# Feature Feedback and User Experience

**Collection Date:** 2026-04-09  
**Sources:** GitHub issues, blog posts, community discussions, documentation

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## Skills System

### Positive Feedback

**Self-Improvement Loop:**
> "The agent can transform what it learns into reusable skills, improve them through experience, store useful information, and even search for previous conversations."

**Progressive Disclosure:**
- Level 0: Skill names/descriptions (~3,000 tokens)
- Level 1: Full skill content when needed
- Level 2: Specific reference files

**Skill Creation:**
- Auto-generated after complex tasks (5+ tool calls)
- Can be hand-written
- Installable from Skills Hub
- Shareable via agentskills.io format

### Community Contributions

**Awesome Hermes Agent:** https://github.com/0xNyk/awesome-hermes-agent
- Curated list of skills, tools, integrations
- Four plugins covering common operational needs
- Inter-agent bridge for multiple Hermes instances
- Hermes-skill-factory (auto-generates skills from workflows)

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## Memory System

### Architecture

**Three Layers:**
1. **Short-term** - Recent context in conversation
2. **Long-term** - MEMORY.md (facts, conventions, lessons)
3. **Episodic** - SQLite FTS5 search across all sessions

**Storage:**
- `MEMORY.md` (~2,200 chars) - Always in context
- `USER.md` (~1,375 chars) - User preferences
- `~/.hermes/state.db` - SQLite with full-text search

### User Confusion Points

**Source:** https://vectorize.io/articles/hermes-agent-memory-not-working

> "Memory is for critical facts that should always be in context. Session search is for 'did we discuss X last week?' queries where the agent needs to recall — it doesn't happen automatically before every response."

**Common Misconception:** Agent should automatically remember everything
**Reality:** User must explicitly ask agent to remember: "Remember that my production database runs on port 5433"

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## Delegation and Subagents

### Performance Benefits

> "Use delegate_task with parallel subtasks. Each subagent runs independently with its own context, and only the final summaries come back — massively reducing your main conversation's token usage."

### Best Practices

1. **Set max_iterations lower** for simple tasks (default: 50)
2. **Be specific in goals** - "Fix the TypeError in api/handlers.py line 47" not "Fix the bug"
3. **Include file paths** - Subagents don't know your project structure
4. **Use for context isolation** - Prevents main conversation bloat

### Multi-Agent Architecture (Future)

**Issue #344 Proposal:**
- L0: Current (exists today)
- L1: Workflow engine
- L2: Checkpointing and recovery
- L3: Full orchestration

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## Cron and Scheduling

### Use Cases

**Examples:**
> "Every morning at 9am, check Hacker News for AI news and send me a summary on Telegram."

> "Weekly dependency audit every Sunday at 6 AM"

### Features
- Output automatically delivered to configured platform
- Job output saved to `~/.hermes/cron/output/<job-id>/<timestamp>.md`
- Test with `/cron run <job_id>` before scheduling

### Limitations
- Agent only sees script stdout
- Background execution requires proper setup

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## Gateway and Messaging

### Supported Platforms

**Full List:**
- Telegram
- Discord
- Slack
- WhatsApp
- Signal
- Email
- SMS
- Home Assistant
- Matrix/Mattermost
- DingTalk/Feishu/WeCom

### Cross-Platform Continuity

> "Instructions are given via Telegram in the morning, and progress is checked via Discord at night. It's seamless."

### Voice Support

- Voice memo transcription on all platforms
- TTS output with `/voice` command
- Discord voice channel support

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## Terminal Backends

### Options

1. **Local** (default)
2. **Docker** (sandboxed)
3. **SSH** (remote server)
4. **Daytona** (serverless persistence)
5. **Singularity**
6. **Modal** (serverless, hibernates when idle)

### Security

- Container hardening with read-only root
- Dropped capabilities
- Namespace isolation
- Dangerous command approval system

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## Browser and Vision

### Browser Tools

**Set:**
- `browser_navigate`
- `browser_click`
- `browser_snapshot`
- `browser_type`
- etc. (11 tools total)

**Cost Impact:**
- Browser tools add ~1,258 tokens to every request (even when unused in messaging)
- Screenshots + vision analysis are high-token operations

### Vision Analysis

**Supported:**
- Image URLs via `vision_analyze`
- Image paste in CLI (with xclip/x11 forwarding)
- Images via messaging platforms

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## Voice Mode

### Features

- **STT:** faster-whisper (local, free)
- **TTS:** Microsoft Edge TTS (free)
- **Recording:** Ctrl+B in CLI
- **Cross-platform:** Works in Telegram, Discord, etc.

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## Comparison: Hermes vs OpenClaw

### Hermes Advantages

| Aspect | Winner | Reason |
|--------|--------|--------|
| Personal companion | Hermes | Continuous learning, personalization |
| Repetitive task automation | Hermes | Skill learning adapts to workflows |
| Voice interaction | Hermes | Native voice support |
| Lightweight deployment | Hermes | 20MB vs 200MB+ |
| Signal support | Hermes | Better multi-platform |
| Local model support | Hermes | Works better with Ollama/llama.cpp |

### OpenClaw Advantages

| Aspect | Winner | Reason |
|--------|--------|--------|
| Multi-agent coordination | OpenClaw | Better fleet management |
| Browser automation | OpenClaw | More mature plugin ecosystem |
| Community/plugins | OpenClaw | 307k stars vs 6k |
| MCP ecosystem | OpenClaw | More mature |

### Community Recommendation

> "Use both. OpenClaw as the 'fleet commander' for multi-agent coordination, Hermes as your 'personal advisor' for one-on-one tasks."

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## User Experience Feedback

### Positive

> "Hermes optimizes for depth of learning. It is smaller, more opinionated, and built by a team that trains the underlying models."

> "For repetitive workflows where agent improvement creates measurable value over time, Hermes is the stronger choice."

> "It just works — installation to first conversation is minutes, not hours."

### Areas for Improvement

1. **Token overhead transparency** - Users surprised by costs
2. **Memory system education** - Users expect automatic memory
3. **Local model guidance** - Need better model recommendations
4. **Gateway debugging** - Error messages can be cryptic
5. **Migration experience** - OpenClaw migration has rough edges

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## Summary

**Strengths:**
- Self-improving skill system
- Excellent multi-platform support
- Strong memory architecture
- Good local model support
- Active development

**Weaknesses:**
- Token overhead can surprise users
- Some migration/tooling rough edges
- Documentation gaps for advanced features
- Memory system requires user education