Langchain Architecture

---
name: langchain-architecture
description: Master the LangChain framework for building sophisticated LLM applications with agents, chains, memory, and tool integration. 
category: AI & Agents
source: antigravity
tags: [python, react, api, ai, agent, llm, workflow, template, document, langchain]
url: https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/langchain-architecture
---


# LangChain Architecture

Master the LangChain framework for building sophisticated LLM applications with agents, chains, memory, and tool integration.

## Do not use this skill when

- The task is unrelated to langchain architecture
- You need a different domain or tool outside this scope

## Instructions

- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open `resources/implementation-playbook.md`.

## Use this skill when

- Building autonomous AI agents with tool access
- Implementing complex multi-step LLM workflows
- Managing conversation memory and state
- Integrating LLMs with external data sources and APIs
- Creating modular, reusable LLM application components
- Implementing document processing pipelines
- Building production-grade LLM applications

## Core Concepts

### 1. Agents
Autonomous systems that use LLMs to decide which actions to take.

**Agent Types:**
- **ReAct**: Reasoning + Acting in interleaved manner
- **OpenAI Functions**: Leverages function calling API
- **Structured Chat**: Handles multi-input tools
- **Conversational**: Optimized for chat interfaces
- **Self-Ask with Search**: Decomposes complex queries

### 2. Chains
Sequences of calls to LLMs or other utilities.

**Chain Types:**
- **LLMChain**: Basic prompt + LLM combination
- **SequentialChain**: Multiple chains in sequence
- **RouterChain**: Routes inputs to specialized chains
- **TransformChain**: Data transformations between steps
- **MapReduceChain**: Parallel processing with aggregation

### 3. Memory
Systems for maintaining context across interactions.

**Memory Types:**
- **ConversationBufferMemory**: Stores all messages
- **ConversationSummaryMemory**: Summarizes older messages
- **ConversationBufferWindowMemory**: Keeps last N messages
- **EntityMemory**: Tracks information about entities
- **VectorStoreMemory**: Semantic similarity retrieval

### 4. Document Processing
Loading, transforming, and storing documents for retrieval.

**Components:**
- **Document Loaders**: Load from various sources
- **Text Splitters**: Chunk documents intelligently
- **Vector Stores**: Store and retrieve embeddings
- **Retrievers**: Fetch relevant documents
- **Indexes**: Organize documents for efficient access

### 5. Callbacks
Hooks for logging, monitoring, and debugging.

**Use Cases:**
- Request/response logging
- Token usage tracking
- Latency monitoring
- Error handling
- Custom metrics collection

## Quick Start

```python
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.llms import OpenAI
from langchain.memory import ConversationBufferMemory

# Initialize LLM
llm = OpenAI(temperature=0)

# Load tools
tools = load_tools(["serpapi", "llm-math"], llm=llm)

# Add memory
memory = ConversationBufferMemory(memory_key="chat_history")

# Create agent
agent = initialize_agent(
    tools,
    llm,
    agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
    memory=memory,
    verbose=True
)

# Run agent
result = agent.run("What's the weather in SF? Then calculate 25 * 4")
```

## Architecture Patterns

### Pattern 1: RAG with LangChain
```python
from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings

# Load and process documents
loader = TextLoader('documents.txt')
documents = loader.load()

text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
texts = text_splitter.split_documents(documents)

# Create vector store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(texts, embeddings)

# Create retrieval chain
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever(),
    return_source_documents=True
)

# Query
result = qa_chain({"query": "What is the main topic?"})
```

### Pattern 2: Custom Agent with Tools
```python
from langchain.agents import Tool, AgentExecutor
from langchain.agents.react.base import ReActDocstoreAgent
from langchain.tools import tool

@tool
def search_database(query: str) -> str:
    """Search internal database for information."""
    # Your database search logic
    return f"Results for: {query}"

@tool
def send_email(recipient: str, content: str) -> str:
    """Send an email to specified recipient."""
    # Email sending logic
    return f"Email sent to {recipient}"

tools = [search_database, send_email]

agent = initialize_agent(
    tools,
    llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True
)
```

### Pattern 3: Multi-Step Chain
```python
from langchain.chains import LLMChain, SequentialChain
from langchain.prompts import PromptTemplate

# Step 1: Extract key information
extract_prompt = PromptTemplate(
    input_variables=["text"],
    templat