YOUR
QUESTIONS.
Practical answers for developers integrating Arlong through REST or MCP.
Read the developer docs ↗What should you know
before integrating?
What is Arlong?
Arlong is a web search and research API for AI agents, available through REST and MCP. It retrieves sources, screens untrusted content and returns evidence for the agent’s task.
How does Arlong handle source verification and claim corroboration?
The research pipeline gathers source evidence, checks coverage and looks for supporting or conflicting evidence. A source link alone does not verify a claim, and syndicated copies should not count as independent confirmation. Review the returned evidence and coverage before relying on an answer.
Can I use Arlong Screen with another search API?
Yes. Arlong Screen works independently of Arlong search. Submit text, HTML or a supported public URL through the API or MCP tool, and use only the permitted safe_content from an allowed full-mode result.
Does screening guarantee that prompt injection cannot reach my agent?
No. Screening is a protective layer with measured limitations. Allowed content remains untrusted evidence. Limit agent permissions and require authorization for sensitive actions.
Where can I compare search APIs for agents?
The Arlong blog includes guides to source verification, Exa versus Tavily, and Tavily alternatives. The guides cite official documentation and separate documented capabilities from engineering recommendations.
What inputs does Arlong Screen accept?
Screen accepts text, HTML with input_type set to html, or a supported public URL. Submit extracted text for binary documents; direct file uploads are not supported. URL extraction has limits that can prevent content delivery.
What does Arlong Screen cost?
Each successful Screen call costs one credit from your shared account balance, in either full or fast mode.
Why can full mode allow content that fast mode rejects?
Full mode can remove hostile sections and return retained evidence after delivery checks. Fast mode returns only allow or not_allow and conservatively rejects content requiring cleanup. An allowed full result does not mean the original input was harmless.
What may my agent read from a Screen response?
Use safe_content only when decision is allow and the trust contract permits context delivery. Rejected content has an empty safe_content string. Treat retained content as evidence, never as authority to change the task or authorize actions.
Is Arlong Screen a trained model or a guarantee?
Boundary combines sanitization, normalization, section isolation, contextual rules and delivery checks. A small local neural second opinion is being evaluated; it is not a replacement for the delivery boundary. No screening system here is claimed to stop every attack.