LLM-agnostic memory is portable, provenance-aware context that works across models like GPT, Claude, and local/Ollama, no lock-in. In short: your strategic memory travels with you between tools and survives model swaps. Many people write it as LLM agnostic (no hyphen) too.
Also called: LLM agnostic memory, model-agnostic memory, provider-agnostic memory.
Key takeaways
- Portable across GPT, Claude, and local/Ollama, no lock-in.
- Provenance on every recall (who/what/when/which model).
- Role- and project-threaded recall; not another notes app.
Most AI tools forget everything the moment you close the tab. Even when “memory” exists, it’s trapped inside a single provider and doesn’t carry the why across time.
Threadbaire tracks your ideas, pivots, decisions, and rationale, then feeds the right slice of context into whatever you’re using next. No prompt roulette. No copy-paste across apps. Just a unified thread of strategic memory.
Examples
- Switching from GPT to Claude without losing decision context or rationale.
- Working offline with a local Llama/Ollama model while keeping the same memory thread.
This isn’t a feature. It’s a survival trait for founders, creators, and researchers working across long timelines and multiple hats.