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Self-Evolving AI Agents Survey
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<h1>A Comprehensive Survey of Self-Evolving AI Agents</h1>
<p>A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems</p>
<div class="hero-actions">
<a href="https://arxiv.org/abs/2508.07407" class="btn" target="_blank">Read Paper</a>
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<h2 class="section-title">Overview</h2>
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<h3>Abstract</h3>
<p>
Recent advances in large language models (LLMs) have sparked growing interest in AI agents capable of solving complex, real-world tasks. However, most existing agent systems rely on manually crafted configurations that remain static after deployment, limiting their ability to adapt to dynamic and evolving environments. To address this limitation, recent research has explored agent <em>evolution</em> techniques that aim to automatically enhance agent systems based on interaction data and environmental feedback. This emerging direction lays the foundation for <em>self-evolving AI agents</em>, which bridge the static capabilities of foundation models with the continuous adaptability required by <em>lifelong agentic systems</em>. In this survey, we provide a comprehensive review of existing techniques for self-evolving agentic systems.
Specifically, we first introduce a <em>unified conceptual framework</em> that abstracts the feedback loop underlying the design of self-evolving agentic systems. The framework highlights four key components: <em>System inputs</em>, <em>Agent System</em>, <em>Environment</em>, and <em>Optimisers</em>, serving as a foundation for understanding and comparing different strategies. Based on this framework, we systematically review a wide range of self-evolving techniques that target different components of the agent system, including foundation models, agent prompts, memory, tools, workflows, and communication mechanisms across agents.
We also investigate domain-specific evolution strategies developed for specialised fields such as biomedicine, programming, and finance, where agent behaviour and optimisation objectives are tightly coupled with domain constraints. In addition, we provide a dedicated discussion on the <em>evaluation, safety, and ethical considerations</em> for self-evolving agentic systems, which are critical to ensuring their effectiveness and reliability.
This survey aims to provide researchers and practitioners with a systematic understanding of self-evolving AI agents, laying the foundation for the development of more adaptive, autonomous, and lifelong agentic systems.
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<h2 class="section-title">Conceptual Framework</h2>
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<h3>System Inputs</h3>
<p>Dynamic data sources that provide information to the agent system. Includes user queries, environmental observations, and interaction data.</p>
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<h3>Agent System</h3>
<p>The core AI agent components including foundation models, planning modules, memory systems, tools, workflows, and communication mechanisms.</p>
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<h3>Environment</h3>
<p>The external world or specialized domain where agents operate, providing feedback and constraints that drive evolution.</p>
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<h3>Optimisers</h3>
<p>Mechanisms that enable agents to self-evolve by analyzing performance, identifying deficiencies, and implementing improvements.</p>
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</section>
<section id="authors" class="section">
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<h2 class="section-title">Research Team</h2>
<div class="authors-list">
<span class="author-item">Jinyuan Fang* (1)</span>
<span class="author-item">Yanwen Peng* (2)</span>
<span class="author-item">Xi Zhang* (1)</span>
<span class="author-item">Yingxu Wang* (3)</span>
<span class="author-item">Xinhao Yi (1)</span>
<span class="author-item">Guibin Zhang (4)</span>
<span class="author-item">Yi Xu (5)</span>
<span class="author-item">Bin Wu (6)</span>
<span class="author-item">Siwei Liu (7)</span>
<span class="author-item">Zihao Li (1)</span>
