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Parsera

Turns unstructured website content into clean markdown and structured data for your database, automating the extraction of pricing, product specs, and company news.

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Why use Ceven?

  1. AI native Parsera integration

    • Describe the outcome and Ceven picks the right Parsera calls, fills the parameters, and checks the result.
    • Structured, agent friendly tool schemas so each call runs reliably instead of by guesswork.
    • Rich coverage for reading, writing, and querying your Parsera data, across all 13 of its actions.

  2. Managed auth

    • Built in OAuth with automatic token refresh and rotation.
    • One place to manage, scope, and revoke Parsera access.
    • Per user and per environment credentials instead of shared keys.

  3. Agent optimized design

    • Actions are tuned from real success and error rates so reliability climbs over time.
    • Full execution logs so you always know what ran in Parsera, when, and on whose behalf.
    • The agent pauses and asks when Parsera is unclear instead of plowing ahead.

  4. Enterprise grade security

    • Fine grained access so you control which agents and people can reach Parsera.
    • Least privilege by default, read scopes first and only the writes a workflow needs.
    • A full audit trail of every Parsera action to support review and sign off.

Supported tools

Every action Ceven's agents can run on Parsera, and when to use it.

Extract Markdown
Use this to pull clean markdown content from a specific URL or uploaded file to remove HTML clutter.
Parse Content
Run this after extracting markdown to turn raw text into structured data based on a specific schema.
Fetch Page Source
Pull the raw HTML of a webpage before passing it to the markdown extractor.
Validate Schema
Check if the parsed output matches your required JSON structure before saving to a database.
Clean Text
Remove boilerplate text and navigation links from a scraped page to reduce token usage.
Batch Process URLs
Send a list of multiple URLs through the markdown extraction pipeline in one sequence.
Map Entities
Identify and label specific data points like prices or dates within the parsed content.
Filter Content
Use this to keep only specific sections of a page based on keywords or headers.
Convert to JSON
Transform the final parsed output into a valid JSON object for API delivery.
Test Selector
Verify if a specific content area is being captured correctly by the LLM parser.
Save Extraction
Commit the parsed structured data to a linked storage system or document.
Refresh Content
Re scrape a previously processed URL to check for updates in the text.
Parse Content with Parsera
Tool to parse and extract structured data from provided html or text. use after obtaining raw content.

13 actions · scroll to see them all

Frequently asked questions

Parsera focuses on the content extraction layer. For pages that require heavy JavaScript execution, you should use a headless browser to render the page first and then pass the resulting HTML to the Parsera extract tool. The LLM logic in Parsera is designed to find the signal in the noise regardless of how the HTML is nested, but it cannot trigger clicks or scroll events on its own. Once the HTML is captured, Parsera excels at turning that messy code into a clean markdown format that is optimized for further processing by other AI agents or data pipelines.
Since Parsera relies on LLMs for structured extraction, you are bound by the context window of the underlying model. Very large pages may exceed these limits. To solve this, use the filter content action to isolate the specific section of the page you need before running the parse content tool. By reducing the input to only the relevant markdown, you ensure higher accuracy and lower costs. We recommend splitting long articles into smaller chunks if you need to extract a high volume of specific entities from a single long form webpage.
Parsera itself is a processing library and does not manage session cookies or login credentials. To scrape gated content, you must provide the HTML source of the page while you are authenticated. You can do this by exporting the page source from your browser or using a proxy that handles the authentication layer. Once the authenticated HTML is passed into the Parsera extraction flow, the agent can parse the private data just as easily as it would a public page, provided the HTML is complete.
Yes, Parsera requests are subject to the rate limits of the LLM provider you have connected to the library. If you send too many concurrent extraction requests, you may encounter a 429 error. To prevent this, we recommend implementing a queue in your Ceven workflow to stagger the requests. The library does not have its own global rate limit, but the cost and speed are directly tied to the token throughput of your chosen model. Monitoring your token usage is key when running batch processes on hundreds of URLs.
Accuracy depends on the clarity of the schema you provide. Parsera uses LLMs to map text to keys, so the more descriptive your key names are, the better the result. For example, using a key called product price in usd is more effective than using a key called price. If you find the agent is missing data, try refining the prompt used during the parse content step. Most errors are solved by providing a few examples of the desired output format within the workflow configuration to guide the model.
Parsera is primarily designed for text and structural extraction. While it can identify image URLs within the markdown extraction phase, it does not perform optical character recognition or image analysis. If you need to extract text from an image on a page, you should first use a separate OCR tool and then feed that text into Parsera for structuring. The current version of the library treats images as reference links in the markdown output rather than analyzing the visual pixels of the image itself.
Traditional scrapers rely on hard coded paths that break when a website changes a single class name. Parsera uses semantic understanding to find data. It looks for the meaning of the content rather than its position in the code. This makes your workflows significantly more resilient to website updates. The trade off is that LLM based extraction is slower and more expensive per page than a simple regex or CSS selector. However, for most business users, the time saved on maintenance far outweighs the marginal increase in compute cost.
Yes, because Ceven treats Parsera as a tool in a larger workflow, the output can be sent anywhere. Once Parsera converts a webpage into a structured JSON object, you can push that data into a Google Sheet, a PostgreSQL database, or a CRM like Salesforce. The typical flow involves using the extract markdown action followed by the parse content action, and finally a write action to your destination system. This allows you to build a fully automated data pipeline from the open web to your internal business tools.

Alternatives to Parsera

Other tools that solve a similar problem. Ceven supports these too, so you can switch or run more than one at once.

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