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.

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 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 .

Core 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.

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.

Minimal Project Templates

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"
  }
}

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"
  }
}

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"
  }
}

Versioning Policy & Upgrade Strategy

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.

Environment Variables

# 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

Common Mistakes

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.

// 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"