# Exa — AI-Native Search API for Agent Pipelines

> Search engine built for AI agents with semantic understanding. Exa returns clean content (not links) using neural search, ideal for RAG pipelines and research automation.

## Install

Save as a script file and run:

## Quick Use

```bash
pip install exa-py
```

```python
from exa_py import Exa

exa = Exa(api_key="...")

# Semantic search — understands meaning, not just keywords
results = exa.search_and_contents(
    "best practices for building RAG pipelines",
    num_results=5,
    text=True,
)

for r in results.results:
    print(f"Title: {r.title}")
    print(f"Content: {r.text[:200]}")
    print("---")
```

## What is Exa?

Exa is a search engine built specifically for AI applications. Unlike Google (designed for humans clicking links), Exa uses neural search to understand query meaning and returns clean, structured content — full text, not just URLs. This makes it ideal for RAG pipelines, research agents, and any AI workflow that needs high-quality web knowledge.

**Answer-Ready**: Exa is an AI-native search API using neural search. Returns clean content (not links) with semantic understanding. Built for RAG pipelines and AI agents. Supports search, similarity finding, and content extraction. MCP server available. Used by leading AI companies.

**Best for**: AI agents needing web search with clean content extraction. **Works with**: Any LLM framework, LangChain, LlamaIndex, Claude Code via MCP. **Setup time**: Under 2 minutes.

## Core Features

### 1. Semantic Search

```python
# Understands meaning, not just keyword matching
results = exa.search(
    "papers about attention mechanisms in transformers",
    num_results=10,
    type="neural",  # or "keyword" for traditional search
)
```

### 2. Search + Contents (One Call)

```python
# Get search results with full text content
results = exa.search_and_contents(
    "Claude Code tips and tricks",
    num_results=5,
    text={"max_characters": 3000},  # Full text extraction
    highlights=True,                 # Key excerpts
)
```

### 3. Find Similar

```python
# Find pages similar to a given URL
results = exa.find_similar_and_contents(
    "https://docs.anthropic.com/en/docs/claude-code",
    num_results=5,
    text=True,
)
```

### 4. Content Extraction Only

```python
# Extract content from known URLs
contents = exa.get_contents(
    ["https://example.com/article1", "https://example.com/article2"],
    text=True,
)
```

### 5. Filtering

```python
results = exa.search(
    "AI coding tools",
    num_results=10,
    start_published_date="2026-01-01",
    include_domains=["github.com", "arxiv.org"],
    exclude_domains=["reddit.com"],
    category="research paper",
)
```

## Exa vs Google/Tavily/Brave

| Feature | Exa | Google | Tavily | Brave |
|---------|-----|--------|--------|-------|
| Neural search | Yes | No | Partial | No |
| Returns content | Yes | Links only | Yes | Snippets |
| Find similar | Yes | No | No | No |
| AI-optimized | Yes | No | Yes | Partial |
| MCP server | Yes | No | Yes | Yes |
| Pricing | Per search | Per search | Per search | Per search |

## MCP Integration

```json
{
  "mcpServers": {
    "exa": {
      "command": "npx",
      "args": ["-y", "exa-mcp-server"],
      "env": { "EXA_API_KEY": "..." }
    }
  }
}
```

## FAQ

**Q: How is it different from Tavily?**
A: Exa uses proprietary neural search (trained embeddings), while Tavily wraps traditional search with AI extraction. Exa's "find similar" feature is unique.

**Q: How much does it cost?**
A: Free tier available with limited searches. Paid plans from $5/month.

**Q: Can Claude Code use it?**
A: Yes, via the Exa MCP server or direct API calls in scripts.

## Source & Thanks

> Created by [Exa](https://exa.ai).
>
> [exa.ai](https://exa.ai) — AI-native search engine

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## Quick Use

```bash
pip install exa-py
```

Run AI semantic search in one line — returns clean content, not links.

## What is Exa?

A search engine designed for AI applications. Neural search understands query semantics and returns clean, full-text content. Ideal for RAG pipelines and research agents.

**TL;DR**: AI-native search API. Neural search returns clean content (not links). Semantic understanding + similarity discovery + content extraction. MCP server available. Top choice for RAG pipelines.

**Best for**: AI agents and RAG pipelines that need high-quality web search.

## Core Features

### 1. Semantic Search — understands meaning, not just keywords
### 2. Search + Content — full-text returned in one call
### 3. Find Similar — discover pages similar to a given URL
### 4. MCP Integration — works directly in Claude Code/Cursor

## FAQ

**Q: How does it differ from Tavily?**
A: Exa uses in-house neural search; Tavily wraps traditional search. Exa uniquely offers "find similar."

## Source & Thanks

> [exa.ai](https://exa.ai) — AI-native search engine

---
Source: https://tokrepo.com/en/workflows/exa-ai-native-search-api-agent-pipelines-f550d644
Author: Script Depot