# AutoEDA: Enabling EDA Flow Automation through Microservice-Based LLM Agents arXiv: https://arxiv.org/abs/2508.01012 ยท HTML: https://mlsanity.com/paper/2508.01012/ ## Key facts - Cited by 6 papers in the map (data through 14 Oct 2026). - Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. - Builds on 23 of its 49 references that are in the map. - First posted on arXiv 1 Aug 2025. ## Placed in - LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/) ## Builds on (most cited first, 23 of 23) - [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023) - [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023) - [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023) - [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024) - [CodeBLEU: a Method for Automatic Evaluation of Code Synthesis](https://mlsanity.com/paper/2009.10297/) (2020) - [ToolACE: Winning the Points of LLM Function Calling](https://mlsanity.com/paper/2409.00920/) (2024) - [ChipNeMo: Domain-Adapted LLMs for Chip Design](https://mlsanity.com/paper/2311.00176/) (2023) - [RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model](https://mlsanity.com/paper/2308.05345/) (2023) - [Chip-Chat: Challenges and Opportunities in Conversational Hardware Design](https://mlsanity.com/paper/2305.13243/) (2023) - [ChatEDA: A Large Language Model Powered Autonomous Agent for EDA](https://mlsanity.com/paper/2308.10204/) (2023) - [Agentic Large Language Models, a survey](https://mlsanity.com/paper/2503.23037/) (2025) - [A survey of agent interoperability protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP)](https://mlsanity.com/paper/2505.02279/) (2025) - [From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future](https://mlsanity.com/paper/2408.02479/) (2024) - [RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solution](https://mlsanity.com/paper/2312.08617/) (2023) - [A Survey on the Optimization of Large Language Model-based Agents](https://mlsanity.com/paper/2503.12434/) (2025) - [A Survey of Research in Large Language Models for Electronic Design Automation](https://mlsanity.com/paper/2501.09655/) (2025) - [LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation](https://mlsanity.com/paper/2401.12224/) (2023) - [A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models](https://mlsanity.com/paper/2410.07265/) (2024) - [Microservices: yesterday, today, and tomorrow](https://mlsanity.com/paper/1606.04036/) (2016) - [A Survey of Circuit Foundation Model: Foundation AI Models for VLSI Circuit Design and EDA](https://mlsanity.com/paper/2504.03711/) (2025) - [The Evolving Landscape of LLM- and VLM-Integrated Reinforcement Learning](https://mlsanity.com/paper/2502.15214/) (2025) - [MCP4EDA: LLM-Powered Model Context Protocol RTL-to-GDSII Automation with Backend Aware Synthesis Optimization](https://mlsanity.com/paper/2507.19570/) (2025) - [Workshops on Extreme Scale Design Automation (ESDA) Challenges and Opportunities for 2025 and Beyond](https://mlsanity.com/paper/2005.01588/) (2020) ## Built on by (most cited first, 6 of 6) - [Bridging the Last Mile of Circuit Design: PostEDA-Bench, a Hierarchical Benchmark for PPA Convergence and DRC Fixing](https://mlsanity.com/paper/2605.06936/) (2026) - [FluxEDA: A Unified Execution Infrastructure for Stateful Agentic EDA](https://mlsanity.com/paper/2603.25243/) (2026) - [ZhuLong: Execution-Grounded LLM Agent for EDA Scripting with Offline API Self-Exploration](https://mlsanity.com/paper/2608.07925/) (2026) - [LLMs in Digital EDA: A perspective on shifting roles from Generation to Orchestration](https://mlsanity.com/paper/2608.27184/) (2026) - [Can AI Agents Really Complete RTL-to-GDS? Lessons from Benchmarking Tool-Interactive EDA Workflows](https://mlsanity.com/paper/2607.17528/) (2026) - [EDATracer: An Agentic Framework for Large-Scale EDA Artifact Analysis](https://mlsanity.com/paper/2608.04032/) (2026) ## Abstract (arXiv) Electronic Design Automation (EDA) remains heavily reliant on tool command language (Tcl) scripting to drive complex RTL-to-GDSII flows. This scripting-based paradigm is labor-intensive, error-prone, and difficult to scale across large design projects. Recent advances in large language models (LLMs) suggest a new paradigm of natural language-driven automation. However, existing EDA efforts remain limited and face key challenges, including the absence of standardized interaction protocols and dependence on external APIs that introduce privacy risks. We present AutoEDA, a framework that leverages the Model Context Protocol (MCP) to enable end-to-end natural language control of RTL-to-GDSII design flows. AutoEDA introduces MCP-based servers for task decomposition, tool selection, and automated error handling, ensuring robust interaction between LLM agents and EDA tools. To enhance reliability and confidentiality, we integrate locally fine-tuned LLM agents. We further contribute a benchmark generation pipeline for diverse EDA scenarios and extend CodeBLEU with Tcl-specific enhancements for domain-aware evaluation. Together, these contributions establish a comprehensive framework for LLM-driven EDA automation, bridging natural language interfaces with modern chip design flows. Empirical results show that AutoEDA achieves up to 9.9 times higher accuracy than naive approaches while reducing token usage by approximately 97% compared to in-context learning.