How to build a multi-agent system: a step-by-step guide

By the end of this guide you will have a working multi-agent system: two or more AI agents with distinct roles that pass work to each other, use tools, and produce one result, with tracing so you can see what each agent did. The software that does the heavy lifting is an agent framework, a library or platform that handles the loop of model calls, tool use, handoffs and state.

To build a multi-agent system well, start with a task that really needs several roles, and keep the design as small as you can. This guide covers the main design patterns, a step-by-step build and test, and a comparison of seven frameworks from our Developer platforms category.

Key takeaways

• Start with one agent and add more only when a job has clearly separate roles, tools or permissions. More agents mean more cost, more failure points and harder debugging.
• The main decision is the framework style: graph-based control, role-based crews, handoff-based delegation, or a visual builder.
• Add tracing, human approval steps and guardrails from the start, because multi-agent failures are hard to reconstruct afterwards.
• Check the maintenance status of any framework before you commit. Some older projects have been archived or succeeded.

What you need before you start

• A task worth splitting. For example, research then write then review, or triage then resolve then escalate.
• Python, or another language the framework supports. Most options here are code-first, though Flowise has a visual canvas.
• A model provider key. Most frameworks are provider-flexible, but you still pay your model provider.
• Test cases. A handful of realistic inputs and what a good result looks like.
• Time. A first working prototype takes under a day. Making it dependable takes much longer.
• Difficulty. Moderate to hard. The framework is the easy part; designing roles, state and failure handling is the work.

Which kind of AI tool fits this job

Pick by how much control you want over the flow.

Graph-based control for long-running, auditable flows. LangGraph describes an agent as a graph of nodes and edges, mixing deterministic steps with model-driven ones. It provides durable execution, streaming, persistence and human-in-the-loop controls, and it is low-level, so you assemble models, tools and prompts yourself. Google's Agent Development Kit uses a graph-based Workflow engine with routing, fan-out and fan-in, loops and retries, plus a Task API for agent-to-agent delegation. Microsoft Agent Framework offers graph-based orchestration with sequential, concurrent, handoff and group collaboration patterns, in Python and .NET.

Role-based crews. CrewAI builds teams of agents that split a job, with agents and tasks defined in YAML or code and run as a crew or as a stateful Flow. Its AMP platform adds a visual editor, tracing and guardrails.

Handoffs between agents. OpenAI Agents SDK configures an agent with instructions, tools, guardrails and handoffs, and agents can delegate to each other either as tools or through handoffs. It adds sessions, human-in-the-loop controls and tracing. AG2, formerly AutoGen, is built around multi-agent conversation, supporting autonomous and human-in-the-loop workflows with different conversation patterns.

A visual builder. Flowise offers Assistant, Chatflow and Agentflow builders on a visual canvas, and Agentflow extends to multi-agent systems and workflow orchestration. It suits teams who prefer to compose flows visually, and it can be self-hosted.

Step 1: Define the job and whether it needs several agents

You finish this step with a role map or a decision to stay with one agent.

1. Write the end-to-end task and the result you want.
2. Break it into stages, and ask for each: does it need a different tool, a different permission or a different prompt?
3. If the answer is no for most stages, start with a single agent that uses tools.
4. If yes, list the roles, such as researcher, writer and reviewer, with one sentence each.

Check it worked by drawing the flow on one page with arrows for who hands work to whom.

Step 2: Choose the coordination pattern

The pattern decides how agents talk to each other.

1. Sequential: each agent hands to the next, like a pipeline.
2. Manager and workers: a lead agent splits work and gathers results.
3. Handoff: an agent passes the conversation to a specialist and steps back.
4. Group conversation: several agents discuss until they agree.
5. Pick the simplest that fits. Deterministic flows are easier to test than free-form conversations.

Check it worked by naming the pattern and the reason it suits your task.

Step 3: Choose a framework

Match the framework to your team and stack.

1. If you want explicit control and durable state, look at LangGraph, ADK or Microsoft Agent Framework.
2. If your team thinks in roles and tasks, look at CrewAI.
3. If you want light handoffs and built-in tracing, look at the OpenAI Agents SDK.
4. If you prefer a canvas to code, look at Flowise.
5. Check language and version requirements in the table below, for example the Python versions CrewAI, ADK and the OpenAI Agents SDK each need.
6. Check maintenance status. Microsoft's listings for Semantic Kernel and AutoGen name Microsoft Agent Framework as the successor, so new Microsoft-stack projects should read its migration guides first.

Check it worked by installing your choice and running its hello-world example.

Step 4: Build one agent first

Make a single agent work before you add the next.

1. Write its instructions: role, goal, limits and output format.
2. Give it only the tools it needs, such as a search function or a database lookup, and keep each tool's permissions narrow.
3. Run it on three test inputs and read the full trace.
4. Fix tool errors and unclear instructions now. Problems multiply with more agents.

Check it worked by getting a correct result on your test inputs with a readable trace.

Step 5: Add the other agents and the handoffs

Connect the agents using your chosen pattern.

1. Create each specialist with its own instructions and tools.
2. Define how work moves: handoffs in the OpenAI Agents SDK, edges in LangGraph, tasks in CrewAI, or an Agentflow on the Flowise canvas.
3. Decide what state is shared. Pass only what the next agent needs, not the whole history.
4. Set limits on loops and number of steps, so two agents cannot talk to each other forever.
5. Add a final agent or step that checks the result against your requirements.

Check it worked by running one test input end to end and following the trace through every agent.

Step 6: Add guardrails, approvals and tracing

Multi-agent systems fail in ways that are hard to see, so build visibility in.

1. Turn on tracing. CrewAI mentions tracing with OpenTelemetry support, the OpenAI Agents SDK has tracing, and Microsoft Agent Framework has OpenTelemetry integration.
2. Add human approval before any step that sends messages, spends money or changes data. LangGraph, ADK, the OpenAI Agents SDK and Flowise all list human-in-the-loop controls.
3. Add input and output guardrails where the framework supports them, and treat any text from outside as untrusted.
4. Set cost limits with your model provider, since agents can make many calls.

Check it worked by triggering an approval step on purpose and confirming the run pauses.

Step 7: Evaluate, then deploy

Prove it works on more than the happy path.

1. Run your test cases, plus awkward ones: missing data, contradictory inputs, tool failures.
2. Compare results against a single-agent baseline. If the multi-agent version is not clearly better, simplify.
3. Review cost and run time per task.
4. Deploy with the option your framework offers. For example, CrewAI lists cloud, VPC, self-hosted and hybrid options, and ADK lists Docker, Cloud Run and Vertex AI Agent Engine.
5. Keep humans in the loop for decisions with legal, financial or medical consequences.

Check it worked by running a small batch of real tasks and reviewing each trace.

Tools for this job compared

Tool	Good fit for	Pricing	Stated limits
LangGraph	Graph-defined, long-running, stateful agents	Open source	Low-level, so you assemble models, tools and prompts yourself; self-hosted and BYOC LangSmith options need Enterprise
CrewAI	Role-based crews in Python or a visual editor	Open source	Requires Python 3.10 or newer and below 3.14; Basic plan capped at 50 workflow executions a month
OpenAI Agents SDK	Handoffs, tools, guardrails and tracing in Python	Open source	Requires Python 3.10 or newer; JavaScript and TypeScript version is a separate package
AG2	Multi-agent conversations, formerly AutoGen	Open source	Requires Python coding; AgentOS platform and managed services have no published prices
Microsoft Agent Framework	Python and .NET graph workflows with OpenTelemetry	Open source	Go SDK is in a separate repository; experimental modules ship in a separate lab package
Agent Development Kit	Code-first agents tuned for Gemini, model-agnostic	Open source	Requires Python 3.11 or newer; main branch can contain experimental changes
Flowise	Visual agent building, self-hosted or cloud	Open source	Cloud Free plan allows 2 flows and 2 assistants; evaluations are Cloud and Enterprise only

Prices and plan limits change, so confirm the current terms on each vendor's site before you commit.

Common mistakes to avoid

• Splitting into agents too early. It feels tidy, but each added agent adds model calls, cost and failure points. Fix it by starting with one agent and splitting only when roles, tools or permissions differ.
• Letting agents loop. Two agents can keep correcting each other. Fix it by setting maximum steps and a clear finishing condition.
• Passing the whole history around. Long context raises cost and confuses agents. Fix it by passing only the state the next agent needs.
• Giving every agent every tool. A broad toolset widens the damage from one mistake or a prompt injection. Fix it by granting each agent the minimum tools and putting approvals on risky actions.
• Choosing an unmaintained framework. Projects change fast, and some are archived or replaced. Fix it by checking the repository status and migration notes before you build.

How we put this together

This is an evidence-based guide built from each tool's listing in our directory, which is compiled from the vendor's own site and pricing page. It is not a review based on using the tools. We don't rank tools, and nobody paid to be included. Some outbound links may be affiliate links; see our disclosure.

Frequently asked questions

Is there a free way to build a multi-agent system?

Yes. LangGraph, the OpenAI Agents SDK, AG2, Microsoft Agent Framework and the Agent Development Kit are open source, CrewAI's core framework is open source with a free Basic platform plan, and Flowise can be self-hosted. You still pay your model provider for usage.

What is the difference between a single agent and a multi-agent system?

A single agent is one model with instructions and tools. A multi-agent system uses several, each with its own role, tools or permissions, passing work between them. It helps when stages need different capabilities, and it hurts when the task is simple.

Which framework should a beginner use?

Beginners can try Flowise for a visual canvas, or CrewAI for role-and-task definitions. LangGraph's own documentation points beginners towards higher-level LangChain agents for prebuilt architectures.

Do I need to write code?

For most of these, yes: they are Python or .NET libraries. Flowise offers a visual canvas, and CrewAI, ADK and AG2 list visual or no-code options on top of their code frameworks.

How do I stop agents from making costly mistakes?

Limit tools, add human approval before risky actions, cap steps and spend, and keep traces so you can review every decision. Have a person review outputs used for legal, financial or medical decisions.

Compare more options in our Developer platforms category.