Creating and Using Agents ✅

Agents in Kastrax are intelligent systems that can autonomously decide on a sequence of actions to perform tasks. They have access to tools, memory, and external systems, enabling them to perform complex tasks and interact with various services. Agents can invoke custom functions, utilize third-party APIs through integrations, and access knowledge bases you have built.

Agent Capabilities ✅

Kastrax agents provide several key capabilities:

Multiple Architectures: Choose from adaptive, goal-oriented, hierarchical, reflective, and creative agent architectures
Memory Management: Store and retrieve information across conversations using different memory types
Tool Integration: Use built-in tools or create custom tools for specific tasks with type-safe interfaces
LLM Integration: Work with various LLM providers including DeepSeek, OpenAI, Anthropic, and Google Gemini
RAG Support: Incorporate knowledge from documents and other data sources with advanced retrieval techniques
Actor Model: Build distributed agent systems using the actor model for scalability and resilience
Versioning: Create, manage, and roll back different versions of your agents
State Management: Track and update agent states throughout their lifecycle
Session Management: Organize conversations into sessions with metadata
Performance Monitoring: Collect metrics and analyze agent behavior for optimization

Creating an Agent ✅

To create a basic agent in Kastrax, you use the agent DSL function:


import ai.kastrax.core.agent.agent
import ai.kastrax.integrations.deepseek.deepSeek
import ai.kastrax.integrations.deepseek.DeepSeekModel
import kotlinx.coroutines.runBlocking
 
fun main() = runBlocking {
    val myAgent = agent {
        name("MyFirstAgent")
        description("A helpful assistant that can answer questions")
 
        // Configure the LLM
        model = deepSeek {
            apiKey("your-deepseek-api-key")
            model(DeepSeekModel.DEEPSEEK_CHAT)
            temperature(0.7)
        }
 
        // Add system prompt
        systemPrompt("""
            You are a helpful, friendly assistant. Your goal is to provide accurate and useful information.
            Be concise but thorough in your responses.
        """.trimIndent())
    }
 
    // Use the agent
    val response = myAgent.generate("What is artificial intelligence?")
    println(response.text)
}

Adding Memory ✅

You can add memory to your agent to help it remember past interactions:


import ai.kastrax.core.agent.agent
import ai.kastrax.core.memory.memory
import ai.kastrax.integrations.deepseek.deepSeek
import ai.kastrax.integrations.deepseek.DeepSeekModel
import kotlinx.coroutines.runBlocking
 
fun main() = runBlocking {
    val myAgent = agent {
        name("AgentWithMemory")
        description("An agent that remembers past interactions")
 
        // Configure the LLM
        model = deepSeek {
            apiKey("your-deepseek-api-key")
            model(DeepSeekModel.DEEPSEEK_CHAT)
            temperature(0.7)
        }
 
        // Add memory
        memory = memory {
            workingMemory(true)
            conversationHistory(10)
            semanticMemory(true)
        }
    }
 
    // Use the agent
    val response1 = myAgent.generate("My name is Alice.")
    println(response1.text)
 
    val response2 = myAgent.generate("What's my name?")
    println(response2.text)
}

Adding Tools ✅

You can add tools to your agent to enable it to perform specific actions:


import ai.kastrax.core.agent.agent
import ai.kastrax.core.tools.tool
import ai.kastrax.integrations.deepseek.deepSeek
import ai.kastrax.integrations.deepseek.DeepSeekModel
import kotlinx.coroutines.runBlocking
import java.time.LocalDateTime
import java.time.format.DateTimeFormatter
 
fun main() = runBlocking {
    val myAgent = agent {
        name("AgentWithTools")
        description("An agent that can use tools")
 
        // Configure the LLM
        model = deepSeek {
            apiKey("your-deepseek-api-key")
            model(DeepSeekModel.DEEPSEEK_CHAT)
            temperature(0.7)
        }
 
        // Add tools
        tools {
            tool("getCurrentTime") {
                description("Get the current time")
                parameters {}
                execute {
                    val currentTime = LocalDateTime.now()
                    val formatter = DateTimeFormatter.ofPattern("HH:mm:ss")
                    "The current time is ${currentTime.format(formatter)}"
                }
            }
 
            tool("calculateSum") {
                description("Calculate the sum of two numbers")
                parameters {
                    parameter("a", "First number", Double::class)
                    parameter("b", "Second number", Double::class)
                }
                execute { params ->
                    val a = params["a"] as Double
                    val b = params["b"] as Double
                    "The sum of $a and $b is ${a + b}"
                }
            }
        }
    }
 
