Briefing
Most AI assistants forget you the moment a conversation ends. Ask the same question next week and you start from scratch, re-explaining who you are, what you do, and how you like things done. Nous Research is betting that the next useful step isn't a smarter model so much as an agent that remembers, and Hermes Agent, released in February 2026, is the result.
Hermes is an open-source agent that keeps a running picture of the person using it: your preferences, how you communicate, what you already know, what you're trying to get done. The idea is that the tool gets more useful the longer you work with it, the way a good assistant does, rather than resetting to zero every session.
For Australian teams weighing up where to put their AI effort, that's the practical hook. An agent that learns your context can take on repeat work, drafting, research, data wrangling, without the constant hand-holding. The catch, as always with fast-moving open-source projects, is separating what the framework actually does from the numbers people quote about it.
What Is Hermes Agent?
Hermes is a learning agent written in Python. What sets it apart is memory that sticks around and changes over time. A stateless agent treats every interaction as a clean slate. Hermes instead builds a model of its user across sessions, tracking preferences, communication style, expertise, and goals. That memory runs on Honcho, a dialectic memory system that records not only facts but the context in which the agent picked them up.
The project ships with 40+ built-in tools covering web search, code execution, file handling, data analysis, and API calls. The tools are built to be composed, so an agent can chain several together into a multi-step job.
The Architecture
The article's authors describe Hermes in three layers. Worth noting up front: the official documentation frames the system around a three-tier memory and a "do, learn, improve" loop, so the Perception/Reasoning/Action split below reads as a useful way to think about it rather than the project's own labelling.
Perception Layer: Takes in user input, context from connected services, and signals from the environment. Handles text, file uploads, and structured data.
Reasoning Layer: A planning engine that breaks a complex request into sub-tasks, picks the right tools, and decides the order to run them in. This is where Honcho memory comes in, the agent checks its stored model of you to personalise what it does next.
Action Layer: Runs the tool calls, formats the output, and writes new observations back to memory. Each interaction sharpens the user model a little more.
Key Statistics
- ~22,000 GitHub stars, reported as an early-weeks figure shortly after the February 2026 release; treat this as unconfirmed, since the live repository shows a far higher star count by mid-2026
- [MIT License](https://github.com/nousresearch/hermes-agent), fully open source
- ~142 active contributors, a figure cited by the project but not independently confirmed
- [40+ built-in tools](https://github.com/nousresearch/hermes-agent), extensible via Python plugins
- [Honcho memory system](https://github.com/plastic-labs/honcho), dialectic user modelling
- Built on [Python 3.11+](https://github.com/nousresearch/hermes-agent), reportedly with async support throughout
Honcho: The Secret Sauce
Honcho is the part that pulls Hermes away from the pack. Instead of a plain key-value store, Honcho uses a dialectic model: it tracks what the agent knows, how it came to know it, where contradictions sit, and how confidence should shift over time.
The approach borrows from dialectical reasoning. When Hermes runs into new information that clashes with its existing model of you, it doesn't just overwrite the old version. It logs the tension and works toward a resolution through later interactions. You end up with a more careful, more human read of the user.
The Nous Research Ecosystem
Hermes doesn't stand alone. It sits inside a wider set of tools from Nous Research that includes Atropos, a reinforcement-learning environments framework for collecting and evaluating LLM trajectories, not just a model evaluation tool, and DisTrO, which handles distributed training over the internet and underpins the Psyche network. Between them, these projects cover building, evaluating, and deploying AI systems.
If you want an agent framework that actually learns and adapts to the person using it, Hermes is one of the more interesting open-source options going. Strong memory, a deep tool set, and an active community make it a project worth keeping an eye on, and worth testing against your own work before you commit to it.