Our first meeting will be Sep 27 (Saturday) to welcome the fall cohorts of researchers!
2025/09/27
OpenAI Agents Framwork & Guardrails
recording: [link]
Topics that we will try to cover through our weekend meetings:
Agent Frameworks:
OpenAI agents
CrewAI agents
Amazon Strands
LlamaIndex Agents
Langchain Agents
Google ADK agents
Agent Safety & Guardrails
Multi-Agents Orchestration
Agent Optimization
Knowledge Graphs
Using VLM to outperform OCR
Evaluation: how to use common datasets for validating agents
2025/10/04
2025/10/18
2025/10/25
Poster & Oral Presentation Notes [link]
LangChain & LangSmith
code: [link]
recording: [link]
Readings:
LangChain v1.0 [link]
2025/11/02
Multi-Agents Orchestration
slides: [link]
code: [link]
recording: [link]
Readings:
Zhipeng Hou, Junyi Tang, Yipeng Wang. “HALO: Hierarchical Autonomous Logic-Oriented Orchestration for Multi-Agent LLM Systems.” arXiv:2505.13516 [cs.MA], May 17, 2025. [arxiv]
2025/11/09
2025/11/16
SCCUR poster & talk schedule:
[schedule]
Date: Nov 22 (Saturday) @ CSU Channel Islands
recording: [link]
2025/11/23 (NO MEETING)
See you in CSU Channel Islands for SCCUR
[schedule]
2025/11/30 (Thanksgiving Weekend)
Building Multi-agents
code: [link]
recording: [link]
If enough people are interested, we will meet to show off the final HALO Orchestrator implementation & MMLU Evaluation!
2025/12/06
Smart Router Agent building
code: [link]
recording: [link]
Readings:
Feijie Wu, Zitao Li, Fei Wei, Yaliang Li, Bolin Ding, Jing Gao, "Talk to Right Specialists: Routing and Planning in Multi-agent System for Question Answering", arXiv:2501.07813, January 14, 2025. [link]
2025/12/13
Multi-agent Orchestration (con't)
Readings:
Junwei Yu, Yepeng Ding, Hiroyuki Sato. DynTaskMAS: A Dynamic Task Graph-driven Framework for Asynchronous and Parallel LLM-based Multi-Agent Systems, arXiv:2503.07675, December 2, 2025. [link]
demonstrates that dynamic task graph orchestration achieves 21-33% execution time reduction and near-linear throughput scaling up to 16 concurrent agents.
Philip Drammeh, Multi-Agent LLM Orchestration Achieves Deterministic, High-Quality Decision Support for Incident Response, arXiv:2511.15755, November 19, 2025. [link]
shows the quality case: multi-agent orchestration achieves 100% actionable recommendations versus 1.7% for single-agent, with zero quality variance -- enabling SLA commitments impossible with simpler approaches.
Jiaqi Wu, Qinlao Zhao, Zefeng Chen, Kai Qin, "GAP: Graph-based Agent Planning with Parallel Tool Use and Reinforcement Learning." arXiv:2510.25320, October 29, 2025. [link]
Anna Alexandra Grigoryan, "Why Do Multi-Agent LLM Systems Fail?", Medium, April 30, 2025. [link]
ByteByteGo, How Anthropic Built a Multi-Agent Research System, ByteByteGo, September 16, 2025. [link]