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Official agent skill

Langchain Dependencies

by langchain-ai in langchain-ai/langchain-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.

OfficialMITAuto-check passedAI & LLM Engineering

Install Langchain Dependencies

skills CLI
$ npx skills add langchain-ai/langchain-skills --skill langchain-dependencies -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install langchain-ai/langchain-skills langchain-dependencies --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/langchain-ai/langchain-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/config/skills/langchain-dependencies .claude/skills/langchain-dependencies && rm -rf skills-src

Use ~/.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/

Facts

Skill name
langchain-dependencies
GitHub stars
1.3k
Token cost
~3.6k tokens
SKILL.md length
1,049 words
Files
1
Skills in repo
22
Repo updated
First seen
Licence
MIT

At a glance

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.

  • Tasks that involve Building AI agents
  • SKILL.md covers Environment Requirements, Framework Choice, Core Packages and Minimal Project Templates, plus 3 more sections
  • Calls node; needs LANGSMITH_API_KEY and OPENAI_API_KEY
  • Tasks that involve LLM observability

What it does

Langchain Dependencies is an agent skill from langchain-ai/langchain-skills, published by the product's own GitHub organization. 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.

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Building AI agents and LLM observability. It works with LangChain, LangGraph, LangSmith and Python. The licence is MIT.

When your agent uses it

  • Tasks that involve Building AI agents
  • Tasks that involve LLM observability

Example prompts

  • “/langchain-dependencies”

Requirements

  • Python 3
  • Node.js
  • A credential in LANGSMITH_API_KEY
  • A credential in OPENAI_API_KEY

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

    Shell commands in SKILL.md call:

    • node

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • python.langchain.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • LANGSMITH_API_KEY
    • OPENAI_API_KEY
    • ANTHROPIC_API_KEY
    • GOOGLE_API_KEY
    • MISTRAL_API_KEY
    • GROQ_API_KEY
    • COHERE_API_KEY
    • FIREWORKS_API_KEY
    • TOGETHER_API_KEY
    • HUGGINGFACEHUB_API_TOKEN
    • TAVILY_API_KEY
    • PINECONE_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Langchain Dependencies loads about 3.6k tokens when it runs. Until then it costs about 91 tokens; SKILL.md has 1,049 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~91
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k

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). 1,049 words, ~3,635 tokens.

Download SKILL.mdSave it as .claude/skills/langchain-dependencies/SKILL.md (or your agent's skills folder).
name
langchain-dependencies
description
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.
<overview>
The LangChain ecosystem is split into focused, independently-versioned packages. Understanding which packages you need — and their version constraints — prevents incompatibilities and keeps upgrades predictable.

Key principles:

  • LangChain 1.0 is the current LTS release. Always start new projects on 1.0+. LangChain 0.3 is legacy maintenance-only — do not use it for new work.
  • langchain-core is the shared foundation: always install it explicitly alongside any other package.
  • langchain-community (Python only) does NOT follow semantic versioning; pin it conservatively.
  • LangGraph vs Deep Agents: choose one orchestration approach based on your use case — they are alternatives, not a required stack (see Framework Choice below).
  • Provider integrations (model, vector store, tools) are installed separately so you only pull in what you use.
    </overview>

Environment Requirements

<environment-requirements>
RequirementPythonTypeScript / Node
Runtime minimumPython 3.10+Node.js 20+
LangChain1.0+ (LTS)1.0+ (LTS)
LangSmith SDK>= 0.3.0>= 0.3.0
</environment-requirements>

Framework Choice

<framework-choice>
Pick **one** agent orchestration layer. You do not need both.
FrameworkWhen to useCore extra package
LangGraphNeed fine-grained graph control, custom workflows, loops, or branchinglanggraph / @langchain/langgraph
Deep AgentsWant batteries-included planning, memory, file context, and skills out of the boxdeepagents (depends on LangGraph; installs it as a transitive dep)

Both sit on top of langchain + langchain-core + langsmith. </framework-choice>

Core Packages

<python-packages>
Python — always required
PackageRoleMin version
langchainAgents, chains, retrieval1.0
langchain-coreBase types & interfaces (peer dep)1.0
langsmithTracing, evaluation, datasets0.3.0
Python — orchestration (pick one)
PackageUse whenMin version
langgraphBuilding custom graphs directly1.0
deepagentsUsing the Deep Agents frameworklatest
Python — model providers (pick the one(s) you use)
PackageProvider
langchain-openaiOpenAI (GPT-4o, o3, …)
langchain-anthropicAnthropic (Claude)
langchain-google-genaiGoogle (Gemini)
langchain-mistralaiMistral
langchain-groqGroq (fast inference)
langchain-cohereCohere
langchain-fireworksFireworks AI
langchain-togetherTogether AI
langchain-huggingfaceHugging Face Hub
langchain-ollamaOllama (local models)
langchain-awsAWS Bedrock
langchain-azure-aiAzure AI Foundry
Python — common tool & retrieval packages

These packages have tighter compatibility requirements — use the latest available version unless you have a specific reason not to.

