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Overview

ScrapeGraphAI allows you to build custom scraping pipelines by composing nodes into directed acyclic graphs (DAGs). This gives you complete control over the scraping workflow, enabling you to create specialized pipelines tailored to your specific needs.

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Using BaseGraph

The BaseGraph class is the foundation for creating custom scraping workflows. It manages the execution flow of interconnected nodes.

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Basic Structure

1

Import Required Modules

from scrapegraphai.graphs import BaseGraph
from scrapegraphai.nodes import (
    FetchNode,
    ParseNode,
    RAGNode,
    GenerateAnswerNode,
    RobotsNode
)
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
2

Configure the LLM

graph_config = {
    "llm": {
        "api_key": "your-api-key",
        "model": "gpt-4o",
    },
}

llm_model = ChatOpenAI(graph_config["llm"])
embedder = OpenAIEmbeddings(api_key=llm_model.openai_api_key)
3

Define Nodes

robot_node = RobotsNode(
    input="url",
    output=["is_scrapable"],
    node_config={
        "llm_model": llm_model,
        "force_scraping": True,
        "verbose": True,
    },
)

fetch_node = FetchNode(
    input="url | local_dir",
    output=["doc"],
    node_config={
        "verbose": True,
        "headless": True,
    },
)

parse_node = ParseNode(
    input="doc",
    output=["parsed_doc"],
    node_config={
        "chunk_size": 4096,
        "verbose": True,
    },
)

rag_node = RAGNode(
    input="user_prompt & (parsed_doc | doc)",
    output=["relevant_chunks"],
    node_config={
        "llm_model": llm_model,
        "embedder_model": embedder,
        "verbose": True,
    },
)

generate_answer_node = GenerateAnswerNode(
    input="user_prompt & (relevant_chunks | parsed_doc | doc)",
    output=["answer"],
    node_config={
        "llm_model": llm_model,
        "verbose": True,
    },
)
4

Create the Graph

graph = BaseGraph(
    nodes=[
        robot_node,
        fetch_node,
        parse_node,
        rag_node,
        generate_answer_node,
    ],
    edges=[
        (robot_node, fetch_node),
        (fetch_node, parse_node),
        (parse_node, rag_node),
        (rag_node, generate_answer_node),
    ],
    entry_point=robot_node,
)
5

Execute the Graph

result, execution_info = graph.execute({
    "user_prompt": "Describe the content",
    "url": "https://example.com/"
})

answer = result.get("answer", "No answer found.")
print(answer)

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Node Configuration

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Input Expressions

Nodes use boolean expressions to define their input requirements:
  • Single input: input="url"
  • OR logic: input="url | local_dir" (accepts either)
  • AND logic: input="user_prompt & parsed_doc" (requires both)
  • Complex: input="user_prompt & (relevant_chunks | parsed_doc | doc)"

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Node Config Dictionary

Each node accepts a node_config dictionary for customization: 
node_config = {
    "llm_model": llm_model,        # LLM instance
    "embedder_model": embedder,    # Embedder instance
    "verbose": True,               # Enable logging
    "chunk_size": 4096,            # Text chunk size
    "headless": True,              # Browser mode
    "timeout": 30,                 # Request timeout
}

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Graph Execution

The execute() method returns a tuple: 
state, execution_info = graph.execute(initial_state)
  • state: Final state dictionary with all outputs
  • execution_info: List of execution metrics per node
[
    {
        "node_name": "Fetch",
        "total_tokens": 0,
        "prompt_tokens": 0,
        "completion_tokens": 0,
        "successful_requests": 0,
        "total_cost_USD": 0.0,
        "exec_time": 1.234
    },
    # ... more nodes
    {
        "node_name": "TOTAL RESULT",
        "total_tokens": 1523,
        "prompt_tokens": 1200,
        "completion_tokens": 323,
        "successful_requests": 3,
        "total_cost_USD": 0.045,
        "exec_time": 5.678
    }
]

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Using GraphBuilder

The GraphBuilder class uses natural language to automatically generate graph configurations.

