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langchain-dependencies

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<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).
```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.
# 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>

Source

git clone https://github.com/langchain-ai/langchain-skills/blob/main/config/skills/langchain-dependencies/SKILL.mdView on GitHub

Overview

The LangChain ecosystem uses independently versioned packages. This skill explains which packages to install, minimum versions, environment requirements, and versioning best practices, covering both Python and TypeScript workflows and common community tooling.

How This Skill Works

Identify core dependencies (langchain-core and core LangChain packages), enforce the 1.0+ LTS baseline, and choose a single orchestration framework (LangGraph or Deep Agents). Install provider integrations only as needed and pin langchain-community conservatively due to non-semantic versioning. Validate setups with environment checks and basic tests.

When to Use It

  • Starting a new LangChain project and deciding which packages to install
  • Verifying minimum versions and environment requirements for Python or TypeScript
  • Choosing between LangGraph and Deep Agents for orchestration
  • Adding a new model/provider integration (OpenAI, Anthropic, etc.) to a project
  • Setting up LangSmith tracing and datasets for monitoring and evaluation

Quick Start

  1. Step 1: Confirm environment: Python 3.10+ or Node.js 20+, LangChain 1.0+ LTS, LangSmith SDK >= 0.3.0
  2. Step 2: Install core packages: langchain, langchain-core, and langsmith; pick one orchestration framework (langgraph or deepagents)
  3. Step 3: Add necessary provider packages and common tools only as you need them; keep version constraints aligned with the core packages

Best Practices

  • Start with LangChain 1.0+ (LTS) and always install langchain-core alongside other packages
  • Pin langchain-community conservatively due to non-semantic versioning
  • Pick one orchestration framework (LangGraph or Deep Agents) rather than both
  • Install provider integrations only for the services you actually use
  • Verify environment requirements (Python 3.10+ or Node.js 20+) and test upgrades in a CI workflow

Example Use Cases

  • Boot a Python project with langchain-core 1.0 and langchain 1.0+ while pinning langchain-community to a minor series
  • Set up a TypeScript project on Node.js 20+ using LangGraph for custom graph workflows
  • Use Deep Agents for batteries-included planning and ensure langgraph is installed as a transitive dep if chosen
  • Add a specific provider (e.g., langchain-openai) and only the necessary tools (text splitters, retrieval, etc.)
  • Configure LangSmith >= 0.3.0 for tracing and dataset evaluation in a new project

Frequently Asked Questions

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