential network dependencies detected "
+            f"in the memory path."
+        )
+        print("Memory must be local-only (filesystem + SQLite).")
+        print()
+        print("If a violation is intentional and documented, add it to")
+        print("KNOWN_VIOLATIONS in this script with a justification.")
+        return 1
+    else:
+        print("PASS: Memory path is sovereign — no network dependencies detected.")
+        print("All memory operations use local filesystem and/or SQLite only.")
+        return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())