Research Agent v4.3.3

Automated deep research verifies claims by decomposing queries into multiple perspectives, performing web searches, extracting raw claims, and checking them against supporting and refuting evidence to synthesize a final report.

AI claim verification works by extracting raw claims from search results, normalizing them into structured data, and searching for counter-evidence to assign statuses like VERIFIED, CONDITIONED, or CONTESTED before synthesis.

Yes, you can use LangChain for LLM capabilities and Playwright for web searching and content reading to extract raw claims and normalize them into structured data for analysis.

The best way to identify knowledge gaps is to normalize extracted claims into structured data, which allows the system to detect missing information and conduct targeted deep research for evidence.

Yes, the approach supports multiple language models through LangChain integrations, including OpenAI, Anthropic, Google Generative AI, and Ollama for claim extraction and verification.

Limitations include relying on Playwright's ability to bypass anti-scraping measures to extract raw claims, and the accuracy of LLM verification depends on the availability of supporting or refuting web evidence.