GraphBit: A Graph-based Agentic Framework for Non-Linear Agent Orchestration
Yeahia Sarker, Md Rahmat Ullah, Musa Molla, Shafiq Joty

TL;DR
GraphBit is a deterministic, graph-based framework for orchestrating agent workflows that improves reproducibility, efficiency, and accuracy over prompted methods by explicitly defining workflows as DAGs.
Contribution
It introduces a novel engine-controlled, DAG-based architecture with a three-tier memory system, outperforming existing frameworks in accuracy and reliability.
Findings
Achieves 67.6% accuracy on GAIA benchmark tasks.
Zero hallucinations observed during execution.
Lowest latency of 11.9 ms overhead.
Abstract
Agentic LLM frameworks that rely on prompted orchestration, where the model itself determines workflow transitions, often suffer from hallucinated routing, infinite loops, and non-reproducible execution. We introduce GraphBit, an engine-orchestrated framework that defines workflows explicitly and deterministically as a directed acyclic graph (DAG). Unlike prompted orchestration, agents in GraphBit operate as typed functions, while a Rust-based engine governs routing, state transitions, and tool invocation, ensuring reproducibility and auditability. The engine supports parallel branch execution, conditional control flow over structured state predicates, and configurable error recovery. A three-tier memory architecture consisting of ephemeral scratch space, structured state, and external connectors isolates context across stages, preventing cascading context bloat that degrades reasoning…
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