Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/ejirocodes/agent-skills/exa-ragnpx skills add ejirocodes/agent-skills --skill exa-raggit clone --depth 1 https://github.com/ejirocodes/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ejirocodes/agent-skills/exa-rag)<a href="https://agentmods.dev/skills/ejirocodes/agent-skills/exa-rag"><img src="https://agentmods.dev/badge/skills/ejirocodes/agent-skills/exa-rag.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00125 | $0.00862 |
| Opus 5 | $0.00063 | $0.00431 |
| Sonnet 5 | $0.00025 | $0.00172 |
| Haiku 4.5 | $0.00013 | $0.00086 |
Grade A, and why
exa-rag scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 |
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 98 lines · 125 tokens per session scan A a7d40f62d0a7
exa-rag is a skill published in the GitHub repository ejirocodes/agent-skills (6 stars, last pushed 2mo ago), licensed MIT. It adds 125 tokens to every session and 862 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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