When agents pass work to one another, someone must make sure the right input reaches the next agent. AI agent collaboration platforms give you ways to organize that work. Choosing one starts with deciding how much of the coordination you want to build.
You might need execution paths in application code, or a workflow that connects your business tools and waits for approval. If colleagues already have agents, a shared place to assign work and discuss results may be enough. We compare ten options using official documentation; we have not run a shared performance benchmark.
What is AI agent collaboration?
AI agent collaboration means agents work toward a common task and use one another's contributions. Take a research task: one agent collects source material, a second drafts a report from it, and a third checks whether the claims match the sources. A person decides whether to use the report. In this illustrative workflow, the draft depends on the research, and the review can send it back for revision.
Three unrelated chats leave that coordination to you. Someone still has to combine the answers and decide what happens next.
The platform choice depends on where that coordination belongs: inside application code, in a business process, or between people and bots. For the underlying control patterns, see AI agent orchestration.
What platforms support multi-agent collaboration?
LangGraph and the other application frameworks below give developers control over how agents run. n8n and Relevance AI organize business workflows, while OpenAgents, Alook, and Paperclip provide different ways for a team to coordinate existing agents.
Start with the work you need to organize:
| Your requirement | Options to examine | What you still need to define |
|---|---|---|
| Build a multi-agent application | LangGraph, OpenAI Agents SDK, Google ADK, CrewAI, Microsoft Agent Framework | Agent responsibilities, tools, execution logic, and deployment |
| Connect agents to business processes | n8n, Relevance AI | Connections, credentials, routing, and review points |
| Work with colleagues and existing bots | OpenAgents, Alook, Paperclip | Conversation or task model, agent connections, and execution permissions |
The numbers identify the ten products in this comparison, not their rank.
Multi-agent frameworks compared
For an application you will build and maintain, compare how each framework represents the work. State, handoffs, and task definitions affect the code your team will own.
1. LangGraph: control over state and execution paths
A report may need another research pass after a reviewer finds a missing source. With LangGraph, you can represent that return path in the application and keep the state needed for the next step. You decide which transitions are fixed and where an agent makes a choice.
That control comes with work: your team builds the graph and its integrations. It matters most when a prompt telling agents to “review, then revise” is too loose for the application you need to maintain. Keep the runtime decision separate from buying LangSmith services for tracing or deployment.
2. OpenAI Agents SDK: specialists as tools or handoffs
The OpenAI Agents SDK makes you answer a useful ownership question: should one agent remain responsible for the reply, or should a specialist take over?
In the SDK's orchestration patterns, a manager can call specialists as tools and assemble their contributions into its answer. A handoff transfers control to another agent instead. A research assistant that needs a specialist's findings may keep the manager in charge; a conversation that should continue with the specialist may call for a handoff. Choose the pattern according to which agent should own the response. You run the SDK inside your application. OpenAI's managed Agents API is a separate hosting choice.
3. Google ADK: agents and workflow nodes
Some steps need a model's judgment; others should just run code. Google ADK's graph workflows let you place both in the same process. A function can process one agent's output before the next agent receives it, with the route defined in code. That is useful when you want an agent to interpret an input but ordinary application logic to determine the next operation.
ADK also offers prebuilt workflow structures for common execution patterns. The graph and dynamic workflow options require ADK 2.0 or higher, and support differs by language. Compare the workflow available in the SDK you will actually deploy, rather than treating every ADK example as interchangeable.
4. CrewAI: roles, tasks, and crew processes
CrewAI crews start with the jobs you want agents to do. Its documented research-and-analysis example gives the analysis task the research task's output as context. That is a concrete way to express a dependency without leaving the analyst to guess where its input should come from.
This role-and-task structure gives a team a place to state the expected output for each job. It does not make the instructions complete: a reviewer, for example, needs a specific instruction to check citations against the source material. CrewAI supports sequential and hierarchical processes. Compare those processes with LangGraph's explicit paths when deciding how much execution logic your application needs to express.
5. Microsoft Agent Framework: workflows in the Microsoft ecosystem
A .NET team can build a Microsoft Agent Framework workflow from typed executors, connecting them through edges and conditions. Each executor receives input and emits output. The graph can send work down parallel branches and bring the results together, with events showing what ran.
That gives an existing .NET application a concrete workflow API to assess, beyond the general promise of “agents working together.” Python also has an experimental functional workflow API, so the language choice affects how you express the process. Teams moving from AutoGen or Semantic Kernel should begin with the migration guidance below. Adopting this framework does not by itself decide where the application will be hosted.
