hindsight vs LightMem
Agent memory that learns, not just recalls: retain/recall/reflect API over Postgres, SOTA on LongMemEval. Self-host via Docker with UI; Python/TS clients, any LLM provider. — versus — ICLR 2026 memory framework for LLMs/agents: LLMLingua pre-compression, topic segmentation and offline memory updates — leading LoCoMo/LongMemEval results at lower token cost.
Both chase LongMemEval leadership from opposite ends: LightMem is a research framework optimizing token cost via pre-compression; Hindsight is a packaged service optimizing accuracy. Benchmark rivals, different deployment realities.
| hindsight | LightMem | |
|---|---|---|
| Stars | 19k | 1.0k |
| Forks | 1.2k | 93 |
| Language | Python | Python |
| License | MIT | MIT |
| Last activity | today | 4 days ago |
| Topics | memory, agents | memory |
| Curated connections | 2 | 3 |
hindsight — the curator's take
Pick it when you want a deployable memory *service* whose pitch is learning — agents that get better over time, not a transcript search. The LongMemEval lead was independently reproduced (Virginia Tech, Washington Post), which is more than most memory vendors offer, and the LLM side is pluggable down to Ollama/LM Studio for fully-local stacks. NOT an embedded library: you run a Docker service with Postgres and talk to it over HTTP — overkill for a single coding agent wanting session notes. The ™ and Hindsight Cloud signal a commercial trajectory; watch where the open/paid line lands.
LightMem — the curator's take
Research-grade memory with receipts: reproduction scripts for LoCoMo/LongMemEval plus a baseline harness that benchmarks Mem0, A-MEM and LangMem side by side — useful even if you adopt none of them. The compression-first pipeline (LLMLingua before storage) is the differentiating idea. Expect paper-adjacent ergonomics: manual model downloads and config dicts, not a polished product.