ToolTrace LlamaIndex Integration

LlamaIndex data reader for the ToolTrace web intelligence API. Load webpages as LlamaIndex Documents with clean Markdown content for RAG pipelines and knowledge bases.

Install

pip install tooltrace-llamaindex

Quick start

from tooltrace_llamaindex import ToolTraceReader

reader = ToolTraceReader(api_key="your-key")

docs = reader.load_data(
    urls=[
        "https://example.com/blog/post-1",
        "https://example.com/blog/post-2",
    ]
)

for doc in docs:
    print(doc.metadata["title"])
    print(doc.text[:200])

RAG pipeline example

from llama_index.core import VectorStoreIndex

from tooltrace_llamaindex import ToolTraceReader

# Load and index
reader = ToolTraceReader(api_key="your-key")
docs = reader.load_data(urls=["https://tooltrace.io/docs"])
index = VectorStoreIndex.from_documents(docs)

# Query
engine = index.as_query_engine()
response = engine.query("How does browser rendering work?")
print(response)

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

Options

reader = ToolTraceReader(
    api_key="your-key",        # or set TOOLTRACE_API_KEY env var
    render="auto",             # "never" (1 credit), "auto", "always" (5 credits)
    continue_on_error=True,    # skip failed URLs instead of raising
)

License

MIT