Multi-agent graphs in LangChain

Introduction

Multi-agent graphs help you organize and connect different agents so they can work together smoothly. It makes managing many agents easier by showing how they relate and share information.

When you have several agents that need to collaborate on a task.

When you want to visualize how different agents communicate or depend on each other.

When you want to manage complex workflows involving multiple AI agents.

When you want to track the flow of information between agents.

When building systems where agents have different roles but must work as a team.

Syntax

LangChain

from langchain.graphs import MultiAgentGraph

# Create a multi-agent graph
multi_agent_graph = MultiAgentGraph()

# Add agents
multi_agent_graph.add_agent(name="agent1", agent=agent1_instance)
multi_agent_graph.add_agent(name="agent2", agent=agent2_instance)

# Connect agents
multi_agent_graph.add_edge(from_agent="agent1", to_agent="agent2", description="passes data")

# Run or visualize the graph
multi_agent_graph.run()
multi_agent_graph.visualize()

You create a MultiAgentGraph object to start.

Add agents by giving each a unique name and the agent instance.

Examples

This shows an empty graph with no agents.

LangChain

multi_agent_graph = MultiAgentGraph()

# No agents added yet
print(len(multi_agent_graph.agents))  # Output: 0

Graph with one agent added.

LangChain

multi_agent_graph.add_agent(name="agent1", agent=agent1_instance)
print(len(multi_agent_graph.agents))  # Output: 1

Connects two agents with a directional edge showing communication.

LangChain

multi_agent_graph.add_agent(name="agent2", agent=agent2_instance)
multi_agent_graph.add_edge(from_agent="agent1", to_agent="agent2", description="sends message")

Shows a visual graph of agents and their connections.

LangChain

multi_agent_graph.visualize()

Sample Program

This program creates two simple agents, adds them to a multi-agent graph, connects them, and shows how data flows from one to the other. It prints what each agent outputs.

LangChain

from langchain.agents import Agent
from langchain.graphs import MultiAgentGraph

# Define two simple agents
class SimpleAgent(Agent):
    def __init__(self, name):
        self.name = name
    def run(self, input_text):
        return f"{self.name} received: {input_text}"

# Create agent instances
agent1 = SimpleAgent("Agent One")
agent2 = SimpleAgent("Agent Two")

# Create a multi-agent graph
multi_agent_graph = MultiAgentGraph()

# Add agents to the graph
multi_agent_graph.add_agent(name="agent1", agent=agent1)
multi_agent_graph.add_agent(name="agent2", agent=agent2)

# Connect agent1 to agent2
multi_agent_graph.add_edge(from_agent="agent1", to_agent="agent2", description="forwards message")

# Simulate running agent1 and passing output to agent2
output1 = agent1.run("Hello")
output2 = agent2.run(output1)

# Print outputs
print("Output from agent1:", output1)
print("Output from agent2:", output2)

# Visualize the graph (this will open a window or save a file depending on environment)
multi_agent_graph.visualize()

Important Notes

The time complexity to add an agent or edge is usually O(1).

Visualizing large graphs may slow down your program.

Common mistake: forgetting to connect agents, so they don't communicate.

Use multi-agent graphs when you want clear structure and flow between agents instead of isolated agents.

Summary

Multi-agent graphs organize multiple agents and their connections.

They help manage communication and workflows between agents.

Adding agents and edges is simple and lets you visualize the system.

Practice

1. What is the main purpose of a multi-agent graph in Langchain?

easy

A. To compile code faster

B. To store large datasets efficiently

C. To create user interfaces for web apps

D. To organize multiple agents and their connections

Solution

1. Step 1: Understand the concept of multi-agent graphs

   Multi-agent graphs are designed to organize agents and show how they connect and communicate.

2. Step 2: Compare options with the concept

   Only To organize multiple agents and their connections correctly describes organizing agents and their connections, which matches the purpose of multi-agent graphs.