    // Use the agent
    val response1 = myAgent.generate("What time is it now?")
    println(response1.text)
 
    val response2 = myAgent.generate("Calculate the sum of 5.2 and 3.8")
    println(response2.text)
}

Using RAG with Agents ✅

You can incorporate Retrieval-Augmented Generation (RAG) into your agents:


import ai.kastrax.core.agent.agent
import ai.kastrax.rag.document.DocumentLoader
import ai.kastrax.rag.document.TextSplitter
import ai.kastrax.rag.embedding.EmbeddingModel
import ai.kastrax.rag.vectorstore.VectorStore
import ai.kastrax.rag.retrieval.Retriever
import ai.kastrax.rag.context.ContextBuilder
import ai.kastrax.integrations.deepseek.deepSeek
import ai.kastrax.integrations.deepseek.DeepSeekModel
import kotlinx.coroutines.runBlocking
 
fun main() = runBlocking {
    // Set up RAG components
    val loader = DocumentLoader.fromFile("knowledge_base.pdf")
    val documents = loader.load()
 
    val splitter = TextSplitter.recursive(chunkSize = 1000, chunkOverlap = 200)
    val chunks = splitter.split(documents)
 
    val embeddingModel = EmbeddingModel.create("all-MiniLM-L6-v2")
    val vectorStore = VectorStore.create("chroma")
    vectorStore.addDocuments(chunks, embeddingModel)
 
    val retriever = Retriever.fromVectorStore(
        vectorStore = vectorStore,
        embeddingModel = embeddingModel,
        topK = 5
    )
 
    val contextBuilder = ContextBuilder.create {
        template("""
            Answer the question based on the following context:
 
            Context:
            {{context}}
 
            Question: {{query}}
 
            Answer:
        """.trimIndent())
    }
 
    // Create a RAG agent
    val ragAgent = agent {
        name("RAGAgent")
        description("An agent with RAG capabilities")
 
        // Configure the LLM
        model = deepSeek {
            apiKey("your-deepseek-api-key")
            model(DeepSeekModel.DEEPSEEK_CHAT)
            temperature(0.7)
        }
 
        // Configure RAG
        rag {
            retriever(retriever)
            contextBuilder(contextBuilder)
            maxTokens(3000)
        }
    }
 
    // Use the RAG agent
    val response = ragAgent.generate("What are the key features of Kastrax?")
    println(response.text)
}

Agent Architectures ✅

Kastrax provides several specialized agent architectures, each designed for different use cases:

Adaptive Agents ✅

Adaptive agents adjust their behavior based on user preferences and feedback:


import ai.kastrax.core.agent.architecture.adaptiveAgent
import ai.kastrax.integrations.deepseek.deepSeek
import ai.kastrax.integrations.deepseek.DeepSeekModel
import kotlinx.coroutines.runBlocking
 
fun main() = runBlocking {
    val adaptiveAgent = adaptiveAgent {
        name("AdaptiveAssistant")
        description("An assistant that adapts to user preferences")
 
        // Configure the LLM
        model = deepSeek {
            apiKey("your-deepseek-api-key")
            model(DeepSeekModel.DEEPSEEK_CHAT)
            temperature(0.7)
        }
 
        // Define user preferences
        preferences {
            // Preference configuration
        }
 
        // Define adaptation strategy
        adaptationStrategy {
            // Adaptation logic
        }
    }
 
    // Use the adaptive agent
    val response = adaptiveAgent.generate("Tell me about quantum computing")
    println(response.text)
}

Goal-Oriented Agents ✅

Goal-oriented agents focus on achieving specific objectives through planning and task execution:


import ai.kastrax.core.agent.architecture.goalOrientedAgent
import ai.kastrax.integrations.deepseek.deepSeek
import ai.kastrax.integrations.deepseek.DeepSeekModel
import kotlinx.coroutines.runBlocking
 
fun main() = runBlocking {
    val goalOrientedAgent = goalOrientedAgent {
        name("ProjectManager")
        description("A goal-oriented agent that helps manage projects")
 
        // Configure the LLM
        model = deepSeek {
            apiKey("your-deepseek-api-key")
            model(DeepSeekModel.DEEPSEEK_CHAT)
            temperature(0.7)
        }
 
        // Define goals and tasks
        goals {
            // Goal configuration
        }
 
        tasks("goal-id") {
            // Task configuration
        }
    }
 
    // Use the goal-oriented agent
    val response = goalOrientedAgent.generate("What's the status of the project?")
    println(response.text)
}

For more detailed information about agent architectures, see the Agent Architectures guide.

Next Steps ✅

Now that you understand the basics of creating and using agents in Kastrax, you can:

Explore different agent architectures
Learn about memory systems
Create custom tools
Build agent workflows