Mem0 Platform SDK
mem0ai/mem0
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INVOKE THIS SKILL when writing ANY LangGraph code. An agent skill from langchain-ai/langchain-skills.
Install Langgraph Fundamentals
$ npx skills add langchain-ai/langchain-skills --skill langgraph-fundamentals -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install langchain-ai/langchain-skills langgraph-fundamentals --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/langchain-ai/langchain-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/config/skills/langgraph-fundamentals .claude/skills/langgraph-fundamentals && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "langgraph-fundamentals" agent skill from https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/langgraph-fundamentals into .claude/skills/langgraph-fundamentals/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langgraph-fundamentals", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/langgraph-fundamentalsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add langchain-ai/langchain-skills --skill langgraph-fundamentals -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install langchain-ai/langchain-skills langgraph-fundamentals --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/langchain-ai/langchain-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/config/skills/langgraph-fundamentals .agents/skills/langgraph-fundamentals && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "langgraph-fundamentals" agent skill from https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/langgraph-fundamentals into .agents/skills/langgraph-fundamentals/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langgraph-fundamentals", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add langchain-ai/langchain-skills --skill langgraph-fundamentals -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install langchain-ai/langchain-skills langgraph-fundamentals --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/langchain-ai/langchain-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/config/skills/langgraph-fundamentals .cursor/skills/langgraph-fundamentals && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "langgraph-fundamentals" agent skill from https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/langgraph-fundamentals into .cursor/skills/langgraph-fundamentals/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langgraph-fundamentals", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/langchain-ai/langchain-skills.git --path config/skills/langgraph-fundamentals--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add langchain-ai/langchain-skills --skill langgraph-fundamentals -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install langchain-ai/langchain-skills langgraph-fundamentals --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/langchain-ai/langchain-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/config/skills/langgraph-fundamentals .gemini/skills/langgraph-fundamentals && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "langgraph-fundamentals" agent skill from https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/langgraph-fundamentals into .gemini/skills/langgraph-fundamentals/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langgraph-fundamentals", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install langchain-ai/langchain-skills langgraph-fundamentalsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add langchain-ai/langchain-skills --skill langgraph-fundamentals -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/langchain-ai/langchain-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/config/skills/langgraph-fundamentals .github/skills/langgraph-fundamentals && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "langgraph-fundamentals" agent skill from https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/langgraph-fundamentals into .github/skills/langgraph-fundamentals/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langgraph-fundamentals", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add langchain-ai/langchain-skills --skill langgraph-fundamentals -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install langchain-ai/langchain-skills langgraph-fundamentals --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/langchain-ai/langchain-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/config/skills/langgraph-fundamentals .opencode/skills/langgraph-fundamentals && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "langgraph-fundamentals" agent skill from https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/langgraph-fundamentals into .opencode/skills/langgraph-fundamentals/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langgraph-fundamentals", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
langgraph-fundamentals- GitHub stars
- 1.3k
- Used in
- 1 other repo
- Token cost
- ~1.5k tokens
- SKILL.md length
- 640 words
- Files
- 3 (incl. references)
- Skills in repo
- 22
- Repo updated
- First seen
- Licence
- MIT
At a glance
INVOKE THIS SKILL when writing ANY LangGraph code. An agent skill from langchain-ai/langchain-skills.
- Works in 5 steps: Map out discrete steps — sketch a… → Identify what each step does —… → Design your state — state is shared… → …
- Tasks that involve Building AI agents
- SKILL.md covers State Management, Nodes, Edges and Command, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Langgraph Fundamentals is an agent skill from langchain-ai/langchain-skills, published by the product's own GitHub organization. INVOKE THIS SKILL when writing ANY LangGraph code. Covers StateGraph, state schemas, nodes, edges, Command, Send, invoke, streaming, and error handling.
Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/python.md` and `references/typescript.md`).
It sits in AI & LLM Engineering, covering Building AI agents. It works with LangGraph and LangChain. The licence is MIT.
When your agent uses it
- Tasks that involve Building AI agents
Example prompts
- “/langgraph-fundamentals”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Map out discrete steps — sketch a flowchart of your workflow. Each step becomes a node.
