Awesome Long-Horizon Agents Survey

Skill by ara.so — AI Agent Skills collection.

This skill helps you navigate and utilize the RUC-NLPIR/Awesome-Long-Horizon-Agents repository, a comprehensive survey and curated reading list covering the landscape of long-horizon AI agents. The resource organizes research into two main pillars: externalized harness engineering (loops, memory, tools, orchestration) and internalized model optimization (training, RL, self-evolution).

Overview

The repository accompanies the paper "Towards Long-Horizon Agents: A Survey" and provides:

• Three-level formalization (H1→H2→H3) of long-horizon tasks
• Evolution timeline from prompt engineering (2020-2023) to runtime harnesses (2025+)
• Systematic taxonomy of agent capabilities and implementation patterns
• Curated paper lists organized by technique and application domain
• Benchmarks and evaluation frameworks for long-horizon tasks

Key Concept: Long-Horizon Defined

The survey defines long-horizon agents across three nested levels: