HALO: an MCP plugin that audits personal agents' self-improvement loops on an L0-L5 scale
liuziwei7 · x · 2026-11-10
HALO, from the LMMS Lab circle, is an MCP server plugin that analyzes whether personal agents are genuinely self-improving.
- Plug-and-play: works with any MCP-capable host (ChatGPT dots, Grok Bot, Claude Code, Codex) without code changes, via a one-line "halo": {"command": "halo", "args": ["serve"]} config
- Channels from links: each logged agent step names earlier steps it used, letting HALO identify information channels (e.g. Memory→Action)
- Evidence for self-improvement: HALO follows action → outcome → revision → later action using that revision to trace the full loop closure
- L0-L5 levels as lower bounds: the report places the trace at a loop-closure level and states what the next level would require
It accompanies the Action-Sensing-Memory systemic analysis of personal multimodal agents.
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