Build an AI Agent That Remembers, Learns, and Gets Better Over Time (Free + Open Source)

Build an AI Agent That Remembers, Learns, and Gets Better Over Time (Free + Open Source)

Most AI tools are goldfish.

You tell them everything about your project, your stack, your workflow, your preferences…

Then the next session starts, and it is all gone.

That is fine for casual chat.
It is terrible for real work.

What I wanted was something closer to this:

remembers my projects
keeps useful context between sessions
saves repeatable workflows
runs tasks on a schedule
becomes more useful the more I use it

That is the idea behind a persistent AI agent.

In this post, I will show you how to build one with Hermes Agent, an open-source framework from Nous Research.

What This Agent Can Actually Do

Once set up, your agent can:

remember context across sessions
execute code
browse the web
manage files
save reusable "skills"
run scheduled tasks
connect to chat platforms like Telegram or Discord

So instead of starting from zero every time, it can build up memory about:

your projects
your environment
your workflow
what worked last time
what failed last time

That is where things start to feel very different from a normal chatbot.

Why This Is Interesting

The real unlock is not "AI can answer questions."

We already have that.

The unlock is this:

your agent stops being a one-off assistant and starts becoming part of your actual workflow

That means less repeated setup.
Less re-explaining context.
Less prompt babysitting.
More continuity.

For developers, that matters a lot.

What You Need

You do not need a huge stack.

You just need:

Linux, macOS, or WSL
Python 3.10+
an LLM provider or local model

You can start with free-tier or local options depending on what you use.

Step 1: Install Hermes Agent

curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash

Then verify it installed:

hermes --help

That gets the framework on your machine and ready to configure.

Step 2: Connect a Model

Hermes supports different model backends, including hosted APIs and local models.

A few ways to get started:

• Nous Research (MiMo-V2-Pro) — Free access may be available
• OpenRouter (Kimi K2.5) — Free-tier availability can vary
• Ollama (Qwen3 14B) — Local option if your hardware supports it

Run:

hermes setup

That opens the interactive setup flow for your model provider and credentials.

If you want the cheapest possible runtime, local models are attractive.
If you want the easiest setup, hosted models are usually faster.

Step 3: Give It Persistent Memory

This is where the magic starts.

Hermes can retain useful context between sessions, including things like:

coding preferences
project conventions
file paths
installed tools
prior fixes and lessons learned

So instead of re-explaining your setup every time, the agent can keep track of it.

That sounds small until you use it for a few days.

Then it becomes obvious how much friction comes from stateless AI.

Step 4: Teach It a Skill Once, Reuse It Forever

One of the coolest ideas in Hermes is skills.

A skill is basically a saved workflow the agent can reuse later.

Example:

Save this as a skill called "github-pages-deploy":

1. Build the static site
2. Push the output to the gh-pages branch
3. Verify the deployment URL
4. Report success or failure

Later, instead of re-prompting the whole process, you can just say:

Deploy this project to GitHub Pages.

Now the agent is not improvising from scratch.
It is loading a known workflow and applying it.

That is a big shift.

Step 5: Run Scheduled Tasks

Hermes also supports scheduled execution.

Example:

hermes cron create "0 9 * * *" --name "morning-check" --prompt "Check system status, review overnight logs, and send a summary"

This is useful for things like:

daily summaries
repo maintenance
system health checks
recurring reminders
overnight monitoring prompts

So yes, your agent can do useful work even when you are not actively chatting with it.

Step 6: Connect It to Telegram, Discord, Slack, and More

You can also connect Hermes to messaging platforms so you can talk to it from wherever you already work.

hermes gateway setup

Once you do that, your agent stops feeling like a local experiment and starts feeling like an actual operational tool.

That is when it gets fun.

What the Learning Loop Looks Like

At a high level, this is the cycle:

Do task → review result → save useful workflow → reuse it next time

Over time, the agent builds up:

context about your projects
memory about your environment
workflows for recurring tasks
better alignment with how you like things done

It is not "AGI."
It is not magic.
It is just a much more practical model of AI assistance.

And honestly, that is more useful.

A Real Example

I tested this with a practical build task.

Prompt:

Build a Python toolkit for small businesses: invoice generator, email responder, inventory tracker, plus several more utility scripts. Package it for distribution.

The agent generated:

multiple Python scripts
documentation
a packaged archive
product listing copy

Did it still need review? Yes.

Did it massively reduce the time between "idea" and "usable draft"? Also yes.

That is the real value:
not replacing judgment,
but compressing execution.

What This Is Not

This is not an autonomous employee.
It is not something you should point at production and blindly trust.
It is not a replacement for engineering judgment.

It is better to think of it as:

a persistent technical copilot with memory, tools, and reusable workflows

That framing keeps expectations sane and makes the actual value easier to appreciate.

Security Matters

If you give an agent access to your shell, files, APIs, or scheduled tasks, treat it like any other powerful automation system.

At minimum:

limit permissions
protect secrets
review outputs
be careful with production access
keep the blast radius small

The more capable the agent becomes, the more this matters.

Why I Think This Is a Big Deal

A lot of AI tooling still feels like demoware.

Fun to try.
Annoying to use every day.

Persistent agents are different.

They can actually fit into real workflows because they keep context, remember what matters, and improve through reuse.

For solo developers, indie hackers, and small teams, that is a very big deal.

Because the gap between:

"I have an idea"

and

"I have something working"

is getting smaller fast.

And tools like this are part of why.

Final Thought

If you have only used AI through normal chat interfaces, persistent agents are one of the most interesting next steps.

Once the agent can remember your environment, save your workflows, and help with recurring tasks, it stops feeling like a toy.

It starts feeling like infrastructure.