agentdb-recall

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

1. Embed the current task description.
2. Call agentdb_reflexion_recall with k=5, minReward=0.5.
3. Call agentdb_pattern_search with the same query, k=5.
4. Dedupe + rank — present the top 3-5 to the user as "things I found that look relevant."
5. If user-confirmed useful: call agentdb_record_feedback (or recordFeedback via 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.