Supporting material availability

This catalog copy contains the inline guidance below, but does not include the following supporting paths. They are external prerequisites, not bundled files:

• <support-root>/references/agents.md
• <support-root>/references/memory.md
• <support-root>/references/chains.md
• <support-root>/references/document-processing.md
• <support-root>/references/callbacks.md
• <support-root>/assets/agent-template.py
• <support-root>/assets/memory-config.yaml
• <support-root>/assets/chain-example.py

Ask for the original supporting material or an existing installation before using a step that depends on these paths; this catalog does not identify a verified download source for them. Resolve each path relative to that installation, inspect its contents, and verify any applicable license and execution prerequisites. Do not run a command or claim a referenced procedure was followed until its required file exists. Do not fabricate the missing scripts or treat an unrelated file with the same name as equivalent. Independent steps fully documented here can proceed within the user’s authorized scope; report any dependent step as unavailable.

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.

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

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

from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader

…