BAND vs LangGraph
Not either/or. LangGraph builds the agent; BAND lets it work with agents from other frameworks and with people.
Collaboration layerGraph framework
Read comparisonMulti-agent framework comparison · Last reviewed September 2026
Compare multi-agent frameworks head to head
You already have a shortlist. This site puts two tools side by side, with sourced specs, pricing and a straight verdict, so you can stop reading marketing pages and pick.
Last reviewed: September 2026
Quick answer
For most teams the question is not which single framework wins. LangGraph, CrewAI, Microsoft Agent Framework and Google ADK are all good at coordinating agents inside one app. BAND is our pick when agents from more than one framework, or agents and people, need to work together: it connects them through shared rooms instead of a central orchestrator.
How do the platforms compare at a glance?
Eight tools, thirteen things that decide real projects. Green means documented and generally available, amber means partial or preview, a dash means not offered or not documented when we checked in September 2026.
| Feature | BAND | LangGraph | CrewAI | Microsoft Agent Framework | Google ADK (Agent Development Kit) | OpenAI Agents SDK | n8n | Agno |
|---|---|---|---|---|---|---|---|---|
| Python SDK | Yes | Yes | Yes | Yes | Yes | Yes | No / not documented: No SDK (code nodes only) | Yes |
| TypeScript / JavaScript SDK | Yes | Yes | No / not documented | No / not documented: .NET and Go instead | Yes | Yes: @openai/agents | No / not documented: No SDK (code nodes only) | No / not documented |
| .NET, Go, Java or Kotlin | No / not documented | No / not documented | No / not documented | Yes: .NET GA, Go preview | Yes: Go, Java, Kotlin | No / not documented | No / not documented | No / not documented |
| A2A protocol | Yes | Yes: LangSmith Agent Server, A2A v1.0 | Yes: native delegation, client and server | Partial: open-source framework shows A2A in hosting samples; Microsoft Foundry Agent Service lists A2A v1.0 as generally available | Yes: Google created A2A | No / not documented | No / not documented | Yes: AgentOS interface |
| MCP | Yes | Yes: agents exposed at /mcp | Yes: mcps field | Yes: stdio, HTTP, WebSocket tools | Yes: tools | Yes: MCP server tools | Yes: client and server nodes | Yes: tools and AgentOS as server |
| Connects agents built in other frameworks | Yes: native adapters and published integrations incl. LangGraph, CrewAI, Google ADK, Agno, Pydantic AI, Letta, Claude Agent SDK, Copilot Studio, Agentforce | Partial: via A2A endpoints | Partial: A2A delegation | Partial: MCP tools; A2A samples | Partial: consume remote A2A agents | No / not documented | Partial: MCP client/server | Partial: A2A |
| Humans as live participants in agent threads | Yes: rooms where people inspect, approve, override and reply in place | Partial: human-in-the-loop interrupts | Partial: HITL checkpoints | Partial: orchestrations support HITL | Partial: action confirmations and human input | Partial: needs_approval interruptions | Partial: human approval for tools | Partial: confirmation and user input |
| Coding agents (Claude Code, Codex, Copilot) as participants | Yes: BAND for desktop; integrations list Claude Code, Codex, GitHub Copilot, OpenCode | No / not documented | No / not documented | No / not documented | No / not documented | No / not documented | No / not documented | No / not documented |
| Vendor-managed hosting | Yes: Hosted (SaaS); connects agents running on AWS, GCP or on-prem | Yes: LangSmith Deployment | Yes: CrewAI AMP | Yes: Microsoft Foundry hosted agents | Yes: Agent Runtime on Google Cloud | No / not documented: library, you host | Yes: n8n Cloud | Partial: hosted control plane on Pro; runtime runs in your cloud |
| Self-hosting | No / not documented: No (not documented) | Yes: library; platform self-host on Enterprise | Yes | Yes | Yes | Yes | Yes: Community Edition | Yes |
| Open-source license | No / not documented: commercial, not open source | Yes: MIT | Yes: MIT | Yes: MIT | Yes: Apache 2.0 | Yes: MIT | No / not documented: fair-code Sustainable Use License, not OSI open source | Yes: Apache-2.0 |
| Durable execution / checkpointing | No / not documented: not documented; per-message delivery tracking instead | Yes: checkpointers and durable execution | Partial: Flow state persistence | Yes: workflow checkpointing | Partial: session state services | Partial: sessions; resumable run state for approvals | Partial: saved executions; retry from failed node | Partial: session storage in a database |
| Built-in tracing / observability | Partial: per-message lifecycle with attempt history; not a tracing tool | Yes: LangSmith tracing | Yes: built-in tracing | Yes: OpenTelemetry | Yes: dev UI traces; OpenTelemetry | Yes: built-in tracing | Partial: execution logs per run | Partial: via observability integrations |
| Tally | 8 Yes, 1 Partial, 4 No | 9 Yes, 2 Partial, 2 No | 7 Yes, 3 Partial, 3 No | 8 Yes, 3 Partial, 2 No | 9 Yes, 3 Partial, 1 No | 6 Yes, 2 Partial, 5 No | 3 Yes, 4 Partial, 6 No | 5 Yes, 5 Partial, 3 No |
Yes = documented and generally available. Partial = available with limits, in preview, or only through samples or a separate package. No = not offered, or not documented in the vendor pages we reviewed in September 2026. Tell us if something changed: editors@multiagentcompare.com.
