- by x32x01 ||
Most AI agents can complete a task and move on to the next one. Hermes Agent takes a different approach: it is designed to remember previous conversations, learn from completed tasks, create reusable skills, and build a better understanding of how you work over time.
Developed by Nous Research, Hermes Agent is an open-source AI agent that combines persistent memory, self-improving skills, subagents, scheduled tasks, multiple execution backends, and messaging platforms such as Telegram, Discord, Slack, WhatsApp, and Signal.
The project is released under the MIT license and is designed to run locally, inside containers, on remote servers, or through serverless infrastructure.
Instead of treating every task as an isolated interaction, Hermes can use previous experiences to improve future work.
After completing complex tasks, it can create new skills that can be reused later. Existing skills can also improve through continued use.
This gives the agent a workflow that looks more like:
Task → Experience → Skill → Reuse → Improvement
That approach can make an agent more useful over time, especially for recurring technical, research, development, and automation tasks.
It can periodically update its memory and search previous conversations using full-text search.
This allows the agent to find relevant information from earlier sessions when working on a new task.
For example, if you previously discussed a project architecture, debugging problem, or workflow, Hermes can potentially use information from those earlier conversations instead of asking you to explain everything again.
The goal is to build a better understanding of your preferences, working style, and recurring needs.
Instead of only remembering individual messages, the agent can use this information to build a broader model of how you work.
This is particularly useful for long-running workflows where the same agent is used repeatedly.
When the agent encounters a complex task, it can create a reusable skill based on what it learned.
Those skills can then be used in future tasks.
This means you can gradually build a collection of specialized capabilities without manually creating every workflow from scratch.
Hermes is also compatible with the open Agent Skills ecosystem, allowing users to add existing skills from supported repositories and skill hubs.
Instead of forcing one agent to handle everything sequentially, different parts of a task can be delegated to specialized subagents.
This can be useful for workflows that involve research, coding, analysis, testing, or multiple independent operations.
It supports several backends, allowing you to choose where the agent should run based on your requirements.
This flexibility makes Hermes useful for both local development and cloud-based automation.
The project supports messaging platforms including:
For example, you could send a task through Telegram while you are away from your computer and check the result later.
Hermes also supports cross-platform continuity, allowing you to continue an ongoing workflow from another device.
The TUI supports features such as multiline editing and slash-command autocomplete, making it easier to manage longer prompts and commands directly from the terminal.
You can also use
Instead of typing every instruction, you can send a voice message and let the system process it as part of your interaction with the agent.
This can be particularly convenient when using Hermes through a mobile messaging platform.
Instead of managing separate API credentials for different services, the portal can provide access to multiple models and tools through one account.
Depending on the available integrations, these capabilities can include:
You can describe a recurring task in natural language and configure it to run at a specific time or interval.
Examples include:
On Linux, macOS, WSL2, or Termux, you can install it with:
On Windows PowerShell, the project provides a native installation command:
After installation, you can start Hermes with:
To connect Hermes to Nous Portal, you can use:
And to start the messaging gateway:
The exact installation requirements and supported environments can change as the project evolves, so check the official repository before deploying Hermes in production.
This means compatible skills from supported skill repositories can be added to Hermes rather than being built from scratch.
The important part is that the skill system is not limited to manually created integrations.
Hermes can also create new skills from its own experience, allowing the skill library to grow as the agent handles more tasks.
Persistent memory and reusable skills change that workflow.
Instead of repeatedly explaining the same project, preferences, or procedures, an agent can use information from previous interactions and build reusable capabilities around recurring tasks.
Over time, this can make the agent feel less like a temporary chatbot and more like a persistent assistant.
The real value is not simply that Hermes can remember a conversation. It is the combination of memory, user modeling, reusable skills, subagents, and automation.
Together, these features create a more persistent agentic workflow.
But if you want an AI agent that can remember previous work, create reusable skills, run scheduled tasks, use multiple tools, delegate work to subagents, and remain accessible through messaging platforms, Hermes is an interesting open-source project to explore.
Its support for local, remote, containerized, and serverless execution also gives developers considerable flexibility when building their own workflows.
The project is particularly interesting for developers, researchers, automation enthusiasts, and advanced AI users who want more control over how an agent works over time.
Instead of treating every conversation as a fresh start, it aims to build continuity through persistent memory, user modeling, reusable skills, and long-running automation.
That makes the project more than just another AI chatbot.
If the learning loop continues to improve, the most interesting part of Hermes may not be what it can do during the first conversation, but how much more useful it can become after weeks or months of real-world use.
You can explore the project and its source code on the official GitHub repository:
https://github.com/NousResearch/hermes-agent
Developed by Nous Research, Hermes Agent is an open-source AI agent that combines persistent memory, self-improving skills, subagents, scheduled tasks, multiple execution backends, and messaging platforms such as Telegram, Discord, Slack, WhatsApp, and Signal.
The project is released under the MIT license and is designed to run locally, inside containers, on remote servers, or through serverless infrastructure.
What Makes Hermes Agent Different?
The biggest idea behind Hermes Agent is its closed learning loop.Instead of treating every task as an isolated interaction, Hermes can use previous experiences to improve future work.
After completing complex tasks, it can create new skills that can be reused later. Existing skills can also improve through continued use.
This gives the agent a workflow that looks more like:
Task → Experience → Skill → Reuse → Improvement
That approach can make an agent more useful over time, especially for recurring technical, research, development, and automation tasks.
Persistent Memory
Hermes Agent is designed to maintain memory across conversations instead of starting from scratch every time you open a new session.It can periodically update its memory and search previous conversations using full-text search.
