LLM Foundry: the boring stack that makes an LLM actually useful Most AI projects are built backwards. People start with the model and only later discover they needed a memory system, semantic retrieval, tool use, tests, and a fallback plan for when one provider decides to nap for no visible reason. That is the part I care about now. LLM Foundry is the workshop around an LLM — not the model itself. It is the layer that makes a model useful for actual work instead of just looking smart in a demo. What changed The current version now has a few things worth showing instead of just claiming: semantic retrieval backed by embeddings, so memory search is not just keyword matching multi-provider support for OpenAI-compatible endpoints, Anthropic, Hugging Face, and failover bundles compression + memory so long tasks can be shrunk into a compact working context agent traces that can be exported into training data benchmark + harness runs so the system is testable instead of vibes-based That last bit matters more than…