Loop Engineering For Everyone
Loop engineering is clearly where we're heading. The missing piece is making it simple enough and affordable enough that every engineer can actually use it.
SylphAI Community
Engineering Insights on AI Agents & LLMs.
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Latest posts
Claude Opus 4.6 is Now Available in AdaL CLI
Claude Opus 4.6 is Now Available in AdaL CLI
The Complete Guide to Prompt Caching: Cut LLM Costs by 90%
Master prompt caching to cut LLM costs by 90% and reduce latency by 75%, by AdaL CLI team.
Building With AdalFlow: From Code to Knowledge — DeepWiki
User Case in Action
Real-World Use Case: LinkedIn Recruitment Agent with AdalFlow
Hiring top talent is one of the most resource-intensive parts of building a company.
Token-Smart Agents: Self-Editing Memory, History Compaction, and Open-Source Integration Part 2
In the previous article, we gave a gentle introduction, using simple examples to explain the core idea behind self-editing memory.
Token-Smart Agents: Self-Editing Memory, History Compaction, and Open-Source Integration Part 1
TL,DR; What you’ll learn
Zero → Hero: A Self-Improving Prompt for Your LLM
AdalFlow Prompt Tuning × Self-Supervised (OvO) Prompt Optimization — Dual-Engine Recipe (with drop-in code & runnable skeleton)
AdalFlow: A PyTorch-Like Framework to Auto-Optimizing Prompt for your LLM agent
Say goodbye to manual prompt engineering.
When2Call: A Benchmark to Teach LLM Agents When (Not) to Call Tools
Large Language Models are no longer just text generators , they are becoming tool-augmented agents. They can fetch real-time data, query APIs, run code, or even control external systems.