AIBackends v0.7.0: Prompt Routing with Liquid AI's LFM2.5 Encoder

Melvin Vivas · X post · 2026-08-27 · Open on X

Topics: LLMOps, Deployment & Monitoring, AI System Design & Architecture, LLM Fundamentals · Level: intermediate

Summary

Melvin Vivas released version 0.7.0 of his AIBackends Python library, which adds prompt routing with Liquid AI's LFM2.5-Encoder-350M. The router classifies each prompt zero-shot (without task-specific training), so complex tasks go to expensive, smarter models and simple ones go to small or free models.

Key points

• Install with: pip install "aibackends[routing]".
• Routing uses Liquid AI's LFM2.5-Encoder-350M, a small encoder that classifies prompts zero-shot.
• Why it matters: as models get more expensive, save the most capable models for complex tasks.
• Send simple prompts to smaller, cheaper or free models to cut costs.

Resources mentioned

• aibackends 0.7.0 (PyPI) · tool · pypi.org · free
  PyPI page for the AIBackends Python package, version 0.7.0, which adds prompt routing.
• Release aibackends v0.7.0 — LFM2.5 Prompt Routing (donvito/aibackends) · repo · github.com · free
  GitHub release notes for AIBackends v0.7.0 with LFM2.5 prompt routing.
• LFM2.5-Encoder-350M · tool · huggingface.co · free
  A 350M-parameter encoder model from Liquid AI that does zero-shot prompt classification and routing against categories you supply at runtime.
  Also in: Zero-Shot Prompt Routing with Liquid AI's LFM 2.5-Encoder-350M (Melvin Vivas on X · notes), Model Routing Fine-Tuned on Your Agent Harness Traces (Melvin Vivas on X · notes), Zero-Shot Prompt Routing with Liquid AI's LFM 2.5-Encoder-350M (Melvin Vivas on X · notes), Zero-Shot Prompt Routing by Task Complexity with LFM2.5-Encoder (Melvin Vivas on X · notes)
• Liquid AI (@liquidai) on X · website · x.com · free
  An AI company that builds efficient foundation models. The quoted post shows its PII handling working on Japanese text.
  Also in: Zero-Shot Prompt Routing with Liquid AI's LFM 2.5-Encoder-350M (Melvin Vivas on X · notes), Liquid AI LFM 2.5 Encoder: a CPU-friendly encoder model (Melvin Vivas on X · notes), Fine-tuning Liquid AI LFM2/LFM2.5 MoE models with the new Halo framework (Melvin Vivas on X · notes), Liquid AI's LFM2-Longevity models for aging-data analysis (Melvin Vivas on X · notes) and 19 more

Try this

• Install aibackends[routing] and try routing prompts to different models.
• Build a cost-aware router that sends simple prompts to a small or free model and hard prompts to a frontier model.