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title: Best LangChain posts — June 2025 | daily.dev
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# Best of LangChain — June 2025

1. 1  
[](https://daily.dev/posts/the-rise-of-context-engineering--va0amuoha "The rise of "context engineering"")  
Article  
![Avatar of langchain](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/0f4f2629e0724a849823f8cd0d913e13)LangChain · 1y  
The rise of "context engineering"  
Context engineering is emerging as a critical skill for AI engineers, focusing on building dynamic systems that provide LLMs with the right information, tools, and formatting to accomplish tasks reliably. Unlike traditional prompt engineering, context engineering emphasizes providing complete, structured context rather than clever wording. The approach addresses the primary cause of agent failures: inadequate context rather than model limitations. Key components include dynamic information retrieval, appropriate tool selection, proper formatting, and comprehensive system design. LangGraph and LangSmith are positioned as enabling technologies for implementing effective context engineering practices.  
113
2. 2  
[](https://daily.dev/posts/how-and-when-to-build-multi-agent-systems-wtn2zhgx0 "How and when to build multi-agent systems")  
Article  
![Avatar of langchain](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/0f4f2629e0724a849823f8cd0d913e13)LangChain · 1y  
How and when to build multi-agent systems  
Multi-agent systems require careful consideration of when and how to implement them effectively. Context engineering emerges as the most critical challenge, requiring sophisticated strategies to ensure each agent has appropriate context for their tasks. Systems focused on reading tasks (like research) are generally easier to implement than those emphasizing writing tasks, as read actions are more parallelizable and less prone to conflicting outputs. Production reliability requires durable execution, comprehensive debugging tools, and proper evaluation frameworks. Multi-agent architectures work best for breadth-first queries with high parallelization potential and tasks valuable enough to justify increased token costs.  
42

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