InterReview

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InterReview is an intelligent post-interview review system for technical interview scenarios, solving post-interview problems of 'vague feedback, difficult review, inability to accumulate capabilities.' Through multi-Agent collaboration, it auto-transforms interviews from audio/text input into structured, analyzable, continuously accumulated capability assets. Transcription Agent handles high-quality audio/video to text transcription; Parsing Agent auto-extracts interview questions, candidate answers and follow-up chains; Analysis Agent generates review reports from technical depth, expression structure and risk dimensions; Follow-up Agent simulates interviewer perspective with potential follow-ups and improvement directions; Conversation Agent supports continuous Q&A optimization around single interviews. Through Agent pipeline and collaboration, InterReview upgrades 'one interview experience' into reviewable, iterable, evolvable personal capability system.

Planning 5 core Agents forming collaboration loop around 'transcription → structuring → analysis → follow-up → interactive optimization' of interview data. Under OpenAgents framework, system uses event-driven Agent collaboration mode: Above Agents communicate decoupled through unified event protocol, previous Agent's output triggers subsequent Agent execution. Project uses OpenAgents Mods mechanism to encapsulate different Agent capabilities (transcription, parsing, analysis, dialogue), achieving state passing and task orchestration through standardized protocols and context sharing mechanisms, building an extensible, pluggable interview review agent system.

Transcription Agent: Handles unified audio/video/text input and high-quality transcription. Parsing Agent: Semantic segmentation of transcription results, extracts question-answer-follow-up chains, generates structured QA data. Analysis Agent: Generates review analysis from technical depth, expression logic and risk dimensions based on structured data. Follow-up Agent: Simulates interviewer perspective, generates potential follow-ups and improvement directions based on candidate answers. Conversation Agent: Provides contextual dialogue to users, supports continuous optimization around single questions or entire interviews.