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Paperclip adapter for Hermes Agent — run Hermes as a managed employee in a Paperclip company
| Date | Stars |
|---|---|
| 2026-07-31 | 1768 |
| 2026-08-06 | 1783 |
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# Paperclip Adapter for Hermes Agent
A [Paperclip](https://paperclip.ing) adapter that lets you run [Hermes Agent](https://github.com/NousResearch/hermes-agent) as a managed employee in a Paperclip company.
Hermes Agent is a full-featured AI agent by [Nous Research](https://nousresearch.com) with 30+ native tools, persistent memory, session persistence, 80+ skills, MCP support, and multi-provider model access.
## Key Features
This adapter provides:
- **8 inference providers** — Anthropic, OpenRouter, OpenAI, Nous, OpenAI Codex, ZAI, Kimi Coding, MiniMax
- **Skills integration** — Scans both Paperclip-managed and Hermes-native skills (`~/.hermes/skills/`), with sync/list/resolve APIs
- **Structured transcript parsing** — Raw Hermes stdout is parsed into typed `TranscriptEntry` objects so Paperclip renders proper tool cards with status icons and expand/collapse
- **Rich post-processing** — Converts Hermes ASCII banners, setext headings, and `+--+` table borders into clean GFM markdown
- **Comment-driven wakes** — Agents wake to respond to issue comments, not just task assignments
- **Auto model detection** — Reads `~/.hermes/config.yaml` to pre-populate the UI with the user's configured model
- **Session codec** — Structured validation and migration of session state across heartbeats
- **Benign stderr reclassification** — MCP init messages and structured logs are reclassified so they don't appear as errors in the UI
- **Session source tagging** — Sessions are tagged as `tool` source so they don't clutter the user's interactive history
- **Filesystem checkpoints** — Optional `--checkpoints` for rollback safety
- **Thinking effort control** — Passes `--reasoning-effort` for thinking/reasoning models
### Hermes Agent Capabilities
| Feature | Claude Code | Codex | Hermes Agent |
|---------|------------|-------|-------------|
| Persistent memory | ❌ | ❌ | ✅ Remembers across sessions |
| Native tools | ~5 | ~5 | 30+ (terminal, file, web, browser, vision, git, etc.) |
| Skills system | ❌ | ❌ | ✅ 80+ loadable skills |
| Session search | ❌ | ❌ | ✅ FTS5 search over past conversations |
| Sub-agent delegation | ❌ | ❌ | ✅ Parallel sub-tasks |
| Context compression | ❌ | ❌ | ✅ Auto-compresses long conversations |
| MCP client | ❌ | ❌ | ✅ Connect to any MCP server |
| Multi-provider | Anthropic only | OpenAI only | ✅ 8 providers out of the box |
## Installation
```bash
npm install hermes-paperclip-adapter
```
### Prerequisites
- [Hermes Agent](https://github.com/NousResearch/hermes-agent) installed (`pip install hermes-agent`)
- Python 3.10+
- At least one LLM API key (Anthropic, OpenRouter, or OpenAI)
## Quick Start
### 1. Register the adapter in your Paperclip server
Add to your Paperclip server's adapter registry (`server/src/adapters/registry.ts`):
```typescript
import * as hermesLocal from "hermes-paperclip-adapter";
import {
execute,
testEnvironment,
detectModel,
listSkills,
syncSkills,
sessionCodec,
} from "hermes-paperclip-adapter/server";
registry.set("hermes_local", {
...hermesLocal,
execute,
testEnvironment,
detectModel,
listSkills,
syncSkills,
sessionCodec,
});
```
### 2. Create a Hermes agent in Paperclip
In the Paperclip UI or via API, create an agent with adapter type `hermes_local`:
```json
{
"name": "Hermes Engineer",
"adapterType": "hermes_local",
"adapterConfig": {
"model": "anthropic/claude-sonnet-4",
"maxIterations": 50,
"timeoutSec": 300,
"persistSession": true,
"enabledToolsets": ["terminal", "file", "web"]
}
}
```
### 3. Assign work
Create issues in Paperclip and assign them to your Hermes agent. On each heartbeat, Hermes will:
1. Receive the task instructions
2. Use its full tool suite to complete the work
3. Report results back to Paperclip
4. Persist session state for continuity
## Configuration Reference
### Core
| Field | Type | Default | Description |
|-------|------|---------|-------------|
| `model` | string | `anthropic/claude-sonneExcerpt of 9,014 characters
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