Multi-user auth via gnexus-auth OAuth + hybrid role/permission model ...

- Integrate gnexus-auth-client-py (GAuthClient) for OAuth flow, token refresh,
  and webhook parsing
- Add navi/auth/ package: User model, Fernet encryptor, client singleton,
  deps (get_current_user, require_admin, require_permission)
- New tables: navi_users, user_auth_sessions (auto-created on startup)
- Session/memory isolation by user_id with legacy NULL support
- Cookie-based auth proxy: /auth/login, /callback, /logout, /me
- Webhook receiver /webhooks/gnexus-auth handling user events, global logout,
  session revocation, role/permission changes
- Admin endpoints (/admin/*) gated by role + permissions
- Webclient auth store with isAdmin/hasPermission guards
- Admin-only profile filtering in /agents/profiles
- 200/200 tests passing

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Enrich memory extractor with tool calls/results in transcript ...

- _EXTRACT_SYSTEM now explains 4 transcript entry types and instructs
  LLM to trust tool results over chat, return source/source_context
- _extract_facts builds tool_call_map, appends [Tool call] and
  [Tool result] lines with truncation (500/200 chars)
- Transcript capped at 12k chars (head+tail, drop middle)
- Parse source/source_context from LLM response; map confidence:
  tool_call/auto_discovery=95, user_explicit=90, default=70
- Add TODO comment about deferred semantic deduplication

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Wire pgvector semantic search into memory system ...

- Add vector(768) column + HNSW index to memory_facts

- Add LLMBackend.embed() with Ollama + fallback implementation

- MemoryStore: cosine-distance search with ILIKE fallback

- New memory tool params: source, confidence, expires_days, source_context

- Update extractor, sqlite_store, deps wiring

- Add pgvector to requirements

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Improve compression and memory prompts

Add long-term user memory system ...

Architecture:
- navi/memory/store.py: MemoryStore backed by SQLite (memory_facts,
  memory_summary, session_memory_state tables in navi.db)
- navi/memory/extractor.py: LLM-based fact extraction from sessions +
  summary regeneration (triggered after session goes idle >30 min)
- Fact upsert uses UNIQUE(category, key) — same key always overwrites,
  no duplicates or stale contradictions
- Keyword search across category + key + value (LIKE-based, no extra deps)

Context injection:
- Memory summary injected as an ephemeral system message on every LLM call
  via Agent._with_memory() — never persisted to session.context

Tools (all profiles):
- memory_search(query): keyword search against fact DB; persona instructs
  model to call it at session start and before personal-context questions
- memory_forget(key, category?): delete a specific fact on user request

Extraction trigger:
- On new session creation, fire-and-forget background task checks all
  sessions idle >30 min with unprocessed messages → runs extraction

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>