Every AI coding agent forgets everything when the session ends. That’s the problem every “agent memory” tool is trying to solve — but they solve it in fundamentally different ways, and the differences matter more than the shared goal.
I run Vestige as my memory system, so I have a strong opinion on this space. When I looked at three newcomers — Pond, ai-memory, and Engram — the first thing that struck me is that they’re not actually competitors. They’re three different answers to three different questions, and only one of them is trying to do what Vestige does.
I wrote this survey for work. The goal wasn’t to win a tool-bake-off or prove a pet stack was superior — it was to answer one honest question: how do the people I build with actually spend their days with AI?
Every section maps to a layer of the modern dev toolchain. If you’re assessing a team’s workflow, onboarding new people, or just want to see where you sit relative to your peers, steal it. It’s licensed by the human copyright office of “you can just use this.”
Your AI can write code, summarize meetings, and research anything. But can it manage your to-do list? With TickTick MCP, it can — your AI assistant becomes a task manager you talk to instead of a UI you click.
Every task manager has a UI, and every AI has a chat box. The friction is the gap between them: you think of something, you have to switch to the app, click through to create a task, set the date, pick the list, tag it. That’s exactly the kind of repetitive friction that AI should be eating.
You trust your AI agent with your repo. Do you trust it with your SSH keys, your ~/.aws, your dotfiles? Most people running Claude Code, Codex, or OpenCode don’t think about it until it’s too late — and by then the agent has already read everything it could reach.
Greywall (Apache 2.0, Go) is a container-free, deny-by-default sandbox built specifically for AI coding agents on Linux and macOS. No Docker, no VMs — kernel-enforced isolation via Bubblewrap namespaces, Landlock, Seccomp BPF, eBPF monitoring, and a TUN-based network capture.
If you’re evaluating AI coding agents — Claude Code, OpenHands, Codex CLI, whatever — you’re probably doing it wrong. Running them against your own infra is dangerous. Running them manually is slow. Running them unrepeatably is pointless.
Harbor fixes that. It’s a framework from the creators of Terminal-Bench that lets you define sandboxed agent tasks, run evaluations against any agent/model combo, and scale across cloud providers.
Harbor wraps each agent task in an isolated environment (Docker locally, or Daytona/Modal/LangSmith/Novita Sandbox in the cloud). You specify:
