LlamaParse documentation

Your first result in under a minute

One API key, one SDK, five products. Get a key, install, run a sample.

Open the quickstart Get API key

Available document APIs

Each is a separate API on the same key and SDK. Chain them (Split, then Extract) or use one on its own.

→

# Quarterly report

| Region | Revenue |
| ------ | ------- |
| North  | 1,240   |
| South  |   980   |

Parse

Agentic OCR for 130+ formats. PDFs, scans, tables, and charts to clean markdown, text, or JSON.

Jordan Lee · Senior engineer

jordan@example.com

Skills: Python, SQL, Rust

{
  "name": "Jordan Lee",
  "email": "jordan@example.com",
  "skills": ["Python", "SQL"]
}

Extract

Pull typed, schema-shaped data out of documents, with citations back to the page.

What was the Q3 revenue by region?↑

Q3 financial summary · p.40.92

Regional performance table · p.70.88

Management discussion · p.20.81

Index

Managed ingestion, embedding, and retrieval for RAG over your document set.

INV-2041.pdfinvoice0.97

scan_0312.jpgreceipt0.91

msa-final.docxcontract0.99

po-88210.pdfpurchase order0.94

Classify

Sort documents by type using natural-language rules, no training data needed.

invoice · p1–3

contract · p4–8

receipt · p9–10

Split

Break combined files into logical sections before you parse or extract.

Try it

The samples run as written once LLAMA_CLOUD_API_KEY is set.

Examples

Python TypeScript cURL

Parse a document PDF in, markdown out Extract structured data Your schema, filled from the file Classify documents Types and confidence per file Split a combined file Page ranges per section

from llama_cloud import LlamaCloud

client = LlamaCloud()  # Uses LLAMA_CLOUD_API_KEY env var

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

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

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);

# Upload the document
curl -X POST \
  https://api.cloud.llamaindex.ai/api/v1/beta/files \
  -H 'Accept: application/json' \
  -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
  -F 'purpose=parse' \
  -F 'file=@/path/to/your/file.pdf;type=application/pdf'

# Start a Parse job with the returned file id
curl -X POST \
  'https://api.cloud.llamaindex.ai/api/v2/parse' \
  -H 'Accept: application/json' \
  -H 'Content-Type: application/json' \
  -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
  --data '{
    "file_id": "<file_id>",
    "tier": "agentic",
    "version": "latest"
  }'

# Fetch the markdown once the job completes
curl -X GET \
  'https://api.cloud.llamaindex.ai/api/v2/parse/<job_id>?expand=markdown' \
  -H 'Accept: application/json' \
  -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"

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()

# Upload and extract
file = client.files.create(file="resume.pdf", purpose="extract")
job = client.extract.run(
    file_input=file.id,
    configuration={
        "data_schema": Resume.model_json_schema(),
        "tier": "agentic",
    },
)
print(job.extract_result)

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();

// Upload and extract
const file = await client.files.create({
  file: fs.createReadStream('resume.pdf'),
  purpose: 'extract',
});
let job = await client.extract.run({
  file_input: file.id,
  configuration: {
    data_schema: z.toJSONSchema(ResumeSchema),
    tier: 'agentic',
  },
});
console.log(job.extract_result);

# Upload the document
curl -X 'POST' \
  'https://api.cloud.llamaindex.ai/api/v1/beta/files' \
  -H 'Content-Type: multipart/form-data' \
  -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
  -F 'file=@/path/to/file' \
  -F purpose='extract'

# Run Extract with an inline JSON schema
curl -X 'POST' \
  'https://api.cloud.llamaindex.ai/api/v2/extract?project_id={PROJECT_ID}' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
  -d '{
    "file_input": "{FILE_ID}",
    "configuration": {
      "tier": "agentic",
      "data_schema": {
        "type": "object",
        "properties": {
          "name": { "type": "string", "description": "Full name of candidate" },
          "email": { "type": "string", "description": "Email address" },
          "skills": { "type": "array", "items": { "type": "string" }, "description": "Technical skills" }
        }
      }
    }
  }'

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}")

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}`);
  }
}

import time

from llama_cloud import LlamaCloud

client = LlamaCloud()

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

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

# Poll until the job reaches a terminal state (Split statuses are lowercase)
result = client.beta.split.get(job.id)
while result.status in ("pending", "processing"):
    time.sleep(2)
    result = client.beta.split.get(job.id)

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

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 job = await client.beta.split.create({
  document_input: { type: 'file_id', value: file.id },
  configuration: {
    categories: [
      {
        name: 'invoice',
        description: 'Commercial document with line items and totals',
      },
      {
        name: 'contract',
        description: 'Legal agreement with terms and signatures',
      },
    ],
  },
});

// Poll until the job reaches a terminal state (Split statuses are lowercase)
let result = await client.beta.split.get(job.id);
while (result.status === 'pending' || result.status === 'processing') {
  await new Promise((resolve) => setTimeout(resolve, 2000));
  result = await client.beta.split.get(job.id);
}

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

# Upload the combined PDF
curl -X 'POST' \
  'https://api.cloud.llamaindex.ai/api/v1/beta/files' \
  -H 'accept: application/json' \
  -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
  -F 'purpose=split' \
  -F 'file=@/path/to/your/file.pdf;type=application/pdf'

# Start a Split job
curl -X 'POST' \
  'https://api.cloud.llamaindex.ai/api/v1/beta/split/jobs' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
  -d '{
    "document_input": { "type": "file_id", "value": "YOUR_FILE_ID" },
    "configuration": {
      "categories": [
        { "name": "invoice", "description": "Commercial document with line items and totals" },
        { "name": "contract", "description": "Legal agreement with terms and signatures" }
      ]
    }
  }'

# Poll the job until it completes
curl -X 'GET' \
  'https://api.cloud.llamaindex.ai/api/v1/beta/split/jobs/YOUR_JOB_ID' \
  -H 'accept: application/json' \
  -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"

Open in playground

Next steps

• Quickstart: install the SDK in Python, TypeScript, Go, Java, or the CLI and run every product end to end.
• API reference: every endpoint, request and response shape, across all SDKs.
• Give your coding agent these docs: claude mcp add llama-index-docs --transport http https://developers.llamaindex.ai/mcp