I wanted to see whether a weak local model could become genuinely useful without pretending the base model was magic.

LLM Foundry is the stack around the model: memory, compression, semantic retrieval, provider support, and a benchmark harness.

The core idea

A useful model workflow usually looks like this:

read the task

recover relevant memory

compress the clutter

ask the model

check the answer

use tools if needed

save traces

benchmark the result

That is the difference between a chatbot and something you can actually trust on real work.

What changed

The current version now has:

embedding-based semantic retrieval

multi-provider support for OpenAI-compatible and Anthropic endpoints

compression + memory so long tasks can be shrunk into compact context

agent traces that can become training data later

benchmarks and harnesses so the system is measurable

The measured part

The proof pack shows:

Benchmark pass rate: 50%

Reasoning harness: 60%

Coding harness: 100%

Tool-use harness:…