exa-rag

Original：🇺🇸 English

Translated

Build RAG pipelines with Exa.ai for real-time web retrieval. Use when building retrieval-augmented generation, integrating Exa with LangChain, LlamaIndex, Vercel AI SDK, or implementing AI agents with web search capabilities. Triggers on: RAG pipeline, retrieval augmented generation, Exa LangChain, Exa LlamaIndex, ExaSearchRetriever, ExaSearchResults, Exa MCP, Exa tool calling, Claude tool use, AI agent web search, grounded generation, citation generation, fact checking, hallucination detection, OpenAI compatibility, chat completions.

3installs

Sourceejirocodes/agent-skills

Added on2026-05-17

NPX Install

npx skill4agent add ejirocodes/agent-skills exa-rag

SKILL.md Content

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Exa RAG Integration

Quick Reference

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

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

exa-rag

NPX Install

Tags

SKILL.md Content

Exa RAG Integration

Quick Reference

Essential Patterns

LangChain Retriever

LlamaIndex Reader

Vercel AI SDK Tool

OpenAI-Compatible Endpoint

Integration Selection

Common Mistakes