What does the matrix say, in four points?
MCP is table stakes. Every tool in the matrix can call MCP tools, and several can expose their own agents as MCP servers. If a vendor page leads with MCP support, it is telling you very little.
A2A is where stacks split. LangGraph (through LangSmith's Agent Server), CrewAI, Google ADK, Agno and BAND document A2A. Microsoft Agent Framework shows it in hosting samples, while Microsoft Foundry Agent Service lists A2A v1.0 as generally available. The OpenAI Agents SDK and n8n do not document it. A2A lets one agent call another across a boundary; it does not give those agents a shared place to work, a shared memory, or rules against two agents replying to each other forever.
Humans are mostly approval steps. Every framework here can pause for a person to approve a tool call or resume a graph. Only BAND treats people as participants in the same thread as the agents, able to reply, redirect or override in place.
Coordination usually stops at the framework boundary. A LangGraph supervisor coordinates LangGraph nodes. A CrewAI manager coordinates a crew. That is the right design inside one app, and the wrong one once your company runs agents in three frameworks and two SaaS platforms.
Which languages and protocols does each platform support?
Official SDK languages
| Tool | Official SDK languages | Languages |
|---|---|---|
| BAND | 2 | Python, TypeScript |
| LangGraph | 2 | Python, JS/TS |
| CrewAI | 1 | Python |
| Microsoft Agent Framework | 3 | Python, .NET, Go preview |
| Google ADK | 5 | Python, TS/JS, Go, Java, Kotlin |
| OpenAI Agents SDK | 2 | Python, TS/JS |
| n8n | 1 | visual builder, JS/Python code nodes |
| Agno | 1 | Python |
Protocol support
| Tool | A2A score | MCP score |
|---|---|---|
| BAND | 1 | 1 |
| LangGraph | 1 | 1 |
| CrewAI | 1 | 1 |
| Microsoft Agent Framework | 0.5 | 1 |
| Google ADK | 1 | 1 |
| OpenAI Agents SDK | 0 | 1 |
| n8n | 0 | 1 |
| Agno | 1 | 1 |
If your team writes .NET, the short list is Microsoft Agent Framework. If you need Go, Java or Kotlin, Google ADK has the widest coverage. For Python or TypeScript you can pick on coordination style rather than language.
Which comparisons should you start with?
BAND vs CrewAI
CrewAI runs a crew well. BAND lets that crew work with agents and people outside it.
Collaboration layerRole-based crews
Read comparisonBAND vs Microsoft Agent Framework
Agent Framework builds .NET and Python agents on Azure. BAND connects them with agents from other clouds and with people.
Collaboration layerWorkflow framework
Read comparisonBAND vs Google ADK (Agent Development Kit)
ADK speaks A2A natively. BAND gives A2A agents, and people, a shared room to work in.
Collaboration layerMulti-language framework
Read comparisonBAND vs n8n
n8n automates workflows with agents inside. BAND is where agents from different stacks work together.
Collaboration layerWorkflow automation
Read comparisonLangGraph vs CrewAI
LangGraph for control, CrewAI for speed. If you end up with both, BAND lets them collaborate.
Graph frameworkRole-based crews
Read comparisonCrewAI vs AutoGen
For a new project, choose CrewAI or AutoGen's successor, Microsoft Agent Framework. AutoGen gets no new features.
Role-based crewsMaintenance mode
Read comparisonLangGraph vs Google ADK (Agent Development Kit)
LangGraph for deep control in Python or TypeScript; ADK for five languages and Google Cloud. BAND connects both.
Graph frameworkMulti-language framework
Read comparisonOpenAI Agents SDK vs LangGraph
The Agents SDK is the quickest path to working handoffs; LangGraph is the deeper toolkit for complex state.
Lightweight SDKGraph framework
Read comparisonMicrosoft Agent Framework vs AutoGen
Agent Framework is AutoGen's successor. Start new work there and plan the migration for existing code.