<span class="author-item">Zhaochun Ren (8)</span>
<span class="author-item">Nikos Aletras (2)</span>
<span class="author-item">Xi Wang (2)</span>
<span class="author-item">Han Zhou (5)</span>
<span class="author-item">Zaiqiao Meng✉ (1)</span>
</div>
<div class="institutions-list">
<span>(1) University of Glasgow</span>
<span>(2) University of Sheffield</span>
<span>(3) Mohamed bin Zayed University of Artificial Intelligence</span>
<span>(4) National University of Singapore</span>
<span>(5) University of Cambridge</span>
<span>(6) University College London</span>
<span>(7) University of Aberdeen</span>
<span>(8) Leiden University</span>
</div>
<p style="text-align: center; margin-top: 30px; color: var(--gray);">
* Equal Contributor | ✉ Corresponding Author
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<h2 class="section-title">EvoAgentX GitHub Repository</h2>
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<h2 class="section-title">Awesome Self-Evolving Agents GitHub Repository</h2>
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<h3 style="text-align: center; margin-bottom: 20px;">Awesome Self-Evolving Agents Repository</h3>
<p style="text-align: center;">This repository contains a comprehensive collection of resources related to self-evolving AI agents, including:</p>
<ul style="max-width: 600px; margin: 20px auto; padding-left: 20px;">
<li>Papers and research articles on agent evolution techniques</li>
<li>Frameworks and methodologies for self-evolving systems</li>
<li>Case studies in domain-specific applications</li>
<li>Evaluation metrics and benchmarks</li>
<li>Safety and ethical considerations resources</li>
</ul>
<div style="text-align: center;">
<a href="https://github.com/EvoAgentX/Awesome-Self-Evolving-Agents" class="btn" target="_blank">Explore Repository</a>
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Self-Evolving AI Agents Survey
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<p>A comprehensive survey of techniques enabling AI agents to autonomously evolve their behaviors</p>
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header.style.boxShadow = '0 4px 20px rgba(0, 0, 0, 0.3)';
} else {
header.style.boxShadow = 'none';
}
});
// Add animation to feature cards when they come into view
const featureCards = document.querySelectorAll('.feature-card');
const observer = new IntersectionObserver((entries) => {
entries.forEach(entry => {
if (entry.isIntersecting) {
entry.target.style.opacity = 1;
entry.target.style.transform = 'translateY(0)';
}
});
}, { threshold: 0.1 });
featureCards.forEach(card => {
card.style.opacity = 0;
card.style.transform = 'translateY(20px)';
card.style.transition = 'opacity 0.5s ease, transform 0.5s ease';
observer.observe(card);
});
// Add animation to stat cards when they come into view
const statCards = document.querySelectorAll('.stat-card');
statCards.forEach(card => {
card.style.opacity = 0;
card.style.transform = 'translateY(20px)';
card.style.transition = 'opacity 0.5s ease, transform 0.5s ease';
observer.observe(card);
});
});
</script>
<script>
const fmt = n => Intl.NumberFormat('en', { notation: 'compact', maximumFractionDigits: 1 }).format(n);
async function updateOneStats(container) {
const repo = container.dataset.repo;
if (!repo) return;
try {
const [repoRes, contribRes] = await Promise.all([
fetch(`https://api.github.com/repos/${repo}`),
fetch(`https://api.github.com/repos/${repo}/contributors?per_page=1&anon=0`)
]);
const repoData = await repoRes.json();
// contributors：优先从 Link 头拿最后一页页码
let contributors = 0;
const link = contribRes.headers.get('link');
if (link && /&page=(\d+)>;\s*rel="last"/.test(link)) {
contributors = parseInt(RegExp.$1, 10);
} else {
const arr = await contribRes.json();
contributors = Array.isArray(arr) ? arr.length : 0;
}
const setNum = (metric, val) => {
const el = container.querySelector(`.number[data-metric="${metric}"]`);
if (el) el.textContent = fmt(val);
};
setNum('stars', repoData.stargazers_count);
setNum('forks', repoData.forks_count);
setNum('issues', repoData.open_issues_count);
setNum('contributors', contributors);
} catch (e) {
console.error('GitHub stats fetch error:', e);
}
}
// 更新页面上所有带 data-repo 的区块
document.querySelectorAll('.github-stats[data-repo]').forEach(updateOneStats);
</script>
</body>
</html> |