Multi-agent graphs in LangChain - Step-by-Step Execution

Concept Flow - Multi-agent graphs

Initialize Agents

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Create Nodes for Agents

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Define Edges (Relationships)

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Build Graph Structure

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Agents Communicate via Graph

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Process Queries or Tasks

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Update Graph or Agent States

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Repeat or Terminate

This flow shows how multiple agents are represented as nodes, connected by edges to form a graph, enabling communication and task processing.

Execution Sample

LangChain

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

agents = [Agent(name='A'), Agent(name='B')]
graph = MultiAgentGraph(agents)
graph.add_edge('A', 'B')
result = graph.communicate('A', 'B', 'Hello')

This code creates two agents, connects them in a graph, and sends a message from agent A to B.

Execution Table

Step	Operation	Graph Nodes	Edges	Communication	Result
1	Initialize agents A and B	['A', 'B']	[]	null	Agents created
2	Create MultiAgentGraph with agents	['A', 'B']	[]	null	Graph initialized
3	Add edge from A to B	['A', 'B']	[('A', 'B')]	null	Edge added
4	Agent A sends 'Hello' to B	['A', 'B']	[('A', 'B')]	A -> B: 'Hello'	Message delivered
5	Process message at B	['A', 'B']	[('A', 'B')]	B receives 'Hello'	Response generated
6	Return communication result	['A', 'B']	[('A', 'B')]	Communication complete	Response returned
7	End of communication	['A', 'B']	[('A', 'B')]	null	Process terminates

💡 Communication completes after message is delivered and response is generated.

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	Final
agents	[]	['A', 'B']	['A', 'B']	['A', 'B']	['A', 'B']
graph.nodes	[]	['A', 'B']	['A', 'B']	['A', 'B']	['A', 'B']
graph.edges	[]	[]	[('A', 'B')]	[('A', 'B')]	[('A', 'B')]
communication	null	null	null	A -> B: 'Hello'	Response returned

Key Moments - 3 Insights

Why do we add edges after creating agents?

Edges represent relationships or communication paths between agents. Without edges, agents exist but cannot interact, as shown in execution_table step 3.

How does the message travel from one agent to another?

The message travels along the edge connecting the agents. In step 4, agent A sends 'Hello' to B via the edge ('A', 'B'), enabling communication.

What happens if there is no edge between agents?

Without an edge, agents cannot communicate directly. The graph structure must have edges to allow message passing, as seen in step 3 where the edge is added before communication.

Visual Quiz - 3 Questions

Look at the execution table, what is the state of graph.edges after step 3?

A []

B [('A', 'B')]

C [('B', 'A')]

D ['A', 'B']

💡 Hint

Check the 'Edges' column in row with Step 3.

At which step does agent B receive the message 'Hello'?

A Step 5

B Step 3

C Step 4

D Step 6

💡 Hint

Look at the 'Communication' column describing message reception.

If we remove the edge addition (step 3), what happens when A tries to send 'Hello' to B?

A Message is delivered successfully

B Message is sent but ignored

C Graph raises an error or message fails

D Agents create a new edge automatically

💡 Hint

Edges define communication paths; without them, messages cannot be routed.

Concept Snapshot

Multi-agent graphs represent agents as nodes connected by edges.
Edges define communication paths.
Agents send messages along edges.
Graph updates as agents interact.
Useful for coordinating multiple agents in LangChain.

Full Transcript

Multi-agent graphs in LangChain involve creating agents as nodes and connecting them with edges to form a graph. This graph allows agents to communicate by sending messages along edges. The process starts by initializing agents, then building the graph, adding edges, and finally enabling communication. Each step updates the graph's state, allowing message passing and response generation. Without edges, agents cannot communicate. This structure helps coordinate multiple agents working together.