Empowers developers to create and manage AI agents with a streamlined DSL, offering error handling, logging, and integration with the ecosystem to transition from PoC to production.
Visit aigentic.io for a complete overview.
Aigentic is a Kotlin Multiplatform library that provides a powerful DSL for building and integrating AI agents into applications. It streamlines the process of creating, deploying, and managing LLM agents within your software ecosystem.
Create agents in a fully type-safe way:
@AigenticParameter
data class WeatherRequest(val location: String)
@AigenticParameter
data class WeatherResponse(
val temperature: String,
val conditions: String,
val location: String
)
val agent = agent<String, WeatherResponse> {
openAIModel {
apiKey("YOUR_API_KEY")
modelIdentifier(OpenAIModelIdentifier.GPT4O)
}
task("Provide weather information") {
addInstruction("You are a helpful weather assistant")
addInstruction("Use the getWeather tool to fetch current weather conditions")
}
// Add a weather lookup tool
addTool("getWeather", "Get current weather for a location") { req: WeatherRequest ->
WeatherResponse(
temperature = "22°C",
conditions = "Partly cloudy",
location = req.location
)
}
}
val run = agent.start("What's the weather like in Amsterdam?")For complete documentation, examples, and guides, visit aigentic.io
Explore ready-to-use examples in the Aigentic Initializr repository or check our example projects.
To use SNAPSHOT versions, add the Sonatype snapshot repository:
repositories {
mavenCentral()
maven { url = uri("https://central.sonatype.com/repository/maven-snapshots/") }
}./gradlew buildAigentic is released under the MIT License. See LICENSE for details.
For questions, issues, or feature requests:
Visit aigentic.io for a complete overview.
Aigentic is a Kotlin Multiplatform library that provides a powerful DSL for building and integrating AI agents into applications. It streamlines the process of creating, deploying, and managing LLM agents within your software ecosystem.
Create agents in a fully type-safe way:
@AigenticParameter
data class WeatherRequest(val location: String)
@AigenticParameter
data class WeatherResponse(
val temperature: String,
val conditions: String,
val location: String
)
val agent = agent<String, WeatherResponse> {
openAIModel {
apiKey("YOUR_API_KEY")
modelIdentifier(OpenAIModelIdentifier.GPT4O)
}
task("Provide weather information") {
addInstruction("You are a helpful weather assistant")
addInstruction("Use the getWeather tool to fetch current weather conditions")
}
// Add a weather lookup tool
addTool("getWeather", "Get current weather for a location") { req: WeatherRequest ->
WeatherResponse(
temperature = "22°C",
conditions = "Partly cloudy",
location = req.location
)
}
}
val run = agent.start("What's the weather like in Amsterdam?")For complete documentation, examples, and guides, visit aigentic.io
Explore ready-to-use examples in the Aigentic Initializr repository or check our example projects.
To use SNAPSHOT versions, add the Sonatype snapshot repository:
repositories {
mavenCentral()
maven { url = uri("https://central.sonatype.com/repository/maven-snapshots/") }
}./gradlew buildAigentic is released under the MIT License. See LICENSE for details.
For questions, issues, or feature requests: