agent.pymarketingLangchainv1.0.0

LangChain: Content Generator

Generates blog outlines and drafts using your existing content as style reference.

Setup time: ~10 min

Model: GPT-4o

Cost: ~$0.15/day

Last updated: Mar 16, 2026

byRunbooks Communitycontributor

Template

agent.py

# Install: pip install langchain langchain-openai langchain-community chromadb

from langchain_community.document_loaders import DirectoryLoader, TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
import os

# --- Config ---
CONTENT_DIR = "./published-content"
CHROMA_DIR = "./chroma_content"

# --- Load existing content for style reference ---
loader = DirectoryLoader(CONTENT_DIR, glob="**/*.md", loader_cls=TextLoader)
docs = loader.load()

splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=200)
chunks = splitter.split_documents(docs)

embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(chunks, embeddings, persist_directory=CHROMA_DIR)
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})

llm = ChatOpenAI(model="gpt-4o", temperature=0.5)

# --- Outline generator ---
outline_prompt = ChatPromptTemplate.from_messages([
    ("system", """You are a content strategist. Generate blog post outlines that match the style of existing content. Use the reference material for tone and structure.

Reference content:
{context}"""),
    ("human", """Create a detailed blog outline for:
Topic: {topic}
Target audience: {audience}
Goal: {goal}

Include: title, intro hook, 4-6 sections with subpoints, conclusion, and a CTA."""),
])

outline_chain = outline_prompt | llm | StrOutputParser()

def generate_outline(topic: str, audience: str = "developers", goal: str = "educate") -> str:
    context_docs = retriever.invoke(topic)
    context = "\n---\n".join(d.page_content for d in context_docs)
    return outline_chain.invoke({
        "context": context,
        "topic": topic,
        "audience": audience,
        "goal": goal,
    })

if __name__ == "__main__":
    topic = input("Blog topic: ")
    audience = input("Target audience (default: developers): ") or "developers"
    goal = input("Goal (educate/convert/engage): ") or "educate"
    print("\n--- Outline ---\n")
    print(generate_outline(topic, audience, goal))

Setup

1
Copy the agent.py content above.
2
Create a Python virtual environment and install dependencies.
3
Set your OPENAI_API_KEY environment variable.
4
Run: python agent.py

Run with LangChain

This is a Python script using LangChain. Set up a virtual environment and install dependencies.

# 1. Create a virtual environment
python -m venv venv && source venv/bin/activate

# 2. Install dependencies
pip install langchain langchain-openai langchain-community chromadb

# 3. Set your API key
export OPENAI_API_KEY="sk-..."

# 4. Save the agent.py from above and run it
python agent.py