Multi-agent workflow platforms compared
Cross-team business tasks often need both a working connection between applications and a clear handoff to the next specialist. n8n and Relevance AI approach that work differently.
6. n8n: agents inside business workflows
n8n can connect the business applications involved in a task. Its main agent delegates work to specialists through the AI Agent Tool, and you decide which tool calls must wait for a person to approve them using human review. The workflow still needs your credentials and tool inputs.
7. Relevance AI: agent teams on a visual canvas
Relevance AI Workforces puts specialist handoffs on a canvas you can edit and inspect. A required next step always passes work onward when an agent finishes. An AI connection leaves the handoff decision to the agent, while conditional routing follows rules you set.
A required review should sit on a required path, rather than depend on the drafting agent deciding it needs help. In n8n, you place agent calls inside a broader application workflow. In Relevance, you connect the specialists directly on the Workforce canvas. Its execution view lets you inspect what each agent received and passed on when a handoff goes wrong.
For concrete processes to try, see multi-agent workflow patterns. The examples can help you define the input and expected output before choosing a tool.
Collaboration platforms for people and existing bots
Colleagues may already have agents they want to work with. The choice then turns on how people participate and how the team passes work to its next owner.
8. OpenAgents: cloud and local agents in a workspace
OpenAgents puts the conversation and the work in one browser workspace. A colleague can discuss a result in a thread and refer to files the agents have produced, rather than receive only the final answer from someone else's private chat.
Teams can begin with hosted agents without connecting a local machine. The Launcher or CLI connects local agents when the work needs their environment. Those choices change where the agent runs, while the shared workspace remains the place teammates follow the work. This is a different starting point from building an agent application in an SDK.
9. Alook: people and bots with connected runtimes
Alook’s multi-agent AI collaboration platform, which publishes this guide, lets colleagues bring their existing bots into the same channel. Someone reviewing a result can ask the bot that produced it a follow-up there, with the other participants able to see the exchange. The bot has its own identity and visible owner.
For a closer look at how people and their agents share a conversation, see our introduction to the Alook AI agent workspace.
Each bot still runs on its owner's connected machine. Joining the discussion does not give a colleague access to that machine or expand the bot's tool permissions. Colleagues can work with one another's agents while keeping their existing setups, but the connected machine and runtime must be available. The collaboration without code guide explains the workflow.
10. Paperclip: goals and assigned work for an agent team
Paperclip makes a task the place where agent work is assigned and followed up. The task stays tied to a goal, with its owner and discussion available to the person supervising it. That helps when the question is “what should this agent work on next?” rather than “which specialist should answer this message?”
Agents can wake on a schedule or a task notification, so the board can organize ongoing work beyond a single conversation. For recurring work, the budget dashboard helps you follow spending alongside the tasks you have assigned. These controls manage the work your agents are assigned; the connected runtimes still need their own tool permissions. The handoff example below shows what changing a task's owner actually does.
Which AI agent collaboration platform fits your team?
Building an application means maintaining its execution logic. Connecting business tools or coordinating existing agents puts different demands on your team. Choose a shortlist around the work you can take on.
Shortlists for application and business teams
Use these documentation-based shortlists to plan a trial:
| Your constraint | Start by comparing | What should decide the choice |
|---|---|---|
| You are building an application with explicit execution requirements | LangGraph, OpenAI Agents SDK, Google ADK | Which state, delegation, and workflow model fits the code you will maintain? |
| Roles and task outputs are the main design unit | CrewAI and LangGraph | Is a role-and-task process sufficient, or do you need explicit graph state and paths? |
| You are extending an AutoGen or Semantic Kernel application | Assess Microsoft Agent Framework through the relevant migration guide first | Which tools, sessions, and workflows can you carry forward, and which need changes? |
| The work connects business tools and needs review points | n8n, Relevance AI | Which connections and control choices can your workflow owner maintain? |
| You need a workspace for existing agents | OpenAgents, Alook, Paperclip | Do shared discussions or assigned tasks better fit how your team coordinates work? |
For an existing Microsoft-stack application, start by inventorying the implementation and reviewing the official AutoGen migration guide or Semantic Kernel migration guide. Compare another SDK only if you have a reason to rebuild and can assess that work; the table does not imply a direct replacement.
Which platforms handle shared business tasks?
A report that must collect data from business applications points toward n8n. Relevance AI makes the specialist handoffs visible when the report passes through several agents. Ongoing work also needs someone to follow up: Paperclip keeps that work on a task board, where you can check progress and reassign a task.
Which platforms make task handoffs and progress clear?
