Building a Content Writing Agent with LangChain: Complete Guide

Understanding Content Writing Agents with LangChain

A Content Writing Agent built with LangChain is an intelligent, autonomous system that leverages large language models (LLMs) to research, plan, draft, and refine written content through a structured chain of reasoning and tool usage. Unlike simple prompt-and-response interactions with ChatGPT, a LangChain agent actively makes decisions—it chooses which tools to invoke, what information to retrieve, and how to iteratively improve its output based on self-critique and feedback loops.

At its core, LangChain provides the orchestration layer that connects the LLM to external resources such as web search APIs, document loaders, vector databases, and even other LLMs. The agent uses a reasoning-and-action loop (often called ReAct) to decompose a high-level writing task into manageable steps. For example, given the instruction "Write a comprehensive blog post about quantum computing for a technical audience," the agent might first search for recent developments, extract key points from articles, outline a structure, draft each section, then revise for clarity and accuracy—all without manual intervention at each stage.

Why Building a Content Writing Agent Matters

Manual content creation doesn't scale well when you need consistent, research-backed output across dozens or hundreds of articles. A LangChain-based agent offers several compelling advantages:

• Research integration: Agents can query live data sources, ensuring content reflects current information rather than the LLM's training cutoff.
• Structured quality control: You can embed editing and fact-checking steps directly into the agent's workflow, reducing hallucinations.
• Reproducibility: The agent's process is codified, so every article follows the same rigorous pipeline.
• Cost efficiency: By breaking tasks into smaller steps, you can use cheaper models for drafting and reserve expensive models for critical reasoning steps.
• Extensibility: Need SEO optimization? Add a tool. Need brand voice compliance? Add a critique step. The architecture is modular by design.

For development teams, marketing agencies, and technical documentation groups, this approach transforms content generation from an artisanal craft into an engineered, measurable process.

Core Architecture of a LangChain Writing Agent

Before diving into code, let's understand the architectural components that make a writing agent tick. The diagram below represents the logical flow:

• The LLM Brain: Typically GPT-4, Claude, or an open-source model via Ollama/HuggingFace. This is the decision-maker.
• The Tool Belt: Functions the agent can call—web search (Tavily, SerpAPI), content scraping, internal knowledge base retrieval, grammar checking, or even a separate "editor" LLM.
• The Orchestrator (LangChain Agent Executor): Manages the ReAct loop: receive observation → reason about next step → call tool or produce final answer.
• Memory: Stores intermediate research, outlines, and drafts so the agent doesn't lose context across steps.
• Prompt Templates: Carefully engineered system prompts that define the agent's persona, writing style, and quality standards.

The agent doesn't just "write"—it plans. A typical workflow might look like: Research → Outline → Section Drafting → Self-Critique → Revision → Final Formatting. Each phase may involve multiple tool calls and reasoning steps.

Setting Up Your Environment

Let's start with the foundational setup. You'll need Python 3.10+ and several LangChain packages. Create a new project directory and set up a virtual environment:

mkdir content-writing-agent
cd content-writing-agent
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install the required dependencies. We'll use LangChain's OpenAI integration, the Tavily search tool for web research, and ChromaDB for optional vector storage:

pip install langchain langchain-openai langchain-community
pip install tavily-python chromadb
pip install python-dotenv beautifulsoup4 markdown

Create a .env file to store your API keys securely:

OPENAI_API_KEY=your-openai-api-key-here
TAVILY_API_KEY=your-tavily-api-key-here

Now let's load these environment variables in our main script:

# config.py
import os
from dotenv import load_dotenv

load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")

if not OPENAI_API_KEY or not TAVILY_API_KEY:
    raise ValueError("Missing API keys in .env file")

Building the Core Tools

Tools are the agent's superpowers. For a content writing agent, we need at least three categories: research tools, content extraction tools, and quality assurance tools. Let's build them step by step.

