MIAPI — Grounded AI Answers API

What

MIAPI is a grounded AI answers API for developers who need responses backed by real-time web search. It returns synthesized answers with inline citations, source links, and confidence scores, and it also exposes raw search, news, image, and streaming endpoints.

The product appears positioned as a developer-first alternative to other grounded answer APIs, with OpenAI-compatible chat completions and a separate answer endpoint for more configurable retrieval. It serves teams building search-assisted assistants, RAG workflows, research features, and applications that need current information rather than model-only responses.

Features

• Grounded answer endpoint: /v1/answer searches the web and returns an AI-generated answer with optional citations, sources, confidence, cache status, and response timing.
• OpenAI-compatible chat API: /v1/chat/completions acts as a drop-in replacement for OpenAI chat code, reducing migration effort for teams that want web-grounded outputs.
• Knowledge mode: The API can answer from user-provided text, the web, or both together, which is useful for lightweight RAG and domain-specific question answering.
• Search-only mode: /v1/search returns raw web results without LLM synthesis, giving developers more control in custom pipelines or bring-your-own-model architectures.
• Streaming responses: SSE support streams sources first and then the answer token by token, which helps improve perceived responsiveness in interactive apps.
• Specialized retrieval endpoints: Separate /v1/news and /v1/images endpoints provide recent articles and web images with structured metadata for use cases beyond plain text answers.

Helpful Tips

• Test grounding quality by query type: Evaluate factual, time-sensitive, and niche-domain prompts separately, because grounded answer APIs often perform differently across broad search, recent news, and proprietary-context questions.
• Use domain controls where accuracy matters: The search_domains and exclude_domains parameters can help reduce noisy sources and make outputs more predictable for business or research workflows.
• Choose endpoint style based on product design: Use /v1/answer when you need retrieval controls and metadata, and /v1/chat/completions when compatibility with existing OpenAI-style tooling is the main priority.
• Validate confidence and sources in your app layer: The API provides confidence scores and citations, but production workflows should still apply human review or rule-based checks for high-stakes use cases.
• Check operational fit beyond core API behavior: The page lists usage, key management, caching headers, rate limits, and an MCP server, so buyers should confirm these operational details match their deployment and governance needs.

OpenClaw Skills

Within the OpenClaw ecosystem, MIAPI could likely serve as a retrieval and grounding layer for agents that need current, cited answers. Likely use cases include research assistants, competitive intelligence agents, customer-support copilots that combine company knowledge with public web data, and monitoring workflows that summarize news with source attribution.

OpenClaw skills could also be built around MIAPI's search-only, news, and image endpoints to separate retrieval from reasoning in multi-agent systems. For example, one agent could gather current sources via MIAPI, another could score credibility or classify findings, and a third could generate a structured brief or response draft. If implemented well, that combination could make knowledge work in product, research, operations, and support more auditable and easier to verify, though the source page does not explicitly confirm a native OpenClaw integration.