I Was Tired of AI Agents Forgetting Everything, So I Built a Zero-Config Persistent Memory Sidecar

Hansjoerg Hofer — Mon, 06 Jul 2026 09:14:33 +0000

I was tired of AI agents forgetting everything.

So I built a zero-configuration persistent memory sidecar for Hermes/OpenClaw that works on both Windows and Linux using PostgreSQL.

AI agents are getting smarter, but their memory model is still fragile.
They can reason well in-session, but across days and weeks, they lose context, preferences, and project history unless you keep feeding them expensive prompts.

I wanted something practical:

Self-hosted
Easy to install
Durable memory
Fast retrieval
Minimal operational overhead
That became HermesClawZero-ConfigSidecar.

The Problem
Most agent workflows break down over time for three reasons:

Session memory is temporary.
Long prompts are costly and still lossy.
Teams need memory that persists beyond one chat window.
I wanted memory to behave like infrastructure, not like a fragile prompt trick.

What I Built
HermesClawZero-ConfigSidecar is a sidecar service that stores and retrieves memory for Hermes/OpenClaw using PostgreSQL + pgvector.

At a high level:

Capture events and notes.
Store embeddings and metadata.
Retrieve by semantic + lexical relevance.
Manage memory health with a dashboard and optimizer tools.
Setup Experience
One design goal was first-run simplicity.

🤖 AI-Agent Installation (Auto-Setup)
You can install this project automatically using an AI agent (like OpenClaw or Hermes). Simply paste this URL to your agent and say: “Install this project from GitHub”: https://github.com/SunMe1977/HermesClawZero-ConfigSidecar

You can run setup scripts on Windows or Linux/macOS, generate configuration, and launch Docker services quickly. The setup flow also supports provider keys and updater defaults.

Dashboard and Operations
I added a dashboard to make memory management operationally clear:

search and review memories
maintenance and optimizer actions
update/version visibility
safer admin flows

Data Layer
Under the hood, memory is persisted in PostgreSQL with pgvector and indexed for practical retrieval workflows.

Security and Reliability Improvements Before Release
Before calling this v1.0-ready, I focused on release hardening instead of feature creep:

added health checks and restart policies
improved auth behavior and endpoint protection
fixed production-facing bugs (rerank crash, delete form binding, transcribe safety)
improved docs, troubleshooting, and release notes
This was important: I wanted the repo to feel like a mature open-source project, not just a prototype.

Performance (Local Snapshot)
Local measurements (example values from my setup):

Hybrid retrieval: ~18 ms
PostgreSQL lexical search: ~12 ms
API startup (warm): ~0.8 s
Idle API memory: ~70 MB
Docker stack startup: ~4 s
Your numbers will vary by machine and provider configuration.

Why This Approach
There are stronger enterprise-grade memory platforms out there, but my goal here was different:

easy self-hosting
low setup friction
straightforward architecture
practical long-term agent memory
Not “the best system in the world.”
A reliable one you can actually run today.

What’s Next
For the next iterations, I plan to improve:

observability and logs
backup/restore workflows
policy controls for memory lifecycle
more deployment templates
Final Thoughts
Persistent memory should be a standard primitive for agents.

If your assistant forgets everything between sessions, you are rebuilding context from scratch every day.
That is expensive, fragile, and avoidable.

This project is my practical answer: a zero-config memory sidecar for Hermes/OpenClaw that works on Windows and Linux with PostgreSQL and Docker.

If you want to try it, I would love your feedback after first install and first real-world usage.

Introducing AI SelfPub ColoringBook Studio

Hansjoerg Hofer — Sun, 28 Sep 2025 12:22:55 +0000

Introducing AI SelfPub ColoringBook Studio
AI SelfPub ColoringBook Studio is a self-hosted automation tool that empowers creators to generate KDP-ready books in minutes - with full control, no cloud lock-in, and no licensing ambiguity.
Live Demo:
Support the Campaign:
Built with Spring Boot (Java) and React, the studio integrates open-source AI models like Gemini Flash and Hugging Face SDXL to generate high-quality line art. It then automates the entire publishing pipeline - from image curation to batch PDF export with unique filenames and embedded metadata.

Why This Matters
Most AI tools today are locked behind paywalls, usage quotas, or vague licensing terms that make commercial publishing risky. My goal was to build something different:

✅ No hidden quotas or surprise service pauses
✅ Full transparency and forensic debugging
✅ Legal compliance for commercial image generation
✅ Customizable output for educators, artists, and authors

Whether you're publishing a themed activity book, a therapeutic art journal, or a creative workbook for kids - this tool gives you speed, autonomy, and creative control.

Built by a Publisher, for Publishers
I've published children's books on Amazon and built SaaS platforms for over 25 years. This project combines both worlds: robust backend automation with creative flexibility.

The studio is already live in demo form, and I've used it to publish real books. Now I'm raising funds to cover:
Microsoft and Google KI registration (for domain/email verification)
Stable hosting infrastructure
Legal licensing for commercial deployment

If you believe in creative freedom and automation for indie creators, I invite you to support the campaign: 👉

Support

Let's Build the Future of Publishing
This isn't just about coloring books. It's about giving creators tools that work for them - not against them. It's about publishing workflows that are fast, affordable, and fully autonomous.
If you're an indie author, educator, or developer, I'd love your feedback. Try the demo, share the campaign, or reach out with questions.
Let's make publishing smarter - and a lot more fun.

DEV Community: Hansjoerg Hofer

I Was Tired of AI Agents Forgetting Everything, So I Built a Zero-Config Persistent Memory Sidecar

Introducing AI SelfPub ColoringBook Studio