Ranking multi-agent frameworks by stars is easy; knowing which collaboration pattern fits your task is not. This guide covers ten frameworks, the three common patterns, and the real cost multiplier you should budget for.

💡 What You Will Learn

Ranking multi-agent frameworks by stars is easy; knowing which collaboration pattern fits your task is not. This guide covers ten frameworks, the three common patterns, and the real cost multiplier yo

📜 Table of Contents

• The ten
• Three patterns
• Budget for the multiplier
• Getting started
• FAQ

Multi-Agent Systems on GitHub in 2026: 10 Frameworks

Multi-agent systems split work across several AI roles. Stars show attention, but the real selection question is which collaboration pattern matches your task. Star counts are from our tracker snapshot (late July 2026).

The ten

Three patterns

Orchestrator-worker: a manager decomposes tasks for specialists, then aggregates. Deterministic and debuggable (LangGraph, Semantic Kernel).

Conversation: agents message each other until convergence. Flexible, unpredictable rounds (AutoGen).

Role-playing: personas argue different positions (CAMEL, AgentVerse).

Rule of thumb: use the first when subtasks are known upfront; use the others when exploration is the point.

Budget for the multiplier

Multi-agent runs typically burn 3-10x the tokens of a single agent, since every role carries a full conversation. Practical limits: cap rounds per agent, use cheap models in the middle, trim shared context instead of copying all history, and measure real average cost on a fixed task set before scaling.

Getting started

Start with CrewAI - two or three roles, one task each. Move to graph orchestration when you need branching, retries, or human review. Judge results on completion rate, average cost per task, and how often a human had to step in. Optimizing only completion rate tends to produce slow, expensive systems.

FAQ

Is multi-agent always better? No. It helps when a task decomposes cleanly; for plain Q&A it only adds latency and cost.

How many agents? Most working systems use two to five. Beyond that, coordination overhead eats the gains.

Debugging? Log every step to files or a tracing tool, then find which role drifted before touching prompts.

Mixing vendors? Yes, and often wise - but add an adapter layer since tool-calling formats differ.

Stars and versions change - check each project's repository.

❓ FAQ

Is multi-agent better than single-agent?

For tasks with separable subtasks, yes - research and industry both show gains; for simple Q and A, no.

What is the token cost?

Multi-agent multiplies token usage - each agent is a full LLM conversation. Budget 3-10x a single agent.