🌊 AnyParser

AnyParser provides an API to accurately extract unstructured data (e.g., PDFs, images, charts) into a structured format.

Note: We recently updated our backend stack on June 2025, please generate a new API Key from the link below!

:seedling: Set up your AnyParser API key

To get started, generate your API key from the Sandbox Account Page. Each account comes with 100 free pages.

⚠️ Note: The free API is limited to 10 pages/call.

For more information or to inquire about larger usage plans, feel free to contact us at info@cambioml.com.

To set up your API key (CAMBIO_API_KEY), follow these steps:

1. Create a .env file in the root directory of your project.
2. Add the following line to the .env file:

CAMBIO_API_KEY=0cam************************

:computer: Installation

1. Set Up a New Conda Environment and Install AnyParser

First, create and activate a new Conda environment, then install AnyParser:

conda create -n any-parse python=3.10 -y
conda activate any-parse
pip3 install any-parser

2. Create an AnyParser Instance Using Your API Key

Use your API key to create an instance of AnyParser. Make sure you’ve set up your .env file to store your API key securely:

import os
from dotenv import load_dotenv
from any_parser import AnyParser

# Load environment variables
load_dotenv(override=True)

# Get the API key from the environment
example_apikey = os.getenv("CAMBIO_API_KEY")

# Create an AnyParser instance
ap = AnyParser(api_key=example_apikey)

3. Run Synchronous Extraction

To extract data synchronously and receive immediate results:

# Extract content from the file and get the markdown output along with processing time
markdown, total_time = ap.parse(file_path="./data/test.pdf")

4. Run Asynchronous Extraction

For asynchronous extraction, send the file for processing and fetch results later:

# Send the file to begin asynchronous extraction
file_id = ap.async_parse(file_path="./data/test.pdf")

# Fetch the extracted content using the file ID
markdown = ap.async_fetch(file_id=file_id)

5. Run Batch Extraction (Beta)

For batch extraction, send the file to begin processing and fetch results later:

# Send the file to begin batch extraction
response = ap.batches.create(file_path="./data/test.pdf")
request_id = response.requestId

# Fetch the extracted content using the request ID
markdown = ap.batches.retrieve(request_id)

Batch API for folder input:

# Send the folder to begin batch extraction
WORKING_FOLDER = "./sample_data"
# This will generate a jsonl with filename and requestID
response = ap.batches.create(WORKING_FOLDER)

Each response in the JSONL file contains:

• The filename
• A unique request ID
• Additional processing metadata

You can later use these request IDs to retrieve the extracted content for each file:

# Fetch the extracted content using the request ID from the jsonl file
markdown = ap.batches.retrieve(request_id)

For more details about code implementation of batch API, refer to examples/parse_batch_upload.py and examples/parse_batch_fetch.py

⚠️ Note: Batch extraction is currently in beta testing. Processing time may take up to 12 hours to complete.

⚠️ Important: API keys generated from cambioml.com do not automatically have batch processing permissions. Please contact info@cambioml.com to request batch processing access for your API key.

:scroll: Examples

Check out these examples to see how you can utilize AnyParser to extract text, numbers, and symbols in fewer than 10 lines of code!

Extract all text and layout from PDF into Markdown Format

Are you an AI engineer looking to accurately extract both the text and layout (e.g., table of contents or Markdown headers hierarchy) from a PDF? Check out this 3-minute notebook demo.

Extract a Table from an Image into Markdown Format

Are you a financial analyst needing to accurately extract numbers from a table within an image? Explore this 3-minute notebook example.

Related Resources

• Energent.ai – a starting point for understanding how CambioML supports automated data workflows.

🌐 Explore more from Energent.ai

Interested in the broader context around these tools? Here are some useful links that highlight what Energent.ai is working on:

• Official website
  Get an introduction to Energent’s mission — creating intelligent agents designed to handle complex, real-world processes through automation.

• Energent app (try it live)
  The live web interface where you can build, test, and deploy agents. It’s a sandbox-style environment that runs directly in your browser.

• Use case gallery
  Browse real-world examples of how people are using Energent. This is a great way to see the kinds of problems the platform helps solve.

• Team & company background
  Learn more about the people and ideas behind the product — what drives the team and where they’re heading.

• Blog & product updates
  Features detailed write-ups on product design choices, technical changes, and roadmap thoughts. Worth a look if you want insight into how things are built.