langchain-ai/deep-agents-memory

INVOCA ESTA HABILIDAD cuando tu Deep Agent necesite memoria, persistencia o acceso al sistema de archivos. Cubre StateBackend (efímero), StoreBackend (persistente), FilesystemMiddleware y CompositeBackend para enrutamiento.

¿Qué es deep-agents-memory?

Esta habilidad proporciona una arquitectura modular de backends de memoria y almacenamiento para Deep Agents. Incluye StateBackend para almacenamiento efímero de estado en memoria, StoreBackend para persistencia a largo plazo, FilesystemMiddleware para operaciones con el sistema de archivos del agente, y CompositeBackend que permite enrutar automáticamente las solicitudes al backend correcto según el contexto. Es útil para agentes que necesitan recordar información entre pasos de ejecución o mantener estado a través de sesiones. Funciona en plataformas como Claude Code, Cursor, Codex, y es compatible con cualquier implementación de Deep Agent que siga esta interfaz. Su diseño permite combinarlos fácilmente para construir sistemas de memoria híbridos y escalables.

Compatible con~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/langchain-ai/langchain-skills/tree/main/skills/deep-agents-memory

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Documentación

¿Qué hace deep-agents-memory?

INVOCA ESTA HABILIDAD cuando tu Deep Agent necesite memoria, persistencia o acceso al sistema de archivos. Cubre StateBackend (efímero), StoreBackend (persistente), FilesystemMiddleware y CompositeBackend para enrutamiento.

Individual skills in this repo

This repo contains 11 individual skills — each has its own dedicated page.

langchain-ai/deep-agents-core

INVOKE THIS SKILL when building ANY Deep Agents application. Covers create_deep_agent(), harness architecture, SKILL.md format, and configuration options.

langchain-ai/deep-agents-orchestration

INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents. Covers SubAgentMiddleware, TodoList for planning, and HITL interrupts.

langchain-ai/framework-selection

INVOKE THIS SKILL at the START of any LangChain/LangGraph/Deep Agents project, before writing any agent code. Determines which framework layer is right for the task: LangChain, LangGraph, Deep Agents, or a combination. Must be consulted before other agent skills.

langchain-ai/langchain-dependencies

INVOKE THIS SKILL when setting up a new project or when asked about package versions, installation, or dependency management for LangChain, LangGraph, LangSmith, or Deep Agents. Covers required packages, minimum versions, environment requirements, versioning best practices, and common community tool packages for both Python and TypeScript.

langchain-ai/langchain-fundamentals

Create LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling.

langchain-ai/langchain-middleware

INVOKE THIS SKILL when you need human-in-the-loop approval, custom middleware, or structured output. Covers HumanInTheLoopMiddleware for human approval of dangerous tool calls, creating custom middleware with hooks, Command resume patterns, and structured output with Pydantic/Zod.

langchain-ai/langchain-rag

INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), and vector stores (Chroma, FAISS, Pinecone).

langchain-ai/langgraph-fundamentals

INVOKE THIS SKILL when writing ANY LangGraph code. Covers StateGraph, state schemas, nodes, edges, Command, Send, invoke, streaming, and error handling.

langchain-ai/langgraph-human-in-the-loop

INVOKE THIS SKILL when implementing human-in-the-loop patterns, pausing for approval, or handling errors in LangGraph. Covers interrupt(), Command(resume=...), approval/validation workflows, and the 4-tier error handling strategy.

langchain-ai/langgraph-persistence

INVOKE THIS SKILL when your LangGraph needs to persist state, remember conversations, travel through history, or configure subgraph checkpointer scoping. Covers checkpointers, thread_id, time travel, Store, and subgraph persistence modes.

langchain-ai/swarm

Dispatches many independent items in parallel: create a table, fan out to subagents, aggregate results. One row = one unit of work.

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