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[LangChain] Enhance memory management with importance scoring and conflict resolution

A standalone memory layer is proposed that includes features like importance scoring, temporal decay, and batch conflict resolution, allowing for better control over memory in LangChain applications.

Original Source

@u/eyepaqmax | 1 pts | 3/14/2026

If you've been using LangChain's built-in memory modules and wanted more control over how memories are scored, decayed, and conflict-resolved, I built widemem as a standalone alternative. Key differences from LangChain memory: \- Importance scoring: each fact gets a 1-10 score, retrieval is weighted by similarity + importance + recency \- Temporal decay: configurable exponential/linear/step decay so old trivia fades naturally \- Batch conflict resolution: adding contradicting info triggers automatic resolution in 1 LLM call \- Hierarchical memory: facts roll up into summaries and themes with automatic query routing \- YMYL prioritization: health/legal/financial facts are immune to decay It's not a LangChain replacement, it handles memory specifically. You can use it alongside LangChain for the rest of your pipeline. Works with OpenAI, Anthropic, Ollama, FAISS, Qdrant, and sentence-transformers. SQLite + FAISS out of the box, zero config. pip install widemem-ai GitHub: [https://github.com/remete618/widemem-ai](https://github.com/remete618/widemem-ai)

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