AI & agent systems
Useful agent memory does not mean loading everything
Keep task state structured, retrieve relevant context when needed, and let an agent find the right memory without stuffing every document into every request.
More context is not always better
A project can collect research, documents, artifacts, and task history quickly. Sending all of that to a model for every step makes it harder to focus on the current question and wastes room that could hold the useful details.
The better goal is to make relevant information findable. Retrieve the pieces that help with the work at hand, then keep the rest available for another time.
Separate state from semantic memory
A task has structured facts: its status, owner, steps, and outputs. Those facts should be stored and updated in a predictable way. Crew uses PostgreSQL for structured application state.
Other knowledge is less rigid. A prior research note or document may be relevant because of its meaning rather than its ID. Semantic retrieval with a vector store such as Qdrant can help surface that context when an agent needs it.
Retrieve with the task in mind
Memory works best when it responds to a specific need: what has already been researched, which artifact answers this question, or what context should the writer know? This keeps retrieval tied to the active task instead of becoming a second unbounded history.
The aim is not to remember everything equally. It is to keep dependable task state and make useful past work easy to recover at the moment it matters.