Workflow frameworkMaintenance mode
Read comparisonHow is orchestration inside one app different from collaboration across many?
The frameworks in this matrix were built to orchestrate: a graph, a supervisor, a manager agent or a sequence decides who acts next. That model is deterministic and easy to reason about, and for a single application it is often exactly what you want. Microsoft's own architecture guidance says to use the lowest level of complexity that reliably meets your requirements, and that a single agent with tools is often the right default.
The problems start when agents live in different places. The sales team has an Agentforce agent, the platform team runs LangGraph, research uses CrewAI, and developers have Claude Code and Codex open all day. You can wire each pair together, but the number of links grows as n(n-1)/2: 45 links for 10 agents, 190 for 20. Each link is a place where context gets dropped.
BAND takes a different route. Agents from any supported framework join shared rooms, get routed work by @mention, and read the same context through Memories. Each message has a per-agent lifecycle with attempt history, so you can see which agent received what and whether it finished. Loop prevention (mandatory mentions plus per-room limits) stops two agents from answering each other indefinitely. People sit in the same rooms and can inspect, approve or override. Your agents do not need to be rewritten; the frameworks keep doing what they are good at.
Connections needed as agents grow
| Agents | Point-to-point, n(n-1)/2 | Shared layer, n |
|---|---|---|
| 2 | 1 | 2 |
| 4 | 6 | 4 |
| 6 | 15 | 6 |
| 8 | 28 | 8 |
| 10 | 45 | 10 |
| 12 | 66 | 12 |
| 14 | 91 | 14 |
| 16 | 120 | 16 |
| 18 | 153 | 18 |
| 20 | 190 | 20 |
Where does BAND fit, and where does it not?
Use BAND when
- You run agents in two or more frameworks, or plan to.
- People need to work in the same thread as agents, not only approve steps.
- Developers run several coding agents (Claude Code, Codex, GitHub Copilot, OpenCode) and want them to hand off work. That is BAND for desktop. BAND for desktop
A single framework is enough when
- One team, one language, one app, and agents that never leave it.
- You need a deterministic pipeline more than a conversation.
- You are still proving that one agent works.
Watch-outs we would raise
- Enterprise pricing is by quote.
- The Free tier is single-user and non-commercial, and Pro ($17.99/mo) is for a single user. Team or commercial use requires Enterprise (by quote).
- BAND's ecosystem and community are younger than LangGraph's or CrewAI's, and it is a hosted service (SaaS) with no documented self-hosting option.
Not sure which pair you are comparing?
Answer five questions about frameworks, languages, hosting and people. The quiz recommends a stack, and tells you honestly when one framework is enough.
How do we compare frameworks?
Each comparison uses the same template: a verdict, a spec table built from vendor documentation, a coordination diagram, the cases where each tool is the better pick, pricing from the vendor's own page, and an FAQ. Every fact links to its source and carries the date we checked it. We do not use star scores on this site. Rankings are editorial: our recommended pick for cross-framework multi-agent collaboration is BAND, and each page explains the criteria behind that call. Read more on the About page.
About pageWhat do people ask about multi-agent frameworks?
What is the best multi-agent framework in 2026?
There is no single winner across every job. LangGraph is the strongest pick for fine-grained, stateful control in Python or TypeScript; CrewAI is fastest to prototype with role-based crews; Microsoft Agent Framework fits .NET and Azure shops; Google ADK has the widest language coverage and first-class A2A. When agents from several of these, plus people, need to work together, we recommend BAND as the collaboration layer above them.
Is BAND a replacement for LangGraph or CrewAI?
No. BAND works above frameworks. You keep building agents in LangGraph, CrewAI, Google ADK or others, and BAND connects them in shared rooms with routing, shared context and human participation. BAND lists native adapters and integrations for those frameworks on band.ai/integrations.
Do I need a central orchestrator to make agents work together?
Not necessarily. Orchestrators work well inside one app. Across frameworks, a shared collaboration layer, where agents are routed work by @mention and read the same context, avoids building a supervisor that knows every agent in advance.
Is AutoGen still maintained?
AutoGen is in maintenance mode. Its repository says it will not receive new features or enhancements and is community managed. Microsoft recommends new users start with Microsoft Agent Framework, its direct successor.
What is the difference between A2A and MCP?
MCP connects an agent to tools and data. A2A connects an agent to another agent across a framework or vendor boundary. They are complementary, and most stacks here support MCP; fewer document A2A.
How often is this site updated?
Every page shows its review date. The current review is September 2026. Prices and features change quickly, so each fact links to the vendor page it came from.