langchain-architecture – Coding skill for AI agents

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

Version and execution scope

The inline code uses legacy LangChain APIs/imports. LangChain v1 moved legacy chains and related functionality into langchain-classic; do not install latest and assume these snippets run unchanged. For new code use the official v1 migration guide and supported agent/state APIs: https://docs.langchain.com/oss/python/migrate/langchain-v1 . Pin compatible provider packages and verify their imports. Tools below are placeholders, not real database/email integrations; never report an email sent without a verified authorized side effect. Bound iterations, tokens and paid tools; isolate tenant memory and treat retrieved text as untrusted. Logs must redact prompts, results and credentials.

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
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

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
    raise NotImplementedError("Configure an authorized email adapter and verify delivery before reporting success")

tools = [search_database, send_email]

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

Pattern 3: Multi-Step Chain

from langchain.chains import LLMChain, SequentialChain
from langchain.prompts import PromptTemplate

# Step 1: Extract key information
extract_prompt = PromptTemplate(
    input_variables=["text"],
    template="Extract key entities from: {text}\n\nEntities:"
)
extract_chain = LLMChain(llm=llm, prompt=extract_prompt, output_key="entities")

# Step 2: Analyze entities
analyze_prompt = PromptTemplate(
    input_variables=["entities"],
    template="Analyze these entities: {entities}\n\nAnalysis:"
)
analyze_chain = LLMChain(llm=llm, prompt=analyze_prompt, output_key="analysis")

# Step 3: Generate summary
summary_prompt = PromptTemplate(
    input_variables=["entities", "analysis"],
    template="Summarize:\nEntities: {entities}\nAnalysis: {analysis}\n\nSummary:"
)
summary_chain = LLMChain(llm=llm, prompt=summary_prompt, output_key="summary")

# Combine into sequential chain
overall_chain = SequentialChain(
    chains=[extract_chain, analyze_chain, summary_chain],
    input_variables=["text"],
    output_variables=["entities", "analysis", "summary"],
    verbose=True
)

Memory Management Best Practices

Choosing the Right Memory Type

# For short conversations (< 10 messages)
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory()

# For long conversations (summarize old messages)
from langchain.memory import ConversationSummaryMemory
memory = ConversationSummaryMemory(llm=llm)

# For sliding window (last N messages)
from langchain.memory import ConversationBufferWindowMemory
memory = ConversationBufferWindowMemory(k=5)

# For entity tracking
from langchain.memory import ConversationEntityMemory
memory = ConversationEntityMemory(llm=llm)

# For semantic retrieval of relevant history
from langchain.memory import VectorStoreRetrieverMemory
memory = VectorStoreRetrieverMemory(retriever=retriever)

Callback System

Custom Callback Handler

from langchain.callbacks.base import BaseCallbackHandler

class CustomCallbackHandler(BaseCallbackHandler):
    def on_llm_start(self, serialized, prompts, **kwargs):
        print("LLM started")

    def on_llm_end(self, response, **kwargs):
        print("LLM ended")

    def on_llm_error(self, error, **kwargs):
        print(f"LLM error: {error}")

    de

… (full content is in the download package)