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Langchain Dependencies

  • 44 installs
  • 111 repo stars
  • Updated July 29, 2026
  • langchain-ai/skills-benchmarks

This is a copy of langchain-dependencies by langchain-ai - installs and ranking accrue to the original listing.

Helps with ai & agent building tasks.

About

langchain-dependencies is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.

  • langchain-dependencies
  • AI & Agent Building
  • AI-coding skill

Langchain Dependencies by the numbers

  • 44 all-time installs (skills.sh)
  • Data as of Aug 4, 2026 (Skillselion catalog sync)
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Installs44
repo stars111
Last updatedJuly 29, 2026
Repositorylangchain-ai/skills-benchmarks

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

<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>

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