---
id: ejirocodes/agent-skills/exa-rag
version: "bf0da573"
license: MIT
install: manual
updated: 2026-06-25
---

# exa-rag — Exa RAG enables you to construct retrieval-augmented generation systems that pull live web data into your AI applications. Integrate with LangChain, LlamaIndex, Vercel AI SDK, or Claude MCP to add grounded search capabilities, citations, and fact-checking to your agents.

Publisher: ejirocodes · Stars: 5 · Updated: 2026-06-25

Install (manual): `git clone https://github.com/ejirocodes/agent-skills`

## SKILL.md

# Exa RAG Integration

## Quick Reference

| Topic | When to Use | Reference |
|-------|-------------|-----------|
| **LangChain** | Building RAG chains with LangChain | [langchain.md](references/langchain.md) |
| **LlamaIndex** | Using Exa as a LlamaIndex data source | [llamaindex.md](references/llamaindex.md) |
| **Vercel AI SDK** | Adding web search to Next.js AI apps | [vercel-ai.md](references/vercel-ai.md) |
| **MCP & Tools** | Claude MCP server, OpenAI tools, function calling | [mcp-tools.md](references/mcp-tools.md) |

## Essential Patterns

### LangChain Retriever

```python
from langchain_exa import ExaSearchRetriever

retriever = ExaSearchRetriever(
    exa_api_key="your-key",
    k=5,
    highlights=True
)

docs = retriever.invoke("latest AI research papers")
```

### LlamaIndex Reader

```python
from llama_index.readers.web import ExaReader

reader = ExaReader(api_key="your-key")
documents = reader.load_data(
    query="machine learning best practices",
    num_results=10
)
```

### Vercel AI SDK Tool

```typescript
import { exa } from "@agentic/exa";
import { createOpenAI } from "@ai-sdk/openai";
import { generateText } from "ai";

const result = await generateText({
  model: openai("gpt-4"),
  tools: { search: exa.searchAndContents },
  prompt: "Search for the latest TypeScript features",
});
```

### OpenAI-Compatible Endpoint

```python
from openai import OpenAI

client = OpenAI(
    base_url="https://api.exa.ai/v1",
    api_key="your-exa-key"
)

response = client.chat.completions.create(
    model="exa",
    messages=[{"role": "user", "content": "What are the latest AI trends?"}]
)
```

## Integration Selection

| Framework | Best For | Key Feature |
|-----------|----------|-------------|
| **LangChain** | Complex chains, agents | ExaSearchRetriever, tool integration |
| **LlamaIndex** | Document indexing, Q&A | ExaReader, query engines |
| **Vercel AI SDK** | Next.js apps, streaming | Tool definitions, edge-ready |
| **OpenAI Compat** | Drop-in replacement | Minimal code changes |
| **Claude MCP** | Claude Desktop, Claude Code | Native tool calling |

## Common Mistakes

1. **Not using highlights for RAG** - Full text wastes context; use `highlights=True` for relevant snippets
2. **Missing source attribution** - Always include `result.url` in citations for grounded responses
3. **Ignoring summaries** - `summary=True` provides concise context without full page overhead
4. **Over-fetching results** - Start with 3-5 results; more isn't always better for RAG quality
5. **Not filtering domains** - Use `include_domains` to limit to authoritative sources
6. **Skipping date filters** - For current events, always add `start_published_date` to avoid stale info
7. **Forgetting async patterns** - Use async retrievers in production for better throughput


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