tutorial

Offload Long-Term AI Context to Persistent Client-Side Memory Stores

Q: What prompt should I use?

I want to implement a 'barista agent' (or similar persistent role) using the Claude Memory tool. Task: Define a memory schema where Claude can store [USER_PREFERENCES] and [PAST_INTERACTIONS] in a directory structure. Show me the Python client-side implementation using the `anthropic` SDK to handle `memory_store` tool calls.

Published 06:02 MYT · Jul 11

Use client-side file operations to let Claude store and recall information across sessions.

Deploy the Claude Memory tool to move information out of the expensive context window and into persistent, searchable storage files.

Claude

The Scenario

You want to build a customer support agent that 'remembers' a user's specific technical setup and past issues across completely separate conversations weeks apart.

Before & after

The old way

Users manually summarized previous chat logs and pasted them into new sessions to maintain continuity, a process taking 10–15 minutes per session.

With AI

By enabling the Memory tool, Claude autonomously creates, reads, and updates files in your local infrastructure in seconds, keeping only necessary context active.

The Prompt

I want to implement a 'barista agent' (or similar persistent role) using the Claude Memory tool. 
Task: Define a memory schema where Claude can store [USER_PREFERENCES] and [PAST_INTERACTIONS] in a directory structure. 
Show me the Python client-side implementation using the `anthropic` SDK to handle `memory_store` tool calls.

The memory tool allows Claude to perform file operations (create, read, update, delete) client-side. This 'just-in-time' retrieval prevents context window overflow by storing experience-based knowledge in files rather than constant system prompts.

Source

Claude Platform - Claude Platform Docs

"Claude can create, read, update, and delete files that persist between sessions, building up knowledge over time without keeping everything in the context window."