microsoft/RecAI

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1195 stars · 130 forks observed · 2026-09-21

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

RecAI is a Microsoft research project exploring ways to integrate large language models into recommender systems (LLM4Rec). It includes a conversational recommender AI agent, selective knowledge plugins via prompting, and LLM-based model explainers.

Use cases

• build a conversational recommender system on top of an existing recommendation model
• make matrix factorization recommenders interactive and explainable with an LLM agent
• inject domain knowledge into an LLM via personalized prompting without fine-tuning
• fine-tune a language model to act as a recommender
• evaluate LLM-based recommendation quality
• use an LLM to explain recommendations from a traditional model

When to choose

• you want to add LLM-driven interactivity or explainability to an existing recommender system
• you are researching LLM4Rec techniques like agents, prompting, or fine-tuning for recommendations
• you want a reference implementation of a recommender AI agent that calls traditional models as tools

When to avoid

• you need a production-ready, turnkey recommender system without LLM integration
• you want a lightweight library with a stable API rather than research code and notebooks
• your project has no need for conversational or explainable recommendation

Facets

library · maturity active

agent-framework llm-inference rag machine-learning chatbot artificial-intelligence large-language-models machine-learning python llm4rec recommender-systems conversational-recommendation jupyter-notebooks microsoft-research ai-agents retrieval-augmented-generation

Member repositories

For agents

markdown · JSON · MCP: product_card(name="microsoft/RecAI")