MemThread sits between your app and any LLM, storing what matters and surfacing it automatically. The model arrives briefed, not blank.
No custom pipelines. No prompt engineering. Three steps.
Point MemThread at your LLM API endpoint or drop in the SDK. Works with Claude, GPT-4, Gemini, or any model behind a standard chat API. Setup is minutes, not a sprint.
After each conversation, MemThread extracts what matters — decisions, preferences, code patterns, constraints, names. Structured, ranked by relevance, stored in your account.
On the next request, relevant memories arrive before your prompt does. The model knows your project, your preferences, your history. No re-explaining. No catching up.
MemThread is for the people who've learned to paste the same notes into every new session.
Stop writing your own retrieval and memory scaffolding for every LLM feature. MemThread handles storage, embedding, and relevance ranking so you can ship the thing that actually matters.
You switch between Claude, GPT-4, and Gemini depending on the task. Your context shouldn't be stranded in one tool's history. MemThread is model-agnostic by design.
When your team shares an AI assistant, knowledge should compound. What one person teaches the model, everyone benefits from. Context becomes infrastructure, not tribal knowledge.