Agent memory

Agents can persist information across runs using a memory canvas. This lets agents remember context from previous executions rather than starting from scratch each time.

How it works

Memory is stored as a dedicated canvas that the agent reads at the start of each run and can update before finishing. This creates a persistent scratchpad that carries forward between executions.

Use cases

• Track trends over time: e.g., "alert me if deployments increase week over week"
• Remember decisions from past runs: avoid repeating analysis or re-asking questions
• Build up context incrementally: aggregate information across multiple runs into a single summary
• Maintain state: keep running counts, lists, or logs that evolve over time