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langchain-dependencies

by @langchain-aiv
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管理LangChain依赖，推荐使用1.0+ LTS版本进行新项目开发，避免使用0.3旧版。

langchaindependency-managementpython-packagingvirtual-environmentsproject-setupGitHub
安装方式
npx skills add langchain-ai/langchain-skills --skill langchain-dependencies
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Before / After 效果对比

1 组
使用前

旧版LangChain依赖常导致兼容性问题，项目开发受阻。调试困难，功能不稳定，耗费大量时间解决版本冲突。

使用后

采用1.0+ LTS版本，项目依赖稳定可靠。开发效率显著提升，减少兼容性烦恼，专注于核心业务逻辑。

SKILL.md

langchain-dependencies

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).
```json
{
  "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"

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统计数据

安装量6.8K
评分4.5 / 5.0
版本
更新日期2026年5月23日
对比案例1 组

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4.5(38)
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4
47%
3
32%
2
5%
1
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兼容平台

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时间线

创建2026年3月17日
最后更新2026年5月23日