agents-features-snapshot
Provides checkpoint functionality for AI agents, allowing saving and restoring agent state at specific points during execution.
Overview
The agents-features-snapshot module enables checkpoint capabilities for AI agents in the Koog framework, allowing you to:
Resume agent execution from a specific point
Roll back to previous states
Persist agent state across sessions
Implement continuous persistence of agent state
Key features include:
Checkpoint creation and management
Multiple storage providers (in-memory, file-based)
Automatic checkpoint creation after each node execution (optional)
Complete state capture including message history, current node, and input data
Using in your project
To use the checkpoint feature in your project, add the following dependency:
dependencies {
implementation("ai.koog.agents:agents-features-snapshot:$version")
}Then, install the AgentCheckpoint feature when creating your agent:
val agent = AIAgent(
// other configuration parameters
) {
install(AgentCheckpoint) {
// Configure the storage provider
snapshotProvider(InMemoryAgentCheckpointStorageProvider())
// Optional: enable automatic checkpoint creation after each node
continuouslyPersistent()
}
}Using in tests
For testing agents with checkpoint capabilities, you can use an in-memory storage provider:
// Create a test agent with checkpoint capability
val testAgent = AIAgent(
// other test configuration
) {
install(AgentCheckpoint) {
// Use in-memory storage for testing
snapshotProvider(InMemoryAgentCheckpointStorageProvider())
}
// Enable testing mode
withTesting()
}Example of usage
Here's an example of using checkpoints to save and restore agent state:
// Create an agent with checkpoint capability
val agent = AIAgent(
executor = simpleOpenAIExecutor(apiKey),
llmModel = OpenAIModels.Chat.GPT4o,
systemPrompt = "You are a helpful assistant.",
installFeatures = {
install(AgentCheckpoint) {
// Use file-based storage for persistence
snapshotProvider(FileAgentCheckpointStorageProvider(
rootPath = Path("checkpoints")
))
}
}
)
// Run the agent and create a checkpoint
val result = agent.run("Tell me about AI agents") { context ->
// Create a checkpoint at this point
val checkpoint = context.persistency().createCheckpoint(
agentContext = context,
nodeId = context.currentNodeId ?: "unknown",
lastInput = "Tell me about AI agents",
lastInputType = typeOf<String>()
)
// Store the checkpoint ID for later use
val checkpointId = checkpoint?.checkpointId
println("Created checkpoint: $checkpointId")
}
// Later, restore from the checkpoint
agent.run("Continue from where we left off") { context ->
// Roll back to the latest checkpoint
val restoredCheckpoint = context.persistency().rollbackToLatestCheckpoint(context)
if (restoredCheckpoint != null) {
println("Restored from checkpoint: ${restoredCheckpoint.checkpointId}")
} else {
println("No checkpoint found")
}
}This example demonstrates creating an agent with checkpoint capability, creating a checkpoint during execution, and later restoring from that checkpoint to continue the conversation.
Provides checkpoint functionality for AI agents, allowing saving and restoring agent state at specific points during execution.
Overview
The agents-features-snapshot module enables checkpoint capabilities for AI agents in the Koog framework, allowing you to:
Resume agent execution from a specific point
Roll back to previous states
Persist agent state across sessions
Implement continuous persistence of agent state
Key features include:
Checkpoint creation and management
Multiple storage providers (in-memory, file-based)
Automatic checkpoint creation after each node execution (optional)
Complete state capture including message history, current node, and input data
Using in your project
To use the checkpoint feature in your project, add the following dependency:
dependencies {
implementation("ai.koog.agents:agents-features-snapshot:$version")
}Then, install the AgentCheckpoint feature when creating your agent:
val agent = AIAgent(
// other configuration parameters
) {
install(AgentCheckpoint) {
// Configure the storage provider
snapshotProvider(InMemoryAgentCheckpointStorageProvider())
// Optional: enable automatic checkpoint creation after each node
continuouslyPersistent()
}
}Using in tests
For testing agents with checkpoint capabilities, you can use an in-memory storage provider:
// Create a test agent with checkpoint capability
val testAgent = AIAgent(
// other test configuration
) {
install(AgentCheckpoint) {
// Use in-memory storage for testing
snapshotProvider(InMemoryAgentCheckpointStorageProvider())
}
// Enable testing mode
withTesting()
}Example of usage
Here's an example of using checkpoints to save and restore agent state:
// Create an agent with checkpoint capability
val agent = AIAgent(
executor = simpleOpenAIExecutor(apiKey),
llmModel = OpenAIModels.Chat.GPT4o,
systemPrompt = "You are a helpful assistant.",
installFeatures = {
install(AgentCheckpoint) {
// Use file-based storage for persistence
snapshotProvider(FileAgentCheckpointStorageProvider(
rootPath = Path("checkpoints")
))
}
}
)
// Run the agent and create a checkpoint
val result = agent.run("Tell me about AI agents") { context ->
// Create a checkpoint at this point
val checkpoint = context.persistency().createCheckpoint(
agentContext = context,
nodeId = context.currentNodeId ?: "unknown",
lastInput = "Tell me about AI agents",
lastInputType = typeOf<String>()
)
// Store the checkpoint ID for later use
val checkpointId = checkpoint?.checkpointId
println("Created checkpoint: $checkpointId")
}
// Later, restore from the checkpoint
agent.run("Continue from where we left off") { context ->
// Roll back to the latest checkpoint
val restoredCheckpoint = context.persistency().rollbackToLatestCheckpoint(context)
if (restoredCheckpoint != null) {
println("Restored from checkpoint: ${restoredCheckpoint.checkpointId}")
} else {
println("No checkpoint found")
}
}This example demonstrates creating an agent with checkpoint capability, creating a checkpoint during execution, and later restoring from that checkpoint to continue the conversation.