PackageAddsNotes
langchain-tavilyTavily web search (TavilySearch)Dedicated integration package; prefer latest
langchain-text-splittersText chunking utilitiesSemver, keep current
langchain-community1000+ integrations (fallback)NOT semver — pin to minor series
faiss-cpuFAISS vector store (local)Via langchain-community; use latest
langchain-chromaChroma vector storeDedicated integration package; prefer latest
langchain-pineconePinecone vector storeDedicated integration package; prefer latest
langchain-qdrantQdrant vector storeDedicated integration package; prefer latest
langchain-weaviateWeaviate vector storeDedicated integration package; prefer latest
langsmith[pytest]pytest plugin for LangSmithRequires langsmith >= 0.3.4

langchain-community stability note: This package is NOT on semantic versioning. Minor releases can contain breaking changes. Prefer dedicated integration packages (e.g. langchain-chroma, langchain-tavily) when they exist — they are independently versioned and more stable.

</python-packages>
<typescript-packages>
TypeScript — always required
PackageRoleMin version
@langchain/coreBase types & interfaces (peer dep)1.0
langchainAgents, chains, retrieval1.0
langsmithTracing, evaluation, datasets0.3.0
TypeScript — orchestration (pick one)
PackageUse whenMin version
@langchain/langgraphBuilding custom graphs directly1.0
deepagentsUsing the Deep Agents frameworklatest
TypeScript — model providers (pick the one(s) you use)
PackageProvider
@langchain/openaiOpenAI (GPT-4o, o3, …)
@langchain/anthropicAnthropic (Claude)
@langchain/google-genaiGoogle (Gemini)
@langchain/mistralaiMistral
@langchain/groqGroq (fast inference)
@langchain/cohereCohere
@langchain/awsAWS Bedrock
@langchain/azure-openaiAzure OpenAI
@langchain/ollamaOllama (local models)
TypeScript — common tool & retrieval packages
PackageAddsNotes
@langchain/tavilyTavily web search (TavilySearch)Dedicated integration package; prefer latest
@langchain/communityBroad set of community integrationsUse sparingly; prefer dedicated packages
@langchain/pineconePinecone vector storeDedicated integration package; prefer latest
@langchain/qdrantQdrant vector storeDedicated integration package; prefer latest
@langchain/weaviateWeaviate vector storeDedicated integration package; prefer latest

@langchain/core must be installed explicitly in yarn workspaces and monorepos — it is a peer dependency and will not always be hoisted automatically.

</typescript-packages>
Show full SKILL.md (440 more words)Show less

Minimal Project Templates

<ex-langgraph-python>
<python>
Minimal dependency set for a LangGraph project (provider-agnostic).
# requirements.txt
langchain>=1.0,<2.0
langchain-core>=1.0,<2.0
langgraph>=1.0,<2.0
langsmith>=0.3.0

# Add your model provider, e.g.:
# langchain-openai
# langchain-anthropic
# langchain-google-genai
</python>
</ex-langgraph-python>
<ex-langgraph-typescript>
<typescript>
Minimal package.json dependencies for a LangGraph project (provider-agnostic).
json
{
  "dependencies": {
    "@langchain/core": "^1.0.0",
    "langchain": "^1.0.0",
    "@langchain/langgraph": "^1.0.0",
    "langsmith": "^0.3.0"
  }
}
</typescript>
</ex-langgraph-typescript>
<ex-deepagents-python>
<python>
Minimal dependency set for a Deep Agents project (provider-agnostic).
# requirements.txt
deepagents            # bundles langgraph internally
langchain>=1.0,<2.0
langchain-core>=1.0,<2.0
langsmith>=0.3.0

# Add your model provider, e.g.:
# langchain-anthropic
# langchain-openai
</python>
</ex-deepagents-python>
<ex-deepagents-typescript>
<typescript>
Minimal package.json dependencies for a Deep Agents project (provider-agnostic).
json
{
  "dependencies": {
    "deepagents": "latest",
    "@langchain/core": "^1.0.0",
    "langchain": "^1.0.0",
    "langsmith": "^0.3.0"
  }
}
</typescript>
</ex-deepagents-typescript>
<ex-with-tools-python>
<python>
Adding Tavily search and a vector store to a LangGraph project.
# requirements.txt
langchain>=1.0,<2.0
langchain-core>=1.0,<2.0
langgraph>=1.0,<2.0
langsmith>=0.3.0

