Welcome to LlamaParse

Build document agents powered by agentic OCR

LlamaParse is the enterprise platform to go from raw documents to production document agents: agentic OCR and parsing, structured extraction, searchable knowledge, and deployable agents—all in one platform.

Install

Python
TypeScript

pip install llama-cloud>=1.0

npm install @llamaindex/llama-cloud

Set your API key:

export LLAMA_CLOUD_API_KEY=llx-...

Get an API key from the LlamaCloud dashboard.

Agentic OCR and parsing for 130+ formats. Turn PDFs and scans into LLM-ready text—the foundation for document agents.

from llama_cloud import AsyncLlamaCloud
import asyncio

async def main():
    client = AsyncLlamaCloud()  # Uses LLAMA_CLOUD_API_KEY env var

    # Upload and parse a document
    file = await client.files.create(file="document.pdf", purpose="parse")
    result = await client.parsing.parse(
        file_id=file.id,
        tier="agentic",
        version="latest",
        expand=["markdown"],
    )

    # Get markdown output
    print(result.markdown.pages[0].markdown)

asyncio.run(main())

Full Guide | Examples | Tiers & Pricing

Structured data from documents with custom schemas. Feed agents with clean entities, tables, and fields.

from pydantic import BaseModel, Field
from llama_cloud import LlamaCloud

# Define your schema
class Resume(BaseModel):
    name: str = Field(description="Full name of candidate")
    email: str = Field(description="Email address")
    skills: list[str] = Field(description="Technical skills")

client = LlamaCloud()

# Create extraction agent with schema
agent = client.extraction.extraction_agents.create(
    name="resume-parser",
    data_schema=Resume.model_json_schema(),
    config={}
)

# Upload and extract
file = client.files.create(file="resume.pdf", purpose="extract")
result = client.extraction.jobs.extract(
    extraction_agent_id=agent.id,
    file_id=file.id,
)
print(result.data)

Full Guide | Examples | Schema Design

Ingest, chunk, and embed into searchable indexes. Power RAG and retrieval for document agents. Index is designed for UI-first setup with SDK integration. Start in the LlamaCloud dashboard to create your index, then integrate:

from llama_cloud import LlamaCloud

client = LlamaCloud()  # Uses LLAMA_CLOUD_API_KEY env var

# Retrieve relevant nodes from the index
results = client.pipelines.retrieve(
    pipeline_id="your-pipeline-id",
    query="Your query here",
    # -- Customize search behavior --
    # dense_similarity_top_k=20,
    # sparse_similarity_top_k=20,
    # alpha=0.5,
    # -- Control reranking behavior --
    # enable_reranking=True,
    # rerank_top_n=5,
)

for n in results.retrieval_nodes:
    print(f"Score: {n.score}, Text: {n.node.text}")

Full Guide | Examples

Categorize documents with natural-language rules. Pre-processing for extraction, parsing, or indexing.

from llama_cloud import LlamaCloud

client = LlamaCloud()

# Upload a document
file = client.files.create(file="document.pdf", purpose="classify")

# Classify with natural language rules
result = client.classifier.classify(
    file_ids=[file.id],
    rules=[
        {
            "type": "invoice",
            "description": "Documents with invoice numbers, line items, and totals"
        },
        {
            "type": "receipt",
            "description": "Short POS receipts with merchant and total"
        },
        {
            "type": "contract",
            "description": "Legal agreements with terms and signatures"
        },
    ],
    mode="FAST",  # or "MULTIMODAL" for visual docs
)

for item in result.items:
    print(f"Type: {item.result.type}, Confidence: {item.result.confidence}")

Full Guide | Examples

Segment concatenated PDFs into logical sections. AI-powered classification to split combined documents.

from llama_cloud import LlamaCloud

client = LlamaCloud()

# Upload a combined PDF
file = client.files.create(file="combined.pdf", purpose="split")

# Split into logical sections
result = await client.beta.split.split(
    categories=[
        {
            "name": "invoice",
            "description": "Commercial document with line items and totals"
        },
        {
            "name": "contract",
            "description": "Legal agreement with terms and signatures"
        },
    ],
    document_input={"type": "file_id", "value": file.id},
)

for segment in result.result.segments:
    print(f"Pages {segment.pages}: {segment.category} ({segment.confidence_category})")

Full Guide | Examples

Extract tables and metadata from messy spreadsheets. Output as Parquet files with rich cell metadata.

from llama_cloud import LlamaCloud

client = LlamaCloud()

# Upload a spreadsheet
file = client.files.create(file="spreadsheet.xlsx", purpose="parse")

# Extract tables and regions
result = client.beta.sheets.parse(
    file_id=file.id,
    config={"generate_additional_metadata": True},
)

# Print extracted regions
print(f"Found {len(result.regions)} regions")
for region in result.regions:
    print(f"  - {region.region_id}: {region.title} ({region.location})")

Full Guide | Examples

Agentic OCR and parsing for 130+ formats. Turn PDFs and scans into LLM-ready text—the foundation for document agents.

