Agent-Skills.md

Agent Skills: Exa RAG Integration

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.

UncategorizedID: ejirocodes/agent-skills/exa-rag

Repository

ejirocodes/agent-skills
ejirocodesLicense: MIT
1

Install this agent skill to your local

pnpm dlx add-skill https://github.com/ejirocodes/agent-skills/tree/HEAD/exa/skills/exa-rag

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exa/skills/exa-rag/SKILL.md

Skill Metadata

Name
exa-rag
Description
"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."

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

  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