Bagas Purwa Sentika
0355858596
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)
81 lines
2.9 KiB
Markdown
81 lines
2.9 KiB
Markdown
# Hermes Agent: Memory System Deep Dive
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## Built-in Memory
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### MEMORY.md (Agent's Notes)
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- Capacity: 2,200 chars (~800 tokens)
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- Stores: environment facts, project conventions, lessons learned, completed work
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- Format: frozen snapshot at session start in system prompt
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- Tool: `memory` with actions add/replace/remove
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- Auto-saves: agent proactively saves facts it discovers
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- Compaction: when full, agent consolidates or removes entries
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### USER.md (User Profile)
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- Capacity: 1,375 chars (~500 tokens)
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- Stores: preferences, communication style, expectations
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- Write gating: `memory.write_approval: true` requires approval before saves
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### Session Search
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- FTS5 full-text search on all past conversations
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- Unlimited storage (all sessions in SQLite)
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- `session_search` tool for the agent; `hermes sessions list` for the user
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- ~20ms queries, no LLM call cost for search
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## External Memory Providers (8 total)
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| Provider | Storage | Cost | Tools | Best For |
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|----------|---------|------|-------|----------|
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| **Honcho** | Cloud | Paid | 5 | Multi-agent, cross-session user modeling |
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| **OpenViking** | Self-hosted | Free | 5 | Structured knowledge hierarchy |
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| **Mem0** | Cloud | Paid | 3 | Hands-off auto extraction |
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| **Hindsight** | Cloud/Local | Free/Paid | 3 | Knowledge graph + entity relationships |
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| **Holographic** | Local SQLite | Free | 2 | No external deps, trust scoring |
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| **RetainDB** | Cloud | $20/mo | 5 | Teams using RetainDB |
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| **ByteRover** | Local/Cloud | Free/Paid | 3 | Portable CLI-based memory |
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| **Supermemory** | Cloud | Paid | 4 | Semantic recall + session graph |
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## Recommended for ngn-agent
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**Phase 2 start:** Built-in memory only — sufficient for ~15 env facts + ~10 user preferences.
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**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).
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## Key Settings
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```yaml
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memory:
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memory_enabled: true
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user_profile_enabled: true
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memory_char_limit: 2200
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user_char_limit: 1375
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write_approval: false # true to require approval before saves
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nudge_interval: 10 # turns between save nudges
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flush_min_turns: 6 # save memories on exit/reset after N turns
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```
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## What the Agent Auto-Saves
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The agent proactively saves:
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- User preferences ("prefers TypeScript" → USER.md)
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- Environment facts ("this server runs Debian 12" → MEMORY.md)
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- Corrections ("don't use sudo for Docker" → MEMORY.md)
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- Conventions ("project uses tabs" → MEMORY.md)
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- Completed work ("migrated DB from MySQL to PG" → MEMORY.md)
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## What It Skips
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- Trivial/obvious info
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- Easily web-searchable facts
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- Raw data dumps
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- Session-specific ephemera
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- Info already in context files
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## Memory Write Approval Flow
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```yaml
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# When write_approval: true:
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# CLI: prompts inline
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# Gateway: stages for /memory pending → /memory approve <id>
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```
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Toggle at runtime: `/memory approval on|off` |