- Identify what each step does — categorize nodes: LLM step, data step, action step, or user input step. For each, determine static context…
- Design your state — state is shared memory for all nodes. Store raw data, format prompts on-demand inside nodes.
- Build your nodes — implement each step as a function that takes state and returns partial updates.
- Wire it together — connect nodes with edges, add conditional routing, compile with a checkpointer if needed.
What it can do on your machine
Read from SKILL.md and the folder at commit 16a992f. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Langgraph Fundamentals loads about 1.5k tokens when it runs, and up to ~6k if it reads all its reference files. Until then it costs about 44 tokens; SKILL.md has 640 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from langchain-ai/langchain-skills at commit 16a992f, republished under its MIT licence (© langchain-ai). 640 words, ~1,471 tokens.
Download SKILL.mdSave it as
.claude/skills/langgraph-fundamentals/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.- name
- langgraph-fundamentals
- description
- INVOKE THIS SKILL when writing ANY LangGraph code. Covers StateGraph, state schemas, nodes, edges, Command, Send, invoke, streaming, and error handling.
<overview>
LangGraph models agent workflows as **directed graphs**:- 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.
</overview>
<design-methodology>Designing a LangGraph application
Follow these 5 steps when building a new graph:
- Map out discrete steps — sketch a flowchart of your workflow. Each step becomes a node.
- 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.
- Design your state — state is shared memory for all nodes. Store raw data, format prompts on-demand inside nodes.
- Build your nodes — implement each step as a function that takes state and returns partial updates.
- Wire it together — connect nodes with edges, add conditional routing, compile with a checkpointer if needed.
</design-methodology><when-to-use-langgraph>| Use LangGraph When | Use Alternatives When |
|---|---|
| Need fine-grained control over agent orchestration | Quick prototyping → LangChain agents |
| Building complex workflows with branching/loops | Simple stateless workflows → LangChain direct |
| Require human-in-the-loop, persistence | Batteries-included features → Deep Agents |
</when-to-use-langgraph>State Management
<state-update-strategies>| Need | Solution | Example |
|---|---|---|
| Overwrite value | No reducer (default) | Simple fields like counters |
| Append to list | Reducer (operator.add / concat) | Message history, logs |
| Custom logic | Custom reducer function | Complex merging |
</state-update-strategies>Nodes
<node-function-signatures>Node functions return partial state updates. Signatures for configuration and runtime access differ by language; use the applicable implementation reference.
</node-function-signatures>Edges
<edge-type-selection>| Need | Edge Type | When to Use |
|---|---|---|
| Always go to same node | add_edge() | Fixed, deterministic flow |
| Route based on state | add_conditional_edges() | Dynamic branching |
| Update state AND route | Command | Combine logic in single node |
| Fan-out to multiple nodes | Send | Parallel processing with dynamic inputs |
</edge-type-selection>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 nextresume: Value to resume afterinterrupt()— see human-in-the-loop skill
<command-return-type-annotations>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.
</command-return-type-annotations><warning-command-static-edges>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.
</warning-command-static-edges>Show full SKILL.md (229 more words)Show less
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
<invoke-basics>Call graph.invoke(input, config) to run a graph to completion and return the final state.
</invoke-basics><stream-mode-selection>| Mode | What it Streams | Use Case |
|---|---|---|
values | Full state after each step | Monitor complete state |
updates | State deltas | Track incremental updates |
messages | LLM tokens + metadata | Chat UIs |
custom | User-defined data | Progress indicators |
</stream-mode-selection>Error Handling
Match the error type to the right handler:
<error-handling-table>| Error Type | Who Fixes | Strategy | Example |
|---|---|---|---|
| Transient (network, rate limits) | System | RetryPolicy(max_attempts=3) | add_node(..., retry_policy=...) |
| LLM-recoverable (tool failures) | LLM | ToolNode(tools, handle_tool_errors=True) | Error returned as ToolMessage |
| User-fixable (missing info) | Human | interrupt({"message": ...}) | Collect missing data (see HITL skill) |
| Unexpected | Developer | Let bubble up | raise |
</error-handling-table>Core boundaries
- Return partial state updates from nodes instead of mutating state directly.
