Building Your First AI Agent from Scratch (No Framework)

BEAM serves as a critical benchmark for AI agents, testing memory across multi-session histories with 100K to 10 million tokens. Unlike older benchmarks, it evaluates the ability to update beliefs and reason across thousands of turns. Meanwhile, the Agent Memory Leaderboard (AML) addresses evaluation inconsistencies in memory systems. Separately, some developers are bypassing agent frameworks like LangChain or CrewAI to build production-shaped agents from scratch using Python to avoid restrictive abstractions and maintain specific workflow control.