AI

Hermes Agent: The Self-Improving AI Agent That Grows With Your Workflows with 157k+ GitHub Stars

Discover Hermes Agent, the open-source AI agent with 157k+ stars that learns from experience, builds its own skills, and integrates with your favorite platforms.

Tosin Akinosho

— 4 min read

Hermes Agent is an open-source, self-improving AI agent built by Nous Research that fundamentally changes how autonomous systems learn and adapt. With 157k+ GitHub stars and active development (commits within minutes), Hermes represents a paradigm shift from stateless chatbots to persistent, learning agents that grow more capable the longer they run. It's the only agent framework with a built-in learning loop that automatically creates skills from experience, improves them during use, and remembers what it learned yesterday.

What is Hermes Agent?

Hermes Agent is a self-hosted, multi-platform AI agent that lives on your server rather than in the cloud. Unlike traditional AI assistants that start fresh with each conversation, Hermes maintains persistent memory, learns from interactions, and autonomously generates new skills to handle novel tasks. The agent integrates with 20+ messaging platforms including Telegram, Discord, Slack, WhatsApp, Signal, and Email, allowing you to interact with it wherever you are.

Built by Nous Research and released in February 2026, Hermes has rapidly become the most-used agent in production environments. The framework emphasizes durability, security, and genuine autonomy—not just prompt engineering. It includes a durable multi-agent Kanban system with heartbeat detection, zombie process cleanup, and retry budgets to ensure tasks actually complete. The architecture supports five sandboxing backends (local, Docker, SSH, Singularity, Modal) with container hardening and namespace isolation for safe code execution.

What sets Hermes apart is its self-improvement loop. The agent doesn't just execute tasks—it learns from them. When it solves a problem, it automatically generates a reusable skill. When it encounters the same problem again, it uses the skill. Over time, the skill library grows and improves, making the agent progressively more capable without manual intervention.

Core Features and Architecture

Persistent Memory and Learning Loop: Hermes maintains session history, skill library, and learned patterns across restarts. The self-improvement loop automatically creates skills from experience, grades them for quality, and consolidates the library. This means the agent gets smarter the longer it runs—a fundamental departure from stateless LLM APIs.

Multi-Platform Integration: Connect Hermes to Telegram, Discord, Slack, WhatsApp, Signal, Email, CLI, and 14+ other platforms. Start a conversation on one platform, pick it up on another. The agent maintains context across all channels through a unified session database.

Durable Multi-Agent Orchestration: The Kanban system enables isolated subagents with their own conversations, terminals, and Python RPC scripts. Tasks include heartbeat detection, stale-task reclaim, zombie process cleanup, and configurable retry budgets. This ensures long-running workflows actually complete, even if individual workers fail.

Real Sandboxing: Five execution backends provide genuine isolation. Local execution for development, Docker for containerization, SSH for remote servers, Singularity for HPC clusters, and Modal for serverless compute. Each backend includes hardening and namespace isolation to prevent privilege escalation.

Full Web and Browser Control: Web search, browser automation, vision analysis, image generation, text-to-speech, and multi-model reasoning are built-in. The agent can research topics, fill out forms, analyze screenshots, and generate images—all without leaving the framework.

Scheduled Automations: Natural language cron scheduling for reports, backups, and briefings. Write "send me a weekly summary of my GitHub activity" and Hermes handles the scheduling, execution, and delivery through your preferred platform.

Pluggable Architecture: Providers, memory backends, platforms, and model providers are all pluggable. Add custom inference providers, memory systems, or messaging platforms without forking the codebase. The plugin system includes lifecycle hooks for initialization and cleanup.

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Getting Started

Installation: The quickest path to a working Hermes setup takes 20 minutes. On macOS/Linux:

curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash

On Windows, use the PowerShell installer. The script handles dependency installation, creates the ~/.hermes/ config directory, and guides you through initial setup.

Configuration: Run hermes setup to configure your LLM provider (OpenAI, Anthropic, local via Ollama, or 20+ others), messaging platforms, and execution backend. The setup wizard walks through each step with sensible defaults.

Verification: Start the agent with hermes and test a simple conversation. The TUI (terminal user interface) shows active sessions, skill library, and system status. Connect your first messaging platform (Telegram is easiest for testing) and verify the agent responds across channels.

Prerequisites: Python 3.10+, pip or uv for package management, and an API key for at least one LLM provider. For browser automation, Playwright is installed automatically. For Docker sandboxing, Docker daemon must be running.

Real-World Use Cases

DevOps Automation: Hermes monitors your infrastructure, runs diagnostics, generates reports, and escalates issues. Schedule daily health checks, weekly capacity reports, and on-demand incident response. The agent learns your infrastructure patterns and suggests optimizations.

Research and Analysis: Ask Hermes to research a topic, compile findings, generate visualizations, and deliver a report. The agent performs web searches, analyzes documents, and synthesizes information—all without manual intervention. Perfect for competitive analysis, market research, and technical deep-dives.

Content Generation and Publishing: Hermes can draft blog posts, social media content, and newsletters. It learns your voice, style, and audience preferences. Schedule weekly content generation, review drafts, and publish automatically. The skill library captures your editorial guidelines.

Customer Support Automation: Deploy Hermes as a first-line support agent across Slack, Discord, and email. It handles common questions, escalates complex issues, and learns from support interactions. The persistent memory means it remembers customer context across conversations.

How It Compares

vs. LangChain/LangGraph: LangChain is a framework for building LLM applications; Hermes is a complete agent runtime. LangChain requires you to orchestrate agents, manage state, and handle persistence. Hermes handles all of this automatically. LangChain is more flexible for custom workflows; Hermes is more complete for production agents.

vs. AutoGen (Microsoft): AutoGen focuses on multi-agent conversations and role-playing. Hermes includes multi-agent orchestration but adds persistent memory, skill generation, and platform integration. AutoGen is better for research simulations; Hermes is better for production automation.

vs. CrewAI: CrewAI simplifies agent creation with a clean API. Hermes goes further with self-improvement, durability, and platform integration. CrewAI is easier to learn; Hermes is more powerful for long-running systems. Both are production-ready, but Hermes emphasizes learning and adaptation.

What is Next

The Hermes roadmap includes expanded provider support (more LLM APIs, inference engines), enhanced skill grading and consolidation, and additional platform integrations. The v0.14.0 release (May 2026) introduced the Curator—an autonomous background process that grades, prunes, and consolidates the skill library. Future releases will focus on multi-agent reasoning, improved delegation, and cross-agent learning.

The community is actively contributing. Recent additions include Microsoft Teams integration, Spotify and Google Meet native support, and ComfyUI + TouchDesigner MCP servers bundled by default. The project maintains a 20+ minute setup time and emphasizes accessibility for new users while supporting advanced deployments.

Sources

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