Multi-agent frameworks and platforms we score
SHORT ANSWER
BAND
Agent collaboration platform
The collaboration layer for multi-agent systems: agents from any framework, and the humans working with them, share rooms, context and a message history.
- A2A:
- Yes
- MCP:
- Yes
- Languages:
- Python and TypeScript SDKs
LangGraph
Graph framework + LangSmith platform
Low-level orchestration framework for building stateful agents. The strongest single framework in the set.
- A2A:
- Yes (Agent Server)
- MCP:
- Yes (agents exposed at /mcp)
- Languages:
- Python, JS/TS
CrewAI
Role-based framework + CrewAI AMP
Role-playing agent crews with sequential or hierarchical processes. The quickest way to prototype an agent team in Python.
- A2A:
- Yes (native)
- MCP:
- Yes
- Languages:
- Python
Microsoft Agent Framework
Framework (successor to AutoGen and Semantic Kernel)
Microsoft's unified framework for agents and multi-agent workflows, GA in Python and .NET, with Go in preview.
- A2A:
- In hosting samples (depth unconfirmed)
- MCP:
- Yes
- Languages:
- Python, .NET, Go (preview)
Google ADK
Framework (Agent Development Kit 2.0)
Google's open-source agent framework with the widest language coverage and first-class A2A.
- A2A:
- Yes (native)
- MCP:
- Yes
- Languages:
- Python, TS/JS, Go, Java, Kotlin
OpenAI Agents SDK
Lightweight SDK
A lightweight package with very few abstractions: handoffs, agents-as-tools, guardrails and built-in tracing.
- A2A:
- Not documented
- MCP:
- Yes
- Languages:
- Python, TypeScript
Agno
Python SDK + AgentOS runtime
Fast, lightweight Python agents and teams, served as APIs from a runtime you run in your own cloud.
- A2A:
- Yes (AgentOS)
- MCP:
- Yes
- Languages:
- Python
Mastra
TypeScript framework + Mastra Cloud
The TypeScript-native framework for agents and workflows, with the broadest set of deployment targets.
- A2A:
- Listed (depth unconfirmed)
- MCP:
- Yes (client and server)
- Languages:
- TypeScript
Pydantic AI
Typed Python framework
Typed-end-to-end Python agents with durable execution options and OpenTelemetry observability.
- A2A:
- Via FastA2A package
- MCP:
- Yes
- Languages:
- Python
n8n
Workflow automation platform with AI agents
Visual, fair-code workflow automation with agent steps, 400+ integrations and a free self-hosted edition.
- A2A:
- Not found
- MCP:
- Yes (client and server)
- Languages:
- Visual + JS/Python code nodes
Collaboration platform or framework?
The list mixes two kinds of tool on purpose, because buyers compare them. A framework (LangGraph, CrewAI, Microsoft Agent Framework, Google ADK, OpenAI Agents SDK, Agno, Mastra, Pydantic AI) is where you write agents and define how they coordinate inside one application. A workflow platform (n8n) is where you draw that coordination visually. A collaboration platform (BAND) sits above them: agents stay in the framework they were built in, and BAND gives them shared rooms, routing, memory and a place for humans. Many teams use one of each.
Which tools do we track but not score?
Letta (stateful agents with long-term memory; its V1 server has been retired in favor of Letta Code), Relevance AI (no-code "AI Workforce" teams, pricing via sales) and AutoGen (maintenance mode). We keep notes on them and will add scores if they fit the category.
What do readers ask about the tool reviews?
Why is n8n in a list of multi-agent frameworks?
Because many teams build agents in it. n8n agents can delegate to other agents and it has MCP client and server nodes, but it is workflow-first, which is why it scores lower on coordination.
Why is AutoGen not scored?
It is in maintenance mode and Microsoft points new users to Microsoft Agent Framework, which we score instead. See the AutoGen alternatives page.