Web Search Tool with Tavily

Tavily is purpose-built for LLM agents—it returns clean, structured search results optimized for downstream processing. Here's how to wrap it as a LangChain tool:

# tools/search_tool.py
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain.tools import tool
from typing import List, Optional
import json

# Configure Tavily for detailed results
tavily_search = TavilySearchResults(
    api_key=TAVILY_API_KEY,
    search_depth="advanced",  # Gets comprehensive results
    max_results=5,
    include_answer=True,      # Provides an AI-generated summary
    include_raw_content=False,
    include_images=False,
)

@tool
def web_search(query: str) -> str:
    """
    Search the web for current information on a given topic.
    Use this for research, fact-checking, and finding recent sources.
    Input should be a specific search query string.
    """
    try:
        results = tavily_search.invoke({"query": query})
        # Format results for readability
        formatted = []
        for i, result in enumerate(results, 1):
            formatted.append(
                f"Source {i}: {result.get('title', 'Untitled')}\n"
                f"URL: {result.get('url', 'N/A')}\n"
                f"Content: {result.get('content', 'No content available')}\n"
            )
        return "\n\n".join(formatted)
    except Exception as e:
        return f"Search failed: {str(e)}. Please try a different query."

Content Scraping Tool

Once the agent identifies promising sources, it needs to extract full article text. We'll build a scraping tool that fetches and cleans web content:

# tools/scraper_tool.py
from langchain.tools import tool
import requests
from bs4 import BeautifulSoup
import re

@tool
def scrape_webpage(url: str) -> str:
    """
    Extract the main text content from a webpage URL.
    Use this to read articles identified during research.
    Input should be a complete URL starting with https://
    """
    try:
        headers = {
            "User-Agent": "Mozilla/5.0 (compatible; ContentAgent/1.0)"
        }
        response = requests.get(url, headers=headers, timeout=15)
        response.raise_for_status()
        
        soup = BeautifulSoup(response.text, 'html.parser')
        
        # Remove script, style, and nav elements
        for element in soup(['script', 'style', 'nav', 'footer', 'header']):
            element.decompose()
        
        # Extract main content, prioritizing article tags
        main_content = soup.find('article') or soup.find('main') or soup.body
        
        if main_content:
            text = main_content.get_text(separator='\n', strip=True)
        else:
            text = soup.get_text(separator='\n', strip=True)
        
        # Clean up excessive whitespace
        text = re.sub(r'\n\s*\n', '\n\n', text)
        text = re.sub(r' +', ' ', text)
        
        # Truncate if too long (agents have context limits)
        if len(text) > 8000:
            text = text[:8000] + "... [content truncated]"
        
        return f"Content from {url}:\n\n{text}"
    except requests.RequestException as e:
        return f"Failed to scrape {url}: {str(e)}"
    except Exception as e:
        return f"Error processing {url}: {str(e)}"

Grammar and Style Checking Tool

A dedicated editing tool allows the agent to polish its output. We'll use a secondary LLM call focused solely on editing:

# tools/editor_tool.py
from langchain.tools import tool
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

# Use a cheaper model for editing tasks
editor_llm = ChatOpenAI(
    model="gpt-3.5-turbo",
    temperature=0.1,  # Low temperature for consistent editing
    max_tokens=2000,
)

editing_prompt = ChatPromptTemplate.from_messages([
    ("system", """You are an expert content editor. Review the provided text and:
1. Fix grammar, spelling, and punctuation errors
2. Improve sentence structure for clarity and flow
3. Ensure consistent tone and voice
4. Flag any factual claims that seem questionable with [NEEDS VERIFICATION]
5. Preserve the original meaning and structure

Return the fully edited version of the text, followed by a brief summary of changes made."""),
    ("human", "{text}"),
])

@tool
def edit_text(text: str) -> str:
    """
    Edit and polish a piece of text for grammar, style, and clarity.
    Use this after drafting content to improve quality.
    Input should be the complete text to edit.
    """
    try:
        chain = editing_prompt | editor_llm
        response = chain.invoke({"text": text})
        return response.content
    except Exception as e:
        return f"Editing failed: {str(e)}. Returning original text:\n\n{text}"

Designing the Agent's Prompt Template

The system prompt is the single most important factor in agent behavior. It defines the agent's identity, writing standards, and decision-making process. Here's a carefully engineered prompt for our content writing agent:

# prompts/writer_prompt.py
SYSTEM_PROMPT = """You are an expert Content Writing Agent, a specialized AI designed to produce high-quality, well-researched written content. Your process is methodical and transparent.