3. Final Answer:

   To organize multiple agents and their connections -> Option D

4. Quick Check:

   Multi-agent graph purpose = Organize agents [OK]

Hint: Remember: multi-agent graphs show agents and links [OK]

Common Mistakes:

• Confusing data storage with agent organization
• Thinking it's for UI design
• Assuming it's for code compilation

2. Which of the following is the correct way to add an agent to a multi-agent graph in Langchain?

easy

A. graph.insert_agent('agent_name')

B. graph.create_agent('agent_name')

C. graph.add_agent('agent_name')

D. graph.push_agent('agent_name')

Solution

1. Step 1: Recall the method to add agents in Langchain multi-agent graphs

   The standard method to add an agent is using add_agent.

2. Step 2: Check each option's method name

   Only graph.add_agent('agent_name') uses add_agent, which is the correct syntax. Others are invalid method names.

3. Final Answer:

   graph.add_agent('agent_name') -> Option C

4. Quick Check:

   Adding agent method = add_agent() [OK]

Hint: Look for 'add_agent' method to add agents [OK]

Common Mistakes:

• Using incorrect method names like insert_agent
• Confusing create_agent with add_agent
• Using push_agent which doesn't exist

3. Given the following code snippet, what will be the output when printing the graph's edges?

graph = MultiAgentGraph()
graph.add_agent('AgentA')
graph.add_agent('AgentB')
graph.add_edge('AgentA', 'AgentB')
print(graph.edges)

medium

A. [('AgentA', 'AgentB')]

B. [('AgentB', 'AgentA')]

C. []

D. Error: add_edge method not found

Solution

1. Step 1: Analyze the code adding agents and an edge

   Two agents 'AgentA' and 'AgentB' are added, then an edge from 'AgentA' to 'AgentB' is created.

2. Step 2: Understand the edges property output

   The edges list will contain a tuple representing the connection from 'AgentA' to 'AgentB'.

3. Final Answer:

   [('AgentA', 'AgentB')] -> Option A

4. Quick Check:

   Edges list = [('AgentA', 'AgentB')] [OK]

Hint: Edges show connections as (from, to) tuples [OK]

Common Mistakes:

• Reversing the edge direction
• Expecting empty edges list
• Assuming add_edge method is missing

4. Identify the error in this code snippet for creating a multi-agent graph:

graph = MultiAgentGraph()
graph.add_agent('Agent1')
graph.add_edge('Agent1', 'Agent2')

medium

A. Agent2 was not added before creating an edge

B. add_edge method requires three arguments

C. add_agent method is misspelled

D. MultiAgentGraph cannot add edges

Solution

1. Step 1: Check agent additions before adding edges

   Only 'Agent1' is added; 'Agent2' is missing before adding an edge.

2. Step 2: Understand edge creation requirements

   Edges require both agents to exist; missing 'Agent2' causes an error.

3. Final Answer:

   Agent2 was not added before creating an edge -> Option A

4. Quick Check:

   Both agents must exist before edge [OK]

Hint: Add both agents before connecting them with edges [OK]

Common Mistakes:

• Assuming add_edge needs three arguments
• Thinking add_agent is misspelled
• Believing edges can't be added

5. You want to build a workflow where AgentX sends data to AgentY, and AgentY processes it and sends results to AgentZ. Which multi-agent graph setup correctly represents this flow?

hard

A. Add agents AgentX, AgentY; add edge AgentX->AgentZ only

B. Add agents AgentX, AgentY, AgentZ; add edges AgentX->AgentY and AgentY->AgentZ

C. Add agents AgentX, AgentY, AgentZ; add edges AgentZ->AgentY and AgentY->AgentX

D. Add agents AgentX, AgentY, AgentZ; no edges needed

Solution

1. Step 1: Identify the data flow between agents

   AgentX sends to AgentY, then AgentY sends to AgentZ, so edges must follow this order.

2. Step 2: Match edges to the described flow

   Add agents AgentX, AgentY, AgentZ; add edges AgentX->AgentY and AgentY->AgentZ correctly adds edges from AgentX to AgentY and AgentY to AgentZ, representing the workflow.

3. Final Answer:

   Add agents AgentX, AgentY, AgentZ; add edges AgentX->AgentY and AgentY->AgentZ -> Option B

4. Quick Check:

   Edges follow data flow direction [OK]

Hint: Edges must follow the exact data flow between agents [OK]

Common Mistakes:

• Reversing edge directions
• Omitting necessary agents or edges
• Assuming edges are optional for workflows