import LlamaCloud from '@llamaindex/llama-cloud';
import fs from 'fs';

const client = new LlamaCloud(); // Uses LLAMA_CLOUD_API_KEY env var

// Upload and parse a document
const file = await client.files.create({
  file: fs.createReadStream('document.pdf'),
  purpose: 'parse',
});
const result = await client.parsing.parse({
  file_id: file.id,
  tier: 'agentic',
  version: 'latest',
  expand: ['markdown']
});

// Get markdown output
console.log(result.markdown.pages[0].markdown);

Full Guide | Examples | Tiers & Pricing

Structured data from documents with custom schemas. Feed agents with clean entities, tables, and fields.

import LlamaCloud from '@llamaindex/llama-cloud';
import { z } from 'zod';
import fs from 'fs';

// Define your schema with Zod
const ResumeSchema = z.object({
  name: z.string().describe('Full name of candidate'),
  email: z.string().describe('Email address'),
  skills: z.array(z.string()).describe('Technical skills'),
});

const client = new LlamaCloud();

// Create extraction agent with schema
const agent = await client.extraction.extractionAgents.create({
  name: 'resume-parser',
  dataSchema: ResumeSchema,
  config: {},
});

// Upload and extract
const file = await client.files.create({
  file: fs.createReadStream('resume.pdf'),
  purpose: 'extract',
});
const result = await client.extraction.jobs.extract({
  extraction_agent_id: agent.id,
  file_id: file.id,
});
console.log(result.data);

Full Guide | Examples | Schema Design

import LlamaCloud from '@llamaindex/llama-cloud';

const client = new LlamaCloud(); // Uses LLAMA_CLOUD_API_KEY env var

// Retrieve relevant nodes from the index
const results = await client.pipelines.retrieve('your-pipeline-id', {
  query: 'Your query here',
  // -- Customize search behavior --
  // dense_similarity_top_k: 20,
  // sparse_similarity_top_k: 20,
  // alpha: 0.5,
  // -- Control reranking behavior --
  // enable_reranking: true,
  // rerank_top_n: 5,
});

for (const node of results.retrieval_nodes || []) {
  console.log(`Score: ${node.score}, Text: ${node.node?.text}`);
}

Full Guide | Examples

Categorize documents with natural-language rules. Pre-processing for extraction, parsing, or indexing.

import LlamaCloud from '@llamaindex/llama-cloud';
import fs from 'fs';

const client = new LlamaCloud();

// Upload a document
const file = await client.files.create({
  file: fs.createReadStream('document.pdf'),
  purpose: 'classify',
});

// Classify with natural language rules
const result = await client.classifier.classify({
  file_ids: [file.id],
  rules: [
    {
      type: 'invoice',
      description: 'Documents with invoice numbers, line items, and totals',
    },
    {
      type: 'receipt',
      description: 'Short POS receipts with merchant and total',
    },
    {
      type: 'contract',
      description: 'Legal agreements with terms and signatures',
    },
  ],
  mode: 'FAST', // or 'MULTIMODAL' for visual docs
});

for (const item of result.items) {
  if (item.result) {
    console.log(`Type: ${item.result.type}, Confidence: ${item.result.confidence}`);
  }
}

Full Guide | Examples

Segment concatenated PDFs into logical sections. AI-powered classification to split combined documents.

import LlamaCloud from '@llamaindex/llama-cloud';
import fs from 'fs';

const client = new LlamaCloud();

// Upload a combined PDF
const file = await client.files.create({
  file: fs.createReadStream('combined.pdf'),
  purpose: 'split',
});

// Split into logical sections
const result = await client.beta.split.split({
  categories: [
    {
      name: 'invoice',
      description: 'Commercial document with line items and totals',
    },
    {
      name: 'contract',
      description: 'Legal agreement with terms and signatures',
    },
  ],
  document_input: { type: 'file_id', value: file.id },
});

for (const segment of result.result.segments) {
  console.log(`Pages ${segment.pages}: ${segment.category} (${segment.confidence_category})`);
}

Full Guide | Examples

Extract tables and metadata from messy spreadsheets. Output as Parquet files with rich cell metadata.

import LlamaCloud from '@llamaindex/llama-cloud';
import fs from 'fs';

const client = new LlamaCloud();

// Upload a spreadsheet
const file = await client.files.create({
  file: fs.createReadStream('spreadsheet.xlsx'),
  purpose: 'parse',
});

// Extract tables and regions
const result = await client.beta.sheets.parse({
  file_id: file.id,
  config: { generate_additional_metadata: true },
});

// Print extracted regions
console.log(`Found ${result.regions?.length || 0} regions`);
for (const region of result.regions || []) {
  console.log(`  - ${region.region_id}: ${region.title} (${region.location})`);
}

Full Guide | Examples

Resources

Web UI - Visual interface for all products
Python SDK - pip install llama-cloud
TypeScript SDK - npm install @llamaindex/llama-cloud
API Reference