File-based markdown memory is the right call for the agent-loop scale. We run something similar for Claude Code sessions: a MEMORY.md index
file plus per-topic .md files split by type (user, feedback, project, reference). The agent loads the index every turn and pulls topic files
on demand.
What we hit:
The index file gets loaded every turn so it has to stay short. Ours truncates past 200 lines, so we keep the actual index under 150. Worth
designing for early.
Concurrent edits across parallel agent sessions race on the same file. Plain markdown has no merge story. The pragmatic answer was
treating the directory as append-mostly and serializing writes through one process.
Five categories is a clean shape but the boundary between decisions and errors gets fuzzy in practice. Does your design nudge the agent
toward one category over the other, or is it free choice?
yeah v0.1.2 solo open-source lol : )
no hard category rules, agent decides freely but we can guide via prompts if needed. keeping it flexible for now, can tighten later if scaling hits.
thanks for the suggestion ,really helpful
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File-based markdown memory is the right call for the agent-loop scale. We run something similar for Claude Code sessions: a MEMORY.md index
file plus per-topic .md files split by type (user, feedback, project, reference). The agent loads the index every turn and pulls topic files
on demand.
What we hit:
Five categories is a clean shape but the boundary between decisions and errors gets fuzzy in practice. Does your design nudge the agent
toward one category over the other, or is it free choice?
yeah
v0.1.2solo open-source lol : )no hard category rules, agent decides freely but we can guide via prompts if needed. keeping it flexible for now, can tighten later if scaling hits.
thanks for the suggestion ,really helpful