## Your Identity
- You are a professional writer with expertise across technology, business, science, and culture.
- You prioritize accuracy, clarity, and engaging prose.
- You always cite sources when using external information.

## Your Workflow
For any content request, follow this structured approach:

1. **ANALYZE**: Understand the topic, audience, tone, and format requested.
2. **RESEARCH**: Use the web_search tool to gather current, relevant information. Execute multiple searches with different angles.
3. **EXTRACT**: Use the scrape_webpage tool to read promising articles in depth.
4. **OUTLINE**: Create a logical structure with clear sections before writing.
5. **DRAFT**: Write each section methodically, incorporating research findings.
6. **EDIT**: Use the edit_text tool to polish the complete draft.
7. **FINALIZE**: Present the finished content with a brief summary of sources used.

## Writing Standards
- Use active voice and concrete language.
- Vary sentence structure for rhythm.
- Include specific examples and data points when available.
- Write in the requested tone (casual, professional, technical, etc.).
- Format output in Markdown with proper headings, lists, and code blocks as appropriate.

## Tool Usage Rules
- Always perform research before writing—never rely solely on training data.
- When search results are insufficient, try different queries.
- After scraping, synthesize information rather than copying verbatim.
- Use the edit_text tool on complete drafts, not fragments.

## Output Format
Present your final content clearly, preceded by a brief "Research Summary" showing the sources consulted. Use the following structure:

---

**Research Summary:** [Brief note on sources and key findings]

[The completed content]

---

Now, let's begin the content creation process."""

HUMAN_PROMPT_TEMPLATE = """Create content based on the following request:

Topic: {topic}
Audience: {audience}
Tone: {tone}
Format: {format}
Additional Requirements: {requirements}

Follow your structured workflow to produce the best possible content."""

Assembling the Agent Executor

Now we bring everything together. The LangChain agent executor combines the LLM, tools, prompts, and memory into a single runnable unit. We'll use the create_tool_calling_agent pattern, which is the modern recommended approach:

# agent.py
from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.memory import ConversationSummaryMemory
from langchain_core.messages import SystemMessage, HumanMessage

from tools.search_tool import web_search
from tools.scraper_tool import scrape_webpage
from tools.editor_tool import edit_text
from prompts.writer_prompt import SYSTEM_PROMPT, HUMAN_PROMPT_TEMPLATE
from config import OPENAI_API_KEY

def create_writing_agent(verbose: bool = True):
    """
    Build and return a configured content writing agent.
    
    Args:
        verbose: Whether to print agent's thought process
    
    Returns:
        An AgentExecutor ready to handle content requests
    """
    
    # Initialize the primary LLM with reasoning capabilities
    llm = ChatOpenAI(
        model="gpt-4-turbo-preview",  # Use gpt-4 for complex reasoning
        temperature=0.7,              # Moderate creativity
        api_key=OPENAI_API_KEY,
        max_tokens=4096,
    )
    
    # Register all tools
    tools = [web_search, scrape_webpage, edit_text]
    
    # Build the prompt with system message and placeholders
    prompt = ChatPromptTemplate.from_messages([
        ("system", SYSTEM_PROMPT),
        ("human", HUMAN_PROMPT_TEMPLATE),
        MessagesPlaceholder(variable_name="agent_scratchpad"),
        MessagesPlaceholder(variable_name="chat_history", optional=True),
    ])
    
    # Create the agent
    agent = create_tool_calling_agent(llm, tools, prompt)
    
    # Wrap with executor for managed execution
    executor = AgentExecutor(
        agent=agent,
        tools=tools,
        verbose=verbose,
        handle_parsing_errors=True,
        max_iterations=15,           # Prevent infinite loops
        max_execution_time=300,      # 5-minute timeout
        return_intermediate_steps=False,
    )
    
    return executor

The create_tool_calling_agent function is the modern LangChain approach that uses native function-calling capabilities of models like GPT-4. The AgentExecutor wrapper handles the execution loop, error recovery, and iteration limits.

