Anyone else hacking on long-horizon reasoning frameworks for LLMs? (github.com)

Long Horizon LLM introduces a cutting-edge framework designed to enable large language models (LLMs) to perform deep, multi-step reasoning through a structured pipeline—classify, plan, execute, critique, and synthesize—rather than simple one-shot responses. Built with a FastAPI backend and a Next.js frontend, it offers both an HTTP API and web UI, allowing interactive experiments with local or “sovereign” models. Its standout feature is a blackboard engine that orchestrates reasoning via a directed acyclic graph (DAG), embedding concurrency controls, QA loops, iterative judges, contradiction detection, and persistent memory storage, which combined enable complex workflows to dynamically plan, self-correct, and compose comprehensive final outputs. This framework is significant for the AI/ML community as it pushes beyond traditional single-step LLM usage toward resilient, adaptive long-horizon reasoning—key for applications demanding logical coherence and multi-faceted problem-solving. Its control-theoretic approach to budgeting and hedging, along with modular judge ensembles and detailed JSON audit trails, highlight a new level of observability and robustness. Although still experimental with limitations like shallow contradiction checks, approximate token budgeting, and file-based memory concurrency issues, Long Horizon LLM serves as a research playground for developing sophisticated reasoning orchestration and lays the groundwork for future agent architectures that can handle complex, prolonged tasks reliably and verifiably.