In an AI landscape dominated by closed, API-gated systems, a new open-source agent is turning heads — and climbing GitHub’s trending charts at a remarkable pace. Hermes Agent, built by Nous Research, has become the only AI agent with a built-in learning loop that genuinely improves from experience. Today it sits at over 42,000 stars on GitHub, with nearly 6,000 of those earned in a single day.
What Makes Hermes Different
Most AI agents are stateless — each conversation starts fresh, with no memory of past interactions. Hermes breaks this pattern entirely. It creates skills from experience, improves them during use, nudges itself to persist knowledge, searches its own past conversations using FTS5 full-text search with LLM summarization, and builds a deepening model of who you are across sessions.
As the Hermes documentation explains: the agent “autonomously creates skills after complex tasks” and those “skills self-improve during use.” This is a fundamentally different paradigm — not just following instructions, but learning and adapting in real time.
Works Everywhere You Work
One of Hermes’s most compelling features is its ubiquity. From a single gateway process, you can connect it to:
- Telegram, Discord, Slack, WhatsApp, and Signal
- Email integration
- Full CLI terminal with multiline editing, slash-command autocomplete, conversation history, and streaming tool output
Talk to it from your phone via Telegram while it runs workloads on a cloud VM. Your agent’s environment persists across platforms — conversation continuity is built in, not bolted on.
Deploy Anywhere: From $5 VPS to GPU Clusters
Hermes runs on six different terminal backends: local, Docker, SSH, Daytona, Singularity, and Modal. Platforms like Daytona and Modal offer serverless persistence — the agent’s environment hibernates when idle and wakes on demand, costing nearly nothing between sessions. You can run it on a $5 VPS or scale up to a full GPU cluster, with no code changes required.
Model flexibility is equally broad. Use Nous Portal, OpenRouter (with access to 200+ models), z.ai/GLM, Kimi/Moonshot, MiniMax, OpenAI, or your own custom endpoint. Switch between providers with a single command — hermes model — no code changes, no lock-in.
Built for Real Work: 40+ Tools and MCP Integration
Hermes ships with 40+ built-in tools covering file operations, web search, code execution, and more. It also supports the Model Context Protocol (MCP) for connecting any MCP server to extend capabilities. The skills system allows autonomous skill creation — after completing a complex task, Hermes can package that knowledge into a reusable skill for future sessions.
Additional capabilities include:
- Built-in cron scheduler for automated tasks with natural language scheduling
- Subagent spawning for parallel workstreams
- Python scripting with RPC tool calls for multi-step pipelines
- Batch trajectory generation for research applications
- Trajectory compression for training the next generation of tool-calling models
The Future of Personal AI Agents
What Nous Research has built with Hermes represents a vision of personal AI that differs sharply from the dominant “AI assistant” paradigm. Rather than a static tool that responds to prompts, Hermes is designed to be a persistent, evolving companion that knows your preferences, learns your workflows, and gets genuinely better at helping you over time.
For the open-source AI community, Hermes also represents something important: proof that cutting-edge agent research doesn’t have to happen behind closed API walls. The project is fully open source, built by a research organization committed to making powerful AI tools accessible to everyone.
As AI agents move from novelty to necessity, Hermes may well be pointing toward the most compelling future: not a single omniscient AI assistant, but a personal agent that grows alongside you, learning your world as deeply as you allow.
Featured image: NousResearch Hermes Agent GitHub repository