Now I have a clear picture of both. Here's the breakdown:
What's in this repo (openclaw-supermemory)
A plugin for OpenClaw that adds persistent memory via the Supermemory cloud service:
- Auto-recall: Semantically searches past memories before each AI turn, injects relevant context
- Auto-capture: Extracts lasting facts from conversations automatically
- Deduplication: Prevents redundant context injection
- Profile building: Maintains a persistent user profile (static facts, dynamic facts)
- Smart filtering: Knows what to remember (preferences, decisions, entities) vs. what to forget (temporary intents, assistant actions)
- Container routing: AI-driven categorization into separate memory namespaces
What's in autoresearch
An autonomous ML experimentation framework where Claude edits train.py, runs 5-minute experiments, logs results, and loops forever. Uses the "Ralph loop" pattern: markdown state files (progress.md, next_ideas.md, results.tsv) as persistent memory between iterations.
How supermemory ideas can level up autoresearch
Here are the concrete cross-pollination opportunities:
- Semantic recall instead of flat file reads
Right now autoresearch reads progress.md and results.tsv linearly every iteration. After 500+ experiments, these files bloat and waste context. Supermemory's approach: semantically search past experiments based on the current idea, retrieving only the 5-10 most relevant prior results. Instead of "read all history", it becomes "what happened last time we tried adjusting RoPE base frequency?"
- Auto-capture for experiment insights
Currently the agent manually updates progress.md with "what works/fails". Supermemory's auto-capture pattern could automatically extract insights from each experiment's logs and diffs, building a searchable knowledge base without relying on the agent to summarize correctly every time.
- Deduplication of experiment ideas
next_ideas.md can accumulate duplicate or near-duplicate ideas across iterations. Supermemory's deduplication logic (similarity-based) would prevent the agent from re-proposing experiments that are essentially the same as previous ones.
- Profile = "research state" that persists across sessions
Supermemory builds a user profile from conversations. Autoresearch could build a "research profile" — GPU constraints, confirmed good hyperparameter ranges, dead-end directions — that persists even when you switch GPUs or restart from scratch. Instead of encoding this in progress.md manually, it's extracted automatically.
- Container tags for experiment categories
Supermemory routes memories into containers (work, personal). Autoresearch could use the same pattern to separate memory by experiment category: lr-tuning, architecture, optimizer, rope, wild-card. The coordinator could then query specific categories when generating new experiments.
- Relative importance scoring
Supermemory returns similarity scores with each memory. Autoresearch could weight experiment history by relevance to current search direction rather than treating all past experiments equally. A RoPE experiment from 200 iterations ago is irrelevant when you're deep in LR tuning — unless you're about to try combining them.
- Forgetting mechanism
Supermemory has supermemory_forget. Autoresearch has no equivalent — failed experiments accumulate forever. A forgetting mechanism that prunes experiments below a relevance threshold would keep the agent's context focused on what matters.
The bottom line: autoresearch's markdown-as-memory pattern is clever but doesn't scale past ~100 experiments. The supermemory approach of semantic search + auto-extraction + deduplication would let autoresearch run thousands of experiments without context degradation. The simplest integration path would be adding a local semantic index (embeddings over results.tsv entries + done/*.md reports) rather than requiring the Supermemory cloud service.
Want me to prototype any of these ideas into autoresearch?