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Dynamic Graph Creation

from scrapegraphai.builders import GraphBuilder

graph_config = {
    "llm": {
        "api_key": "your-api-key",
        "model": "gpt-4o",
        "temperature": 0,
    },
}

# Create builder with natural language prompt
builder = GraphBuilder(
    prompt="I need to scrape product prices and descriptions from an e-commerce site",
    config=graph_config
)

# Generate graph configuration
graph_json = builder.build_graph()

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Visualizing Graphs

Convert your graph to a visual diagram: 
from scrapegraphai.builders import GraphBuilder

# Generate visualization
graph_diagram = GraphBuilder.convert_json_to_graphviz(
    graph_json,
    format="pdf"  # or "png", "svg"
)

# Render to file
graph_diagram.render("my_scraping_graph")
Graphviz must be installed on your system. Download from graphviz.org/download.

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Adding Nodes Dynamically

You can append nodes to an existing graph: 
from scrapegraphai.nodes import DescriptionNode

# Create graph
graph = BaseGraph(
    nodes=[fetch_node, parse_node],
    edges=[(fetch_node, parse_node)],
    entry_point=fetch_node
)

# Add new node
description_node = DescriptionNode(
    input="parsed_doc",
    output=["description"],
    node_config={"llm_model": llm_model}
)

graph.append_node(description_node)
Node names must be unique within a graph. The append_node() method will raise a ValueError if a node with the same name already exists.

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Complete Example

Here’s a full example combining all concepts:
~/workspace/source/examples/custom_graph/openai/custom_graph_openai.py
import os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from scrapegraphai.graphs import BaseGraph
from scrapegraphai.nodes import (
    FetchNode,
    GenerateAnswerNode,
    ParseNode,
    RAGNode,
    RobotsNode,
)

load_dotenv()

# Configuration
openai_key = os.getenv("OPENAI_APIKEY")
graph_config = {
    "llm": {
        "api_key": openai_key,
        "model": "gpt-4o",
    },
}

# Initialize models
llm_model = ChatOpenAI(graph_config["llm"])
embedder = OpenAIEmbeddings(api_key=llm_model.openai_api_key)

# Define nodes
robot_node = RobotsNode(
    input="url",
    output=["is_scrapable"],
    node_config={
        "llm_model": llm_model,
        "force_scraping": True,
        "verbose": True,
    },
)

fetch_node = FetchNode(
    input="url | local_dir",
    output=["doc"],
    node_config={
        "verbose": True,
        "headless": True,
    },
)

parse_node = ParseNode(
    input="doc",
    output=["parsed_doc"],
    node_config={
        "chunk_size": 4096,
        "verbose": True,
    },
)

rag_node = RAGNode(
    input="user_prompt & (parsed_doc | doc)",
    output=["relevant_chunks"],
    node_config={
        "llm_model": llm_model,
        "embedder_model": embedder,
        "verbose": True,
    },
)

generate_answer_node = GenerateAnswerNode(
    input="user_prompt & (relevant_chunks | parsed_doc | doc)",
    output=["answer"],
    node_config={
        "llm_model": llm_model,
        "verbose": True,
    },
)

# Create graph
graph = BaseGraph(
    nodes=[
        robot_node,
        fetch_node,
        parse_node,
        rag_node,
        generate_answer_node,
    ],
    edges=[
        (robot_node, fetch_node),
        (fetch_node, parse_node),
        (parse_node, rag_node),
        (rag_node, generate_answer_node),
    ],
    entry_point=robot_node,
)

# Execute
result, execution_info = graph.execute({
    "user_prompt": "Describe the content",
    "url": "https://example.com/"
})

result = result.get("answer", "No answer found.")
print(result)

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Best Practices

  1. Entry Point: Always ensure the first node in the nodes list matches the entry_point parameter
  2. Error Handling: Wrap graph execution in try-except blocks to handle node failures
  3. Verbose Mode: Enable verbose: True during development for detailed logging
  4. Chunk Size: Adjust chunk_size based on your LLM’s token limits
  5. Timeouts: Set appropriate timeout values to prevent hanging requests

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Next Steps

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