Hermes Agent is an open-source AI assistant that learns from its own work, maintains persistent memory across conversations, and runs on any infrastructure you choose. Built by Nous Research and licensed under MIT, it represents a fundamentally different approach to AI agents compared to stateless chatbots that forget everything between sessions. The project has attracted 8,700 GitHub stars, 142 contributors, and 2,293 commits as of late March 2026, signaling growing developer interest in agents that actually remember and improve over time .

Most AI agents today operate as one-off conversation tools. You ask a question, get an answer, and start fresh next time. Hermes flips this model by building a learning loop into its core architecture. After completing complex tasks, the agent can autonomously create reusable "skills" that capture procedures, pitfalls, and verification steps. The next time a similar task appears, Hermes loads the skill instead of figuring everything out from scratch. These skills can even self-improve during use when the agent discovers a better approach .

How Does Hermes Actually Remember Things?

Memory in Hermes is bounded and intentional, not a hack. The agent maintains two small, curated files: MEMORY.md for environment facts, conventions, and lessons learned, and USER.md for your preferences and communication style. These files are injected into the system prompt at the start of each session. MEMORY.md is limited to 2,200 characters while USER.md holds 1,375 characters, totaling roughly 1,300 tokens. This constraint keeps memory from bloating the context window while still holding 15 to 20 useful entries .

For deeper recall, Hermes can search all past sessions using SQLite full-text search combined with LLM summarization. This on-demand capability lets the agent find and reference conversations from weeks ago without keeping everything in the active prompt. The agent also manages its own memory, adding entries when it learns something useful, replacing entries when information changes, and consolidating entries when memory gets full. Security scanning on memory entries prevents prompt injection attacks .

What Makes Hermes Different From Other AI Agent Frameworks?

Hermes stands out in several ways that matter for developers building production systems. First, it is not tied to your laptop. You can run the agent on a $5 virtual private server, inside a Docker container, over SSH to a remote server, or on serverless infrastructure like Modal or Daytona that hibernates when idle and wakes on demand. The conversation continues seamlessly across platforms whether you talk to it from Telegram, Discord, Slack, WhatsApp, Signal, or the terminal .

Second, Hermes is not locked into a single LLM provider. You can plug in whatever provider you want, including OpenAI, Anthropic, OpenRouter (which gives access to 200+ models), or your own self-hosted endpoint running Ollama, vLLM, or SGLang. Switching providers is a single command with no code changes required. This flexibility means you can experiment with different models or migrate to new providers without rewriting your agent .

Third, Hermes supports the Model Context Protocol (MCP) out of the box. You can connect any MCP server by adding a few lines to the config file, enabling the agent to interact with GitHub, databases, or any service that exposes an MCP endpoint. This extensibility makes it practical for integrating with existing tools and workflows .

Steps to Get Hermes Running on Your Machine

• Install the agent: Run the installer script with curl, which handles Python, Node.js, and all dependencies. You only need git installed beforehand. Windows users should use WSL2 instead of native Windows.
• Configure your LLM provider: Run the model selection command to choose from Nous Portal, OpenRouter, OpenAI, Anthropic, or a custom endpoint like Ollama. You can switch providers at any time by running the command again.
• Start a conversation: Launch Hermes with a single command and begin typing messages. The agent displays your active model, available tools, and installed skills in a welcome banner.
• Set up sandboxed execution: For safety, configure Docker isolation or SSH to a remote server so the agent runs commands in a protected environment rather than directly on your machine.
• Connect messaging platforms: If you want to talk to Hermes from Telegram, Discord, Slack, or other platforms, run the gateway setup command to configure messaging and start the gateway process.

What Can Hermes Actually Do?

Hermes supports six ways to execute commands: local, Docker, SSH, Daytona, Singularity, and Modal. Docker and SSH provide sandboxed execution, while Daytona and Modal offer serverless persistence that hibernates when idle and wakes on demand. The gateway is a long-running process that connects the agent to messaging platforms, allowing you to use the same slash commands across all platforms from your phone .

The core of Hermes is a synchronous orchestration engine that handles provider selection, prompt construction, tool execution, retries, compression, and persistence. Skills are stored as on-demand knowledge documents in a progressive disclosure pattern to minimize token usage. Level 0 shows the agent a list of skill names and descriptions (about 3,000 tokens). Level 1 loads the full content of a specific skill when needed. Level 2 loads a specific reference file within a skill. Each skill is a directory with a SKILL.md file and optional reference materials, templates, and scripts using YAML front matter for metadata .

Hermes also includes research-grade infrastructure for training better tool-calling models. If you are working on building the next generation of agent models, Hermes provides batch trajectory generation, Atropos reinforcement learning environments, and trajectory compression. This means the tool serves both end users who want a practical AI agent and researchers developing improved agent models .

Why Should Developers Care About This Approach?

The self-improving aspect addresses a real pain point in AI development. Most agents require constant human guidance because they do not learn from their own work. Hermes changes this by creating a feedback loop where the agent captures what it learns and reuses that knowledge. This reduces redundant work, improves consistency, and makes the agent more useful over time without requiring retraining .

The ability to run on your own infrastructure matters for privacy, cost, and control. You are not locked into paying per-API-call pricing or sending all your data to a cloud provider. You can run Hermes on modest hardware, a cheap VPS, or serverless infrastructure that only charges when the agent is actually working. The flexibility to switch LLM providers means you can chase better models, cheaper options, or specialized providers without rewriting your system .

Hermes is written primarily in Python, making it accessible to a large developer community. The project has attracted 142 contributors, suggesting an active ecosystem around development and improvement. For teams building AI-powered workflows, internal tools, or automation systems, Hermes offers a foundation that learns, remembers, and adapts without requiring constant human intervention .