awesome-ai-model-routing
A curated list of awesome solutions and research in AI model routing. Other awesome lists can be found in the awesome-awesomeness list.
Intelligent AI model routing
Open-source tools and software solutions for AI model routing (ordered alphabetically). * indicates that an entry is open source.
- Martian: Dynamically routes requests to the best LLM in real-time.
- Neutrino AI: Intelligently route queries to the best-suited LLM for the prompt.
- Not Diamond: An AI model router that automatically determines which LLM is best-suited to respond to any query.
- notdiamond-0001*: Automatically determines whether to send queries to GPT-3.5 or GPT-4.
- Pulze AI KNN router*: A minimal server for generating a ranked list of targets, for a query, based on its k-nearest semantic neighbors. Written in Go.
- OpenRouter: Prompts will be sent to Llama 3 70B Instruct, Claude 3.5 Sonnet (self-moderated) or GPT-4o.
- Requesty: Configurable smart routing algorithms to optimize your performance, cost or latency.
- RoRF: SOTA open-source pairwise router based on a random forest architecture.
- RouteLLM*: RouteLLM is a framework for serving and evaluating LLM routers.
- Semantic Router*: Route inputs to different models using semantic embeddings.
- Unify: Improve quality, cost and speed by routing to the perfect model and provider for each individual prompt.
AI model routing papers
Research papers on AI model routing (ordered by recency)
- EmbedLLM: Learning Compact Representations of Large Language Models, 2024-10-03: A matrix-factorization based LLM router which extracts "model embeddings" that captures LLM characteristics.
- RouteLLM: Learning to Route LLMs with Preference Data, 2024-07-21: A training framework for strong vs weak LLM routers leveraging human preference data and data augmentation.
- MetaLLM: A High-performant and Cost-efficient Dynamic Framework for Wrapping LLMs, 2024-07-15: Dynamically route each query to the optimal LLM for classification tasks.
- Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models, 2024-07-08: Comprehensive overview of merging, ensembling, and cooperation in LLMs.
- AutoMix: Automatically Mixing Language Models, 2024-06-28: Strategically routes queries to larger LMs based on the approximate correctness of outputs from a smaller LM.
- OptLLM: Optimal Assignment of Queries to Large Language Models, 2023-05-24: Predicts the performance of candidate LLMs on each query using a multi-label classification model with uncertainty estimation.
- Optimising Calls to Large Language Models with Uncertainty-Based Two-Tier Selection, 2024-05-03: Uses the uncertainty of the generations of a small LLM as the decision criterion for whether to use a strong LLM.
- Harnessing the Power of Multiple Minds: Lessons Learned from LLM Routing, 2024-05-01: Explores whether it is feasible to direct input queries to the most suitable LLM on reasoning tasks.
- Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing, 2024-04-22: Uses a router that assigns queries to a small or large model based on the predicted query difficulty and the desired quality level.
- Predictive Human Preference: From Model Ranking to Model Routing, 2024-02-28: Predictive human preference aims to predict which model individual users might prefer for a specific query based on their preferences.
- Routoo: Learning to Route to Large Language Models Effectively, 2024-01-25: Optimize the selection of LLMs for specific prompts based on performance, cost, and efficiency.
- Fly-Swat or Cannon? Cost-Effective Language Model Choice via Meta-Modeling, 2023-12-18: Assigns each input to an LM predicted to do well on the input according to a so-called meta-model, aiming to achieve high overall performance at low cost.
- Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models, 2023-11-15: A reward-guided routing method distilling rewards on training queries to train a routing function, which can precisely route queries to the best LLM.
- Cache & Distil: Optimising API Calls to Large Language Models, 2023-10-20: A student-teacher framework that includes a policy that learns which requests should be processed by the student alone and which should be redirected to the teacher LLM.
- EcoAssistant: Using LLM Assistant More Affordably and Accurately, 2023-10-03: Answers code-driven queries by using a hierarchy of LLM assistants, which attempts to answer the query with cheaper LLMs before trying more expensive ones.
- Large Language Model Routing with Benchmark Datasets, 2023-09-27: Repurposes benchmark datasets to learn a "router" model for LLM selection.
- Tryage: Real-time, intelligent Routing of User Prompts to Large Language Models, 2023-08-23: Leverages a language model router for optimal selection of expert models from a model library based on analysis of individual input prompts.
- LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion, 2023-06-30: An ensembling framework to improve performance by leveraging the diverse strengths of multiple open-source LLMs
- FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance, 2023-05-09: Proposes an LLM cascade which learns which combinations of LLMs to use for different queries in order to reduce cost and improve accuracy.
Contributing
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