graph-based-agents

Graph-based agents

With graph-based agents, you model the behavior as an explicit state machine: nodes of a graph strategy represent actions (LLM calls, tool execution) and edges represent data flow between nodes.

The main advantages of graph-based agents are:

• Easy to visualize
• State persistence
• Composable architecture

??? note "Prerequisites"

--8<-- "quickstart-snippets.md:prerequisites"

--8<-- "quickstart-snippets.md:dependencies"

--8<-- "quickstart-snippets.md:api-key"

Examples on this page assume that you are running Llama 3.2 locally via Ollama.

This page describes how to re-create the strategy graph used by basic agents. It sends a request to an LLM and then either outputs the response (if the LLM responded with an assistant message) or executes a tool (if the LLM requested a tool call). In case of a tool call, the agent sends the tool result to the LLM and then either outputs the response or executes a tool.

Here is an illustration of the strategy graph:

---
config:
  flowchart:
    defaultRenderer: "elk"
---
graph TB
    subgraph nodeStart
        Input
    end
    
    subgraph nodeFinish
        Output
    end
    
    subgraph nodeSendInput
        llmRequest(Request LLM)
    end
    
    subgraph nodeExecuteTool
        executeTool(Execute tool call)
    end
    
    subgraph nodeSendToolResult
        sendToolResult(Request LLM)
    end
    
    Input --String--> llmRequest
    llmRequest --Message.Assistant--> onToolCalls{{onToolCalls}}
    llmRequest --Message.Assistant--> onTextMessage{{onTextMessage}}
    onTextMessage --String--> Output
    onToolCalls --ToolCalls--> executeTool --ReceivedToolResults--> sendToolResult
    sendToolResult --Message.Assistant--> onToolCalls
    sendToolResult --Message.Assistant--> onTextMessage

Build a strategy graph

In Koog, you implement a strategy using AIAgentGraphStrategyBuilder. Just like every node has an input and output type, the strategy as a whole also defines some input and output type. This example assumes that the input and output types are strings, which means the agent implementing this strategy will expect a string and return a string.