langgraph-persistence
INVOKE THIS SKILL when your LangGraph needs to persist state, remember conversations, travel through history, or configure subgraph checkpoi
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About
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.
Details
- Kind
- Agent skills
- Topic
- Maps, weather & travel
- Publisher
- langchain-ai
- Origin
- skillssh
- Category
- ferramentas
- Stars
- 1,197
- Forks
- 90
- Open pull requests
- 3
- Last push
- 2026-08-29T01:39:54Z
- Repository state
- ativo
- Language
- TypeScript
- Added
- 2026-08-30 15:21:15
- Updated
- 2026-09-10 11:02:45
- Origin id
langchain-ai/langchain-skills/langgraph-persistence
README
# LangChain Skills > **⚠️** — This project is in early development. APIs and skill content may change. Agent skills for building agents with LangChain, LangGraph, and Deep Agents. > For LangSmith-specific trace and dataset workflows, use [langsmith-skills](https://github.com/langchain-ai/langsmith-skills). ## Supported Coding Agents These skills can be installed via [`npx skills`](https://github.com/vercel-labs/skills) for any agent that supports the [Agent Skills specification](https://skills.sh), including Claude Code, Cursor, Windsurf, and more. ## Installation ### Quick Install Using [`npx skills`](https://github.com/vercel-labs/skills): **Local** (current project): ```bash npx skills add langchain-ai/langchain-skills --skill '*' --yes ``` **Global** (all projects): ```bash npx skills add langchain-ai/langchain-skills --skill '*' --yes --global ``` To link skills to a specific agent (e.g. Claude Code): ```bash npx skills add langchain-ai/langchain-skills --agent claude-code --skill '*' --yes --global ``` --- ### Claude Code Plugin Install directly as a [Claude Code plugin](https://code.claude.com/docs/en/plugins): ```bash /plugin marketplace add langchain-ai/langchain-skills /plugin install langchain-skills@langchain-skills ``` --- ### Install Script (Claude Code & Deep Agents CLI only) Alternatively, clone the repo and use the install script: ```bash # Install for Claude Code in current directory (default) ./install.sh # Install for Claude Code in a specific project directory ./install.sh ~/my-project # Install for Claude Code globally ./install.sh --global # Install for Deep Agents CLI in a specific project directory ./install.sh --deepagents ~/my-project # Install for Deep Agents CLI globally (includes agent persona) ./install.sh --deepagents --global ``` | Flag / Argument | Description | |------|-------------| | `DIRECTORY` | Target project directory (default: current directory, ignored with `--global`) | | `--claude` | Install for Claude Code (default) | | `--deepagents` | Install for Deep Agents CLI | | `--global`, `-g` | Install globally instead of current directory | | `--force`, `-f` | Overwrite skills with same names as this package | | `--yes`, `-y` | Skip confirmation prompts | ## Usage After installation, set your API keys: ```bash export OPENAI_API_KEY=<your-key> # For OpenAI models export ANTHROPIC_API_KEY=<your-key> # For Anthropic models ``` Then run your coding agent from the directory where you installed (for local installs) or from anywhere (for global installs). ### Eval Engineering To install only the eval-engineering skill: ```bash npx skills add langchain-ai/langchain-skills --skill eval-engineering --yes ``` Or ask Codex: “Install the `eval-engineering` skill from `langchain-ai/langchain-skills`.” Eval tasks require [Harbor](https://www.harborframework.com/docs). Run Harbor locally with Docker or use a supported cloud environment. Then ask your coding agent: ```text Use the eval-engineering skill to create a new eval Task for this project. Review the current repository and existing evals. Traces for this agent can be found here [optional Tracing Project/Location]. ``` ## Available Skills (21) ### Getting Started - **ecosystem-primer** - Start-here primer: framework selection (LangChain vs LangGraph vs Deep Agents), env setup, and which skill to load next - **langchain-dependencies** - Full package version and dependency management reference (Python + TypeScript) ### Quickstarts (local) Thin wrappers around the official Mintlify quickstarts — ask for provider/model (default `anthropic:claude-sonnet-5`), new directory, provider API key only: - **langchain-python-quickstart** / **langchain-typescript-quickstart** → [Python](https://docs.langchain.com/oss/python/langchain/quickstart) / [JS](https://docs.langchain.com/oss/javascript/langchain/quickstart) (weather) - **langgraph-python-quickstart** / **langgraph-typescript-quickstart** → [Python](https://docs.langchain.com/oss/python/langgraph/quickstart) / [JS](https://docs.langchain.com/oss/javascript/langgraph/quickstart) (math) - **deepagents-python-quickstart** / **deepagents-typescript-quickstart** → [Python](https://docs.langchain.com/oss/python/deepagents/quickstart) / [JS](https://docs.langchain.com/oss/javascript/deepagents/quickstart) (research; provider web search instead of Tavily) ### Deep Agents - **deep-agents-core** - Agent architecture, harness setup, and SKILL.md format - **deep-agents-memory** - Memory, persistence, filesystem middleware - **deep-agents-orchestration** - Subagents, task planning, human-in-the-loop - **managed-deep-agents** - Managed Deep Agents: deploy with the CLI, use the SDKs, stream runs, connect MCP tools, and build React `useStream` UIs ### LangChain - **langchain-fundamentals** - Agents with create_agent, tools, structured output, middleware basics - **langchain-middleware** - Human-in-the-loop approval, custom middleware, Command resume patterns - **langchain-rag** - RAG pipeline (document loaders, embeddings, vector stores) ### LangGraph - **langgraph-fundamentals** - StateGraph, nodes, edges, state reducers - **langgraph-persistence** - Checkpointers, thread_id, cross-thread memory - **langgraph-cli** - CLI lifecycle: scaffold, dev, build, deploy, langgraph.json config - **langgraph-human-in-the-loop** - Interrupts, human review, approval workflows ### Evaluation - **eval-engineering** - Design and audit Harbor Tasks while building reusable project World knowledge with human review ### Utilities - **swarm** - Dispatch independent work items in parallel and aggregate the results