AI is systems engineering. The hardest part of production AI agents is the infrastructure around the models: off-LLM guarantees, verification loops, response guards, per-role model routing.
📰 news.llm-works.ai: AI news aggregation across 70+ sources with theme clustering, daily digests, and persona-based presentation
🔬 xray.llm-works.ai: Decision-grade deep research with calibrated verdicts through adversarial + hypothesis + grader verification loops, plan-as-protagonist architecture, knowledge-graph synthesis
| Package | What it does |
|---|---|
| 🤖 llm-gent | Trait-based agent framework with production HTTP runtime |
| 🧩 llm-saia | Verb-vocabulary LLM interaction (ask · verify · critique · refine · ...) |
| 🧠 llm-kelt | Persistent memory + embeddings + LoRA/DPO adapter training (Postgres+pgvector) |
| 🔌 llm-infer | Unified inference (vLLM · Ollama · native torch), multi-provider client |
| 🔩 appinfra | Production Python infrastructure (logging · DB · lifecycle) |
📊 ~500K lines across all packages · 🧪 ~1:1 test-to-code ratio · 💻 Local Qwen3.5-27B on RTX PRO 6000
📝 Blog · 🌐 llm-works.ai · 🏢 github.com/llm-works



