Multi-agents: making multiple AIs collaborate

Multi-agents: making multiple AIs collaborate

The essentials

Multi-agents involves making multiple specialized artificial intelligences collaborate to accomplish a complex task, inspired by the principle of the division of labor. According to Microsoft's research (2024) and LangChain's benchmarks (2025), this architecture clearly outperforms a single agent as soon as the task requires more than two different areas of expertise or cross-checks. The approach relies on three fundamental architectures (Orchestrator/Workers, Peer-to-Peer, Hierarchical) and is supported by specialized frameworks like CrewAI, AutoGen or LangGraph.

What if a single AI was no longer enough? Welcome to the era of multi-agents — where multiple artificial intelligences collaborate, divide up tasks, and produce results that an isolated agent could never achieve.

In this advanced guide, we explore multi-agent architectures, the major frameworks (CrewAI, AutoGen, LangGraph) and build a concrete pipeline together with three specialized agents.

🏗️ Why multi-agents?

A single AI agent is powerful. But as soon as the task becomes more complex — writing an SEO-optimized article in 3 languages, analyzing a dataset and then generating a report, orchestrating a deployment — its limitations become apparent:

• Saturated context window: a single agent doing everything accumulates unnecessary context
• Impossible specialization: the "SEO expert + copywriter + translator" prompt yields mediocre results across the board
• No cross-checking: no one proofreads the work
• Limited scalability: impossible to parallelize

Multi-agents solves all of this by applying a principle as old as time: the division of labor.

🧠 The 3 fundamental architectures

Orchestrator / Workers Architecture

This is the most common model. An orchestrator agent (the "project manager") distributes tasks to specialized worker agents. The orchestrator receives the overall task, breaks it down into subtasks, each worker executes its part, and then the orchestrator aggregates and validates the results.

Advantages:
- Centralized control
- Easy to debug (single decision point)
- Workers are interchangeable

Disadvantages:
- Single point of failure (the orchestrator)
- The orchestrator must be highly competent
- Latency if everything goes through it

Peer-to-Peer Architecture

Agents communicate directly with each other, without a centralized leader. Each agent decides when to pass the baton.

Use cases: AI debates, brainstorming, cross-checking.

Advantages:
- No central bottleneck
- Resilient (if one agent fails, the others continue)
- Emergence of creative solutions

Disadvantages:
- Risk of infinite loops (agents keep relaunching endlessly)
- Difficult to control and predict
- Complex debugging

Hierarchical Architecture

A mix of the two: multiple levels of management. A main orchestrator delegates to sub-orchestrators, who themselves manage workers.

Use cases: complex projects with many agents (10+).

To understand how these architectures are applied in practice, you can check out Les 5 patterns d'agents IA qui marchent.

🛠️ Multi-agent frameworks

CrewAI — The ready-to-use AI team

CrewAI is the most accessible framework. It models a team (Crew) composed of agents with roles, goals, and tools. Configuration is done in three steps: first, you define each agent with its role, goal, and backstory, then you create the associated tasks by specifying the description and the expected output, finally you assemble everything in a Crew object that manages the sequential or parallel chaining. Context passing between agents is handled automatically by the framework.

CrewAI strengths:
- Simple and intuitive API
- Automatic management of context passing between agents
- Support for many LLMs (OpenAI, Anthropic, local)
- Built-in tools (web search, file reading)

Limitations:
- Less flexible than LangGraph for complex flows
- No native conditional branching

AutoGen — The Microsoft framework

AutoGen from Microsoft Research is designed for multi-agent conversations. Agents talk to each other to solve a problem. You configure assistant agents with specific system messages, group them in a GroupChat with a maximum number of turns, and then a GroupChatManager orchestrates the discussion. Code execution is integrated directly into the flow via a UserProxyAgent.

AutoGen strengths:
- Natural conversations between agents
- Integrated code execution
- GroupChat for multi-agent discussions
- Very good for AI pair-programming

Limitations:
- More complex API
- Less structured than CrewAI for pipelines

LangGraph — The workflow graph

LangGraph (by LangChain) models workflows as directed graphs. Each node is an agent or a function, the edges define the flow. You start by defining a typed shared state (via TypedDict), then you create functions for each agent that take and return this state. The graph is built by adding nodes and transitions, with the possibility of adding conditional branches — for example, if a verification agent considers the quality insufficient, the flow loops back to the drafting step.

LangGraph strengths:
- Conditional branching (if quality insufficient → start over)
- Workflow visualization as a graph
- Typed shared state
- Persistence and resumption after error

Limitations:
- Steeper learning curve
- Verbose for simple cases

To master the underlying mechanisms used by these frameworks, our guide MCP, Function Calling, Tool Use: the complete guide details everything you need to know.

