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Langgraph Fundamentals

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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.

Overview

Publisherlangchain-ai
Repositorylangchain-skills
Skill namelanggraph-fundamentals
Stars
1.2K
Forks
95
Bundled files
2
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by langchain-ai on GitHub. Read the source before you install it.

Installation

Install the Langgraph Fundamentals AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/langchain-ai/langchain-skills.git /tmp/langchain-skills
mkdir -p .claude/skills
cp -r /tmp/langchain-skills/config/skills/langgraph-fundamentals .claude/skills/langgraph-fundamentals
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Langgraph Fundamentals in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Langgraph Fundamentals on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Langgraph Fundamentals is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

  • StateGraph: Main class for building stateful graphs
  • Nodes: Functions that perform work and update state
  • Edges: Define execution order (static or conditional)
  • START/END: Special nodes marking entry and exit points
  • State with Reducers: Control how state updates are merged

Graphs must be compile()d before execution.

Designing a LangGraph application

Follow these 5 steps when building a new graph:

  1. Map out discrete steps — sketch a flowchart of your workflow. Each step becomes a node.
  2. Identify what each step does — categorize nodes: LLM step, data step, action step, or user input step. For each, determine static context (prompt), dynamic context (from state), retry strategy, and desired outcome.
  3. Design your state — state is shared memory for all nodes. Store raw data, format prompts on-demand inside nodes.
  4. Build your nodes — implement each step as a function that takes state and returns partial updates.
  5. Wire it together — connect nodes with edges, add conditional routing, compile with a checkpointer if needed.
Use LangGraph WhenUse Alternatives When
Need fine-grained control over agent orchestrationQuick prototyping → LangChain agents
Building complex workflows with branching/loopsSimple stateless workflows → LangChain direct
Require human-in-the-loop, persistenceBatteries-included features → Deep Agents

State Management

NeedSolutionExample
Overwrite valueNo reducer (default)Simple fields like counters
Append to listReducer (operator.add / concat)Message history, logs
Custom logicCustom reducer functionComplex merging

Nodes

Node functions return partial state updates. Signatures for configuration and runtime access differ by language; use the applicable implementation reference.


Edges

NeedEdge TypeWhen to Use
Always go to same nodeadd_edge()Fixed, deterministic flow
Route based on stateadd_conditional_edges()Dynamic branching
Update state AND routeCommandCombine logic in single node
Fan-out to multiple nodesSendParallel processing with dynamic inputs

Command

Command combines state updates and routing in a single return value. Fields:

  • update: State updates to apply (like returning a dict from a node)
  • goto: Node name(s) to navigate to next
  • resume: Value to resume after interrupt() — see human-in-the-loop skill

Python: Use Command[Literal["node_a", "node_b"]] as the return type annotation to declare valid goto destinations.

TypeScript: Pass { ends: ["node_a", "node_b"] } as the third argument to addNode to declare valid goto destinations.

Warning: Command only adds dynamic edges — static edges defined with add_edge / addEdge still execute. If node_a returns Command(goto="node_c") and you also have graph.add_edge("node_a", "node_b"), both node_b and node_c will run.


Send API

Fan-out with Send: return [Send("worker", {...})] from a conditional edge to spawn parallel workers. Requires a reducer on the results field.


Running Graphs: Invoke and Stream

Call graph.invoke(input, config) to run a graph to completion and return the final state.

ModeWhat it StreamsUse Case
valuesFull state after each stepMonitor complete state
updatesState deltasTrack incremental updates
messagesLLM tokens + metadataChat UIs
customUser-defined dataProgress indicators

Error Handling

Match the error type to the right handler:

Error TypeWho FixesStrategyExample
Transient (network, rate limits)SystemRetryPolicy(max_attempts=3)add_node(..., retry_policy=...)
LLM-recoverable (tool failures)LLMToolNode(tools, handle_tool_errors=True)Error returned as ToolMessage
User-fixable (missing info)Humaninterrupt({"message": ...})Collect missing data (see HITL skill)
UnexpectedDeveloperLet bubble upraise

Core boundaries

  • Return partial state updates from nodes instead of mutating state directly.
  • Route loops through a named node; START is entry-only.
  • Define reducers for accumulated list fields; otherwise, the last write wins.
  • Account for static edges when using Command with goto, because both routes execute.

Implementation references

If writing, modifying, or debugging LangGraph code, determine the project's language from its existing files, then read the applicable reference before implementing:

Read both only when the task covers both languages. For conceptual questions that require no code, do not load either reference.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Langgraph Fundamentals AI skill do?

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

Why use Langgraph Fundamentals on TypingMind?

Because you install it once and use it with any model. Langgraph Fundamentals is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Langgraph Fundamentals in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/langgraph-fundamentals. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Langgraph Fundamentals?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Langgraph Fundamentals?

As many as you like. As long as a model supports skills, you can use Langgraph Fundamentals with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Langgraph Fundamentals AI skill free?

It is published on GitHub by langchain-ai. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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