langchain-dependencies

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.

---

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

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.

---

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

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

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"
  }
}

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

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"
  }
}

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.

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"
  }
}

---

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

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

---

Common Mistakes

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

`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).

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

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.

`@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"
  }
}

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"

Node.js below 20 is not officially supported.

# Verify before installing
node --version   # must be v20.x or higher