LLM as Router: Intent Classification for a Local Telegram Email Agent

Jun 3, 2026 · 2:00 PM UTC ·15 min read · 0 reactions · 0 comments · 42 views

TL;DR · WeSearch summary

The article discusses the development of a local AI email agent that utilizes a three-tier routing system for intent classification. It emphasizes the importance of handling natural language commands effectively while maintaining the efficiency of explicit commands. The system aims to provide a more intuitive user experience by allowing users to interact with the agent in a conversational manner.

Key facts

▪The AI email agent integrates various technologies including Gmail, Telegram, and SQLite.
▪It features a three-tier routing system that distinguishes between slash commands, direct compound commands, and natural language queries.
▪The router uses a local LLM to classify natural language inputs, ensuring a more user-friendly interaction.

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Record

Original publisher	DEV.to (Top)
Canonical URL	https://dev.to/sviat_barbutsa/llm-as-router-intent-classification-for-a-local-telegram-email-agent-23l6
Publication time	Wed, 03 Jun 2026 14:00:48 +0000
Retrieval time	2026-06-03T14:12:09.529Z
Last seen	2026-06-03T14:12:09.529Z
Headline source	Publisher (no WeSearch rewrite)
Excerpt source	publisher body
Excerpt method	First ~120 words (~800 chars) of extracted publisher body, fair-use limited.
Summary	WeSearch · cerebras-chat (WeSearch summarizer)
Summary source text	contentText
Citation coverage	Summary is a WeSearch-generated derivative; primary citation is the original publisher URL.
Cluster	yZxpv-9Wpf7x
Cluster logic	Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison.
Ranking reason	Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking.
Publisher visit	Yes — open original
Substitutes article?	No — link-out required for full text

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Unknown

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WeSearch handling by dimension

Indexing	May the item be indexed (stored, ranked, made findable)?	Allowed
Snippet	May a short excerpt of the publisher's text be shown?	Allowed
AI summary	May WeSearch generate its own short summary of the article?	Limited
Retrieval / RAG	May the content be exposed for third-party retrieval-augmented generation?	Not asserted
Model training	May the content be used to train AI models?	Not asserted
Commercial reuse	May the content be reused commercially?	Not permitted

Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.

Open original publisher → Source rights JSON → Machine-readable provenance → Rights framework → WESEARCH-STORY-PROVENANCE-V1

Opening excerpt (first ~120 words) tap to expand

try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3854341) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Sviatoslav Barbutsa Posted on Jun 3 LLM as Router: Intent Classification for a Local Telegram Email Agent #ai #programming #architecture #software Building a Private, Local AI Email Agent (3 Part Series) 1 From Inbox to Character: Building a Private, Local AI Email Agent 2 How /search and /ask Work: Local Hybrid RAG with ChromaDB + SQLite FTS5 3 LLM as Router: Intent Classification for a Local Telegram Email Agent In the first article, I showed the whole Llamail system: Gmail,…

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