GenAI

GenAI

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Why agent infrastructure matters
Why agent infrastructure matters
Learn why agent infrastructure is essential to handling stateful, long-running tasks — and how LangGraph Platform provides the runtime support needed to build and scale reliable agents.
·blog.langchain.com·
Why agent infrastructure matters
a2a-community/a2a-ui
a2a-community/a2a-ui
Contribute to a2a-community/a2a-ui development by creating an account on GitHub.
·github.com·
a2a-community/a2a-ui
GitHub - AdemBoukhris457/Docs_Parsing_Techniques: Jupyter notebooks testing different OCR models for document parsing (Dolphin, MonkeyOCR, Marker, Nanonets, ...)
GitHub - AdemBoukhris457/Docs_Parsing_Techniques: Jupyter notebooks testing different OCR models for document parsing (Dolphin, MonkeyOCR, Marker, Nanonets, ...)
Jupyter notebooks testing different OCR models for document parsing (Dolphin, MonkeyOCR, Marker, Nanonets, ...) - AdemBoukhris457/Docs_Parsing_Techniques
·github.com·
GitHub - AdemBoukhris457/Docs_Parsing_Techniques: Jupyter notebooks testing different OCR models for document parsing (Dolphin, MonkeyOCR, Marker, Nanonets, ...)
A2A vs ACP Protocol Comparison Analysis Report
A2A vs ACP Protocol Comparison Analysis Report
A2A (Agent2Agent Protocol) and ACP (Agent Communication Protocol) represent two mainstream technical approaches in AI multi-agent system communication: 'cross-platform interoperability' and 'local/edge autonomy' respectively. A2A, with its powerful cross-vendor interconnection capabilities and rich task collaboration mechanisms, has become the preferred choice for cloud-based and distributed multi-agent scenarios; while ACP, with its low-latency, local-first, cloud-independent characteristics, is suitable for privacy-sensitive, bandwidth-constrained, or edge computing environments. Both protocols have their own focus in protocol design, ecosystem construction, and standardization governance, and are expected to further converge in openness in the future. Developers are advised to choose the most suitable protocol stack based on actual business needs.
·a2aprotocol.ai·
A2A vs ACP Protocol Comparison Analysis Report
GitHub - TencentQQGYLab/AppAgent: AppAgent: Multimodal Agents as Smartphone Users, an LLM-based multimodal agent framework designed to operate smartphone apps.
GitHub - TencentQQGYLab/AppAgent: AppAgent: Multimodal Agents as Smartphone Users, an LLM-based multimodal agent framework designed to operate smartphone apps.
AppAgent: Multimodal Agents as Smartphone Users, an LLM-based multimodal agent framework designed to operate smartphone apps. - TencentQQGYLab/AppAgent
·github.com·
GitHub - TencentQQGYLab/AppAgent: AppAgent: Multimodal Agents as Smartphone Users, an LLM-based multimodal agent framework designed to operate smartphone apps.
LangGraph Rollout: Evolving VeRL’s Multi-Turn Capabilities for Agent RL
LangGraph Rollout: Evolving VeRL’s Multi-Turn Capabilities for Agent RL
After completing our multi-turn tokenization and masking refactoring, we eliminated a critical bottleneck that was preventing us from building a more consistent and flexible rollout system for our Agent RL research. This breakthrough enabled us to implement a LangGraph-based rollout for VeRL in just a few days, which we’ve already successfully deployed in our Agent RL experiments. In this article, I’ll share our journey from VeRL’s native multi-turn implementation to our new LangGraph-based solution, explaining both the motivations driving this evolution and the technical details of our implementation.
·jybsuper.github.io·
LangGraph Rollout: Evolving VeRL’s Multi-Turn Capabilities for Agent RL
Context Engineering Guide
Context Engineering Guide
Context Engineering Guide By DAIR.AI Academy Table of Contents What is Context Engineering? Context Engineering is Action System Prompt Instructions User Input Structured Inputs and Outputs Tool Calling RAG & Memory State & Historical Context Advanced Context Engineering Resources What is Co...
·docs.google.com·
Context Engineering Guide
Context Engineering
Context Engineering
TL;DR Agents need context to perform tasks. Context engineering is the art and science of filling the context window with just the right information at each step of an agent’s trajectory. In this post, we break down some common strategies — write, select, compress, and isolate — for context engineering
·blog.langchain.com·
Context Engineering