● Covered by 1 source · 1 reportLow impact1 neutral
Lossless-memory Project Creates Personal AI Memory System That Avoids Summarization
Key points
- Stores raw conversation logs without summarization.
- Timestamps every record for temporal indexing.
- Prioritizes time-based search over keyword search.
- Local, file-based system using JSONL and SQLite.
Lossless Memory Approach
The Lossless-memory project proposes a new method for personal AI long-term memory. Unlike common systems that summarize conversations or use embeddings for retrieval, this project retains every line of conversation. The design emphasizes preserving the original content and chronological order of interactions.
Temporal Backbone and Search
Every record, including utterances, actions, and document chunks, is timestamped, forming a "Temporal Backbone." This allows for time-first searching, where queries like "Yesterday evening, about the budget" narrow the search range chronologically before keyword ranking. Results are presented unsummarized and in chronological order.
Local and Dedicated System
The system is designed for a single user and a single AI assistant, operating locally on one machine without server or cloud dependencies. It utilizes JSONL logs and SQLite for storage, with FTS5 for exact search and sqlite-vec for semantic search. A small index, LLL, injects the current topic into the model's context to maintain continuity across sessions.
Distinction from Other Systems
Lossless-memory explicitly avoids being a vector database wrapper or a summarizer, as it deliberately omits any summarization steps. It is not a benchmark-driven research system but a working implementation with an operational record since July 2026 for its single user.
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Primary sources
GitHub cmc_internal/api GitHub github/collect GitHub aru-labs/lossless-memory GitHub _private/browser GitHub get-started/accessibility GitHub open-source/sponsorsReporting from
The Lossless-memory project introduces a personal AI memory system that stores raw conversation logs with timestamps, avoiding summarization and embedding. This approach aims to preserve the exact content and temporal context of interactions for a single user's AI assistant.