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Bagas Purwa Sentika 0355858596 docs: update project state through Phase 3

Phase 2: memory, session search, git worktree configured
Phase 3: Telegram gateway connected, DM pairing approved
Phase 4: custom skills created (aws-diagnostics, jira-query, confluence-search, bitbucket-pr)

2026-06-14 12:02:04 +08:00

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Hermes Agent: Memory System Deep Dive

Built-in Memory

MEMORY.md (Agent's Notes)

Capacity: 2,200 chars (~800 tokens)
Stores: environment facts, project conventions, lessons learned, completed work
Format: frozen snapshot at session start in system prompt
Tool: memory with actions add/replace/remove
Auto-saves: agent proactively saves facts it discovers
Compaction: when full, agent consolidates or removes entries

USER.md (User Profile)

Capacity: 1,375 chars (~500 tokens)
Stores: preferences, communication style, expectations
Write gating: memory.write_approval: true requires approval before saves

Session Search

FTS5 full-text search on all past conversations
Unlimited storage (all sessions in SQLite)
session_search tool for the agent; hermes sessions list for the user
~20ms queries, no LLM call cost for search

External Memory Providers (8 total)

Provider	Storage	Cost	Tools	Best For
Honcho	Cloud	Paid	5	Multi-agent, cross-session user modeling
OpenViking	Self-hosted	Free	5	Structured knowledge hierarchy
Mem0	Cloud	Paid	3	Hands-off auto extraction
Hindsight	Cloud/Local	Free/Paid	3	Knowledge graph + entity relationships
Holographic	Local SQLite	Free	2	No external deps, trust scoring
RetainDB	Cloud	$20/mo	5	Teams using RetainDB
ByteRover	Local/Cloud	Free/Paid	3	Portable CLI-based memory
Supermemory	Cloud	Paid	4	Semantic recall + session graph

Recommended for ngn-agent

Phase 2 start: Built-in memory only — sufficient for ~15 env facts + ~10 user preferences.

If more capacity needed: Holographic (local SQLite + FTS5, zero external deps) or Honcho (cross-session user modeling with dialectic reasoning, good for platform engineering context that spans multiple projects).

Key Settings

memory:
  memory_enabled: true
  user_profile_enabled: true
  memory_char_limit: 2200
  user_char_limit: 1375
  write_approval: false     # true to require approval before saves
  nudge_interval: 10        # turns between save nudges
  flush_min_turns: 6        # save memories on exit/reset after N turns

What the Agent Auto-Saves

The agent proactively saves:

User preferences ("prefers TypeScript" → USER.md)
Environment facts ("this server runs Debian 12" → MEMORY.md)
Corrections ("don't use sudo for Docker" → MEMORY.md)
Conventions ("project uses tabs" → MEMORY.md)
Completed work ("migrated DB from MySQL to PG" → MEMORY.md)

What It Skips

Trivial/obvious info
Easily web-searchable facts
Raw data dumps
Session-specific ephemera
Info already in context files

Memory Write Approval Flow

# When write_approval: true:
# CLI: prompts inline
# Gateway: stages for /memory pending → /memory approve <id>

Toggle at runtime: /memory approval on|off

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