Agent Memory
A language model remembers nothing. Every single call starts from an empty room, and anything it seems to remember was pasted back into the prompt by your code. Once you internalize that, most agent bugs become obvious.
Four kinds, four jobs
Scratchpad
What happened during this run: thoughts, tool calls, observations. Lives for one task, then goes away. This is what turns a sequence of calls into a loop.
Conversation
Earlier turns with the same person. Cheap to add, and the thing users notice missing first.
Long term
Facts worth keeping across sessions: preferences, account IDs, decisions already made. A store your code writes to and reads from. Here it is your browser's localStorage.
Retrieval
Too much to fit in a prompt, so you fetch only the relevant slice at question time. This is RAG, and it is memory with a search engine in front of it.
Optional: load a real model
Memory on, memory off
Talk to the agent for a few turns, then ask it something that depends on an earlier turn, such as "what did I say my name was?". Switch the toggle and ask the same thing again. Nothing about the model changes. Only what your code chose to send changes.
Long term memory you can inspect
Facts saved here persist across page loads, exactly as an agent's memory store would. They are written into the prompt on every turn above, and they stay on this device.
Next: planning and breaking a goal into steps.
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