# Web search
langchain-tavily          # use latest; partner package, semver

# Vector store — pick one:
langchain-chroma          # use latest; partner package, semver
# langchain-pinecone      # use latest; partner package, semver
# langchain-qdrant        # use latest; partner package, semver

# Text processing
langchain-text-splitters  # use latest; semver

# Your model provider:
# langchain-openai / langchain-anthropic / etc.
</python>
</ex-with-tools-python>
<ex-with-tools-typescript>
<typescript>
Adding Tavily search and a vector store to a LangGraph project.
json
{
  "dependencies": {
    "@langchain/core": "^1.0.0",
    "langchain": "^1.0.0",
    "@langchain/langgraph": "^1.0.0",
    "langsmith": "^0.3.0",
    "@langchain/tavily": "latest",
    "@langchain/pinecone": "latest"
  }
}
</typescript>
</ex-with-tools-typescript>

Versioning Policy & Upgrade Strategy

<versioning-policy>
Package groupVersioningSafe upgrade strategy
langchain, langchain-coreStrict semver (1.0 LTS)Allow minor: >=1.0,<2.0
langgraph / @langchain/langgraphStrict semver (v1 LTS)Allow minor: >=1.0,<2.0
langsmithStrict semverAllow minor: >=0.3.0
Dedicated integration packages (e.g. langchain-tavily, langchain-chroma)Independently versionedAllow minor updates; use latest
langchain-communityNOT semverPin exact minor: >=0.4.0,<0.5.0
deepagentsFollow project releasesPin to tested version in production

Breaking changes only happen in major versions (1.x → 2.x) for all semver-compliant packages. Deprecated features remain functional across the entire 1.x series with warnings.

Prefer dedicated integration packages over langchain-community. When a dedicated package exists (e.g. langchain-chroma instead of langchain-community's Chroma integration), use it — dedicated packages are independently versioned and better tested.

Community tool packages (Tavily, vector stores, etc.) should be kept at latest unless your project requires a locked environment. These packages frequently release compatibility fixes alongside LangChain/LangGraph updates.

</versioning-policy>

Environment Variables

<environment-variables>
All keys are read from the environment at runtime. Set only the keys for services you actually use.
bash
# LangSmith (always recommended for observability)
LANGSMITH_API_KEY=<your-key>
LANGSMITH_PROJECT=<project-name>   # optional, defaults to "default"

# Model provider — set the one(s) you use
OPENAI_API_KEY=<your-key>
ANTHROPIC_API_KEY=<your-key>
GOOGLE_API_KEY=<your-key>
MISTRAL_API_KEY=<your-key>
GROQ_API_KEY=<your-key>
COHERE_API_KEY=<your-key>
FIREWORKS_API_KEY=<your-key>
TOGETHER_API_KEY=<your-key>
HUGGINGFACEHUB_API_TOKEN=<your-key>

# Common tool/retrieval services
TAVILY_API_KEY=<your-key>          # for Tavily search
PINECONE_API_KEY=<your-key>        # for Pinecone
</environment-variables>

Common Mistakes

<fix-legacy-version>
Never start a new project on LangChain 0.3. It is maintenance-only until December 2026.
# WRONG: legacy, no new features, security patches only
langchain>=0.3,<0.4

# CORRECT: LangChain 1.0 LTS
langchain>=1.0,<2.0
</fix-legacy-version>
<fix-community-unpinned>
`langchain-community` can break on minor version bumps — it does not follow semver.
# WRONG: allows minor-version updates that may be breaking
langchain-community>=0.4

# CORRECT: pin to exact minor series
langchain-community>=0.4.0,<0.5.0

Also consider switching to the equivalent dedicated integration package if one exists (e.g. langchain-chroma instead of the community Chroma integration). </fix-community-unpinned>

<fix-community-tool-outdated>
Community tool packages like `langchain-tavily` and vector store integrations release compatibility fixes alongside LangChain updates. Using an old pinned version can cause import errors or broken tool schemas.
# RISKY: old pin may be incompatible with LangChain 1.0
langchain-tavily==0.0.1

# BETTER: allow latest within the current major
langchain-tavily>=0.1
</fix-community-tool-outdated>
<fix-community-import-deprecated>
Many tools that used to live in `langchain-community` now have dedicated packages with updated import paths. Always prefer the dedicated package import.
python
# WRONG — deprecated community import path
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.tools import WikipediaQueryRun
from langchain_community.vectorstores import Chroma
from langchain_community.vectorstores import Pinecone

