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Overview

Tool identifier load_memory. System tools are built-in tools that VeADK mounts automatically based on the agent’s configuration — you don’t add them to the tools list yourself. When you pass a long_term_memory to the agent, VeADK mounts the load_memory tool. Across sessions, the agent decides on its own when to query long-term memory to retain user preferences, history, and other long-lived state. For long-term memory backends and configuration, see the long-term memory docs.

Prerequisites

Install python -m pip install "veadk-python[extensions]", then configure the model and local backend. Local storage still sends text to the configured embedding service. Ingest data before the first search.

Usage

Notes

System tools are managed by the framework: just provide a long_term_memory — you don’t add load_memory to the tools list yourself. The agent decides when to call it based on its instruction and user input.

Retrieval input and checks

The memories list contains retrieved long-term memories. This example stores a language preference and retrieves it from another session; the expected answer includes Chinese. A local instance does not filter by user or application, so do not mix private memories from different users. Select appropriate isolation and persistence for production. The model still decides whether to call the tool. If the answer does not reflect the data, inspect tool events and retrieval results. If direct retrieval fails, check ingestion, embedding configuration, and backend permissions first.
Last modified on September 19, 2026