> For the complete documentation index, see [llms.txt](https://docs.graphlit.dev/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.graphlit.dev/api-guides/use-cases/feeds/web/feed-create-exa-search.md).

# Create Exa Search Feed

## User Intent

"How do I search the web with semantic/neural search using Exa? Show me Exa search feed configuration."

**Note**: Exa API key is not required. Graphlit provides Exa access by default and bills you for usage.

## Operation

**SDK Method**: `createFeed()` with FeedTypes.Search\
**Auth**: None required (Graphlit provides Exa access, bills you for usage)

***

## Code Example (TypeScript)

```typescript
import { Graphlit } from 'graphlit-client';
import { FeedTypes, SearchServiceTypes } from 'graphlit-client/dist/generated/graphql-types';

const graphlit = new Graphlit();

const feed = await graphlit.createFeed({
  name: 'AI Research Papers',
  type: FeedTypes.Search,
  search: {
    type: SearchServiceTypes.Exa,
    text: 'machine learning transformers research',
    readLimit: 20,
  },
  // Optional: add workflow for content processing
  // workflow: { id: workflow.createWorkflow.id }
});

console.log(`Created Exa search feed: ${feed.createFeed.id}`);
```

***

## Configuration

**text**: Natural language search query\
**readLimit**: Number of results per execution\
**type**: `SearchServiceTypes.Exa`

***

## What Makes Exa Different

**Neural Search**: Uses embeddings to find semantically similar content, not just keyword matches

**Content Quality**: Optimized for high-quality web content (articles, research, documentation)

**Recency**: Can prioritize recent content

***

## Use Cases

**Research Papers**:

```typescript
text: 'transformer architecture improvements 2024'
```

**Technical Articles**:

```typescript
text: 'distributed systems consensus algorithms explained'
```

**Company Research**:

```typescript
text: 'startups using GraphQL in production'
```

**Thought Leadership**:

```typescript
text: 'AI safety alignment research latest developments'
```

***

## Exa vs Tavily

**Use Exa when:**

* You want semantic/neural search
* Looking for high-quality long-form content
* Researching specific topics in depth
* Need technical documentation or papers

**Use Tavily when:**

* You want traditional keyword search
* Need real-time news updates
* Prefer broader web coverage
* Want faster results

***

***

## ⚠️ ExaCode Limitation

**ExaCode is not available as a feed**. ExaCode returns only Markdown content (no URLs to ingest), so it can only be used via the `searchWeb()` SDK method for direct retrieval, not as a continuous feed.

For code and technical documentation search as a feed, use regular Exa search with code-focused queries.

***

## Related

* [Tavily Search](/api-guides/use-cases/feeds/web/feed-create-tavily-search.md) - Traditional web search
* [Podscan Search](/api-guides/use-cases/feeds/web/feed-create-podscan-search.md) - Podcast search