Paperclip’s task handoffs give you a direct answer to “who has this now?”: look at the task's assignee. Assign it to another agent and Paperclip queues work for that agent. Hand it to a person and the task waits on that person. A name in a comment alone is not a handoff.
In a Relevance AI research-to-review workflow, a required review should be visible in the path between the two specialists. In Alook, colleagues can discuss the result with their own bots before deciding what happens next. The exchange stays in a shared channel or thread, and each bot still works within its owner's tool and file permissions. OpenAgents also keeps people and agents in shared threads, with a Tasks board alongside the conversation.
How do hosted platforms compare for cross-company projects?
Partners can join an OpenAgents workspace by email invitation. Hosted agents let them begin without connecting a local machine. They can discuss a task alongside its files, and a Viewer can follow the work without editing it. Everyone invited sees the workspace's shared material, so put only the joint project's material there.
Relevance AI’s Enterprise Chat role lets partners run agents they have permission to use without opening the main builder app. Enterprise RBAC and the Chat role are still being rolled out, and a user running a Workforce also needs permission to run its agents. There is a further catch if each partner must connect their own service account through dynamic OAuth in MCP: Chat does not support that setup. That setup requires a Member role in a dedicated project instead. In both products, the partners join a shared space managed by a host organization. Their separate company accounts do not automatically share access.
Can a team review an agent's plan before it runs?
Paperclip has a built-in approval step for its CEO’s strategic plan. You can ask for changes before the CEO assigns work based on it. This is not a general gate for every agent plan. A development team can build its own plan-review flow with LangGraph, showing the plan to a reviewer and pausing the application until they respond. The application needs a durable record of the paused work so a restart does not lose it.
Code changes need a repository review path too. On GitHub, a CODEOWNERS file identifies the responsible reviewers, but requiring their approval means enabling Require review from Code Owners on the target branch. On a repository plan that supports the rule, it can block merging until the code owners approve. Anyone with permission to bypass it may still merge without that approval.
Where can teams share configuration and context?
n8n workflow sharing lets colleagues on the same instance work on a shared workflow, including its agent setup. The available sharing options depend on the plan. Editors can use credentials already attached to that workflow, so sharing is also an execution-access decision.
A team can keep shared files and knowledge beside its discussion in OpenAgents. Alook keeps a channel and thread record that participants can use to pass on the current task. Each Alook bot retains its own runtime context, so a visible conversation does not give every bot the same memory.
With LangGraph Store, an application can save a goal or task for another agent to retrieve in a later session. The application writes to the store and selects what to retrieve for the next model call, using access rules your team implements. This works within an application you control. It does not synchronize the private context of unrelated agents.
History and execution recovery require different checks
Saved conversation history and resumable execution state serve different purposes. Check both before relying on a long-running workflow.
Saved messages and files
Can the next agent obtain the required input, identify its version, and see the review result? A transcript may explain an earlier decision without recording which tool calls completed. Check what is stored, who can read it, and how a new run loads it.
Resuming an interrupted run
Which saved execution state allows work to continue? What can run again?
If a LangGraph process restarts, an in-memory checkpointer loses the saved execution state. Use durable checkpoints for work that must survive that restart; a cross-thread store serves a different purpose.
In the OpenAI SDK, retaining the conversation history does not preserve everything needed to resume a run waiting for approval. Save the run-state snapshot with its pending approvals.
With ADK's resume feature, a tool interrupted before its result was saved can run again. A purchase tool, for example, needs its own duplicate prevention before you rely on recovery.
A “memory supported” checkbox hides these differences. Record how each candidate saves execution state and where recovery stops being reliable.
Try one task that produces a reviewable output from a requirements document. Pause it before a business action, then interrupt the run. On recovery, check whether it uses the right input version and still waits for approval. Look for any tool calls that ran twice. This is a proposed evaluation, not a test performed for this guide.
Multi-agent platform FAQs
Do you need multiple agents for every workflow?
No. Use multiple agents when their separate responsibilities justify the coordination work. One agent may already handle the task, and a predictable step may be better implemented as an ordinary function. Keep the simpler implementation until another agent has a clear job and a useful output to pass on.
What costs sit outside the platform subscription?
Budget for model usage and tool services as well as the infrastructure you run. Hosting or local machines and storage can sit outside the platform subscription. Determine which are included and which are billed separately for the deployment you choose. Someone must keep the connections working when credentials expire or an integration changes. Include that maintenance time and the time spent investigating failed runs. A useful cost comparison follows the same workload through each candidate, rather than comparing entry-plan prices alone.
Originally published on Alook.
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