Building the Complete Application

Let's create the main entry point that ties everything together with a clean interface. We'll support both interactive mode and batch processing:

# main.py
import sys
import json
from datetime import datetime
from agent import create_writing_agent
from prompts.writer_prompt import HUMAN_PROMPT_TEMPLATE

class ContentWritingApp:
    """Main application class for the Content Writing Agent."""
    
    def __init__(self, verbose: bool = True):
        self.agent = create_writing_agent(verbose=verbose)
        self.verbose = verbose
    
    def generate_content(
        self,
        topic: str,
        audience: str = "General readers",
        tone: str = "Informative and engaging",
        format: str = "Blog post",
        requirements: str = "Include practical examples and actionable takeaways"
    ) -> str:
        """
        Generate content using the writing agent.
        
        Args:
            topic: The main subject to write about
            audience: Target audience description
            tone: Desired writing tone
            format: Content format (blog post, article, guide, etc.)
            requirements: Any specific instructions
            
        Returns:
            The generated content as a string
        """
        
        # Prepare the input for the agent
        input_data = {
            "topic": topic,
            "audience": audience,
            "tone": tone,
            "format": format,
            "requirements": requirements,
        }
        
        print(f"\n{'='*60}")
        print(f"Starting content generation for: {topic}")
        print(f"Audience: {audience} | Tone: {tone} | Format: {format}")
        print(f"{'='*60}\n")
        
        try:
            result = self.agent.invoke({
                "input": HUMAN_PROMPT_TEMPLATE.format(**input_data)
            })
            
            output = result.get("output", "No output generated")
            
            # Save the output to a file
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            filename = f"output_{timestamp}.md"
            with open(filename, "w", encoding="utf-8") as f:
                f.write(output)
            
            print(f"\n{'='*60}")
            print(f"Content generation complete! Saved to: {filename}")
            print(f"{'='*60}\n")
            
            return output
            
        except Exception as e:
            error_msg = f"Agent execution failed: {str(e)}"
            print(error_msg, file=sys.stderr)
            return error_msg

def run_interactive_mode():
    """Run the agent in interactive mode for testing."""
    app = ContentWritingApp(verbose=True)
    
    print("\n📝 Content Writing Agent - Interactive Mode")
    print("Type 'exit' at any prompt to quit\n")
    
    while True:
        topic = input("Topic: ").strip()
        if topic.lower() == 'exit':
            break
        
        audience = input("Target Audience (default: General readers): ").strip()
        if not audience:
            audience = "General readers"
        if audience.lower() == 'exit':
            break
        
        tone = input("Tone (default: Informative and engaging): ").strip()
        if not tone:
            tone = "Informative and engaging"
        if tone.lower() == 'exit':
            break
        
        format_type = input("Format (default: Blog post): ").strip()
        if not format_type:
            format_type = "Blog post"
        if format_type.lower() == 'exit':
            break
        
        requirements = input("Additional requirements (or press Enter): ").strip()
        if requirements.lower() == 'exit':
            break
        
        app.generate_content(
            topic=topic,
            audience=audience,
            tone=tone,
            format=format_type,
            requirements=requirements
        )

def run_batch_mode(config_file: str):
    """Process multiple content requests from a JSON config file."""
    with open(config_file, 'r') as f:
        configs = json.load(f)
    
    app = ContentWritingApp(verbose=False)  # Less verbose for batch
    
    for i, config in enumerate(configs, 1):
        print(f"\nProcessing batch item {i}/{len(configs)}...")
        app.generate_content(**config)

if __name__ == "__main__":
    if len(sys.argv) > 1 and sys.argv[1] == "--batch":
        if len(sys.argv) < 3:
            print("Usage: python main.py --batch config.json")
            sys.exit(1)
        run_batch_mode(sys.argv[2])
    else:
        run_interactive_mode()

