ruvnet Recall
Pull relevant past memories into the current context.
When to use
- Task start — auto-load any prior episodes / patterns / skills that look relevant
- When stuck — search for past failures + critiques on similar tasks
- User asks — "have we done this?", "what do we know about X?"
- Decision point — surface successful strategies before committing to one
Pick the right tool
| Need | Tool | Returns |
|---|---|---|
| Similar past tasks | agentdb_reflexion_recall |
Episodes, top-k by similarity |
| Lessons from past failures | agentdb_critique_summary |
Combined critique text |
| What worked last time | agentdb_success_strategies |
Approach summaries from high-reward episodes |
| Generic patterns / facts | agentdb_pattern_search |
Patterns ranked by similarity |
| Reusable skills by intent | agentdb_skill_search |
Skills ranked by precondition match |
Standard recall flow at task start
- Embed the current task description.
- Call
agentdb_reflexion_recallwithk=5,minReward=0.5. - Call
agentdb_pattern_searchwith the same query,k=5. - Dedupe + rank — present the top 3-5 to the user as "things I found that look relevant."
- If user-confirmed useful: call
agentdb_record_feedback(orrecordFeedbackvia the library) so the bandit learns.
Filters worth knowing
minReward— drop low-quality matches (default 0.3).onlyFailures— explicitly query the postmortem set when debugging.onlySuccesses— only winning approaches when copying a strategy.timeWindowDays— recent context only when the codebase has shifted.
Don't
- Don't dump all 50 hits — pick 3-5. Cognitive overload tanks quality.
- Don't recall and ignore. If you used a memory, record feedback. If you didn't, record negative feedback. The bandit needs the signal.
- Don't recall episode content verbatim into prompts when the task changes. Summarize the lesson, not the artifact.