togethercomputer/MoA

Health v2 · maintenance only

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Adoption not part of the score

2970 stars · 386 forks observed · 2026-08-28

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

Together AI's Mixture-of-Agents (MoA) is a Python implementation of a layered LLM architecture where multiple open-source models answer prompts and an aggregator model combines their outputs. It achieved 65.1% on AlpacaEval 2.0, surpassing GPT-4 Omni using only open-source models.

Use cases

• improve response quality by combining multiple open-source LLMs
• build a multi-layer mixture-of-agents pipeline
• run a multi-turn chatbot that aggregates answers from several models
• replicate the MoA paper results on AlpacaEval
• ensemble LLM outputs without using GPT-4

When to choose

• you want GPT-4-level quality using only open-source models via the Together API
• you need a simple, minimal-code implementation of the MoA technique
• you want to experiment with layered LLM agent architectures

When to avoid

• you need a production-grade agent framework with tooling and integrations
• you want to avoid multiple LLM API calls and their latency/cost
• you need local model execution rather than the Together API

Facets

library · maturity active

agent-framework llm-inference chatbot cli large-language-models artificial-intelligence python cli mixture-of-agents llm-orchestration open-source-models alpacaeval together-ai ai-agents

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