# CORRECT — use dedicated package imports
from langchain_tavily import TavilySearch                  # pip: langchain-tavily (TavilySearchResults is deprecated)
from langchain_community.tools import WikipediaQueryRun  # no dedicated pkg yet
from langchain_chroma import Chroma                       # pip: langchain-chroma
from langchain_pinecone import PineconeVectorStore        # pip: langchain-pinecone

To find the current canonical import for any integration, search the integrations directory: https://python.langchain.com/docs/integrations/tools/

Each entry shows the correct package and import path. If a dedicated package exists, use it — the community path may still work but is considered legacy. </fix-community-import-deprecated>

<fix-core-not-installed>
<typescript>
`@langchain/core` is a peer dependency — it must be in your package.json, especially in monorepos.
json
// WRONG: missing @langchain/core (breaks in yarn workspaces / strict hoisting)
{
  "dependencies": {
    "@langchain/langgraph": "^1.0.0"
  }
}

// CORRECT: always list @langchain/core explicitly
{
  "dependencies": {
    "@langchain/core": "^1.0.0",
    "@langchain/langgraph": "^1.0.0"
  }
}
</typescript>
</fix-core-not-installed>
<fix-python-version>
<python>
Python 3.9 and below are not supported by LangChain 1.0.
python
# Verify before installing
import sys
assert sys.version_info >= (3, 10), "Python 3.10+ required for LangChain 1.0"
</python>
</fix-python-version>
<fix-node-version>
<typescript>
Node.js below 20 is not officially supported.
bash
# Verify before installing
node --version   # must be v20.x or higher
</typescript>
</fix-node-version>

© 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

Just SKILL.md in config/skills/langchain-dependencies of langchain-ai/langchain-skills.

Open the folder on GitHubat commit 16a992f

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders. This page covers the copy in langchain-ai/langchain-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Langchain Dependencies 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.

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Add Example AgentGetBindu/Bindu10k—~1.1kAutomated safety check: NotesCustom licence

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Works with

Categories

Questions about Langchain Dependencies

What does Langchain Dependencies do?

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. Langchain Dependencies is an agent skill from langchain-ai/langchain-skills, published by the product's own GitHub organization. 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.

When should I use Langchain Dependencies?

Langchain Dependencies fits situations like: tasks that involve Building AI agents; tasks that involve LLM observability.

How do I install Langchain Dependencies in Claude Code?

Run `npx skills add langchain-ai/langchain-skills --skill langchain-dependencies -a claude-code`. Or copy the skill folder (config/skills/langchain-dependencies in langchain-ai/langchain-skills) into .claude/skills/langchain-dependencies in your project. Claude Code loads it when a task matches its description.

How do I install Langchain Dependencies in Codex?

Run `npx skills add langchain-ai/langchain-skills --skill langchain-dependencies -a codex`. Or copy the skill folder (config/skills/langchain-dependencies in langchain-ai/langchain-skills) into .agents/skills/langchain-dependencies in your project. Codex loads it when a task matches its description.

Can I use Langchain Dependencies 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 langchain-dependencies -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/langchain-dependencies, .gemini/skills/langchain-dependencies, .github/skills/langchain-dependencies and .opencode/skills/langchain-dependencies in your project.

What does Langchain Dependencies need to run?

Going by SKILL.md and its folder, Langchain Dependencies needs the command-line tools its instructions call (node) and credentials named LANGSMITH_API_KEY, OPENAI_API_KEY, ANTHROPIC_API_KEY and GOOGLE_API_KEY. Our summary lists: Python 3; Node.js; A credential in LANGSMITH_API_KEY; A credential in OPENAI_API_KEY.

Does Langchain Dependencies access the network?

SKILL.md names 1 domain. As links in the text: python.langchain.com. This is read from the text; nothing was executed.

Is Langchain Dependencies 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 Langchain Dependencies use?

Langchain Dependencies 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 Langchain Dependencies use?

About 3.6k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Langchain Dependencies?

Skills that share tags, products or a category with Langchain Dependencies: Langgraph Testing Evaluation (soba-labs/langchain-agent-skills, 107 stars), Failproof AI SDK Integration (FailproofAI/failproofai, 5.3k stars), Langchain Observability (jeremylongshore/tons-of-skills-marketplace, 2.8k stars) and LangSmith Trace Debugging (ComposioHQ/awesome-claude-skills, 77k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Langchain Dependencies?

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.