agent-data: Structured Web Data for OpenClaw Agents, 70% Cheaper Than Browser Automation

agent-data is a new tool that gives AI agents like OpenClaw access to structured web data through purpose-built Python API endpoints. It covers sources like X and Reddit posts, flight fares and statuses, and live job postings — all without browser automation.
Key Details
- Each endpoint is a pure Python API designed for AI agent consumption, with intuitive parameters and structured output by default.
- Progressive disclosure for token management helps control costs.
- The author benchmarked agent-data against browser automation and ad-hoc scraping, finding it both significantly cheaper and more reliable. Full benchmark results: agent-data.dev/blog/benchmarking-ai-agent-web-access/
- Use cases include daily summaries of r/OpenClaw posts or notifications when roundtrip flights to SFO drop below $300.
Who It's For
Developers using OpenClaw (or similar AI agents) who need reliable, cost-effective access to structured web data for recurring tasks.
📖 Read the full source: r/openclaw
👀 See Also

Skill Scaffolder: Build OpenClaw Skills Without Writing Code
Skill Scaffolder is an open-source tool that lets users create OpenClaw skills by describing what they want in plain English. It handles the entire process—interviewing users, writing skill files, testing, and installation—without requiring YAML, Python, or config files.

Ante: A Single-Binary Coding Agent That Runs Offline
Ante is a self-contained Rust binary that works like Claude Code or Codex but with zero runtime dependencies and full offline support. It scores 82.7% on Terminal-Bench 2.1 using DeepSeek V4 Flash.

AgentSwarms: Free Hands-On Playground for Learning Agentic AI
AgentSwarms offers 5 tracks, 40+ lessons, and 30+ runnable agents for free — no setup or API keys required to start. Learn by building from prompts to multi-agent swarms.

Ctxpact: Context Compaction Proxy for Local LLMs
Ctxpact is an OpenAI-compatible proxy that compresses oversized inputs for local LLMs with 16k context windows, using a 3-stage pipeline that includes DCP, summarization, and extraction strategies. Benchmarks show 110k tokens compressed to 12k with 8/8 reading comprehension accuracy.