langchain-ai/langchain-skills10k installs

langchain-dependencies

Q: How do I install this agent skill?

Run npx skills add https://github.com/langchain-ai/langchain-skills --skill langchain-dependencies.

Q: Is this agent skill safe to install?

Review the displayed partner audits, complete source, permissions, dependencies, and exact revision before installing.

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.

How do I install this agent skill?

npx skills add https://github.com/langchain-ai/langchain-skills --skill langchain-dependencies

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Is this agent skill safe to install?

Gen Agent Trust Hubpass
This skill serves as a comprehensive configuration guide for LangChain ecosystem dependencies. It provides established best practices for package versioning, environment setup, and integration management without introducing any security risks.
Socketpass
No alerts
Snykpass
Risk: LOW · No issues
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1 file scanned · No issues
ZeroLeakspass
Score: 93/100 · 2 sections analyzed

What does this agent skill do?

<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>

Requirement	Python	TypeScript / Node
Runtime minimum	Python 3.10+	Node.js 20+
LangChain	1.0+ (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.

Framework	When to use	Core extra package
LangGraph	Need fine-grained graph control, custom workflows, loops, or branching	`langgraph` / `@langchain/langgraph`
Deep Agents	Want batteries-included planning, memory, file context, and skills out of the box	`deepagents` (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

Package	Role	Min version
`langchain`	Agents, chains, retrieval	1.0
`langchain-core`	Base types & interfaces (peer dep)	1.0
`langsmith`	Tracing, evaluation, datasets	0.3.0

Python — orchestration (pick one)

Package	Use when	Min version
`langgraph`	Building custom graphs directly	1.0
`deepagents`	Using the Deep Agents framework	latest

Python — model providers (pick the one(s) you use)

Package	Provider
`langchain-openai`	OpenAI (GPT-4o, o3, …)
`langchain-anthropic`	Anthropic (Claude)
`langchain-google-genai`	Google (Gemini)
`langchain-mistralai`	Mistral
`langchain-groq`	Groq (fast inference)
`langchain-cohere`	Cohere
`langchain-fireworks`	Fireworks AI
`langchain-together`	Together AI
`langchain-huggingface`	Hugging Face Hub
`langchain-ollama`	Ollama (local models)
`langchain-aws`	AWS Bedrock
`langchain-azure-ai`	Azure 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.

Package	Adds	Notes
`langchain-tavily`	Tavily web search (`TavilySearch`)	Dedicated integration package; prefer latest
`langchain-text-splitters`	Text chunking utilities	Semver, keep current
`langchain-community`	1000+ integrations (fallback)	NOT semver — pin to minor series
`faiss-cpu`	FAISS vector store (local)	Via `langchain-community`; use latest
`langchain-chroma`	Chroma vector store	Dedicated integration package; prefer latest
`langchain-pinecone`	Pinecone vector store	Dedicated integration package; prefer latest
`langchain-qdrant`	Qdrant vector store	Dedicated integration package; prefer latest
`langchain-weaviate`	Weaviate vector store	Dedicated integration package; prefer latest
`langsmith[pytest]`	pytest plugin for LangSmith	Requires 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

Package	Role	Min version
`@langchain/core`	Base types & interfaces (peer dep)	1.0
`langchain`	Agents, chains, retrieval	1.0
`langsmith`	Tracing, evaluation, datasets	0.3.0

TypeScript — orchestration (pick one)

Package	Use when	Min version
`@langchain/langgraph`	Building custom graphs directly	1.0
`deepagents`	Using the Deep Agents framework	latest

TypeScript — model providers (pick the one(s) you use)

Package	Provider
`@langchain/openai`	OpenAI (GPT-4o, o3, …)
`@langchain/anthropic`	Anthropic (Claude)
`@langchain/google-genai`	Google (Gemini)
`@langchain/mistralai`	Mistral
`@langchain/groq`	Groq (fast inference)
`@langchain/cohere`	Cohere
`@langchain/aws`	AWS Bedrock
`@langchain/azure-openai`	Azure OpenAI
`@langchain/ollama`	Ollama (local models)

TypeScript — common tool & retrieval packages

Package	Adds	Notes
`@langchain/tavily`	Tavily web search (`TavilySearch`)	Dedicated integration package; prefer latest
`@langchain/community`	Broad set of community integrations	Use sparingly; prefer dedicated packages
`@langchain/pinecone`	Pinecone vector store	Dedicated integration package; prefer latest
`@langchain/qdrant`	Qdrant vector store	Dedicated integration package; prefer latest
`@langchain/weaviate`	Weaviate vector store	Dedicated 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>

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).

{
  "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).

{
  "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.

{
  "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 group	Versioning	Safe upgrade strategy
`langchain`, `langchain-core`	Strict semver (1.0 LTS)	Allow minor: `>=1.0,<2.0`
`langgraph` / `@langchain/langgraph`	Strict semver (v1 LTS)	Allow minor: `>=1.0,<2.0`
`langsmith`	Strict semver	Allow minor: `>=0.3.0`
Dedicated integration packages (e.g. `langchain-tavily`, `langchain-chroma`)	Independently versioned	Allow minor updates; use latest
`langchain-community`	NOT semver	Pin exact minor: `>=0.4.0,<0.5.0`
`deepagents`	Follow project releases	Pin 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.

# 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.

# 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.

// 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.

# 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.

# Verify before installing
node --version   # must be v20.x or higher

</typescript> </fix-node-version>

How can the creator link this skill?

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/langchain-ai/langchain-skills/langchain-dependencies">View langchain-dependencies on skillZs</a>