diff --git a/labs/AgentStream/README.md b/labs/AgentStream/README.md
new file mode 100644
index 00000000..58fb6d24
--- /dev/null
+++ b/labs/AgentStream/README.md
@@ -0,0 +1,40 @@
+<div align="center">
+
+<h1>AgentStream: How Well Do Self-Evolving LLM Agents Perform Under Streaming Tasks?</h1>
+
+<p>
+  Dong Yan<sup>1,2,3</sup>,
+  Jian Liang<sup>1,3†</sup>,
+  Dapeng Hu<sup>2†</sup>,
+  Ran He<sup>1,3</sup>,
+  Nicholas Jing Yuan<sup>2</sup>,
+  Qi Zhang<sup>2</sup>,
+  Tieniu Tan<sup>1,3,4</sup>
+</p>
+
+<p>
+  <sup>1</sup>School of Artificial Intelligence, University of Chinese Academy of Sciences<br>
+  <sup>2</sup>Microsoft<br>
+  <sup>3</sup>Institute of Automation, Chinese Academy of Sciences<br>
+  <sup>4</sup>Nanjing University
+</p>
+
+<p>
+  📧 <code>liangjian92@gmail.com</code> &nbsp;
+  <code>dapenghu@microsoft.com</code>
+</p>
+
+</div>
+
+## 🚀 News
+* **[2026/07]** Code is under preparation. Stay tuned!
+
+## 📖 Overview
+Large language model (LLM) agents can self-evolve by continually improving from their own accumulated experience.
+However, existing studies predominantly adopt independent evaluation.
+Consequently, the behavior of self-evolving agents in realistic streaming settings, where agents adapt to diverse and complex task streams, remains poorly understood.
+To address this gap, we introduce AgentStream, a unified framework that evaluates self-evolving agents spanning diverse evolution components by organizing agentic benchmarks into a configurable task stream and instantiating the `Isolated`, `Sequential`, and `Interleaved` streaming scenarios at test time, which progressively vary the scope and domain composition of the stream.
+Over these scenarios, we combinatorially evaluate five representative self-evolving methods across three frontier foundation models, disentangling how model capability, method architecture, and streaming scenario jointly shape self-evolution.
+Our results show that self-evolution reliability varies across streaming scenarios, the benefit of self-evolution is gated by model capability and non-monotonic in model strength, and no single method dominates across models and scenarios.
+These findings offer concrete guidance for selecting self-evolving methods across models and streaming scenarios.
+Overall, we advocate that self-evolving agents should be evaluated under realistic task streams rather than isolated single-task settings.