AI Agents Need Live Social Data. Social Fetch Wants to Be the Pipe.

The developer tool connects Claude, ChatGPT, Cursor and custom apps to public data from LinkedIn, Instagram, TikTok, X, YouTube, Reddit and 20 other platforms.

Ask an AI assistant what people are saying about a brand this week, and you will often get a polite apology. Large language models are trained on data that is months old, and most of them cannot see what is happening on social platforms right now. For the growing number of companies building AI agents for marketing, sales and research, that gap is becoming a real problem.

Social Fetch is one of the developer tools trying to close it. The service gives apps and AI agents live access to public social media data through a single API, and it has added a direct connection for AI assistants through the Model Context Protocol (MCP).

One connection, 26 platforms

Social Fetch currently covers 26 platforms through 278 endpoints, according to the company. Its largest endpoint sets are for Instagram (48), LinkedIn (41), TikTok (30) and X (29), followed by YouTube and Reddit. Beyond the major networks, it also reaches Facebook, Threads, Pinterest, Snapchat, Bluesky, Telegram, Twitch, Spotify, GitHub, Hacker News, Yelp, Amazon and the Google Ad Library, among others.

Developers get public profiles, posts, videos, comments, follower data, search results, engagement metrics and video transcripts back as JSON in the same format, whatever the source. Social Fetch runs and maintains the scrapers behind the scenes, so customers do not have to rebuild their pipelines every time a platform changes its layout.

How the AI connection works

The most notable part of the product for AI builders is its MCP server. Instead of writing integration code, a developer adds a single Social Fetch URL to an MCP-compatible client such as Claude, ChatGPT or Cursor and signs in. Each endpoint then appears to the assistant as a tool it can call on its own.

In practice, that means an agent asked to “summarise this week’s TikTok reaction to our product launch” or “check the LinkedIn profiles of the speakers at this conference” can fetch the data itself rather than relying on what it remembers. Social Fetch also offers a downloadable agent skill that tells coding agents which routes exist, with setup guides for Claude Code, Cursor and Codex.

For teams that prefer traditional development, there is a standard REST API with a single API key header, a TypeScript SDK and ready-made connections for automation platforms including n8n, Make, Apify and Pipedream.

Why LinkedIn data is in demand

Among the platforms covered, LinkedIn is one of the most sought-after. Recruiters, sales teams and B2B researchers rely on public professional data, but LinkedIn is also one of the hardest networks to collect from reliably. A dependable LinkedIn scraper API with dozens of endpoints is therefore a key selling point for tools in sales intelligence, recruiting and lead research.

The same applies to marketing and creator platforms. Influencer agencies use public Instagram and TikTok metrics to vet creators, while brand teams track mentions across X, Reddit and YouTube. Social Fetch also offers monitoring features, including tools for tracking competitor ads and follower changes, with monitor limits that scale by plan.

Performance and pricing

Social Fetch says it fetches data live on every request rather than serving it from a shared cache. The company reports an average response time of about 3.2 seconds and 99.8% uptime over the past 90 days, and says it handles more than 8 million API requests per day for over 3,200 developers. These figures come from the company itself.

Pricing follows a simple credit model: one request costs one credit. New users get 100 free credits without a credit card, and the free requests use the same live endpoints as paid plans. Monthly plans start at $29 for 18,000 credits, with Growth at $79 for 50,000 credits and Scale at $225 for 150,000 credits. Pay-as-you-go credits do not expire, and the company lists a 30-day money-back guarantee. Larger plans add team seats, shared workspaces and monthly credit limits per API key – a useful control for agencies running several client projects.

Public data, shared responsibility

Any social media scraper API raises questions about privacy and platform rules, and AI agents make those questions more pressing, because an automated system can collect data far faster than a person. Social Fetch states that it returns only publicly available data, never asks users for their social media passwords, encrypts data in transit and at rest, and aligns its privacy practices with UK and EU GDPR. It also publishes privacy, security, data processing and subprocessor documentation.

Like other providers in the space, the company makes clear that customers remain responsible for how they use the data under platform terms and applicable law. Businesses building agents on top of social data would be wise to collect only what they need and to avoid sensitive personal information.

A growing category

Official social media APIs have become more restricted and more expensive in recent years, while demand for real-time social data has grown – first from analytics and marketing teams, and now from AI developers. That combination has created room for unified social media API providers that handle the maintenance work and offer one consistent interface.

For developers experimenting with AI agents, the appeal of tools like Social Fetch is straightforward: an assistant that can look up what is actually happening online is far more useful than one limited to what it learned during training. As more teams move from chatbots to agents that take real actions, live data connections are likely to become a standard part of the toolkit.