What makes Hermes Agent different from a standard chatbot?

Hermes Agent is designed for persistence. It doesn't lose context after a session ends; instead, it curates its own memory and creates 'skills'—reusable procedures it learns from solving tasks—allowing it to become more efficient over time.

Does it require a specific AI model?

While optimized for Nous Research's Hermes models (like Hermes 3 and 4), it is model-agnostic and works with OpenRouter, OpenAI, Anthropic, or any local endpoint like Ollama.

Can I run it on my phone?

Yes, it can be installed on Android via Termux using the standard Linux installer script, or accessed through mobile messaging apps like Telegram.

What are 'Skills' in Hermes Agent?

Skills are procedural memories that the agent generates after successfully completing a complex task. They are stored as documents the agent can load in the future to repeat the task without starting from scratch.

Is it safe to let it run commands on my computer?

Hermes includes security features like command approval, namespace isolation, and container sandboxing (via Docker or Singularity) to protect your host system.

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Hermes Agent is live in the directory, but no verified owner controls the page.

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Agents

Free

Hermes Agent

A self-improving autonomous agent by Nous Research that learns from experience, builds reusable skills, and maintains persistent memory across sessions and platforms.

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Upvotes

3 months ago

Listed

Agents

Autonomous Agents

Open Source

Self-Improving AI

Productivity

Developer Tools

Automation

AI Framework

Machine Learning

Tool information

Provider

Nous Research

Platforms

Linux

macOS

Windows

Docker

Android

Discord

Slack

Languages

English

API Available

Yes

Added to directory

3 months ago

Last updated

about 2 months ago

Rating

Not available

Pricing

Free

About Hermes Agent

What the tool does and who it's for

Hermes Agent is a persistent, self-improving autonomous AI agent developed by Nous Research. Unlike standard session-based chatbots, it features a built-in learning loop that enables it to create and refine skills from experience, curate cross-session memory, and develop a deepening model of the user over time. It can be deployed across various environments—from local machines and Docker to serverless infrastructure like Modal—and interacted with via multiple platforms including Telegram, Discord, and Slack. With support for over 70 built-in tools, multi-platform messaging gateways, and scheduled automations, it serves as a long-lived digital companion that grows more capable the longer it runs.

Key capabilities

Closed Learning Loop: Automatically creates and refines skills based on past task performance.

Persistent Cross-Session Memory: Uses SQLite FTS5 and LLM summarization to recall past interactions.

Multi-Platform Messaging: Access the agent via Telegram, Discord, Slack, WhatsApp, and Signal.

Scheduled Automations: Supports natural language cron scheduling for recurring tasks and reports.

Subagent Delegation: Spawns isolated subagents to handle parallel workstreams and complex tasks.

Flexible Deployment: Runs on local hardware, VPS, GPU clusters, or serverless backends like Modal.

Extensive Toolset: Over 70 built-in tools for web search, file editing, and terminal execution.

Browser Automation: Integrated web browsing and data extraction using Playwright or Browserbase.

MCP Integration: Full support for the Model Context Protocol to connect with external data sources.

Voice Mode: Real-time voice interaction available in CLI and Discord voice channels.

Code Execution: Sandboxed Python RPC execution to collapse multi-step workflows into single turns.

Privacy-Focused: Supports local LLMs and self-hosted deployments for full data control.

Pricing

Free — plans below

Open Source

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