 | Hey all, I’m building TurboMemory — a local long-term memory system for AI agents / chatbots. Main idea: store semantic memory using TurboQuant-style compression 4-bit / 6-bit / 8-bit packed embeddings SQLite index for fast lookup topic centroid prefilter to reduce search cost daemon consolidation (merge/prune old memory automatically) contradiction detection + confidence decay It’s meant to be a lightweight “Claude-style memory” that runs on your laptop. Repo: https://github.com/Kubenew/TurboMemory� I’m looking for early contributors (Python + systems/ML folks). Good first issues: benchmarks, packaging, improving retrieval/scoring, tests. If you build agents, I’d love feedback: what features are missing? [link] [comments] |
TurboMemory: Claude-style long-term memory with 4-bit/6-bit embeddings (runs locally) – looking for contributors
Reddit r/LocalLLaMA / 4/2/2026
💬 OpinionDeveloper Stack & InfrastructureSignals & Early TrendsTools & Practical Usage
Key Points
- TurboMemory is a locally runnable, Claude-style long-term memory system for AI agents/chatbots that stores semantic memory using TurboQuant-style embedding compression at 4-bit/6-bit/8-bit precision.
- The approach combines compressed packed embeddings with a SQLite index for fast retrieval, plus topic-centroid prefiltering to reduce search cost.
- It includes ongoing memory management via a daemon that consolidates (merges/prunes) stored memories automatically, along with contradiction detection and confidence decay to keep memories up to date.
- The project is seeking early contributors, especially people with Python and systems/ML skills, with suggested first issues covering benchmarks, packaging, retrieval/scoring improvements, and testing.
- Feedback is invited from builders of AI agents on what features are missing from the current design, indicating an early-stage community development effort.
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