LangChain

SocialCrawl does not ship a LangChain package, and it does not need one: the API is one authenticated GET request, so you wrap it in a tool() in about 30 lines. Once bound to your model, your agent can pull current public data from 48 platforms through a single tool; documented endpoint caches apply.

How do I add social media data to a LangChain agent?

Define a tool with a zod schema that takes a platform, a resource, and the endpoint's query params, then call the SocialCrawl REST API inside the tool function with your key in the x-api-key header. Hand the tool to createAgent and it runs the tool loop for you.

// socialcrawl-langchain.ts
// Run with: SOCIALCRAWL_API_KEY=sc_... ANTHROPIC_API_KEY=... npx tsx socialcrawl-langchain.ts
import { createAgent, tool } from "langchain";
import * as z from "zod";

const API_KEY = process.env.SOCIALCRAWL_API_KEY;
if (!API_KEY) throw new Error("Set SOCIALCRAWL_API_KEY");

const socialcrawl = tool(
  async ({ platform, resource, params }) => {
    const url = new URL(
      `https://www.socialcrawl.dev/v1/${platform}/${resource}`,
    );
    for (const [key, value] of Object.entries(params ?? {})) {
      url.searchParams.set(key, String(value));
    }
    const res = await fetch(url, { headers: { "x-api-key": API_KEY } });
    const json = await res.json();
    if (!json.success) {
      // Hand the model a directive it can act on, not a raw error envelope.
      return `SocialCrawl error ${json.error.type}: ${json.error.message}. See ${json.error.doc_url}`;
    }
    // Return a string so the model can read it back as a tool message.
    return JSON.stringify(json.data);
  },
  {
    name: "socialcrawl",
    description:
      "Fetch current public social media, commerce, and review data from SocialCrawl; documented endpoint caches apply. " +
      "Covers TikTok, Instagram, YouTube, LinkedIn, Reddit, Amazon and more. " +
      "Set `platform` (e.g. 'tiktok'), `resource` (e.g. 'profile'), and `params` " +
      "(the endpoint's query fields, e.g. { handle: 'charlidamelio' }).",
    schema: z.object({
      platform: z
        .string()
        .describe("Platform slug, e.g. 'tiktok' or 'youtube'"),
      resource: z
        .string()
        .describe("Endpoint resource, e.g. 'profile' or 'search'"),
      params: z
        .record(z.string(), z.union([z.string(), z.number()]))
        .describe("Query parameters for the endpoint"),
    }),
  },
);

const agent = createAgent({
  model: "claude-sonnet-5",
  tools: [socialcrawl],
});

const result = await agent.invoke({
  messages: [
    {
      role: "user",
      content: "How many followers does @charlidamelio have on TikTok?",
    },
  ],
});

console.log(result.messages.at(-1)?.content);

Install the packages first (check the LangChain docs for the current versions):

npm install langchain @langchain/anthropic zod

createAgent from the langchain package is the LangChain v1 standard — it replaced createReactAgent and runs the model-calls-tool-calls-model loop itself, so a query needing two rounds of tool calls still works. Swap "claude-sonnet-5" for any model string LangChain resolves. If you need to drive the loop by hand, new ChatAnthropic({ model }).bindTools([socialcrawl]) still works: invoke the model, iterate response.tool_calls, push a ToolMessage per call, and invoke again until the model stops asking for tools.

Why one tool instead of one per endpoint?

Every SocialCrawl endpoint shares the same shape: a GET at /v1/{platform}/{resource} with query parameters and one x-api-key header, returning the same JSON envelope. A single tool that takes platform, resource, and params therefore reaches all 381 endpoints without you writing a new tool each time. Point the model at the platform directory or the machine-readable llms.txt so it knows which platform and resource to pass.

How do I keep the agent from spending too many credits?

Credits are billed exactly as the REST API bills them — most endpoints are 1 credit, heavier ones 5 or 10, and composite or bundle endpoints carry their own price (for example search/everywhere is a flat 20). Cache hits cost 0 credits. See Endpoint pricing for the exact figure per endpoint. Empty results and upstream errors are auto-refunded. Read credits_remaining from the envelope after each call to track spend, and see Credits for how the ledger works.

Where to go next

• Get a key at socialcrawl.dev (100 free credits) and read Authentication.
• Prefer the Vercel AI SDK? The same pattern in that framework is on the Vercel AI SDK page.
• Using an MCP client instead? Claude Code and Skills & MCP skip the DIY tool entirely.
• Fetching many items at once? Batch endpoints replace a loop of tool calls with one request.
• Explore the platform directory or start with the Quickstart.