- Route loops through a named node;
STARTis entry-only. - Define reducers for accumulated list fields; otherwise, the last write wins.
- Account for static edges when using
Commandwithgoto, 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:
- For Python, read references/python.md.
- For TypeScript, read references/typescript.md.
Read both only when the task covers both languages. For conceptual questions that require no code, do not load either reference.
© langchain-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 2 other files (references) in config/skills/langgraph-fundamentals of langchain-ai/langchain-skills.
- SKILL.md
- references/python.md
- references/typescript.md
Open the folder on GitHubat commit 16a992f
Used in 2 other repositories
We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in langchain-ai/langchain-skills, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Langgraph Fundamentals next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Langgraph Fundamentals this skilllangchain-ai/langchain-skills | 1.3k | 1 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Mem0 Platform SDKmem0ai/mem0 | 67k | 1 repos | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| LangSmith Trace DebuggingComposioHQ/awesome-claude-skills | 77k | 8 repos | ~2.7k | Automated safety check: Pass | None | |
| Add Example AgentGetBindu/Bindu | 10k | — | ~1.1k | Automated safety check: Notes | Custom licence | |
| Failproof AI SDK IntegrationFailproofAI/failproofai | 5.3k | — | ~6k | Automated safety check: Pass | Custom licence | |
| Edgeone Makers MigrationTencentEdgeOne/edgeone-makers-tools | 1.9k | 1 repos | ~4.1k | Automated safety check: Pass | MIT |
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Add a new self-contained example agent under examples/. An agent skill from GetBindu/Bindu.
Failproof AI SDK Integration
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Related
Categories
Questions about Langgraph Fundamentals
What does Langgraph Fundamentals do?
INVOKE THIS SKILL when writing ANY LangGraph code. An agent skill from langchain-ai/langchain-skills. Langgraph Fundamentals is an agent skill from langchain-ai/langchain-skills, published by the product's own GitHub organization. INVOKE THIS SKILL when writing ANY LangGraph code.
When should I use Langgraph Fundamentals?
Langgraph Fundamentals fits situations like: tasks that involve Building AI agents.
How do I install Langgraph Fundamentals in Claude Code?
Run `npx skills add langchain-ai/langchain-skills --skill langgraph-fundamentals -a claude-code`. Or copy the skill folder (config/skills/langgraph-fundamentals in langchain-ai/langchain-skills) into .claude/skills/langgraph-fundamentals in your project. Claude Code loads it when a task matches its description.
How do I install Langgraph Fundamentals in Codex?
Run `npx skills add langchain-ai/langchain-skills --skill langgraph-fundamentals -a codex`. Or copy the skill folder (config/skills/langgraph-fundamentals in langchain-ai/langchain-skills) into .agents/skills/langgraph-fundamentals in your project. Codex loads it when a task matches its description.
Can I use Langgraph Fundamentals in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add langchain-ai/langchain-skills --skill langgraph-fundamentals -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/langgraph-fundamentals, .gemini/skills/langgraph-fundamentals, .github/skills/langgraph-fundamentals and .opencode/skills/langgraph-fundamentals in your project.
What does Langgraph Fundamentals need to run?
SKILL.md names no scripts, command-line tools or credentials: Langgraph Fundamentals is instructions for the agent only.
Does Langgraph Fundamentals access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Langgraph Fundamentals safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Langgraph Fundamentals use?
Langgraph Fundamentals is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Langgraph Fundamentals use?
About 1.5k tokens (SKILL.md is roughly 5.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.6k tokens, read only when the agent opens those files.
What are the alternatives to Langgraph Fundamentals?
Skills that share tags, products or a category with Langgraph Fundamentals: Mem0 Platform SDK (mem0ai/mem0, 67k stars), LangSmith Trace Debugging (ComposioHQ/awesome-claude-skills, 77k stars), Add Example Agent (GetBindu/Bindu, 10k stars) and Failproof AI SDK Integration (FailproofAI/failproofai, 5.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Langgraph Fundamentals?
langchain-ai (a GitHub organization, an official publisher) maintains it in langchain-ai/langchain-skills, which has 1,276 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on October 8, 2026.
Source: langchain-ai/langchain-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.