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- Python 3.10+ (for Python projects) or Node.js 20+ (for TypeScript projects)
- Basic familiarity with pip (Python) or npm/yarn (TypeScript)
How to use langchain-dependencies
- 1.Identify your runtime: Python 3.10+ or Node.js 20+
- 2.Choose your orchestration framework: LangGraph for fine-grained control, or Deep Agents for batteries-included features
- 3.Select your model provider package (langchain-openai, langchain-anthropic, etc.) based on which LLM you use
- 4.Copy the minimal project template for your framework and language, then add provider and tool packages as needed
- 5.Pin langchain-community conservatively to a minor version if you use it; prefer dedicated integration packages (langchain-chroma, langchain-tavily) when available
- 6.Install dependencies and verify versions match the documented minimums
Use cases
- 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
- 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
Always use LangChain 1.0+ for new projects. Version 0.3 is legacy maintenance-only and should not be used for new work.
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.
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.
You must explicitly install @langchain/core (TypeScript) or langchain-core (Python) as a peer dependency. It will not always be hoisted automatically in workspaces.
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>| 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 |
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 |
</python-packages> <typescript-packages>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.
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 |
</typescript-packages>
@langchain/coremust be installed explicitly in yarn workspaces and monorepos — it is a peer dependency and will not always be hoisted automatically.
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>
# 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>Related skills
More from langchain-ai/langchain-skills and the wider catalog.
deep-agents-memory
Pluggable memory and file backends for Deep Agents: ephemeral, persistent, or hybrid routing.
langgraph-fundamentals
Build stateful agent workflows as directed graphs with nodes, edges, and state management.
langgraph-persistence
Persist LangGraph state with checkpointers, thread IDs, and time travel for durable multi-turn conversations.
langchain-fundamentals
Build production LangChain agents with create_agent(), tools, and middleware for human-in-the-loop control.
langchain-rag
Build retrieval-augmented generation (RAG) systems with document loading, chunking, embeddings, and vector stores.
langgraph-human-in-the-loop
Pause LangGraph execution for human approval, validation, or input—then resume with their decision.