@inproceedings{sarker-etal-2026-graphbit,
title = "{G}raph{B}it: A Graph-based Agentic Framework for Non-Linear Agent Orchestration",
author = "Sarker, Yeahia and
Ullah, Md Rahmat and
Molla, Musa and
Joty, Shafiq",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.203/",
pages = "3412--3425",
ISBN = "979-8-89176-460-6",
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 (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 shows excellent performance achieving the highest accuracy, zero orchestration errors, the lowest latency, and the highest throughput. Ablation studies demonstrate that each memory tier contributes measurably to performance with similar configuration due to architectural changes, with deterministic execution providing the greatest gains on tool-intensive tasks representative of real-world deployments.\par Code and data: \url{https://github.com/InfinitiBit/graphbit}"
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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 (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 shows excellent performance achieving the highest accuracy, zero orchestration errors, the lowest latency, and the highest throughput. Ablation studies demonstrate that each memory tier contributes measurably to performance with similar configuration due to architectural changes, with deterministic execution providing the greatest gains on tool-intensive tasks representative of real-world deployments.\par Code and data: https://github.com/InfinitiBit/graphbit</abstract>
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%0 Conference Proceedings
%T GraphBit: A Graph-based Agentic Framework for Non-Linear Agent Orchestration
%A Sarker, Yeahia
%A Ullah, Md Rahmat
%A Molla, Musa
%A Joty, Shafiq
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F sarker-etal-2026-graphbit
%X 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 (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 shows excellent performance achieving the highest accuracy, zero orchestration errors, the lowest latency, and the highest throughput. Ablation studies demonstrate that each memory tier contributes measurably to performance with similar configuration due to architectural changes, with deterministic execution providing the greatest gains on tool-intensive tasks representative of real-world deployments.\par Code and data: https://github.com/InfinitiBit/graphbit
%U https://aclanthology.org/2026.emnlp-industry.203/
%P 3412-3425
Markdown (Informal)
[GraphBit: A Graph-based Agentic Framework for Non-Linear Agent Orchestration](https://aclanthology.org/2026.emnlp-industry.203/) (Sarker et al., EMNLP 2026)
ACL