Introduction
Multi-AI systems utilizing Large Language Model (LLM) agents represent a significant advance in artificial intelligence. Instead of relying on a single AI model to perform complex tasks, multi-agent systems combine several specialized AI agents working collaboratively to solve intricate problems. This article covers their concepts, benefits, usage scenarios, limitations, and provides a practical implementation using Python and LangGraph. This implementation uses local LLMs for environments with privacy, performance, or cost constraints, and we briefly introduce the Google A2A (Agent-to-Agent) protocol as a promising mechanism for agent communication. As a precondition we require an installation of the Open Source Ollama tool.
What are Multi-AI (LLM) Agents?
Multi-AI (LLM) agents are independent AI components, each possibly using its own LLM, specialized for specific tasks. These agents communicate, share information, delegate tasks, and collaboratively solve problems that a single agent would struggle with or be inefficient at solving alone. These agents can use cloud-based models (e.g., OpenAI) or locally hosted models, depending on system constraints and preferences. Importantly, each agent in the system may use a different LLM instance, either for specialization or due to differing requirements regarding performance, precision, or hardware compatibility.
The Role of Google A2A Protocol
Google’s A2A (Agent-to-Agent) protocol, introduced in 2024, is a lightweight communication protocol specifically designed to support interactions between AI agents. A2A focuses on structured message-passing between agents using a JSON schema, enabling standardization, traceability, and type-checking for agent messages. A2A supports turn-taking, action framing, and intent resolution, making it ideal for coordination in multi-agent environments. Although still evolving, A2A is gaining adoption as a common substrate for interoperable AI systems across vendors and platforms.
Integrating A2A in LangGraph or other frameworks involves designing each node or agent to accept and emit A2A-compliant message envelopes. This enhances compatibility and logging, and potentially allows agents developed independently to cooperate.
Benefits
A key benefit of Multi-AI agent systems is scalability. By dividing labor among multiple agents, workloads can be distributed and processed in parallel, significantly improving throughput. Specialization is another major advantage: each agent can be fine-tuned or configured for a specific domain, such as legal analysis, customer support, or data extraction. This leads to more accurate and relevant responses. Efficiency also increases, since tasks are often handled simultaneously rather than sequentially. Moreover, the architecture lends itself to robustness; if one agent fails or becomes unresponsive, the system can often compensate through others. Finally, modularity simplifies system maintenance. Each agent is an encapsulated component, which makes upgrades, debugging, and testing more manageable.
When to Use Multi-AI Agents
Multi-agent systems are ideal for applications involving complex or multi-step reasoning, such as decision support systems, document understanding pipelines, or autonomous agents coordinating plans. They are also highly useful in real-time systems requiring rapid responses across domains, such as financial advisors or customer care chatbots. In research environments, they allow rapid prototyping and extension by plugging in or modifying specific agents.
When Not to Use Multi-AI Agents
In contrast, simple tasks that can be fully handled by a single, well-tuned LLM do not benefit from multi-agent complexity. For example, summarizing a short email or classifying a tweet does not justify the communication overhead. Similarly, environments with restricted compute or memory budgets (e.g., mobile devices or embedded systems) may not be suitable, unless extremely lightweight models are employed. Finally, if the coordination and orchestration required between agents is non-trivial and domain-specific, the complexity introduced may outweigh the modular benefits.
Constituents of Multi-AI Systems
Multi-agent systems consist of several core components. First are the agents themselves: autonomous, task-specific modules that accept input and produce output, possibly by calling an LLM. Second is the communication protocol that enables agents to share state, data, and instructions with each other. This may be as simple as function chaining or as complex as message passing or blackboard systems. Third, many systems use a coordinator: a central agent responsible for orchestration, task delegation, or fault recovery. Lastly, there is the environment interface, which bridges the agents with external data sources, users, or services. Modern systems are increasingly adopting the A2A protocol to formalize inter-agent communication.
Practical Example: Using LangGraph
Below is a complete implementation using Python and LangGraph, a popular library designed explicitly for multi-agent systems. We provide only one option: using local models hosted via Ollama for environments that require privacy or local execution. Each agent can independently choose its LLM backend, enabling hybrid setups with both local and remote models. In the example though, we use mistral which you may download via ollama pull mistral.
The applicastion consists of three agents: the Analyzer reads and categorizes the prompt, the Fetcher tries to find information thast falls under this category, and the Formulator creates a polite response for the customer respectively user. You may also distibute the agents across different nodes which requires a technology like Google A2A so that the agents can communicate with each other.
For the implementation the following packages are imported:
pip install langgraph ollama
Then pull the model:
ollama pull mistral
The code of the simple Multi AI agent system looks like follows:
import os
import ollama
from langgraph.graph import Graph
OLLAMA_MODELS = {
"analyze": "mistral",
"fetch": "mistral",
"formulate": "mistral"
}
def run_llm(messages, agent_name):
prompt = "\n".join(f"{m['role'].capitalize()}: {m['content']}" for m in messages) + "\nAssistant:"
result = ollama.chat(model=OLLAMA_MODELS[agent_name], messages=[{"role": "user", "content": prompt}])
return result['message']['content'].strip()
def analyze_query(state):
messages = [
{"role": "system", "content": "Categorize the customer query."},
{"role": "user", "content": state["query"]}
]
state["category"] = run_llm(messages, "analyze")
return state
def fetch_product_info(state):
messages = [
{"role": "system", "content": "Provide product data for the category."},
{"role": "user", "content": state["category"]}
]
state["info"] = run_llm(messages, "fetch")
return state
def formulate_response(state):
messages = [
{"role": "system", "content": "Formulate a polite customer response."},
{"role": "user", "content": f"Customer asked about {state['category']}. {state['info']}"}
]
state["response"] = run_llm(messages, "formulate")
return state
graph = Graph()
graph.add_node("analyze", analyze_query)
graph.add_node("fetch", fetch_product_info)
graph.add_node("formulate", formulate_response)
graph.add_edge("analyze", "fetch")
graph.add_edge("fetch", "formulate")
graph.set_entry_point("analyze")
graph.set_finish_point("formulate")
app = graph.compile()
if __name__ == '__main__':
initial_state = {"query": "Can you tell me about your premium headphones?"}
result = app.invoke(initial_state)
print("Final Response:")
print(result["response"])
Disadvantages and Limitations
Multi-agent systems are not without downsides. The coordination logic can introduce significant complexity, particularly when dependencies or control flow are dynamic. Debugging becomes difficult as state is passed across many functions or networked components. Communication overhead, particularly in synchronous systems, can lead to bottlenecks or latency. Moreover, running multiple LLMs—especially if each agent is backed by a separate model—can be computationally expensive and may require GPU or large-memory systems. Local LLMs also require hardware compatibility, disk space, and careful optimization to ensure reasonable latency. Protocols like A2A, while standardizing communication, may introduce additional verbosity and schema management overhead.
Summary
Multi-AI LLM systems leverage modular, collaborative agents to tackle complex problems with enhanced scalability, specialization, and fault tolerance. Frameworks like LangGraph help streamline development, making it easier to orchestrate task-specific agents with both cloud-based and local LLMs. Each agent can independently select its backend model, allowing for heterogeneous architectures that mix open-source and hosted models as needed. Protocols like Google’s A2A offer a promising path toward interoperable, modular AI ecosystems. While these systems are powerful, they should be adopted with a clear understanding of their computational demands and coordination complexity. When used judiciously, they offer a promising blueprint for building intelligent, adaptable applications.
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