exa-rag

📁 ejirocodes/agent-skills 📅 Jan 24, 2026

总安装量

周安装量

#22349

全站排名

安装命令

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

Agent 安装分布

claude-code 6

windsurf 5

antigravity 5

codex 5

gemini-cli 5

opencode 5

Skill 文档

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

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

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

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