Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A curated collection of AI agent research papers released in 2026, covering agent engineering, memory, evaluation, workflows, and autonomous systems.
| Date | Stars |
|---|---|
| 2026-07-24 | 1614 |
| 2026-07-25 | 1616 |
| 2026-07-28 | 1616 |
| 2026-07-30 | 1616 |
| 2026-07-31 | 1636 |
| 2026-08-06 | 1636 |
Today
— stars today
This week
+20 stars this week
This month
— stars this month
Momentum
5.0
growth rate 1.24%/day
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<a href="https://github.com/VoltAgent/voltagent">
<img width="1500" height="500" alt="cover-image" src="https://github.com/user-attachments/assets/23718e60-9ad3-4105-999c-8372713c3fbb" />
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<strong>Hand-picked research papers on the AI agent ecosystem, published in 2026.
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[](https://awesome.re)
<a href="https://github.com/VoltAgent/voltagent">
<img alt="VoltAgent" src="https://cdn.voltagent.dev/website/logo/logo-2-svg.svg" height="20" />
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[](https://s.voltagent.dev/discord)
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<strong>More awesome collections for developers</strong>
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# Awesome AI Agent Papers
A curated collection of research papers **published in 2026** and sourced from arXiv, covering core topics from the AI agent ecosystem like **multi-agent coordination**, **memory & RAG**, **tooling**, **evaluation & observability**, and **security**.
Whether you're an AI engineer building agent systems, a researcher exploring new architectures, or a developer integrating LLM agents into products, these papers help you stay on top of what's actually working, what's breaking, and where the field is heading. Updated weekly from arXiv.
### Why this list exists
Hundreds of papers are published on arXiv every week, and a growing number of them touch on AI agents. We go through them all, filter the ones that are directly relevant to the AI agent ecosystem, and categorize them so you don't have to. This list only includes papers published from January 2026 onward.
### Table of Contents
- [Multi-Agent](#multi-agent) (53)
- [Memory & RAG](#memory--rag) (57)
- [Eval & Observability](#eval--observability) (80)
- [Agent Tooling](#agent-tooling) (95)
- [AI Agent Security](#ai-agent-security) (82)
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<details open id="multi-agent">
<summary><h3 style="display:inline">Multi-Agent (53)</h3></summary>
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| Paper | arXiv ID |
|---|:---:|
| **[PerceptUI: LLM Agents as Human-Aligned Synthetic Users for UI/UX Evaluation](https://arxiv.org/pdf/2606.05697)** - A persona-conditioned framework that predicts how a specific user would answer UI/UX evaluation questions and explains why in natural language. Trained via contrastive reflection fine-tuning and reflective prompt evolution, reaching human-level realism and generalizing to unseen questions and personas. | <a href="https://arxiv.org/abs/2606.05697"><img src="https://img.shields.io/badge/arXiv-2606.05697-b31b1b.svg" alt="arXiv" /></a> |
| **[AutoNumerics: An Autonomous, PDE-Agnostic Multi-Agent Pipeline for Scientific Computing](https://arxiv.org/pdf/2602.17607)** - A multi-agent pipeline that reads a PDE problem description in plain text and writes, debugs, and validates a classical numerical solver end-to-end. Generates spectral and finite-difference code (no neural networks), scoring ~6 orders of magnitude below FNO and CodePDE baselines. | <a href="https://arxiv.org/abs/2602.17607"><img src="https://img.shields.io/badge/arXiv-2602.17607-b31b1b.svg" alt="arXiv" /></a> |
| **[Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation](https://arxiv.org/abs/2604.09549)** - Evaluates recommender systems via agent-RS interactions. | <a href="https://arxiv.org/abs/2604.09549"><img src="https://img.shields.io/badge/arXiv-2602.06039-b31b1b.svg" alt="arXiv" /></a> |
| **[CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery](https://arxiv.org/pdf/2604.01658)** - Introduces long-running multi-agent systems that self-evolve via shared persistent memory, Excerpt of 169,527 characters
Read on GitHub
Necati Özmen · @VoltAgent
54
Youran
2
2
Yuxuan Zhang
2
Zijian Zhou · National University of Singapore · Singapore
1
1
1
Oscar Wu · Australia
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:edb0cc14f4804c5a, topic:ai-agents, name:ai agent, desc:ai agent
matched fp:edb0cc14f4804c5a, topic:rag, topic:memory
matched fp:edb0cc14f4804c5a, topic:awesome, topic:awesome-list