MemEx: A Programmable Scratchpad for LLM Agents

MemEx enables LLM agents to manage context windows using persistent Python code blocks instead of JSON/XML tool calls.

• •Filters and composes tool outputs in Python before materialization, preventing context window overflow
• •Typed Python objects persist across agent turns, eliminating re-parsing overhead
• •Achieves 2-5% accuracy gains at 25-30% less token cost on enterprise retrieval tasks
• •Supports asynchronous sub-agents and analyzing complete agentic trajectories

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