Memory is a core toolbox connector, it is foundational to most agent workflows where the agent runtime needs to reason over large inputs without bloating the prompt.
Capabilities
The Memory connector can:- Search large bodies of context for information matching a query
- Analyze long documents or files and return targeted facts
- Surface relevant excerpts so the agent runtime can ground its synthesis
- Reduce the size of the agent runtime’s working prompt by offloading bulky lookups
Use cases
- Querying a long clinical document or attached file for specific facts
- Pulling targeted passages out of a large transcript or note
- Summarizing or extracting from inputs that are too large to keep in the agent runtime prompt
- Providing analyzed excerpts as evidence for a downstream connector
How to invoke
Send the relevant query or high-level objective as thetext parameter. Do not pass new raw facts to the Memory connector. Provide only the question or objective so it can search and analyze the existing in-request context itself.
For questions that can be answered directly from the agent runtime’s own prompt without consulting bulky context, you can answer directly without invoking the Memory connector.