Framework comparison

🔧 Use case: writer + SEO + translator pipeline

Let's put this into practice with CrewAI by building a complete pipeline: a Writer Agent writes a blog post, an SEO Agent optimizes the title, H2s, and keywords, and a Translator Agent translates it into English.

Setup

Installation requires the crewai, crewai-tools, and langchain-openai packages, as well as configuring environment variables for the OpenAI and Anthropic API keys.

The complete code

The pipeline logic is structured in four blocks. First, we instantiate the three agents with specific roles, goals, and backstories: the Writer (GPT-4o, with a web search tool), the SEO Consultant (GPT-4o), and the Translator (Claude 3.5 Sonnet). Next, we define the sequential tasks: the writing task requests a 2000-2500 word article with an H2/H3 structure, the SEO task requires optimizing the title, meta description, keyword density, and readability, and the translation task adapts the content into English while preserving the markdown. All of this is assembled into a Crew object with the sequential process, memory enabled, and a rate limit of 10 requests per minute. Execution is launched via crew.kickoff() with the topic and target audience as parameters.

Execution and result

Upon execution, the Writer performs its research and writes the article (~2340 words), then the SEO Consultant analyzes and optimizes it (SEO score: 87/100), and finally the Translator produces the English version (~2280 words). The final result includes the optimized French article, the English article, and the SEO meta tags.

To see another example of a complete pipeline in action, check out our article Automating a complete pipeline with an agent.

⚡ Advanced patterns

Pattern 1: Cross-verification

Two agents write independently, a third compares and merges. With CrewAI, you simply configure a Crew object containing the two writers and the editor, with three tasks executed sequentially: writing 1, writing 2, then merging.

Pattern 2: Feedback loop

The critic agent sends the work back to the writer until satisfied. With LangGraph, you implement this logic via a conditional branching: an evaluation function checks the quality score and the number of iterations, then returns either "publish" or "rewrite", redirecting the flow to the appropriate node.

Pattern 3: Agents with shared memory

Use a vector database as common memory. CrewAI offers this feature via its long_term_memory parameter, which accepts a storage backend like ChromaDB to persist information between sessions.

Pattern 4: Multi-models

Each agent uses the LLM best suited to its task:

Recommended tools

• CrewAI — The most accessible framework for getting started with multi-agents, ideal for sequential pipelines with specialized agents.
• LangGraph — The preferred choice for complex workflows requiring conditional branching, persistence, and graph visualization.
• AutoGen — Microsoft Research's framework optimized for multi-agent conversations and AI pair-programming.

Common mistakes

1. The infinite loop

Two agents keep correcting each other endlessly. Solution: always set a max_iter (in CrewAI) or a max_round (in AutoGen) to limit the number of iterations.

2. Diluted responsibility

If no one is "responsible" for the final result, quality drops. Solution: appoint a final agent whose only role is to validate and consolidate the result.

3. Exploding costs

3 agents × 3 iterations × GPT-4o = a hefty bill. Solution: use cheaper models for simple tasks (GPT-4o-mini at ~$0.30/M tokens or Claude 3 Haiku at ~$0.25/M tokens instead of GPT-4o at ~$10/M tokens).

4. Lost context

Agents don't pass enough info to each other. Solution: write very precise expected_output in tasks to force each agent to produce a structured and complete result for the next one.

🚀 Multi-agents with OpenClaw

OpenClaw natively uses multi-agents with its sessions and subagents system: a main agent (orchestrator) spawns subagents for long tasks (writing, SEO analysis, translation), each with its own context and instructions. This is exactly the orchestrator/workers architecture.

The advantages in OpenClaw:
- Isolated context: each subagent has its own context, no pollution
- Parallelism: several subagents work at the same time
- Specialization: each subagent can have a different model
- Automatic reporting: the result goes back up to the main agent

⚠️ The pitfalls of multi-agents

Beyond the common errors detailed above, other pitfalls deserve attention. The dilution of responsibility occurs when no agent is explicitly in charge of the final quality — you must always plan for a validator agent. Costs can explode quickly with the stacking of LLM calls: the best practice is to assign the most powerful model to the critical agent (writer) and lightweight models to structured task agents (SEO). Finally, the loss of context between agents is corrected by being extremely precise about the expected output formats in each task.

📊 When to use multi-agents?

Simple rule: if the task requires more than 2 different areas of expertise or cross-checking, multi-agents are worth it.

If you are a beginner in this field, our tutorial Créer son premier agent IA autonome is an excellent starting point. For an advanced use case of multi-agents in production, Dexter : un agent IA autonome qui fait de la recherche financière profonde concretely illustrates these principles.