A production-ready decentralized search API for AI agents that provides privacy-first, uncensored web results via a distributed node network.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install decentralized-search
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install decentralized-search using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
Presearch Search is a powerful tool within the Openclaw Skills ecosystem designed for developers who prioritize data sovereignty and privacy. By utilizing a decentralized node infrastructure, this skill allows AI agents to perform web searches without the risk of tracking, profiling, or centralized censorship. It provides a reliable bridge between autonomous agents and the vast, distributed web, ensuring that information retrieval is both comprehensive and secure.
This skill is built for production environments where privacy is a non-negotiable requirement. Whether you are building research tools, data aggregators, or autonomous assistants, incorporating Presearch via Openclaw Skills ensures your application benefits from a distributed network that values user anonymity and data integrity.
To get started, obtain an API key from Presearch and configure it in your environment. Use the following bash command to set up your credentials:
export PRESEARCH_API_KEY="YOUR_API_KEY_HERE"
You can then initialize the skill in your preferred environment using the standard Openclaw Skills integration patterns for Python or JavaScript.
The skill returns a structured response that is easy for LLMs to parse. Below is the metadata taxonomy for the search results:
| Field | Description |
|---|---|
standardResults |
An array of search result objects (title, link, description). |
pagination |
Metadata for navigating result pages, including has_next. |
q |
The original search query processed. |
lang |
The language code used for filtering results. |
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