TLDR: LLMs forget everything between sessions. You re-explain yourself constantly, lose progress on long-running work, and have no reliable way to pick up where you left off — especially when switching between models or tools. I built PMM to fix that. @Deacon Wardlow helped me improve it by identifying a specific gap: session continuity — what changed, what’s still open, where you stopped. That’s now live in PMM v 2.8.​ Edit: I figured screenshots tell a better story => same memory on three different tools with 3 different models, where I've asked the LLM to basically recall outstanding tasks across the 4-5 current projects. Slightly different perspectives because I'm working a slightly different project with each model. I initially built PMM to remember stuff beyond the context window to fix an issue i had with the LLMs I use failing to remember and recall over long conversations. I used it to have the same conversation, while switching between Claude Code CLI and Co-work. Eventually it became a tool for helping me with continuity in my conversations with different LLMs on different apps and harnesses ( I switch a lot between Claude, OpenCode and GitHub Co-Pilot). I now use it to give multiple agents long-term memory (while they switch between different models on Claude, Gemini, Model Ark and a couple of smaller local models) without the use of model routing. I currently deploy it (along with with another agentic plugin I developed) to give agents long-term individual and collective organisaitonal memory in their conversations with multiple users over telegram in a small pilot. @Deacon Wardlow tested PMM in his own workflows, identified a gap in session memory management, and tried a couple of changes, which he outlined in another thread discussing Session Memory Layer + PMM

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