Octopus_mem
An agent memory system built on a skill + memory index architecture following a radical-minimalism principle ('jsonl, md, at most sqlite'), with a dual-repo split: open framework, private data.
View source on GitHubKey takeaways
- 01
A memory index file loaded every session is the core mechanism
- 02
Minimal formats (jsonl/md/sqlite) keep memory inspectable
- 03
Framework-public, data-private is a clean split
Flows built on this research
Memory & Context
Design a Memory Index Loaded Every Session
Build the pattern behind minimalist memory systems: a compact index file the agent always loads, pointing to detail files it loads on demand.
3 steps · 60-90 minutes
Memory & Context
Add File-Based Persistent Memory
Give your agent durable memory with plain files - the radical-minimalism approach production practitioners keep converging on.
4 steps · 45-75 minutes
From the archive
Verbatim excerpts mined from our local archive of this repository — the prompts, schemas, and patterns worth stealing.
Skill-scoped memory retrieval with time decay
Retrieval strategy: - Conversation-as-retrieval: every conversation automatically retrieves related memories - Skill-centric: only retrieve memories related to the orchestrator skill - Time decay: more recent memories weigh higher (7-day decay) - Minimal results: return the 5 most relevant memories Configuration (memory_index_config.json): - Retrieval params: max_results=5, decay_days=7 - Time decay: mixed linear + exponential decay - Cache strategy: 300s TTL cache
[translated] A multi-agent memory system where each agent role gets its own skill-scoped index, retrieval is automatic per conversation, and recency decays over 7 days.
skills/memory_molt/SKILL.md