Context.dev launched Answers, an API endpoint that executes autonomous web research and returns structured, schema-compliant JSON with source citations in a single request.
Context.dev has introduced Answers, a web extraction API endpoint designed to collapse multi-step web research workflows into an atomic call. Instead of forcing developers to stitch together separate search APIs, headless scraping infrastructure, anti-bot bypasses, and LLM extraction prompts, Answers takes a high-level research prompt alongside a target JSON format. The engine autonomously discovers relevant web pages, navigates and parses sources, and returns structured data matching the specified schema with citations. Offering both "Fast" and "Ultra" modes for differing depth requirements, it is built to power AI agents, lead enrichment pipelines, competitive intelligence tooling, and automated data collection workflows.
Consolidating search, scraping, and schema-constrained LLM synthesis into an opinionated atomic API is rapidly becoming the essential building block for agentic architectures.
- –Eliminates orchestration sprawl: Developers bypass fragile bespoke pipelines that combine SERP lookups, proxy rotation, HTML extraction, and parsing prompts.
- –Engineered for autonomous reliability: Strict JSON schema conformance paired with source verification helps prevent downstream hallucination in programmatic workflows.
- –Execution speed vs. depth tradeoffs: Multi-page exploration naturally incurs API latency; performance in production will depend on whether "Fast" mode can reliably deliver timely responses for interactive applications.
- –Saturated market: Enters a highly competitive niche populated by specialized agent search and retrieval APIs like Tavily, Exa, Firecrawl, and Perplexity.
DISCOVERED
1h ago
2026-09-20
PUBLISHED
6h ago
2026-09-20
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