This allows the agent to find relevant information from earlier sessions when working on a new task.
For example, if you previously discussed a project architecture, debugging problem, or workflow, Hermes can potentially use information from those earlier conversations instead of asking you to explain everything again.
User Modeling With Honcho
Hermes also integrates with Honcho for user modeling.The goal is to build a better understanding of your preferences, working style, and recurring needs.
Instead of only remembering individual messages, the agent can use this information to build a broader model of how you work.
This is particularly useful for long-running workflows where the same agent is used repeatedly.
Self-Improving Skills
One of Hermes Agent's most interesting features is its skill system.When the agent encounters a complex task, it can create a reusable skill based on what it learned.
Those skills can then be used in future tasks.
This means you can gradually build a collection of specialized capabilities without manually creating every workflow from scratch.
Hermes is also compatible with the open Agent Skills ecosystem, allowing users to add existing skills from supported repositories and skill hubs.
Subagents for Complex Tasks
Hermes Agent can use subagents to divide complex work into smaller tasks.Instead of forcing one agent to handle everything sequentially, different parts of a task can be delegated to specialized subagents.
This can be useful for workflows that involve research, coding, analysis, testing, or multiple independent operations.
Run Hermes Agent Almost Anywhere
Hermes is not tied to a single machine or execution environment.It supports several backends, allowing you to choose where the agent should run based on your requirements.
| Backend | Typical Use |
|---|---|
| Local | Run the agent directly on your computer |
| Docker | Run the agent inside an isolated container |
| SSH | Execute tasks on a remote server |
| Singularity | Run workloads on HPC clusters |
| Modal | Use serverless infrastructure and GPUs |
| Daytona | Use persistent serverless environments |
| Vercel Sandbox | Run short-lived execution tasks |
Use Hermes From Messaging Apps
You do not have to interact with Hermes exclusively through the terminal.The project supports messaging platforms including:
- Telegram
- Discord
- Slack
- Signal
For example, you could send a task through Telegram while you are away from your computer and check the result later.
Hermes also supports cross-platform continuity, allowing you to continue an ongoing workflow from another device.
Terminal Interface
For users who prefer the command line, Hermes includes a full terminal interface.The TUI supports features such as multiline editing and slash-command autocomplete, making it easier to manage longer prompts and commands directly from the terminal.
You can also use
/model to select a supported model when using the appropriate backend or provider.Voice Memos
Hermes can also work with voice memos.Instead of typing every instruction, you can send a voice message and let the system process it as part of your interaction with the agent.
This can be particularly convenient when using Hermes through a mobile messaging platform.
Nous Portal
Nous Research also provides Nous Portal as an option for users who want access to multiple AI capabilities through a single subscription.Instead of managing separate API credentials for different services, the portal can provide access to multiple models and tools through one account.
Depending on the available integrations, these capabilities can include:
- Multiple AI models
- Web search
- Image generation
- Text-to-speech
- Cloud browser capabilities
Built-In Cron Scheduler
Hermes includes a scheduler for recurring tasks.You can describe a recurring task in natural language and configure it to run at a specific time or interval.
Examples include:
- Sending daily reports by email or Telegram
- Running nightly backups
- Performing weekly project audits
- Checking recurring tasks automatically
- Generating scheduled summaries
Installation
Hermes provides installation scripts for several environments.On Linux, macOS, WSL2, or Termux, you can install it with:
Bash:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash Code:
iex (irm https://hermes-agent.nousresearch.com/install.ps1) Bash:
source ~/.bashrc
hermes Bash:
hermes setup --portal Bash:
hermes gatewaySkills Hub and Agent Skills Compatibility
Hermes is designed to work with the open Agent Skills ecosystem.This means compatible skills from supported skill repositories can be added to Hermes rather than being built from scratch.
The important part is that the skill system is not limited to manually created integrations.
Hermes can also create new skills from its own experience, allowing the skill library to grow as the agent handles more tasks.
Why Persistent Learning Matters
Many AI agents are good at completing individual conversations, but the experience can feel repetitive when the agent does not retain useful knowledge from previous sessions.Persistent memory and reusable skills change that workflow.
Instead of repeatedly explaining the same project, preferences, or procedures, an agent can use information from previous interactions and build reusable capabilities around recurring tasks.
Over time, this can make the agent feel less like a temporary chatbot and more like a persistent assistant.
The real value is not simply that Hermes can remember a conversation. It is the combination of memory, user modeling, reusable skills, subagents, and automation.
Together, these features create a more persistent agentic workflow.
Is Hermes Agent Worth Trying?
If you are looking for a traditional chatbot, Hermes Agent may be more powerful than you need.But if you want an AI agent that can remember previous work, create reusable skills, run scheduled tasks, use multiple tools, delegate work to subagents, and remain accessible through messaging platforms, Hermes is an interesting open-source project to explore.
Its support for local, remote, containerized, and serverless execution also gives developers considerable flexibility when building their own workflows.
The project is particularly interesting for developers, researchers, automation enthusiasts, and advanced AI users who want more control over how an agent works over time.
Final Thoughts
Hermes Agent represents a different direction for AI assistants.Instead of treating every conversation as a fresh start, it aims to build continuity through persistent memory, user modeling, reusable skills, and long-running automation.
That makes the project more than just another AI chatbot.
If the learning loop continues to improve, the most interesting part of Hermes may not be what it can do during the first conversation, but how much more useful it can become after weeks or months of real-world use.
You can explore the project and its source code on the official GitHub repository:
https://github.com/NousResearch/hermes-agent