Analyze user inquiries through multi-agent LangGraph orchestration with iterative refinement.
npx skills add https://github.com/ulf1/mas-inquiry --skill inquiry-bot
Please help me install this Agent Skill. Skill: inquiry_bot Source: https://github.com/ulf1/mas-inquiry/tree/main/.agent/skills/inquiry_bot Command: npx skills add https://github.com/ulf1/mas-inquiry --skill inquiry-bot
This Skill tackles complex human inquiries by dissecting them through multiple analytical lenses, providing a comprehensive and structured understanding beyond simple keyword matching.
Analyze the inquiry "What is the current state of AI agents?" using the inquiry bot skill.
High-intent search queries and answers about installing and using this skill.
Multi-agent inquiry analysis uses a sparsely-connected architecture orchestrated by LangGraph to dissect questions through specialized worker agents like Causal and Temporal, iteratively refining answers to produce a synthesized summary.
You can analyze a complex news report by processing the inquiry through multi-dimensional analytical agents that break down causal, temporal, and spatial dimensions, culminating in a synthesized and structured summary.
LangGraph orchestrates iterative refinement by using a state machine to pass inquiries through various worker agents, improving relevance and reducing redundancy across multiple passes before merging the final output.
Yes, this multi-agent system embeds optimization algorithms directly into LLM prompts to achieve native data deduplication and merging during the inquiry analysis process.
No, this multi-agent inquiry analysis system operates without external dependencies, using a LangGraph state machine and algorithmic prompting to manage worker agents and refine outputs.
While effective for deep inquiry analysis, sparsely-connected multi-agent systems require multiple iterative passes through worker agents, which may increase processing time compared to single-agent LLM queries.
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