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langchain-dependencies

langchain-ai/langchain-skills

Manage LangChain ecosystem package versions, dependencies, and environment setup for Python and TypeScript projects.

What is langchain-dependencies?

This skill guides you through the LangChain package ecosystem, covering required versions, environment requirements, and dependency management for LangChain 1.0+, LangGraph, Deep Agents, and provider integrations. Use it when setting up new projects, resolving version conflicts, or choosing between orchestration frameworks.

  • Specifies minimum versions for core packages (langchain, langchain-core, langsmith) across Python and TypeScript
  • Identifies which model provider packages to install based on your LLM choice (OpenAI, Anthropic, Google, Mistral, etc.)
  • Explains framework choice between LangGraph (custom graph control) and Deep Agents (batteries-included planning)
  • Provides minimal project templates for LangGraph and Deep Agents in both Python and TypeScript
  • Documents common tool and retrieval packages (vector stores, web search, text splitters) with stability notes
  • Clarifies that langchain-community is not semver and should be pinned conservatively

How to install langchain-dependencies

npx skills add https://github.com/langchain-ai/langchain-skills --skill langchain-dependencies
Prerequisites
  • Python 3.10+ (for Python projects) or Node.js 20+ (for TypeScript projects)
  • Basic familiarity with pip (Python) or npm/yarn (TypeScript)
Claude Code
Cursor
Windsurf
Cline

How to use langchain-dependencies

  1. 1.Identify your runtime: Python 3.10+ or Node.js 20+
  2. 2.Choose your orchestration framework: LangGraph for fine-grained control, or Deep Agents for batteries-included features
  3. 3.Select your model provider package (langchain-openai, langchain-anthropic, etc.) based on which LLM you use
  4. 4.Copy the minimal project template for your framework and language, then add provider and tool packages as needed
  5. 5.Pin langchain-community conservatively to a minor version if you use it; prefer dedicated integration packages (langchain-chroma, langchain-tavily) when available
  6. 6.Install dependencies and verify versions match the documented minimums

Use cases

Good for
  • Starting a new LangChain 1.0 project and determining which packages to install
  • Choosing between LangGraph and Deep Agents for your agent orchestration layer
  • Adding a specific model provider (e.g., Claude, GPT-4o, Gemini) to an existing project
  • Selecting vector store integrations (Pinecone, Qdrant, Chroma, Weaviate) with correct versions
  • Troubleshooting dependency conflicts by understanding version constraints and peer dependencies
Who it's for
  • Backend engineers building LangChain agents or retrieval systems
  • Full-stack developers integrating LLMs into Node.js or Python applications
  • DevOps/platform engineers managing dependency versions across teams
  • AI engineers evaluating LangGraph vs. Deep Agents for their use case

langchain-dependencies FAQ

Should I use LangChain 0.3 or 1.0 for a new project?

Always use LangChain 1.0+ for new projects. Version 0.3 is legacy maintenance-only and should not be used for new work.

Do I need to install both LangGraph and Deep Agents?

No. Choose one orchestration approach based on your needs. LangGraph gives you fine-grained graph control; Deep Agents provides batteries-included planning, memory, and file context. They are alternatives, not a required stack.

Why should I pin langchain-community conservatively?

langchain-community does not follow semantic versioning, so minor releases can contain breaking changes. Prefer dedicated integration packages (langchain-chroma, langchain-tavily, langchain-pinecone) when they exist—they are independently versioned and more stable.

What if I'm using a monorepo or yarn workspace?

You must explicitly install @langchain/core (TypeScript) or langchain-core (Python) as a peer dependency. It will not always be hoisted automatically in workspaces.

Which model provider package should I install?

Install only the package for the LLM you use: langchain-openai for GPT-4o, langchain-anthropic for Claude, langchain-google-genai for Gemini, etc. This keeps your dependencies minimal.

Full instructions (SKILL.md)

Source of truth, from langchain-ai/langchain-skills.


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>

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 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.
# 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>