Hermes: an agent that remembers the context across dialogs
Sh0ny
22 июля 20262 min read
Updated 25 июля 2026In short
Most LLM assistants start every conversation from scratch. Hermes Agent from Nous Research retains facts, searches past conversations, and turns successful solutions into reusable skills. Let’s take a closer look at what “self-learning” means and why you might want to run such an agent on your own server.
Anyone who has worked with LLM assistants on a real-world project knows the main pain point: a new conversation is a blank slate. You have to explain the code structure all over again, remind the agent of decisions made, and list the bugs you’ve already found. After a few iterations, this stops being just an inconvenience and turns into a systemic problem: context is lost or duplicated, and productivity drops.
Hermes Agent from Nous Research takes a different approach. The agent retains key facts from conversations, searches for information in previous dialogs, and turns successful problem-solving methods into skills that it applies going forward. This isn’t just memory—it’s the accumulation of procedural experience.
What exactly constitutes “self-learning” in Hermes is the key question. If the agent simply stores a history, that’s not yet learning. But if it identifies repeatable patterns from successful solutions and applies them to new tasks—that’s the transition from a chatbot to an agent with growing competence. The line between these two things determines the tool’s true value.
A practical detail: Hermes can receive messages from Telegram. This means the agent integrates into the workflow without a separate interface—you type in the messenger and receive a response that takes into account all the accumulated context. For teams that already use Telegram, the barrier to entry is minimal.
Running the agent on a remote server isn’t a whim—it’s a matter of control. An agent that stores facts about your project and accumulates skills must run on your infrastructure, not in someone else’s cloud. Otherwise, you’re handing over your project’s context to a third party and becoming dependent on its availability.
The main trade-off here is complexity versus autonomy. A typical chatbot is easy to set up, but it starts from scratch every time. Hermes requires server configuration and an understanding of how its memory works, but in return, it provides an agent that gets smarter with every conversation. The question is how often your tasks repeat—if the patterns are consistent, the accumulation of skills will pay off quickly.