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From exa
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.
npx claudepluginhub ejirocodes/agent-skills --plugin exaHow this skill is triggered — by the user, by Claude, or both
Slash command
/exa:exa-ragThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
| Topic | When to Use | Reference |
Builds a RAG pipeline or custom search engine using Bright Data's Discover API for intent-ranked web results and parsed page content as retrieval/ingestion layer for LLMs or vector stores.
Executes Exa neural search with content extraction, date/domain filters for RAG pipelines, semantic queries, and web search features.
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| 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 |
from langchain_exa import ExaSearchRetriever
retriever = ExaSearchRetriever(
exa_api_key="your-key",
k=5,
highlights=True
)
docs = retriever.invoke("latest AI research papers")
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
)
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",
});
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?"}]
)
| 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 |
highlights=True for relevant snippetsresult.url in citations for grounded responsessummary=True provides concise context without full page overheadinclude_domains to limit to authoritative sourcesstart_published_date to avoid stale info