TypeScript ai-ollama-local-llm-agent-framework-typescript Projects
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Project mention: I Replaced My AI Agent's Flat Fact Store with a Graph Database | news.ycombinator.com | 2026-06-03
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Importance uses a 1-5 tier (critical health/family = 5, job/identity = 4, preference = 3, context = 2, ephemeral = 1). A moderately relevant but critical fact scores higher than a highly relevant but ephemeral one. Your wife's health condition surfaces above yesterday's weather.
## What I Learned
The model computes nothing. Code handles which facts changed, which are duplicates, what the scores are. The model handles what it means. The moment you let a model do arithmetic or hash-based dedup you get failures you can't explain.
Importance tiers need concrete examples in the extraction prompt. phi4:14b defaulted everything to tier 2 until I added few-shot examples with emotional weight. Abstract instructions don't calibrate a model.
The graph beats flat storage the moment you need relationship reasoning. SUPERSEDES chain alone justified the migration.
Runs entirely on a Mac Mini. 85MB for the graph. Everything local.
GitHub: https://github.com/PeterGreenAppliedAI/LocalClaw
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TypeScript ai-ollama-local-llm-agent-framework-typescript discussion
Index
| # | Project | Stars |
|---|---|---|
| 1 | LocalClaw | 28 |
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