Agent skill
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.
Install this agent skill to your Project
npx add-skill https://github.com/ejirocodes/agent-skills/tree/main/exa/skills/exa-rag
Metadata
Additional technical details for this skill
- author
- ejirocodes
- version
- 1.0.0
SKILL.md
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=Truefor relevant snippets - Missing source attribution - Always include
result.urlin citations for grounded responses - Ignoring summaries -
summary=Trueprovides 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_domainsto limit to authoritative sources - Skipping date filters - For current events, always add
start_published_dateto avoid stale info - Forgetting async patterns - Use async retrievers in production for better throughput
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