deep-agents-memory 是做什麼的？
當你的 Deep Agent 需要記憶、持久化或檔案系統存取時，請呼叫此技能。涵蓋 StateBackend（短暫記憶）、StoreBackend（持久化儲存）、FilesystemMiddleware 以及用於路由的 CompositeBackend。
當你的 Deep Agent 需要記憶、持久化或檔案系統存取時，請呼叫此技能。涵蓋 StateBackend（短暫記憶）、StoreBackend（持久化儲存）、FilesystemMiddleware 以及用於路由的 CompositeBackend。
此技能專為 Deep Agent 設計，提供完整的記憶與持久化功能。它支援四種後端類型：StateBackend 提供短暫的執行階段記憶，StoreBackend 用於長期持久化儲存，FilesystemMiddleware 讓代理能夠存取與操作檔案系統，而 CompositeBackend 則可智慧路由請求至適當的後端。此技能對於需要維持跨會話狀態或處理檔案的代理尤為重要。它適用於 langchain-ai 生態系統中的代理，透過清晰的後端分離簡化了記憶管理與檔案互動的實作。開發人員無需自行實現複雜的儲存邏輯，即可輕鬆整合高階持久化功能。
npx skills add https://github.com/langchain-ai/langchain-skills/tree/main/skills/deep-agents-memoryInstalled? Explore more 生產力與協作 skills: steipete/gemini, steipete/gh-issues, steipete/skill-creator · View all 6 →
當你的 Deep Agent 需要記憶、持久化或檔案系統存取時，請呼叫此技能。涵蓋 StateBackend（短暫記憶）、StoreBackend（持久化儲存）、FilesystemMiddleware 以及用於路由的 CompositeBackend。
This repo contains 11 individual skills — each has its own dedicated page.
INVOKE THIS SKILL when building ANY Deep Agents application. Covers create_deep_agent(), harness architecture, SKILL.md format, and configuration options.
INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents. Covers SubAgentMiddleware, TodoList for planning, and HITL interrupts.
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.
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.
Create LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling.
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.
INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), and vector stores (Chroma, FAISS, Pinecone).
INVOKE THIS SKILL when writing ANY LangGraph code. Covers StateGraph, state schemas, nodes, edges, Command, Send, invoke, streaming, and error handling.
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.
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.
Dispatches many independent items in parallel: create a table, fan out to subagents, aggregate results. One row = one unit of work.
Gemini CLI one-shot prompts, summaries, generation, skills, hooks, MCP, or Gemma routing.
Fetch GitHub issues, select candidates, spawn background fix agents, open PRs, and optionally process PR review comments.
Create, edit, audit, tidy, validate, or restructure AgentSkills and SKILL.md files.
Search, install, update, sync, or publish agent skills with the ClawHub CLI and registry.
Delegate coding work to Codex, Claude Code, or OpenCode as background workers; not simple edits or read-only code lookup.
List, configure, authenticate, call, and inspect MCP servers/tools with mcporter over HTTP or stdio.