System Report

Open‑source LLM guardrails surface as reliability gap widens

Forge lifts local models into reliable agentic performers

Forge’s guardrail stack pushed an 8‑billion‑parameter model from single‑digit success rates to 84 % on a 26‑scenario evaluation suite. The same stack raised Anthropic’s Sonnet 4.6 from 85 % to 98 % on the identical wo…

The system works as a drop‑in proxy that sits between any OpenAI‑compatible client and a local model server. It speaks both the OpenAI chat‑completion API and Anthropic’s messages API. Clients such as opencode, Contin…

Forge also ships a WorkflowRunner that defines tools, picks a backend, and manages the full lifecycle: system prompts, tool execution, context compaction, and guardrails. A SlotWorker adds priority‑queued GPU access f…

Read the full story

Continue on System Report

Open article