Quickstart

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.

Using a coding agent?

Give your AI agent access to these docs: claude mcp add llama-index-docs --transport http https://developers.llamaindex.ai/mcp — More options

Install

Python

pip install llama-cloud>=1.0

TypeScript

npm install @llamaindex/llama-cloud

Set your API key:

export LLAMA_CLOUD_API_KEY=llx-...

Get an API key from the LlamaCloud dashboard.

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Quick Start

Python

Parse

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

Copy as agent context

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

Extract

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

Copy as agent context

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

Index

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:

Copy as agent context

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

Classify

Beta

This feature is currently in beta.

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

Copy as agent context

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

Split

Beta

This feature is currently in beta.

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

Copy as agent context

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

Sheets

Beta

This feature is currently in beta.

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

Copy as agent context

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

TypeScript

Parse

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

Copy as agent context

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

Extract

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

Copy as agent context

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

Index

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:

Copy as agent context

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

Classify

Beta

This feature is currently in beta.

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

Copy as agent context

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

Split

Beta

This feature is currently in beta.

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

Copy as agent context

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

Sheets

Beta

This feature is currently in beta.

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

Copy as agent context

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

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LlamaParse Agent Skills

Download Skills

Available Skills

• llamaparse: Advanced parsing for PDFs, docs, presentations and images (charts, tables, embedded visuals). Requires LLAMA_CLOUD_API_KEY and Node 18+.
• liteparse: Local-first, fast parsing for text-dense PDFs and docs. No API key needed, requires @llamaindex/liteparse globally installed and Node 18+.

Installation

You can install LlamaParse Agent Skills using the skills CLI:

npx skills add run-llama/llamaparse-agent-skills

Or, if you wish to download only one skill:

npx skills add run-llama/llamaparse-agent-skills --skill llamaparse # or the name of another skill

You can also download the skills folder in .zip format from GitHub Releases.

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Resources

Python SDK pip install llama-cloud

TypeScript SDK npm install @llamaindex/llama-cloud