# SeaView: Software Engineering Agent Visual Interface for Enhanced Workflow

arXiv: https://arxiv.org/abs/2504.08696 · HTML: https://mlsanity.com/paper/2504.08696/

## Key facts

- Cited by 7 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 9 of its 23 references that are in the map.
- First posted on arXiv 11 Apr 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, 9 of 9)

- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [Large Language Monkeys: Scaling Inference Compute with Repeated Sampling](https://mlsanity.com/paper/2407.21787/) (2024)
- [Training Software Engineering Agents and Verifiers with SWE-Gym](https://mlsanity.com/paper/2412.21139/) (2024)
- [SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement](https://mlsanity.com/paper/2410.20285/) (2024)
- [CodeMonkeys: Scaling Test-Time Compute for Software Engineering](https://mlsanity.com/paper/2501.14723/) (2025)
- [TDD-Bench Verified: Can LLMs Generate Tests for Issues Before They Get Resolved?](https://mlsanity.com/paper/2412.02883/) (2024)

## Built on by (most cited first, 7 of 7)

- [Understanding Code Agent Behaviour: An Empirical Study of Success and Failure Trajectories](https://mlsanity.com/paper/2511.00197/) (2025)
- [Context Engineering for Multi-Agent LLM Code Assistants Using Elicit, NotebookLM, ChatGPT, and Claude Code](https://mlsanity.com/paper/2508.08322/) (2025)
- [What Resolve Rate Hides: Trajectory Structure Diagnostics for Coding Agents](https://mlsanity.com/paper/2607.06184/) (2026)
- [Agent-Environment Alignment via Automated Interface Generation](https://mlsanity.com/paper/2505.21055/) (2025)
- [TraceView: Interactive Visualization of Agentic Program Repair Trajectories](https://mlsanity.com/paper/2606.22110/) (2026)
- [AgentTrails: Towards Trust and Reuse for Agentic Tasks](https://mlsanity.com/paper/2607.18816/) (2026)
- [Graphectory Viewer: A Tool for Process-Centric Analysis of Agentic Software Trajectories](https://mlsanity.com/paper/2608.17195/) (2026)

## Abstract (arXiv)

Auto-regressive LLM-based software engineering (SWE) agents, henceforth SWE agents, have made tremendous progress (>60% on SWE-Bench Verified) on real-world coding challenges including GitHub issue resolution. SWE agents use a combination of reasoning, environment interaction and self-reflection to resolve issues thereby generating "trajectories". Analysis of SWE agent trajectories is difficult, not only as they exceed LLM sequence length (sometimes, greater than 128k) but also because it involves a relatively prolonged interaction between an LLM and the environment managed by the agent. In case of an agent error, it can be hard to decipher, locate and understand its scope. Similarly, it can be hard to track improvements or regression over multiple runs or experiments. While a lot of research has gone into making these SWE agents reach state-of-the-art, much less focus has been put into creating tools to help analyze and visualize agent output. We propose a novel tool called SeaView: Software Engineering Agent Visual Interface for Enhanced Workflow, with a vision to assist SWE-agent researchers to visualize and inspect their experiments. SeaView's novel mechanisms help compare experimental runs with varying hyper-parameters or LLMs, and quickly get an understanding of LLM or environment related problems. Based on our user study, experienced researchers spend between 10 and 30 minutes to gather the information provided by SeaView, while researchers with little experience can spend between 30 minutes to 1 hour to diagnose their experiment.