widemem: open-source memory layer for LLMs with importance scoring, decay, and conflict resolution

eyepaq
Sun, 15 Mar 2026 01:13:38 +0000

Hi everyone, sharing an open-source project I’ve been working on.

widemem is a Python memory layer for LLMs that goes beyond basic vector search. Most memory systems treat all facts equally and let contradictions accumulate silently. widemem tries to fix that.

What it does

Importance scoring: each extracted fact gets a 1-10 score. Retrieval ranks by a weighted mix of similarity, importance, and recency

Time decay: configurable exponential/linear/step decay. Old trivia fades, critical facts stick

Batch conflict resolution: “I moved to Paris” after “I live in Berlin” gets resolved in a single LLM call instead of storing both

YMYL prioritization: health, legal, and financial facts get higher importance floors and decay immunity

Hierarchical memory: facts roll up into summaries and themes with automatic query routing

MCP server: works as a Model Context Protocol server for Claude Desktop and other MCP clients

Runs fully local

Works with Ollama + sentence-transformers + SQLite + FAISS out of the box. No cloud, no API keys needed. Also supports OpenAI, Anthropic, and Qdrant if you want.

pip install widemem-ai

140 tests passing. Apache 2.0.

GitHub: GitHub - remete618/widemem-ai: Next-gen AI memory layer with importance scoring, temporal decay, hierarchical memory, and YMYL prioritization · GitHub

PyPI: widemem-ai · PyPI

Site: https://widemem.ai

Would love feedback, especially from anyone working on agent memory or RAG pipelines. What approaches are you using for memory in your projects?

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