This is a submission for the Hermes Agent Challenge
Most AI agents today have a memory problem—not the amusing “it forgot my name” kind, but the expensive engineering kind.
You let an agent spend thousands of tokens learning your environment, inspecting your repository, debugging issues, and working through a useful task—sometimes even discovering a workflow that actually works. Then you close the session, come back the next day, and much of that context is gone. The repository is still there, but the agent has forgotten what it learned about it, how it solved the problem, and which workflow got the job done.
For short-lived tasks, that’s fine. But once agents start handling repeated engineering work, automation, or operational workflows, rediscovering the same solution every time becomes expensive.
That’s what makes Hermes Agent from Nous Research interesting. Instead of behaving like just another coding copilot or chat wrapper, Hermes aims to retain useful workflows from successful execution and reuse them later. If that works well in practice, it could meaningfully change what open-source agents become.
The Stateless Agent Problem
A lot of current agent frameworks effectively behave like this:
Goal → Plan → Tool Calls → Execute → Return Result → Forget
That lifecycle works well until the work becomes repetitive.
Imagine asking an agent to scan a repository, identify missing license headers, generate patches, run tests, and commit the changes. A capable system might spend significant time inspecting the filesystem, inferring project conventions, and recovering from failures.
Now run the exact same task a week later.
Most agents start from scratch as though the previous execution never happened. The same applies to recurring issues like flaky CI failures—agents often rediscover the fix instead of reusing what they already learned.
That’s the inefficiency. A human engineer would remember the pattern or document the solution. Stateless agents generally do neither.
What Hermes Is Trying to Change
Hermes attempts to insert a learning loop into that lifecycle.
Instead of behaving like this:
Goal → Plan → Execute → Forget
The intended model looks more like:
Observe → Plan → Execute → Evaluate → Crystallize Skill → Reuse
The key difference is what happens after execution.
Rather than treating a completed task as the end of the interaction, Hermes attempts to evaluate whether the workflow it just used is worth keeping. Did the task succeed? Which actions actually mattered? Was the solution a one-off hack, or does it represent a reusable pattern?
If the answer is yes, Hermes can retain that workflow as a reusable Skill rather than forcing the model to rediscover the entire process during the next session.
That’s a much more compelling idea than simple persistent chat history.
What a Skill Actually Looks Like
“Procedural memory” sounds abstract until you think about what is actually being stored. A simplified representation might look something like this:
# repo-license-remediation
version: 1.2
tags:
- python
- repository
- compliance
inputs:
- repo_path
- license_header
required_tools:
- filesystem
- regex_match
- file_write
- terminal
- git
steps:
1. Scan Python files
2. Detect missing headers
3. Generate patch
4. Run tests
5. Commit validated changes
This is fundamentally different from remembering prior prompts or retaining conversation snippets. It is operational knowledge.
Remembering that I prefer concise responses is personalization. Remembering how to safely repair a repository issue is actual capability.
Procedural Memory Is More Interesting Than Chat Memory
A lot of AI products advertise memory, but most of the time that means conversation continuity, user preferences, or retained prompt context.
That is useful, but it does not necessarily make the system better at doing work. The more meaningful distinction is between remembering facts and remembering procedures.
Humans become effective engineers because they internalize repeatable workflows. You do not memorize every exact command forever, but you do remember how to debug a broken deployment, how to approach an integration issue, or how to remediate a familiar failure pattern.
Hermes appears much closer to that model. Its architecture can be thought of in layers.
Working Memory
This is the short-lived execution state that exists during active task handling:
- current task context
- temporary variables
- recent tool outputs
- active execution state
This is standard agent behavior.
Episodic Memory
This represents longer-lived contextual recall:
- project metadata
- user preferences
- prior interactions
- historical decisions
This helps continuity.
Procedural Memory
This is the genuinely interesting layer because it stores reusable workflows such as:
- debugging routines
- deployment procedures
- remediation pipelines
- environment setup workflows
- integration playbooks
If that layer works well, the system improves with repetition rather than simply remembering conversations.
The Scaling Problem
Persistent memory sounds great until you hit the obvious practical problem.
What happens when the agent has accumulated hundreds of reusable workflows?
Dumping everything into the context window every time would be terrible for latency, token efficiency, and reasoning quality.
A staged retrieval approach makes much more sense.
Discovery Stub (~20 tokens)
Start with the absolute minimum:
- skill name
- short description
Example:
Python repository license remediation workflow
That is enough to determine whether the workflow might be relevant.
