LangChainBeginner⏱ ~15 minutes

Documentation Generator

Turn your codebase into professional documentation automatically — never write docs manually again

★ 4.6(31 reviews)•Developer Tools

Preview Code ↓

$89$178

✓ Full source code & documentation
✓ Commercial license included
✓ 30-day email support
✓ Free updates for 1 year

What You Get

Everything included in this template package

💻

Working Agent Code

3 LangChain agents for parsing, generation, and formatting

⚙️

Configuration File

Output format, language settings, and template styles

💬

Prompt Templates

8 prompts for different documentation types

📖

Setup Guide

Quick start for Python, JS/TS, and Go projects

↔️

Example I/O

Sample codebases with generated documentation

📐

Architecture Diagram

Documentation pipeline flow

😤

The Problem

Documentation is always outdated. Developers hate writing it, and by the time someone does, the code has already changed. New team members waste days figuring out undocumented systems. Your API has endpoints no one knows about.

✨

The Solution

This agent parses your actual codebase, understands functions, classes, and API endpoints, then generates clear documentation with examples. When code changes, re-run it to update docs automatically. Always accurate, always current.

How It Works

Your AI crew handles the entire workflow

Input

Your task description, data, or trigger event

→

↓

AI Agents

Code ParserAnalyzes code structure, extracts functions, classes, and API endpoints

Doc WriterGenerates clear, well-organized documentation with examples

Format CheckerEnsures consistency, proper markdown formatting, and completeness

→

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Output

Structured results, reports, and actionable insights

Code Preview

Sample of the LangChain agent setup

Preview only

crew.py

from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain
from parsers import CodebaseParser

parser = CodebaseParser(language="python")
llm = ChatOpenAI(model="gpt-4", temperature=0.3)

def generate_docs(repo_path: str):
    # Parse the codebase
    modules = parser.parse(repo_path)

    docs = []
    for module in modules:
        chain = LLMChain(
            llm=llm,
            prompt=DOC_PROMPT
        )
        doc = chain.run(
            code=module.source,
            functions=module.functions,
            classes=module.classes
        )
        docs.append(doc)

    return compile_documentation(docs)

Example Input & Output

See what goes in and what comes out

Input

Repository: /path/to/my-api-project
Language: Python (FastAPI)
Output format: Markdown
Include: API endpoints, data models, setup instructions

Output

# My API Project

## Quick Start
```bash
pip install -r requirements.txt
uvicorn main:app --reload
```

## API Endpoints

### POST /users
Create a new user account.

**Request Body:**
```json
{"email": "user@example.com", "name": "John Doe"}
```

**Response (201):**
```json
{"id": "usr_123", "email": "user@example.com", "created_at": "2025-01-15"}
```

### GET /users/{id}
Retrieve user details by ID...

## Data Models
| Field | Type | Required | Description |
|-------|------|----------|-------------|