deep-agents-memory

Manage persistent and ephemeral memory with filesystem operations via backend routing.

2|Updated Sep 9, 2024

Search Tags:#backend #agent #memory #persistence #filesystem #storage

Ready to run

npx skills add https://github.com/ThiNepo/prompt-caller --skill deep-agents-memory-thinepo

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: deep-agents-memory
Source: https://github.com/ThiNepo/prompt-caller/tree/main/.continue/skills/deep-agents-memory
Command: npx skills add https://github.com/ThiNepo/prompt-caller --skill deep-agents-memory-thinepo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides agents with robust memory and filesystem capabilities, enabling them to store and retrieve information across sessions and interact with the file system.

Core Features & Use Cases

StateBackend: Ephemeral, short-term memory within a single thread.
StoreBackend: Persistent, long-term memory across threads and sessions.
FilesystemMiddleware: Tools for file operations like ls, read_file, write_file, edit_file, glob, grep.
CompositeBackend: Routes requests to different backends based on path.
Use Case: An agent needs to remember user preferences across multiple interactions and save generated reports to disk. This skill allows the agent to use StoreBackend for preferences and FilesystemMiddleware for report saving.

Quick Start

Use the deep-agents-memory skill to save a file to persistent storage.

Frequently Asked Questions about deep-agents-memory

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I add persistent memory to an AI agent across multiple sessions?▼

Persistent memory for an AI agent is achieved using a StoreBackend to retain information across threads and sessions. This approach saves user preferences and states long-term for continuous interactions.

What is the best way to give an agent filesystem read and write access?▼

Filesystem access for agents is handled by FilesystemMiddleware, providing tools like `read_file`, `write_file`, and `edit_file`. It enables automated agents to save generated reports directly to disk.

How do I manage both ephemeral and long-term agent memory in one system?▼

Managing both ephemeral and long-term agent memory is done with a CompositeBackend, routing requests to StateBackend for short-term thread state and StoreBackend for persistent storage.

Can an agent use filesystem tools like grep and glob for file operations?▼

Yes, agents can use filesystem tools like grep and glob through FilesystemMiddleware. This integration allows agents to search, list, and manipulate files directly during automated workflows.

When should I use ephemeral memory instead of persistent storage for my agent?▼

Ephemeral memory, via StateBackend, should be used for short-term data within a single thread. Persistent storage is necessary when the agent must recall information across multiple separate sessions.