Signature Layer (~200 tokens)
If the workflow looks promising, the agent can retrieve:
- expected inputs
- required tools
- assumptions
- configuration details
That allows validation without loading the full implementation.
Blueprint Layer (~1000+ tokens)
Only when the workflow is actually needed should the agent load:
- complete execution logic
- command sequences
- implementation details
- tool invocation steps
That model is far more scalable than brute-force memory stuffing.
One caveat here: if you are evaluating Hermes critically, it is worth verifying which parts of this architecture are already implemented versus which represent design direction. But conceptually, this is the right shape of solution.
Trying Hermes Yourself
If you want to test the core idea locally:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
hermes
A worthwhile test is giving it a genuinely multi-step engineering task rather than a toy prompt.
For example:
Scan this repository, identify missing license headers, propose a remediation workflow, and generate a patch.
The interesting question is not whether the task completes—plenty of agent systems can do that. The real question is whether the second execution becomes meaningfully smarter.
Why Hermes Feels Different
Procedural learning is the most interesting part of Hermes, but it’s not the only reason it stands out.
Hermes feels less like a chatbot and more like an operational agent. It can run across CLI, Slack, Discord, Telegram, WhatsApp, Signal, and email, which makes far more sense for long-running workflows than being trapped in a browser tab.
It also supports recurring automation, isolated subagents for parallel execution, and a broader tool surface than typical terminal agents, including web search, browser automation, vision, image generation, text-to-speech, and multiple model backends. Taken together, Hermes feels closer to a general-purpose operational agent than a narrow automation wrapper.
Where Hermes Fits Compared to Other Frameworks
LangChain gives developers enormous flexibility, but that flexibility often comes with a lot of assembly work. If you want full control over orchestration, integrations, and memory patterns, LangChain remains powerful. Hermes feels much more opinionated, which can either be a strength or a limitation depending on what you want.
AutoGen shines in conversation-driven multi-agent workflows, but those architectures can become noisy and expensive quickly. Hermes feels less focused on agent conversation and more focused on execution workflows.
OpenDevin is much more obviously aligned with software engineering automation. Hermes feels broader, aiming at operational agent behavior rather than specifically AI-assisted software development.
OpenClaw feels adjacent rather than directly competitive. OpenClaw is stronger around orchestration and communications routing, while Hermes is more interesting around procedural learning and self-improving execution.
Infrastructure Design Matters
A powerful agent without execution isolation is a liability. Giving unrestricted shell access to an LLM is not a serious production strategy.
Hermes supports multiple execution backends, including restricted local execution, Docker containers, SSH environments, Singularity, and remote sandbox environments like Modal.
That matters because practical automation requires isolation.
A realistic example might be a Slack-triggered infrastructure audit where Hermes launches an isolated environment, validates deployment state, detects a known failure pattern, applies a previously learned remediation workflow, and reports back. That starts looking much less like a toy demo and much more like something operations teams could actually use.
Where This Can Go Wrong
The risks here are not hypothetical.
Skill Drift
A workflow that worked six weeks ago may be wrong today because dependencies changed, APIs evolved, or CLI flags broke. Without revalidation, saved procedural memory becomes stale automation debt.
Faulty Generalization
An agent might incorrectly promote a weird edge-case fix into a reusable standard workflow. That becomes dangerous quickly because repeated incorrect automation is often worse than forcing fresh reasoning every time.
Security Risk
Persistent procedural memory can preserve unsafe commands, environment-specific assumptions, production shortcuts, or even patterns that risk credential leakage. Any self-improving system that executes actions needs serious governance.
Practical Safeguards
If anyone plans to use something like this in production, the minimum checklist probably includes:
- human approval for newly created skills
- multiple successful runs before autonomous reuse
- smoke testing when dependencies change
- provenance metadata and versioning
- rollback support
- least-privilege execution
- periodic workflow audits
Without those controls, procedural memory becomes accumulated operational risk rather than useful automation.
Why This Matters for Open Source
The bigger issue here is ownership.
A lot of proprietary AI systems assume persistent memory belongs inside vendor infrastructure. That creates lock-in, opaque automation logic, poor auditability, and painful migration stories.
If an open agent can retain reusable operational knowledge in inspectable, version-controlled artifacts, teams can audit agent behavior, share playbooks, migrate freely, and actually own the workflows the system learns.
If your agent discovers something useful, that knowledge should live in infrastructure you control—not disappear into a hosted memory layer.
That feels like the more important shift here.
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