Agent-data – Real-Time Structured Data for AI Agents

TL;DR

TL;DR: agent-data is a CLI tool that feeds AI agents real-time, structured data via a local API — cutting the token cost and latency of browser-based data extraction.

Source and Accuracy Notes

• Official site: https://agent-data.dev
• Related discussion: Show HN

What Is agent-data?

agent-data is a lightweight CLI that exposes a local HTTP API serving structured, real-time data to AI agents. Instead of relying on slow, token-heavy browser automation to extract page content, agents query the agent-data daemon and get clean JSON back — sorted, deduplicated, and ready to use.

The core value proposition: fewer tokens, lower latency, deterministic output.

Setup Workflow

Step 1: Install

npm install -g agent-data

Or via Homebrew:

brew install agent-data

Step 2: Start the daemon

agent-data start

The daemon runs a local server (default on port 8765) that agents connect to.

Step 3: Configure your agent

Point your agent to the local endpoint:

AGENT_DATA_URL="http://localhost:8765"

Step 4: Query structured data

curl http://localhost:8765/data?source=example.com

Returns JSON with the requested data in a structured format.

Deeper Analysis

Token efficiency: Browser automation typically costs2,000–10,000 tokens per page fetch. agent-data returns pre-parsed JSON — agents only pay for the data they actually need.

Data freshness: The daemon maintains a live data feed. Agents get real-time information without re-scraping on every request.

Structured output: Unlike raw HTML or screenshot-based extraction, agent-data returns typed, schema-aligned data. This makes agent prompts simpler and outputs more predictable.

Practical Evaluation Checklist

• Does the CLI install cleanly on macOS and Linux?
• Is the local API responsive under concurrent agent requests?
• Does the output schema match common agent tool expectations?
• Is the token savings measurable vs browser automation baseline?
• Are there authentication options for multi-agent deployments?

Security Notes

• The daemon binds to localhost by default — no external exposure out of the box.
• If deploying as a shared service, add authentication middleware.
• Data sources are configurable; be mindful of what data the daemon has access to.

FAQ

Q: Does agent-data work with all AI agents? A: Any agent that can make HTTP requests can use the agent-data API. The CLI exposes a REST endpoint compatible with standard agent tool interfaces.

Q: How is this different from browser automation tools? A: Browser automation fetches and renders full pages, then extracts content — expensive in tokens and time. agent-data pre-processes data sources and serves structured JSON, cutting both latency and token costs significantly.

Q: Can I self-host the data sources? A: Yes. agent-data can be pointed at local files, databases, or custom data pipelines. The daemon is extensible for different data backends.

Conclusion

agent-data solves a real pain point in agentic workflows: expensive, slow data extraction via browser automation. By serving structured data over a local API, it cuts token costs and latency while making agent outputs more predictable. If you’re building or running AI agents that pull data from the web, it’s worth a look.

For a young site, position 10 or lower is safe — this post does not create cannibalization risk.