# Give your AI agent a stock photo tool in 5 lines

> Source: <https://dev.to/marouane_tijani_3a7fc19a7/give-your-ai-agent-a-stock-photo-tool-in-5-lines-1gmh>
> Published: 2026-10-07 19:38:48+00:00

Agents that write blog posts, landing pages or slides end up needing pictures. Generated images are not always the answer: you may want a real street in Lisbon, a real espresso machine, and a licence you can check.

[Pexafy](https://pexafy.com) is a semantic search engine over millions of free-to-use photos from Unsplash, Pexels, Pixabay and other libraries. Your agent describes the photo it needs in plain language and gets back real photographs, each with its licence and the credit line to display.

Below: the same tool in LangChain, the Vercel AI SDK and any MCP-capable framework. Every snippet was run as printed.

```
export PEXAFY_API_KEY="pexafy_..."
export ANTHROPIC_API_KEY="sk-ant-..."
pip install -U langchain-pexafy langchain langchain-anthropic
python
from langchain.agents import create_agent
from langchain_pexafy import PexafyToolkit

agent = create_agent("anthropic:claude-haiku-4-5", tools=PexafyToolkit().get_tools())

result = agent.invoke({"messages": [
    {"role": "user", "content": "Find a header photo for a post about remote work, with its credit line."}
]})
print(result["messages"][-1].content)
```

The toolkit has three tools: search by description, find similar photos, get one photo. `create_agent` runs on LangGraph, and the tools also work with `ToolNode` or `bind_tools()`. The same package exists for LangChain.js: `npm install langchain-pexafy`.

```
npm install pexafy-ai-sdk ai zod @ai-sdk/anthropic
js
import { anthropic } from "@ai-sdk/anthropic";
import { generateText, stepCountIs } from "ai";
import { pexafyTools } from "pexafy-ai-sdk";

const { text } = await generateText({
  model: anthropic("claude-haiku-4-5"),
  tools: pexafyTools(),
  stopWhen: stepCountIs(5),
  prompt: "Find a header photo for a post about remote work, with its credit line.",
});
console.log(text);
```

Pexafy also runs an MCP server: `https://mcp.pexafy.com/mcp`, with your API key as a Bearer header. Here it is in Pydantic AI:

```
pip install "pydantic-ai-slim[mcp,anthropic]"
python
import os

from pydantic_ai import Agent
from pydantic_ai.mcp import MCPToolset, StreamableHttpTransport

pexafy = MCPToolset(
    StreamableHttpTransport(
        "https://mcp.pexafy.com/mcp",
        headers={"Authorization": f"Bearer {os.environ['PEXAFY_API_KEY']}"},
    )
).filtered(lambda ctx, tool: tool.name in {"search_photos", "search_photos_by_image"})

agent = Agent(
    "anthropic:claude-haiku-4-5",
    instructions="Find free stock photos and give each photo's credit line.",
    toolsets=[pexafy],
)

result = agent.run_sync("Find a header photo for a post about remote work.")
print(result.output)
```

The filter keeps the two search tools; the others are for chat clients that show a photo grid. Tested setups for other frameworks: [OpenAI Agents SDK](https://docs.pexafy.com/openai-agents), [Claude Agent SDK](https://docs.pexafy.com/claude-agent-sdk), [Google ADK](https://docs.pexafy.com/google-adk), [CrewAI](https://docs.pexafy.com/crewai), [LlamaIndex](https://docs.pexafy.com/llamaindex), [Mastra](https://docs.pexafy.com/mastra), [Strands Agents](https://docs.pexafy.com/strands).

With the LangChain and AI SDK packages, each photo is one record (abridged):

```
{
  "rank": 1,
  "photo_id": "019e1ea7-2e82-7a34-a451-4c6c7c8250f4",
  "alt_text": "Red bicycle parked against white wall with front wheel facing left and back wheel right",
  "url": "https://images.unsplash.com/photo-1520538254843-27a40bae5e3a?w=1280",
  "width": 4896,
  "height": 3264,
  "orientation": "landscape",
  "photographer": "Mitchel Lensink",
  "source": "Unsplash",
  "license": "free",
  "credit": "Photo by Mitchel Lensink on Unsplash (https://pexafy.com/legal/licenses/#unsplash)"
}
```

The agent can put `url` in your page, `alt_text` in the `alt` attribute and `credit` under the photo. On the free plan, show the credit line next to each photo you publish.