Hermes Agent
Self-improving AI agent from Nous Research. 76.6k stars, MIT. The agent creates skills from experience, improves them during use, and maintains memory across sessions.
Not a framework for building agents. This is a ready-to-run agent with learning built in.
What makes it different
Learning loop: agent creates skills autonomously, then self-improves them at runtime. Skills are compatible with agentskills.io open standard.
Cross-session memory: FTS5 session search + LLM summarization. Plus “Honcho dialectic user modeling” that builds a profile of the user over time.
Multi-platform native: one gateway process handles Telegram, Discord, Slack, WhatsApp, Signal, Email. Same conversation continues across platforms. Voice memos transcribed automatically.
Six deployment backends:
- Local, Docker, SSH (standard)
- Daytona, Modal (serverless with hibernation, near-zero cost when idle)
- Singularity (containers)
200+ models: any OpenAI-compatible API. Switch with hermes model, no code changes.
Architecture
CLI TUI / Telegram / Discord / Slack / WhatsApp / Signal / Email
↓
Gateway (single process, all platforms)
↓
Agent Loop (tool calls, responses, learning)
↓
Terminal Backend (local / Docker / SSH / Daytona / Modal / Singularity)
40+ built-in tools, MCP server integration, parallel subagents, Python RPC, cron scheduler.
Research angle
Nous uses Hermes for training data: batch trajectory generation, trajectory compression, Atropos RL environments. The agent’s tool-calling traces become training data for the next generation of models.
This connects to decision-traces-compound: every agent action becomes a training signal. And to asi-evolve-ai-research-agents: both systems use agent outputs to improve the underlying models.
How it compares to our stack
| Hermes Agent | Our approach (sgr-agent + Claude Code) | |
|---|---|---|
| Language | Python | Rust |
| Learning | Auto-creates skills | Skills authored in SKILL.md, hot-reloaded |
| Memory | FTS5 + LLM summarization | Solograph (FalkorDB + vectors) |
| Deployment | 6 backends including serverless | Local + competition RPC |
| Models | 200+ via OpenRouter | Provider-agnostic (config.toml) |
| Tools | 40+ Python | 14 generic + domain middleware |
Key insight from Hermes: skill self-improvement at runtime is something our self-evolution pattern describes in theory (fixed/mutable partition). Hermes implements it in production.
The serverless backends (Daytona, Modal) are also interesting for infra-two-tools: agent hibernates when idle, wakes on demand.
Links
- GitHub – 76.6k stars, MIT
- Docs
- Skills Hub
- Discord