WebCoach: Self-Evolving Web Agents with Cross-Session Memory Guidance

TL;DR

WebCoach enhances web navigation agents by integrating persistent cross-session memory, enabling continual learning and improved robustness without retraining, demonstrated by significant performance gains on the WebVoyager benchmark.

Contribution

WebCoach introduces a novel self-evolving framework with memory components that allow web agents to learn from past experiences across sessions, improving long-term planning and robustness.

Findings

Increases task success rate from 47% to 61% with a 38B model.

Enables smaller models to perform comparably to GPT-4-based agents.

Improves robustness and efficiency in complex web browsing tasks.

Abstract

Multimodal LLM-powered agents have recently demonstrated impressive capabilities in web navigation, enabling agents to complete complex browsing tasks across diverse domains. However, current agents struggle with repetitive errors and lack the ability to learn from past experiences across sessions, limiting their long-term robustness and sample efficiency. We introduce WebCoach, a model-agnostic self-evolving framework that equips web browsing agents with persistent cross-session memory, enabling improved long-term planning, reflection, and continual learning without retraining. WebCoach consists of three key components: (1) a WebCondenser, which standardizes raw navigation logs into concise summaries; (2) an External Memory Store, which organizes complete trajectories as episodic experiences; and (3) a Coach, which retrieves relevant experiences based on similarity and recency, and decides whether to inject task-specific advice into the agent via runtime hooks. This design empowers web agents to access long-term memory beyond their native context window, improving robustness in complex browsing tasks. Moreover, WebCoach achieves self-evolution by continuously curating episodic memory from new navigation trajectories, enabling agents to improve over time without retraining. Evaluations on the WebVoyager benchmark demonstrate that WebCoach consistently improves the performance of browser-use agents across three different LLM backbones. With a 38B model, it increases task success rates from 47% to 61% while reducing or maintaining the average number of steps. Notably, smaller base models with WebCoach achieve performance comparable to the same web agent using GPT-4o.