5-Layer Design for Open-Source Agent Self-Evolution

yujiezha · reddit · 2026-07-13

The author outlines a "self-evolution" design for open-source AI agents. The core goal isn't just memory retrieval, but enabling the agent to progressively refine its skills, prompts, and even update its code under controlled conditions.

Capability changes are divided into 5 layers:

Base Memory & Knowledge: Memories, knowledge bases, and prompts are stored as editable files; long-term memory goes into MEMORY.md, while daily logs are saved by date.
Context Summarization: When context limits are exceeded, it doesn't just drop messages. It truncates overly long tool results first, then prunes by full turns, summarizing the pruned content back into the daily memory.
Post-Session Review: When idle, the agent reviews the entire interaction to fix or create skills, retry failed tasks, and update memories/knowledge/prompts.
Nightly Consolidation: Periodically organizes short-term information into long-term memory.
Source Code Self-Update: Modifying the codebase is only allowed upon explicit request.

The guiding principle emphasized is: Fix the root cause, don't just log the symptoms. For instance, if a skill fails, it shouldn't just leave a memory saying "it's broken"; it should directly modify the skill file.

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5-Layer Design for Open-Source Agent Self-Evolution

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