Example Batch Config File

For processing multiple articles at once, create a JSON configuration file:

// batch_config.json
[
    {
        "topic": "The Future of Quantum Computing in 2025",
        "audience": "Technology professionals and CTOs",
        "tone": "Authoritative and forward-looking",
        "format": "Technical analysis article",
        "requirements": "Include timeline predictions, key players, and investment trends. Cite specific companies and research labs."
    },
    {
        "topic": "Sustainable Urban Gardening for Beginners",
        "audience": "Urban apartment dwellers with no gardening experience",
        "tone": "Encouraging and practical",
        "format": "Step-by-step guide",
        "requirements": "Focus on small spaces, container gardening, and low-maintenance plants. Include cost estimates."
    },
    {
        "topic": "Understanding Kubernetes Service Meshes",
        "audience": "DevOps engineers and platform architects",
        "tone": "Technical and educational",
        "format": "In-depth tutorial",
        "requirements": "Compare Istio, Linkerd, and Consul. Include code snippets for basic configurations."
    }
]

Running the Agent

Execute the agent in interactive mode to test single content generation:

python main.py

Sample interaction:

📝 Content Writing Agent - Interactive Mode
Type 'exit' at any prompt to quit

Topic: The impact of AI on modern journalism
Target Audience (default: General readers): Media professionals and journalists
Tone (default: Informative and engaging): Balanced and nuanced
Format (default: Blog post): Long-form article
Additional requirements (or press Enter): Include real-world case studies from major news organizations

============================================================
Starting content generation for: The impact of AI on modern journalism
Audience: Media professionals and journalists | Tone: Balanced and nuanced | Format: Long-form article
============================================================

[Agent begins its reasoning loop - searching, scraping, outlining, drafting...]

============================================================
Content generation complete! Saved to: output_20250115_143022.md
============================================================

For batch processing, use:

python main.py --batch batch_config.json

Advanced Techniques and Customizations

Adding a Vector Store for Research Memory

For agents that write multiple articles on related topics, a vector store prevents redundant research. Here's how to integrate ChromaDB as a research cache:

# tools/vector_memory.py
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain.tools import tool
from langchain_core.documents import Document

# Initialize embeddings and vector store
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vector_store = Chroma(
    collection_name="research_cache",
    embedding_function=embeddings,
    persist_directory="./research_db",
)

@tool
def query_research_cache(topic: str) -> str:
    """
    Search previously stored research for relevant information on a topic.
    Use this BEFORE conducting new web searches to avoid duplication.
    Input should be a descriptive topic or question.
    """
    results = vector_store.similarity_search(topic, k=3)
    if not results:
        return "No relevant cached research found. Proceed with web_search."
    
    formatted = []
    for doc in results:
        formatted.append(
            f"[CACHED] Source: {doc.metadata.get('source', 'Unknown')}\n"
            f"{doc.page_content[:1000]}..."
        )
    return "\n\n".join(formatted)

@tool
def store_research(research_text: str) -> str:
    """
    Store valuable research findings for future use.
    Input should be a summary of key findings with source attribution.
    """
    doc = Document(
        page_content=research_text,
        metadata={"timestamp": datetime.now().isoformat()}
    )
    vector_store.add_documents([doc])
    return "Research stored successfully for future queries."

Add these tools to your agent's tool list in create_writing_agent for persistent research memory across sessions.

Implementing a Self-Critique Loop

For higher quality output, implement a dedicated critique-and-revise cycle within the agent. This can be done by adding a critique tool:

# tools/critique_tool.py
from langchain.tools import tool
from langchain_openai import ChatOpenAI

critic_llm = ChatOpenAI(
    model="gpt-4-turbo-preview",
    temperature=0.3,
)

@tool
def critique_content(content: str, criteria: str = "general") -> str:
    """
    Critically evaluate written content against specified criteria.
    Use this to identify weaknesses before finalizing.
    
