> ## Documentation Index
> Fetch the complete documentation index at: https://exa.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Company Search Reference

> Self-contained reference for coding agents using Exa Company Search

## Overview

**Endpoint:** `POST https://api.exa.ai/search` with `"category": "company"`

**What it searches:** 50M+ company pages including LinkedIn company profiles, official websites, and Crunchbase-style data. Semantic search over industry, funding stage, headcount, geography, and technology attributes. Natural language queries return relevance-ranked company results.

For creating lists or enriching over many companies at scale, use [Websets](/websets/api-guide).

## Minimal Working Example

```bash theme={null}
curl -X POST "https://api.exa.ai/search" \
  -H "Content-Type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -d '{"query": "fintech companies in Switzerland", "category": "company", "contents": {"highlights": true}}'
```

```python theme={null}
from exa_py import Exa
exa = Exa(api_key="YOUR_API_KEY")
result = exa.search("fintech companies in Switzerland", category="company", contents={"highlights": True})
```

```javascript theme={null}
import Exa from "exa-js";
const exa = new Exa("YOUR_API_KEY");
const result = await exa.search("fintech companies in Switzerland", { category: "company", contents: { highlights: true } });
```

## Parameter Restrictions

The `company` category does **not** support the following parameters. Using them returns a **400 error**:

| Unsupported Parameter | Workaround                                                       |
| --------------------- | ---------------------------------------------------------------- |
| `startPublishedDate`  | Not available. Use natural language (e.g. "founded after 2020"). |
| `endPublishedDate`    | Not available.                                                   |
| `excludeDomains`      | Not available.                                                   |

## Supported Parameters

| Parameter    | Type    | Notes                                                                                       |
| ------------ | ------- | ------------------------------------------------------------------------------------------- |
| `query`      | string  | Natural language. Supports industry, geography, funding, headcount, technology, similarity. |
| `category`   | string  | Must be `"company"`.                                                                        |
| `type`       | string  | `"auto"` recommended. `"deep"` and `"deep-reasoning"` also work.                            |
| `numResults` | integer | 1–100. Default 10.                                                                          |
| `contents`   | object  | `text`, `highlights`, `summary`, all nested under `contents`.                               |

## Query Patterns

**Named lookup:**

```
"Sakana AI company"
"Tell me about exa.ai"
```

**Attribute filtering:**

```
"fintech companies in Switzerland"
"Japanese AI companies founded in 2023"
```

**Funding queries:**

```
"agtech companies in the US that have raised series A"
"startups that raised 30M to 80M"
```

**Composite queries:**

```
"Israeli security companies founded after 2015"
"German enterprise SaaS companies with more than 500 employees"
```

**Semantic / similarity:**

```
"Companies like Bell Labs"
"Companies working on making space travel cheaper"
"competitors of Notion"
```

## Common Mistakes

| Wrong                                                         | Correct                                                                                                    |
| ------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
| `excludeDomains: [...]` with `category: "company"`            | Remove `excludeDomains`. Not supported for company. Returns 400.                                           |
| `startPublishedDate: "2023-01-01"` with `category: "company"` | Remove date filters. Use natural language like "founded after 2023" in query.                              |
| Missing `category: "company"`                                 | Without `category`, the search runs against the general web index. Always include `"category": "company"`. |

## Patterns and Gotchas

* **Always set `category: "company"`.** Without it, you search the general web index and won't get company-specific results.
* **Natural language handles what filters can't.** Since date/text/exclude filters aren't supported, put all constraints in your query: "Series A fintech companies in Europe with 50-200 employees founded after 2020".
* **Use `highlights` for agent workflows.** Company pages are long. Highlights extract key details (industry, funding, headcount) efficiently.
* **Similarity queries work well.** "Companies like X" and "competitors of X" leverage semantic understanding of the company index.
* **Python SDK uses snake\_case.** `numResults` → `num_results`, `maxCharacters` → `max_characters`.
* **Combine with deep search for structured extraction.** Use `type: "deep"` with `outputSchema` to extract fields like company name, industry, funding, employee count.