# AAFLOW: Scalable Patterns for Agentic AI Workflows

arXiv: https://arxiv.org/abs/2605.02162 · HTML: https://mlsanity.com/paper/2605.02162/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 32 of its 57 references that are in the map.
- First posted on arXiv 4 May 2026.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 30 of 32)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [SGLang: Efficient Execution of Structured Language Model Programs](https://mlsanity.com/paper/2312.07104/) (2023)
- [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://mlsanity.com/paper/2310.03714/) (2023)
- [Ray: A Distributed Framework for Emerging AI Applications](https://mlsanity.com/paper/1712.05889/) (2017)
- [From RAG to Memory: Non-Parametric Continual Learning for Large Language Models](https://mlsanity.com/paper/2502.14802/) (2025)
- [MemoRAG: Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentation](https://mlsanity.com/paper/2409.05591/) (2024)
- [Pathways: Asynchronous Distributed Dataflow for ML](https://mlsanity.com/paper/2203.12533/) (2022)
- [Parsl: Pervasive Parallel Programming in Python](https://mlsanity.com/paper/1905.02158/) (2019)
- [Exploration of LLM Multi-Agent Application Implementation Based on LangGraph+CrewAI](https://mlsanity.com/paper/2411.18241/) (2024)
- [DataFlow: An LLM-Driven Framework for Unified Data Preparation and Workflow Automation in the Era of Data-Centric AI](https://mlsanity.com/paper/2512.16676/) (2025)
- [Towards Scalable Dataframe Systems](https://mlsanity.com/paper/2001.00888/) (2020)
- [OneFlow: Redesign the Distributed Deep Learning Framework from Scratch](https://mlsanity.com/paper/2110.15032/) (2021)
- [High Performance Data Engineering Everywhere](https://mlsanity.com/paper/2007.09589/) (2020)
- [Design and Performance Characterization of RADICAL-Pilot on Leadership-class Platforms](https://mlsanity.com/paper/2103.00091/) (2021)
- [Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol](https://mlsanity.com/paper/2508.20737/) (2025)
- [Supercharging Distributed Computing Environments For High Performance Data Engineering](https://mlsanity.com/paper/2301.07896/) (2023)
- [RADICAL-Pilot and Parsl: Executing Heterogeneous Workflows on HPC Platforms](https://mlsanity.com/paper/2105.13185/) (2021)
- [High Performance Dataframes from Parallel Processing Patterns](https://mlsanity.com/paper/2209.06146/) (2022)
- [Hybrid Cloud and HPC Approach to High-Performance Dataframes](https://mlsanity.com/paper/2212.13732/) (2022)
- [In-depth Analysis On Parallel Processing Patterns for High-Performance Dataframes](https://mlsanity.com/paper/2307.01394/) (2023)
- [Deep RC: A Scalable Data Engineering and Deep Learning Pipeline](https://mlsanity.com/paper/2502.20724/) (2025)
- [On The Reproducibility Limitations of RAG Systems](https://mlsanity.com/paper/2509.18869/) (2025)
- [Performance comparison of Dask and Apache Spark on HPC systems for Neuroimaging](https://mlsanity.com/paper/2406.01409/) (2024)
- [Tuning LLMs by RAG Principles: Towards LLM-native Memory](https://mlsanity.com/paper/2503.16071/) (2025)
- [RHAPSODY: Execution of Hybrid AI-HPC Workflows at Scale](https://mlsanity.com/paper/2512.20795/) (2025)

## Built on by (most cited first, 2 of 2)

- [[AAFLOW+] Stateful Operator Abstraction with Zero-Copy Distributed KV Cache Orchestration for Multi-Agent Workflows](https://mlsanity.com/paper/2607.10987/) (2026)
- [OpRAG: A Resource-Deterministic Runtime for GPU-Backed Multi-Stage RAG Workflows](https://mlsanity.com/paper/2608.08340/) (2026)

## Abstract (arXiv)

Agentic workflows in large language model systems integrate retrieval, reasoning, and memory, but existing frameworks suffer from scalability and reproducibility limitations due to fragmented data orchestration, serialization overhead, and non-deterministic execution. Although these frameworks increase flexibility, they don't have a formal execution model that adheres to the principles of high-performance computing. We introduce AAFLOW, a unified distributed runtime that creates communication-efficient execution plans by modeling agentic workflows as an operator abstraction. Using Apache Arrow and Cylon, AAFLOW creates a zero-copy data plane that allows direct interoperability between preprocessing, embedding, and vector retrieval without the need for serialization overhead. To lower coordination costs, it uses resource-deterministic scheduling and asynchronous batching. While retaining comparable LLM generation throughput, experimental results demonstrate up to 4.64 times pipeline speedup and 2.8 times gains in embedding and upsert phases. Rather than LLM inference acceleration, these advantages result from enhanced data flow, batching, and communication efficiency.