MCPInstall

exa-rag

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.

Installs
227
Last 24h
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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/ejirocodes/agent-skills --skill exa-rag
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads exa-rag automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Exa RAG Integration

Quick Reference

TopicWhen to UseReference
LangChainBuilding RAG chains with LangChainlangchain.md
LlamaIndexUsing Exa as a LlamaIndex data sourcellamaindex.md
Vercel AI SDKAdding web search to Next.js AI appsvercel-ai.md
MCP & ToolsClaude MCP server, OpenAI tools, function callingmcp-tools.md

Essential Patterns

LangChain Retriever

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

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

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

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

FrameworkBest ForKey Feature
LangChainComplex chains, agentsExaSearchRetriever, tool integration
LlamaIndexDocument indexing, Q&AExaReader, query engines
Vercel AI SDKNext.js apps, streamingTool definitions, edge-ready
OpenAI CompatDrop-in replacementMinimal code changes
Claude MCPClaude Desktop, Claude CodeNative 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

FAQ

What does the exa-rag skill do?

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.

How do I install exa-rag?

Run npx skills add https://github.com/ejirocodes/agent-skills --skill exa-rag in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/exa-rag/ (personal) or .claude/skills/exa-rag/ (project).

Which AI agents can use exa-rag?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is exa-rag safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains exa-rag?

It is published by ejirocodes in ejirocodes/agent-skills. It has 227 recorded installs.

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