    Args:
        content: The complete draft to evaluate
        criteria: What to evaluate - 'general', 'accuracy', 'engagement', 
                  'structure', or 'tone'
    """
    critique_prompt = f"""You are a strict content critic. Evaluate the following 
content based on these criteria: {criteria}.

Provide:
1. A score from 1-10 for each: Clarity, Accuracy, Engagement, Structure, Tone
2. Specific weaknesses with examples from the text
3. Concrete suggestions for improvement
4. Any factual claims that need verification

Content to evaluate:
{content[:5000]}  # Truncate very long content
"""
    response = critic_llm.invoke(critique_prompt)
    return response.content

Best Practices for Production-Ready Writing Agents

Building a prototype is straightforward, but production deployment requires additional considerations. Here are the most important practices distilled from real-world implementations:

1. Structured Output Validation

Never trust the agent's raw output blindly. Implement validation that checks for required sections, word count ranges, and formatting compliance. Use LangChain's output parsers or custom validation functions:

# validators/content_validator.py
def validate_content(output: str, min_words: int = 300, max_words: int = 3000) -> dict:
    """Validate generated content against quality standards."""
    word_count = len(output.split())
    issues = []
    
    if word_count < min_words:
        issues.append(f"Content too short: {word_count} words (minimum: {min_words})")
    if word_count > max_words:
        issues.append(f"Content exceeds maximum: {word_count} words")
    if "Research Summary" not in output:
        issues.append("Missing required 'Research Summary' section")
    
    return {
        "valid": len(issues) == 0,
        "word_count": word_count,
        "issues": issues,
    }

2. Cost Management with Model Tiering

Use GPT-4 for reasoning and planning steps, but route drafting and editing to cheaper models. You can implement this with LangChain's model routing:

# Use cost-effective models for specific tasks
drafting_llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.8)  # Creative drafting
editing_llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.1)   # Precise editing
reasoning_llm = ChatOpenAI(model="gpt-4-turbo-preview", temperature=0.7)  # Complex decisions

3. Implementing Guardrails

Content writing agents can sometimes produce off-brand, inaccurate, or inappropriate content. Implement guardrails at multiple levels:

• Input guardrails: Validate and sanitize topic requests before processing.
• Process guardrails: Monitor tool usage patterns—excessive searching may indicate the agent is stuck.
• Output guardrails: Run content through moderation APIs and brand voice classifiers before delivery.

4. Logging and Observability

Agent behavior can be unpredictable. Comprehensive logging is essential for debugging and improvement:

# logging_config.py
import logging
import json
from datetime import datetime

# Configure structured logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s | %(levelname)s | %(name)s | %(message)s',
    handlers=[
        logging.FileHandler(f'agent_logs_{datetime.now():%Y%m%d}.log'),
        logging.StreamHandler()
    ]
)

logger = logging.getLogger("content_agent")

# Log each tool invocation with timing
def log_tool_call(tool_name: str, input_data: str, output_data: str, duration_ms: float):
    logger.info(json.dumps({
        "event": "tool_call",
        "tool": tool_name,
        "input_length": len(input_data),
        "output_length": len(output_data),
        "duration_ms": round(duration_ms, 2),
        "timestamp": datetime.now().isoformat(),
    }))

5. Human-in-the-Loop Integration

For critical content, insert a human review step before publication. LangChain supports interrupt points:

# The agent can pause and wait for human approval
# Configure with agent_executor options
executor = AgentExecutor(
    agent=agent,
    tools=tools,
    # Add interrupt before final output
    interrupt_before=["final_output"],
    # Human approves, then agent continues
)

6. Iterative Prompt Engineering

The system prompt is never finished on the first attempt. Maintain a version-controlled prompt library and A/B test different phrasings. Key areas to experiment with:

• How strictly the agent follows its workflow (too strict = rigid, too loose = chaotic)
• The balance between research depth and writing speed
• Tone consistency instructions
• Citation formatting requirements

7. Handling Edge Cases Gracefully

Your agent will encounter topics where web search returns conflicting information, or where scraping fails entirely. Build fallback chains:

