Building multi-agent systems with LangGraph and Amazon Bedrock

Introduction

LLMs make natural language interactions possible, but tasks, such as scheduling a doctor’s appointment, entail sophisticated workflows and coordination among systems. LLM agents carry out such tasks but are ineffective in tool proficiency, context handling, and specialization.

A multi-agent architecture utilizes specific agents under a supervisor’s control to achieve the highest scalability and efficiency. Such tools as LangGraph and Amazon Bedrock enable developers to write multi-agent systems. AWS now supports cooperative Amazon Bedrock Agents for task delegation and better outcomes in complex processes.

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Challenges with multi-agent systems

Single-agent systems decompose tasks into steps, while multi-agent systems require task allocation and coordination. This creates problems like synchronization, dependencies, and resource sharing, requiring robust frameworks.

Memory in multi-agent systems is intricate, requiring synchronization of context and shared access, as opposed to the short-term and long-term memory of single-agent systems.

Agent frameworks simplify workflow and coordination. LangGraph, developed by LangChain, manages complex workflows with graph-based controls. Amazon Bedrock makes efficient multi-agent LLM applications possible with monitoring and debugging features.

Understanding LangGraph and LangGraph Studio

LangGraph uses state machines and directed graphs to control multi-agent workflows with state and flow precisely. The three workflow components are:

State: A data structure that shows the app’s current state.
Nodes: Python functions defining agent logic.
Edges: Actions that choose the next node according to state, allowing branching or deterministic transitions.

LangGraph has a persistence layer for:

Memory: Maintains state across sessions, enabling conversational memory and updates.
Human-in-the-loop: Execution may pause for human input or correction.

LangGraph Studio, a feature-oriented IDE, improves development with:

Visual agent graphs-based intuitive architecture design.
Real-time debugging and interactive agent control.
Stateful, adaptive agents within the graph.

fig1

Figure 1: LangGraph Studio UI

Solution overview

This is an example of the supervisor agentic pattern, in which a master Supervisor Agent oversees specialized agents. One scratchpad per agent is dedicated to a particular task, and the supervisor handles communication and delegation, improving efficiency, parallelism, and scalability.

For the query/ user input :

“Suggest a travel destination and search for a flight and hotel. I want to travel on 15-March-2025 for 5 days”. The workflow is:

Supervisor decomposes the query into tasks.
Destination Agent examines the user profile to recommend a destination.
Flight Agent searches for flights on March 15, 2025.
Hotel Agent retrieves hotels for a 5-day stay.
Supervisor assembles and returns the full itinerary.

fig2

Figure 2: Multi-agent workflow

Core Components of the Worker Agents

Each agent consists of two major constituents:

graph.py – This script formalizes the agent’s workflow and decision-making strategy. It provides the LangGraph state machine implementation for controlling agent behavior and configures the flow of communication between various components.
py – This file holds the concrete classes for agent capabilities. It defines the business logic for every operation, as well as data access and manipulation.

Isolating the graph (workflow) from tools (task execution) allows for a clean architecture where decision-making is separated from implementation. With LangGraph’s state-based system, agents exchange information through a structured graph, with the Supervisor Agent controlling task flow and information exchange between specialized agents.

Pre-requisites of the implementation

A valid AWS account.
An AWS Identity and Access Management (IAM) role in the account that possesses sufficient permissions to create the resources that are needed.
Availability of Anthropic’s Claude 3 Sonnet and Claude 3.5 Sonnet on Amazon Bedrock.
A LangGraph application up and running locally

Steps to set up Amazon Bedrock with LangGraph

Install the necessary packages:

pip install boto3 langchain-aws

1	pip install boto3 langchain-aws

These packages are necessary for AWS Bedrock integration:

boto: AWS SDK for Python, manages AWS service communication
langchain-aws: Offers LangChain integrations for AWS services

Import the modules:

from langchain_aws import ChatBedrockConverse

from langchain_aws import ChatBedrock

from langchain_aws import ChatBedrockConverse

from langchain_aws import ChatBedrock

Create an LLM object:

bedrock_client = boto3.client("bedrock-runtime”,                region_name="<region_name>")

llm = ChatBedrockConverse(

        model="anthropic.claude-3-haiku-20240307-v1:0",

        temperature=0,

        max_tokens=None,

        client=bedrock_client,

        # other params...

    )

bedrock_client = boto3.client("bedrock-runtime”, region_name="<region_name>")

llm = ChatBedrockConverse(

model="anthropic.claude-3-haiku-20240307-v1:0",

temperature=0,

max_tokens=None,

client=bedrock_client,

# other params...

)

LangGraph studio configuration:

The langgraph.json file defines your app’s structure and dependencies, enabling LangGraph Studio to run, visualize, and debug multi-agent workflows in real-time.

{

"dependencies": [

"boto3>=1.35.87",

"langchain-aws>=0.2.10",

"."

],

"graphs": {

"supervisor": "./src/supervisor_agent/graph.py:graph",

"flight": "./src/flight_agent/graph.py:graph",

"hotel": "./src/hotel_agent/graph.py:graph"

},

"env": "./.env"

}

{

"dependencies": [

"boto3>=1.35.87",

"langchain-aws>=0.2.10",

"."

"graphs": {

"supervisor": "./src/supervisor_agent/graph.py:graph",

"flight": "./src/flight_agent/graph.py:graph",

"hotel": "./src/hotel_agent/graph.py:graph"

"env": "./.env"

}

Testing and debugging

You can now try the multi-agent travel assistant by executing langgraph dev, which begins the LangGraph API server in development mode with hot reloading and debugging. The interface provides a way to choose a graph through the dropdown and set test parameters using the Manage Configuration button, giving a full environment for real-time testing and debugging.

fig3

Figure 3: LangGraph studio with Destination Agent recommendation

LangGraph Studio allows you to control multiple versions of configurations (e.g., v1, v2, v3) in an easy-to-use interface to quickly switch configurations, such as using user_id to retrieve past data when testing and debugging your multi-agent system.

fig4

Figure 4: Runnable configuration details

In this case, user_id is set to assist tools in fetching user history. The Planner Agent utilizes this to suggest destinations through the compare_and_recommend_destination tool. LangGraph’s checkpoint memory, which is controlled by threads, enables simple tracking and management within LangGraph Studio.

fig5

Figure 5: View graph state in the thread

The destination_agent employs a tool whose output can be examined. Likewise, flight_agent and hotel_agent can be tested separately. After all agents function properly, the entire workflow can be tested by checking each agent’s inputs and outputs, as illustrated in the Supervisor Agent’s complete view.

fig6

Figure 6: Supervisor Agent with a complete workflow

Considerations

Multi-agent systems need coordination, state management, and fault handling.
Graph-based architectures accommodate complex, adaptive workflows and parallelism.
They require advanced configuration and more computational resources.
Ensure consistency with AI governance, safety, and data policies.
Enforce thorough monitoring and fallback procedures for failures.

Clean up

Remove any AWS IAM role and policy set up solely for this implementation. If you no longer want access to an Amazon Bedrock FM, remove access from it.

Conclusion

The integration of LangGraph and Amazon Bedrock offers benefits to multi-agent systems with the integration of LangGraph’s orchestration and Amazon Bedrock’s FMs for scalable, high-performance AI applications. It solves issues like state management, agent coordination, and workflow orchestration, complemented by memory management, error handling, and human-in-the-loop.

LangGraph Studio features help in designing and managing intricate agent interactions, with a solid base for next-gen systems that provide reliable results and efficient resource usage.

Drop a query if you have any questions regarding LangGraph or Amazon Bedrock and we will get back to you quickly.

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