metadata
language:
- en
license: apache-2.0
library_name: transformers
tags:
- text-classification
- intent-classification
- query-routing
- agent
- llm-router
pipeline_tag: text-classification
⚡ AgentRouter
Ultra-fast intent classification for LLM query routing. Classifies user queries into 10 intent categories in <5ms on GPU.
Built on MiniLM (33M params) — small enough for CPU inference, fast enough for real-time routing.
🚀 Usage
from transformers import pipeline
router = pipeline("text-classification", model="ENTUM-AI/AgentRouter")
router("Write a Python function to sort a list")
# [{'label': 'code_generation', 'score': 0.98}]
router("Why am I getting a TypeError?")
# [{'label': 'code_debugging', 'score': 0.97}]
router("Translate hello to Spanish")
# [{'label': 'translation', 'score': 0.99}]
router("What is quantum computing?")
# [{'label': 'information_retrieval', 'score': 0.96}]
🏷️ Intent Classes
| Intent | Description | Suggested Tools |
|---|---|---|
code_generation |
Write new code | code_interpreter, file_editor |
code_debugging |
Fix bugs and errors | code_interpreter, debugger |
math_reasoning |
Solve math problems | calculator, wolfram_alpha |
creative_writing |
Write stories, poems, essays | — |
summarization |
Summarize text | file_reader |
translation |
Translate between languages | translator |
information_retrieval |
Answer questions, explain topics | knowledge_base |
data_analysis |
Analyze data, create charts | code_interpreter, data_visualizer |
web_search |
Search the web for current info | web_browser, search_engine |
general_chat |
Casual conversation | — |
🔍 Use Cases
- LLM routing — route queries to specialized models or tools
- Agent frameworks — decide which tool to invoke
- Cost optimization — use cheap models for simple intents, expensive for complex
- Latency optimization — skip heavy pipelines for general chat
⚠️ Limitations
- English only
- 10 fixed intent categories