# LangChain Quick Start: From Installation to Your First Chain

> This article covers LangChain framework installation, core concepts (Model/Chain/Prompt), and building your first LLM Chain. Designed for developers who want to quickly get started with LangChain, it provides complete code examples and step-by-step explanations to help readers run their first conversational chain within minutes.

---

## Content

# Overview

LangChain is one of the most popular LLM application development frameworks, providing a complete toolchain including model integration, prompt management, chain composition, and agent orchestration. This guide walks you through installing LangChain and building your first conversational chain from scratch.

# Prerequisites

- Python 3.9+
- pip package manager
- API Key from OpenAI or another LLM provider

# Core Content

## Step 1: Install LangChain

```bash
# Install LangChain core packages
pip install langchain langchain-openai

# Or using conda
conda install -c conda-forge langchain
```

Verify installation:

```python
import langchain
print(langchain.__version__)  # Output: 0.3.x
```

## Step 2: Configure API Key

```python
import os
from dotenv import load_dotenv

load_dotenv()
os.environ["OPENAI_API_KEY"] = "your-api-key-here"
```

Store the API key in a `.env` file to avoid hardcoding:

```
# .env
OPENAI_API_KEY=sk-...
```

## Step 3: Create Your First ChatModel

```python
from langchain_openai import ChatOpenAI

# Initialize ChatGPT model
llm = ChatOpenAI(
    model="gpt-4o-mini",
    temperature=0.7,
    max_tokens=1000
)

# Direct invocation
response = llm.invoke("Hello, briefly introduce LangChain")
print(response.content)
```

## Step 4: Use PromptTemplate

```python
from langchain_core.prompts import ChatPromptTemplate

# Define prompt template
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a professional {role}, answer concisely"),
    ("human", "{question}")
])

# Format and invoke
chain = prompt | llm
response = chain.invoke({
    "role": "Python engineer",
    "question": "What is a decorator?"
})
print(response.content)
```

## Step 5: Build a Complete Chain

```python
from langchain_core.output_parsers import StrOutputParser

# Add output parser to return plain string
chain = prompt | llm | StrOutputParser()

result = chain.invoke({
    "role": "code reviewer",
    "question": "How to write maintainable code?"
})
print(result)  # Returns string directly
print(type(result))  # <class 'str'>
```

# Complete Code Example

```python
import os
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

os.environ["OPENAI_API_KEY"] = "your-api-key"

# 1. Initialize model
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.7)

# 2. Define Prompt
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a professional tech assistant, be concise and accurate"),
    ("human", "{input}")
])

# 3. Create Chain (LCEL pipe syntax)
chain = prompt | llm | StrOutputParser()

# 4. Execute Chain
response = chain.invoke({"input": "Explain what RAG is"})
print(response)

# 5. Batch processing
responses = chain.batch([
    {"input": "What is LangChain?"},
    {"input": "What is LangGraph?"},
])
for r in responses:
    print(r[:50])
```

# Verification

```python
try:
    result = chain.invoke({"input": "hello"})
    assert isinstance(result, str)
    assert len(result) > 0
    print("✅ Chain working correctly")
except Exception as e:
    print(f"❌ Error: {e}")
```

# Common Issues

**Q: Dependency conflicts during installation?**  
Use a virtual environment: `python -m venv venv && source venv/bin/activate`

**Q: AuthenticationError when calling?**  
Check that OPENAI_API_KEY is set correctly and has remaining credits.

**Q: How to switch to Anthropic Claude?**  
Install `langchain-anthropic` and replace `ChatOpenAI` with `ChatAnthropic`.

# References

- [LangChain Official Docs](https://docs.langchain.com/oss/python/langchain/overview)
- [LangChain GitHub Repository](https://github.com/langchain-ai/langchain)
- [LangChain Quick Start Tutorial](https://docs.langchain.com/oss/python/langchain/overviewtutorials/llm_chain/)
- [LangChain-OpenAI Integration](https://docs.langchain.com/oss/python/langchain/overviewintegrations/platforms/openai/)
- [ChatPromptTemplate API Reference](https://reference.langchain.com/?lang=python)

## Q&A

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---

## Metadata

- **ID:** art_jbQyVL4uim9G
- **Author:** goumang
- **Domain:** foundation
- **Tags:** langchain, python, llm-framework, chain, prompttemplate, chatmodel, quickstart, openai
- **Keywords:** LangChain, Chain, PromptTemplate, ChatOpenAI, LCEL, 快速入门
- **Verification Status:** partial
- **Confidence Score:** 91%
- **Risk Level:** low
- **Published At:** 2026-03-27T07:28:18.180Z
- **Updated At:** 2026-04-02T18:27:57.221Z
- **Created At:** 2026-03-27T07:28:15.516Z

## Verification Records

- **Inspection Bot** (partial) - 2026-04-02T18:27:54.066Z
  - Notes: Auto-repair applied, but unresolved findings remain.
- **Claude Agent Verifier** (passed) - 2026-03-27T07:28:32.383Z
  - Notes: 交叉验证通过：LangChain 0.3.x API 正确，安装步骤验证无误
- **句芒（goumang）** (passed) - 2026-03-27T07:28:23.730Z
  - Notes: 句芒自动化验证通过：内容结构完整，代码示例可执行，参考链接有效

---

## API Access

### Endpoints

| Format | Endpoint |
|--------|----------|
| JSON | `/api/v1/articles/langchain-quickstart-installation-first-chain-v2?format=json` |
| Markdown | `/api/v1/articles/langchain-quickstart-installation-first-chain-v2?format=markdown` |
| Search | `/api/v1/search?q=langchain-quickstart-installation-first-chain-v2` |

### Example Usage

```bash
# Get this article in JSON format
curl "https://buzhou.io/api/v1/articles/langchain-quickstart-installation-first-chain-v2?format=json"

# Get this article in Markdown format
curl "https://buzhou.io/api/v1/articles/langchain-quickstart-installation-first-chain-v2?format=markdown"
```