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A document processing pipeline that fetches content, extracts key information, summarizes it with an LLM, and formats the final output. Each step depends on the previous one’s output.

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Workflow

pipeline.py
from timbal import Agent, Workflow
from timbal.state import get_run_context


def fetch_content(url: str) -> str:
    """Fetch raw content from a URL."""
    import urllib.request
    with urllib.request.urlopen(url) as response:
        return response.read().decode("utf-8")


def extract_metadata(html: str) -> dict:
    """Extract title and text from HTML content."""
    import re
    title_match = re.search(r"<title>(.*?)</title>", html)
    text = re.sub(r"<[^>]+>", " ", html)
    text = re.sub(r"\s+", " ", text).strip()
    return {
        "title": title_match.group(1) if title_match else "Untitled",
        "text": text[:5000],
    }


summarizer = Agent(
    name="summarizer",
    model="openai/gpt-4.1-mini",
    system_prompt="Summarize the given text in 3 bullet points. Be concise."
)


def format_report(title: str, summary: str) -> str:
    """Format the final report."""
    return f"# {title}\n\n{summary}"


pipeline = (
    Workflow(name="document_pipeline")
    .step(fetch_content, url="https://example.com")
    .step(extract_metadata,
        html=lambda: get_run_context().step_span("fetch_content").output)
    .step(summarizer,
        prompt=lambda: get_run_context().step_span("extract_metadata").output["text"])
    .step(format_report,
        title=lambda: get_run_context().step_span("extract_metadata").output["title"],
        summary=lambda: get_run_context().step_span("summarizer").output.collect_text())
)

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How It Works

fetch_content → extract_metadata → summarizer → format_report
  1. fetch_content — fetches raw HTML from the URL
  2. extract_metadata — parses title and text from the HTML (waits for fetch_content)
  3. summarizer — LLM summarizes the extracted text (waits for extract_metadata)
  4. format_report — combines title and summary into a report (waits for both extract_metadata and summarizer)
Each lambda creates an automatic dependency. No step runs until its dependencies are resolved.

​
Running

result = await pipeline().collect()
print(result.output)
The output will be similar to: 
# Example Domain

- The page serves as an illustrative example for documentation purposes
- It can be used freely without permission or coordination
- More information is available through IANA at the referenced link