deep-agents

Name: deep-agents
Availability: InStock
Author: alex1980alex

Orchestrate LangChain agents with modular skill loading and persistent memory backends.

1|Updated Sep 20, 2025

Search Tags:#middleware #langchain #skills #memory #sandbox #autonomous-agents

Ready to run

npx skills add https://github.com/Alex1980Alex/1C-Enterprise_Framework --skill deep-agents

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: deep-agents
Source: https://github.com/Alex1980Alex/1C-Enterprise_Framework/tree/main/.claude/skills/deep-agents
Command: npx skills add https://github.com/Alex1980Alex/1C-Enterprise_Framework --skill deep-agents

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enables building and orchestrating autonomous agents with memory, skills loaded from SKILL.md, and flexible backends for persistent context and safe delegation.

Core Features & Use Cases

Load and compose modular skills described by SKILL.md for autonomous workflows
Maintain memory across sessions using multiple backends (StoreBackend, FilesystemBackend, StateBackend)
Support sub-agent delegation, middleware routing, and HITL/safety controls
Provide a clear quick-start path for interactive agent sessions in LangChain ecosystems

Quick Start

Start the Deep Agents CLI and load skills from a local skills directory to begin an interactive agent session.

Frequently Asked Questions about deep-agents

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

FAQPage Schema

How do I coordinate autonomous agents with persistent memory in a LangChain workflow?▼

To coordinate autonomous agents with persistent memory in a LangChain workflow, you can use a framework that loads modular skills from SKILL.md files and maintains context across sessions using multiple backends like StoreBackend, FilesystemBackend, and StateBackend.

What is the best way to load modular skills for autonomous agents from markdown files?▼

The best way to load modular skills for autonomous agents from markdown files is to use a system that parses SKILL.md documents, enabling dynamic skill composition and middleware routing for task delegation within an interactive agent session.

Can I use sandboxed execution and human-in-the-loop controls for sub-agent delegation?▼

Yes, you can use sandboxed execution and human-in-the-loop (HITL) controls for sub-agent delegation by leveraging multi-backend isolation features that ensure safe task execution and auto-eviction across interactive agent sessions.

How does middleware routing work for managing memory and task flow in autonomous agents?▼

Middleware routing for managing memory and task flow in autonomous agents works by directing context and execution paths between isolated backends, ensuring persistent state and safe delegation across multiple sessions.

Do I need multiple storage backends to maintain agent memory across different sessions?▼

You need multiple storage backends like FilesystemBackend, StoreBackend, and StateBackend to maintain agent memory across different sessions, allowing flexible context persistence and state isolation for complex autonomous workflows.

Are there limitations when using auto-eviction and multi-backend isolation for interactive agent workflows?▼

Limitations when using auto-eviction and multi-backend isolation for interactive agent workflows include managing state consistency across FilesystemBackend, StoreBackend, and StateBackend, which requires careful middleware routing and HITL oversight to prevent context loss during sub-agent delegation.