# In your agent configuration, handle tool failures gracefully
executor = AgentExecutor(
    agent=agent,
    tools=tools,
    handle_parsing_errors=True,     # Recover from malformed tool inputs
    max_iterations=15,              # Prevent infinite reasoning loops
    max_execution_time=300,         # Hard timeout in seconds
    early_stopping_method="generate",  # Produce best answer if time runs out
)

Testing Your Content Writing Agent

A thorough testing strategy ensures your agent behaves reliably. Here's a testing framework:

# test_agent.py
import pytest
from unittest.mock import patch, MagicMock
from agent import create_writing_agent

def test_agent_initialization():
    """Verify agent builds without errors."""
    agent = create_writing_agent(verbose=False)
    assert agent is not None
    assert len(agent.tools) >= 2  # At least search and edit tools

def test_simple_content_generation():
    """Test basic content generation with mocked tools."""
    agent = create_writing_agent(verbose=False)
    
    with patch('tools.search_tool.web_search') as mock_search:
        mock_search.return_value = "Mock search results about Python programming"
        
        result = agent.invoke({
            "input": "Create content about Python list comprehensions for beginners"
        })
        
        assert "output" in result
        assert len(result["output"]) > 100  # Reasonable content length

def test_tool_selection_logic():
    """Verify agent chooses appropriate tools."""
    agent = create_writing_agent(verbose=False)
    
    # The agent should use web_search for research tasks
    result = agent.invoke({
        "input": "Research the latest developments in fusion energy"
    })
    
    # Check intermediate steps for tool usage
    # (Requires capturing intermediate_steps)

def test_error_recovery():
    """Test agent handles tool failures gracefully."""
    agent = create_writing_agent(verbose=False)
    
    with patch('tools.search_tool.web_search', side_effect=Exception("API Error")):
        result = agent.invoke({
            "input": "Write about climate change solutions"
        })
        
        # Agent should still produce output even if search fails
        assert "output" in result

Run the test suite with:

pytest test_agent.py -v

Deployment Considerations

When moving from development to production, consider these deployment patterns:

• API Wrapper: Wrap your agent in a FastAPI or Flask endpoint for integration with content management systems.
• Queue-Based Processing: Use Celery or Redis Queue for asynchronous batch content generation.
• Containerization: Package the agent with Docker for consistent deployment across environments.
• Monitoring: Track token usage, generation time, and success rates with tools like LangSmith or custom dashboards.
• Rate Limiting: Implement exponential backoff for API calls to respect provider limits.

Here's a minimal FastAPI wrapper example:

# api.py
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
from typing import Optional
from main import ContentWritingApp

app = FastAPI(title="Content Writing Agent API")
writer = ContentWritingApp(verbose=False)

class ContentRequest(BaseModel):
    topic: str = Field(..., description="Main topic for content generation")
    audience: Optional[str] = "General readers"
    tone: Optional[str] = "Informative and engaging"
    format: Optional[str] = "Blog post"
    requirements: Optional[str] = "Include practical examples"

class ContentResponse(BaseModel):
    content: str
    word_count: int
    timestamp: str

@app.post("/generate", response_model=ContentResponse)
async def generate_content(request: ContentRequest):
    try:
        result = writer.generate_content(
            topic=request.topic,
            audience=request.audience,
            tone=request.tone,
            format=request.format,
            requirements=request.requirements,
        )
        return ContentResponse(
            content=result,
            word_count=len(result.split()),
            timestamp=datetime.now().isoformat(),
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/health")
async def health_check():
    return {"status": "healthy", "agent_ready": True}

Conclusion

Building a Content Writing Agent with LangChain transforms content creation from a manual, linear process into an intelligent, tool-assisted workflow. Throughout this guide, we've constructed a complete agent that researches topics via web search, extracts deep content from sources, drafts structured articles, and polishes them through automated editing—all orchestrated by a reasoning LLM that makes autonomous decisions about which tools to use and when.

The architecture we've built is modular and extensible. You can swap in different LL