I am not a programmer. I am a Data Engineer. I have been working in the IT industry solving data problems for enterprises for over 15 years, working in Machine Learning for over 10 of those, and for the last two years I have been building LLM integrations and agents for enterprise use cases. So naturally, when OpenClaw hit the news, I tried it. I created an agent for all my personal projects, to pick up unfinished work, stay organized, keep context across sessions. I used it heavily. And like everyone else, my agent started showing memory problems. It just kept forgetting things. Just like the early Sonnet 3.5 and 3.7 days. I was constantly reminding my agent to memorize things. All that jazz. OpenClaw's filesystem-based memory simply wasn't cutting it for me. It didn't hold up once context piled up. And that is a particular kind of frustrating, when you know something can work, that the problem is solvable, but the current solution just doesn't get there. I know OpenClaw has since addressed some of these issues, the QMD architecture is a real step forward. But their solutions are tightly coupled to the framework itself. I wanted something broader. Memory that is portable across agents, across frameworks. Your agent can change. Your framework can change. Your memory shouldn't have to. It should travel with you. I want to be clear: I am not complaining about OpenClaw. We all love it. But I wanted to build something that layers cleanly on top of it, and on top of any *Claw clone, rather than replace what's already working. Not a framework. A harness. So I went to work and built a solution for exactly that problem. To be honest, I built it for myself. To fix my own agent. Then it felt natural to give it a name, open source it, and see how the community felt about it. This is built for serious power users, people who use OpenClaw or other *Claw frameworks for real work, have long-term plans to stick with them, and run their agents on private servers or in the cloud. If that describes you, you will feel the difference immediately. I named it OpenStinger. It works in three tiers: memory, reasoning, and alignment. The alignment tier is built on a mathematical model of empathy published by Brian Roemmele, his love equation, which I have implemented as a real-time inference layer rather than a training-time concept. I will go deep on the architecture in a follow-up post. Check out the website and the GitHub repo. If it's useful to you, give it a star, it helps others find it. And please share your thoughts. I want to know what the community thinks. - Srikanth Bellary

OpenStinger

A Portable Memory Harness for Autonomous Agents

A Portable Memory Harness for Autonomous Agents