Hi, I’d love to request an analysis of my project: https://github.com/codysnider/tagmem
tagmem is a local memory storage and retrieval system for LLM agents.
It is:
- local-first
- open source
- Docker-friendly
- MCP-compatible
- focused on tagged, depth-aware memory
A few details that may be relevant for analysis:
- written in Go
- supports MCP, CLI, and local Docker-based deployment
- uses local embeddings
- current published benchmark default is bge-small-en-v1.5
- includes reproducible benchmark artifacts and methodology in-repo
- current published LongMemEval result: 99.0% R@5
What may be interesting to evaluate:
- architecture and retrieval model
- memory representation (entries, tags, depth, facts, diary)
- ingestion / tagging pipeline
- MCP integration
- benchmark methodology and reproducibility
- tradeoffs vs. other agent-memory systems
- strengths, weaknesses, and where the design is novel or standard
Benchmark docs are here:
If you’re open to it, I’d really appreciate a full analysis. Happy to answer implementation or benchmark questions if useful.
Hi, I’d love to request an analysis of my project: https://github.com/codysnider/tagmem
tagmem is a local memory storage and retrieval system for LLM agents.
It is:
A few details that may be relevant for analysis:
What may be interesting to evaluate:
Benchmark docs are here:
If you’re open to it, I’d really appreciate a full analysis. Happy to answer implementation or benchmark questions if useful.