ToolTrace LangChain Integration

LangChain document loader and tools for the ToolTrace web intelligence API. Load webpages as LangChain Documents for RAG pipelines, or give your agents web scraping, SEO audit, and tech stack detection capabilities.

Install

pip install tooltrace-langchain

Document Loader

Load webpages as LangChain Documents with clean Markdown content and rich metadata:

from tooltrace_langchain import ToolTraceLoader

loader = ToolTraceLoader(
    urls=[
        "https://example.com/blog/post-1",
        "https://example.com/blog/post-2",
    ],
    api_key="your-key",
)

docs = loader.load()
for doc in docs:
    print(doc.metadata["title"])
    print(doc.page_content[:200])

Document metadata

Each document includes:

• source: Final URL after redirects
• title: Page title
• canonical_url: Canonical URL
• author: Author name
• language: Content language
• published_at: Publication date
• word_count: Word count
• render_method: Whether static or browser rendering was used
• content_hash: Content hash for change detection

Agent Tools

Give LangChain agents web intelligence capabilities:

from tooltrace_langchain import (
    ToolTraceExtractTool,
    ToolTraceMetadataTool,
    ToolTraceSeoAuditTool,
    ToolTraceTechStackTool,
)

tools = [
    ToolTraceExtractTool(api_key="your-key"),
    ToolTraceMetadataTool(api_key="your-key"),
    ToolTraceSeoAuditTool(api_key="your-key"),
    ToolTraceTechStackTool(api_key="your-key"),
]

# Use with any LangChain agent
from langchain.agents import AgentExecutor, create_tool_calling_agent

agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)
result = executor.invoke({"input": "What technologies does example.com use?"})

RAG pipeline example

from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter

from tooltrace_langchain import ToolTraceLoader

# Load pages
loader = ToolTraceLoader(
    urls=["https://tooltrace.io/docs"],
    api_key="your-key",
)
docs = loader.load()

# Split and index
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = splitter.split_documents(docs)
vectorstore = FAISS.from_documents(chunks, OpenAIEmbeddings())

# Query
results = vectorstore.similarity_search("How does rendering work?")

License

MIT