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Query all memory stores at once and get a ranked, token-budgeted briefing.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "recall" yet — see the docs or source repo.
Use recall to search brain, team, reading, and code memories for “payment refactor,” then return a relevance-ranked briefing with key findings and source cues.
A token-budgeted summary with interleaved results from multiple memory stores, ranked by relevance.
Use recall to query all memory stores for “Q3 launch risks” and produce a concise briefing, prioritizing highly relevant team discussions and code signals.
A cross-source risk recap that helps the user quickly grasp existing knowledge.
First use recall to retrieve memories related to “vector index migration plan,” then provide a background summary for follow-up Q&A.
A background briefing for continued conversation, condensing key facts across memory stores.
When an agent needs to continue a complex task, it can query brain, team, reading, and code memories in one call to recover relevant context. This reduces repeated searching and helps it respond faster.
When researchers, product managers, or developers need to understand a topic, this tool can produce a ranked briefing across memory sources. It works well for information consolidation before deeper analysis or execution.
When context windows are limited, this tool returns a token-budgeted briefing. Users can see multi-source results and priority ranking within a constrained output size.
It lets agents query multiple memory stores in one call, including brain, team, reading, and code. Results are ranked by relevance and returned as a token-budgeted briefing.
Based on the provided information, results are interleaved across sources and assembled into a ranked briefing. This lets you see relevant content from multiple memory stores in one output.
The provided material does not include installation steps or prerequisites. See the source repository for setup details.
Semantically search and expand session history to quickly recall past discussions.
Give AI agents persistent memory across sessions with automatic context retrieval.
Give AI coding agents persistent memory across sessions for people, decisions, and context.
Give AI agents persistent local memory with searchable long-term context.
Give AI agents long-term memory with user-scoped storage, recall, and deletion.
Provides local persistent memory for coding agents with low-cost context retrieval.