GraphBit: A Graph-based Agentic Framework for Non-Linear Agent Orchestration
GraphBit is an engine-orchestrated framework that defines workflows as directed acyclic graphs (DAGs), addressing hallucinated routing, infinite loops, and non-reproducible execution in prompted orchestration. A Rust-based engine handles routing, state transitions, and tool invocation, supporting parallel branches, conditional control flow, and configurable error recovery. A three-tier memory architecture isolates context to prevent bloat. On GAIA benchmarks, GraphBit achieves 67.6% accuracy, zero framework-induced hallucinations, 11.9ms overhead latency, and highest throughput, outperforming six existing frameworks.
[2605.13848] GraphBit: A Graph-based Agentic Framework for Non-Linear Agent Orchestration
[Submitted on 8 Mar 2026]
Title:GraphBit: A Graph-based Agentic Framework for Non-Linear Agent Orchestration
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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 in long-running pipelines. Across GAIA benchmark tasks spanning zero-tool, document-augmented, and web-enabled workflows, GraphBit outperforms six existing frameworks, achieving the highest accuracy (67.6 percent), zero framework-induced hallucinations, the lowest latency (11.9 ms overhead), and the highest throughput. Ablation studies demonstrate that each memory tier contributes measurably to performance, with deterministic execution providing the greatest gains on tool-intensive tasks representative of real-world deployments.
Comments: 12 pages, 5 figures, 4 tables. Submitted to arXiv, under review
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
ACM classes: I.2.11; D.4.7; I.2.7
Cite as: arXiv:2605.13848 [cs.AI]
(or arXiv:2605.13848v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.13848
arXiv-issued DOI via DataCite
Submission history
From: Rahmat Ullah [view email] [v1] Sun, 8 Mar 2026 18:32:28 UTC (2,619 KB)
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