Author Strategy Skill

Process steps in order. Do not skip ahead.

Step 1 — Pick Input/Output Types

Ask the user, one question at a time:

• What's the strategy's input type? String (default for chat-shaped agents), Message.User (when you control the message shape upstream), or a typed app object?
• What's the output type? String is the common default; typed outputs require matching node types

Proceed immediately to Step 2.

Step 2 — Sketch the Topology Before Writing Code

Before opening the editor, write the topology as a numbered list:

• Each step: what it does, what type it produces
• Each branch: predicate, target
• Each loop: back-edge condition

If you can't name every step and edge in plain English, you don't yet know the strategy — go back and clarify with the user. The DSL captures the topology; if the topology is fuzzy, the DSL output will be too.

Proceed immediately to Step 3.

Step 3 — Choose Node Variants by Input Type

This is the most common source of bugs. Match the node family to its expected input:

• String-input nodes (use at the top of a strategy, or after another node returns String):

  • nodeLLMRequest() — call the LLM with the current message as a user turn
  • nodeLLMRequestOnlyCallingTools() — like above but the LLM cannot reply with text, only tool calls
  • nodeLLMRequestWithoutTools() — text-only reply
  • nodeLLMRequestForceOneTool(tool) — force a specific tool call (covered in use-llm-node-variants)
  • nodeLLMRequestStructured() — typed JSON output (covered in add-structured-output)
  • nodeLLMRequestMultipleChoices() / nodeLLMRequestStreaming() — covered in use-llm-node-variants
  • nodeLLMModerateText() — content moderation

• Message.User-input nodes (use mid-graph where the previous node produced a Message.User):

  • nodeLLMSendMessage() / variants
  • nodeLLMSendToolResults() / variants — pair with nodeExecuteTools
  • nodeLLMModerateMessage()

Picking the wrong family produces compile-time type errors that read like generic DSL noise. The fix is almost always swapping Request for SendMessage or vice versa.

Proceed immediately to Step 4.

Step 4 — Chain nodeExecuteTools Explicitly

In 1.0, nodeExecuteTools() returns ReceivedToolResults directly — the auto-writeback variant from earlier versions is gone. Always:

val nodeCallLLM by nodeLLMRequest()
val nodeExecuteTool by nodeExecuteTools()
val nodeSendToolResult by nodeLLMSendToolResults()

edge(nodeStart forwardTo nodeCallLLM)
edge(nodeCallLLM forwardTo nodeExecuteTool onToolCalls { true })
edge(nodeCallLLM forwardTo nodeFinish onTextMessage { true })
edge(nodeExecuteTool forwardTo nodeSendToolResult)
edge(nodeSendToolResult forwardTo nodeExecuteTool onToolCalls { true })
edge(nodeSendToolResult forwardTo nodeFinish onTextMessage { true })

Without the nodeExecuteTool forwardTo nodeSendToolResult edge, tool outputs never reach the LLM and the agent silently loses memory of what it ran.

Proceed immediately to Step 5.

Step 5 — Build Edges with the Infix Vocabulary

Edges declare topology and branching. Vocabulary:

• edge(a forwardTo b) — unconditional
• edge(a forwardTo b onToolCalls { predicate(it) }) — when the LLM produced tool calls
• edge(a forwardTo b onTextMessage { predicate(it) }) — when the LLM produced a text reply
• edge(a forwardTo b onCondition { boolean predicate(it) }) — arbitrary predicate
• edge(a forwardTo b onIsInstance SomeClass::class) — type-discriminated branching
• edge(a forwardTo b transformed { reshape(it) }) — change shape between nodes
• a then b — shorthand for an unconditional sequential edge

Member vs extension — which imports you need. forwardTo, onCondition, and transformed are infix members (no import). The other primitives (onToolCalls, onTextMessage, onIsInstance, onMessageParts, onSuccessful, onFailure, asUserMessage, asToolResultMessage) are top-level extensions in ai.koog.agents.core.dsl.extension.* and need explicit imports. Full table:

skills/author-strategy/references/edge-primitive-imports.md

Branching logic belongs in edge predicates, not inside node bodies. Lifting it to edges keeps the topology inspectable.

Proceed immediately to Step 6.

Step 6 — Lift Steps into Subgraphs

If a step needs several LLM calls, its own tool subset, or its own model, lift it into a subgraph. Reference: examples/simple-examples/.../subgraphwithtask/CustomStrategy.kt.

import ai.koog.agents.ext.agent.subgraphWithTask
import ai.koog.agents.ext.agent.subgraphWithVerification
import ai.koog.agents.ext.agent.CriticResult

val generate by subgraphWithTask<Unit, String>(generateTools, llmModel = OpenAIModels.Chat.GPT4o) { input ->
    "system prompt for the generation step..."
}

val verify by subgraphWithVerification(tools = verifyTools, parallelTools = false) { input: String ->
    "system prompt for verifying the generated result..."
}

val fix by subgraphWithTask<CriticResult<String>, String>(tools = fixTools, llmModel = AnthropicModels.Opus_4_6) { vr ->
    "system prompt that incorporates ${vr.feedback}..."
}

edge(verify forwardTo fix onCondition { !it.successful })
edge(verify forwardTo nodeFinish onCondition { it.successful } transformed { "Project is correct." })
edge(fix forwardTo verify)

The generate → verify → fix shape is the canonical "real agentic workflow" demo — it shows iterative correction without inventing a planner.

subgraphWithTask, subgraphWithVerification, and CriticResult live in package ai.koog.agents.ext.agent but ship inside the agents-core artifact, which the koog-agents umbrella already pulls. No extra dependency needed — the standalone ai.koog:agents-ext artifact is a separate 1.0.0-beta module and is NOT required for these APIs.

If you type-annotate the returned strategy, the strategy type lives in ai.koog.agents.core.agent.entity.AIAgentGraphStrategy (not bare ai.koog.agents.core.agent).

Proceed immediately to Step 7.

Step 7 — Name Nodes by Behavior

val classify by nodeLLMRequest() reads as edge(classify forwardTo ...). Names like node1, step1, llm erase what the strategy is doing.

Proceed immediately to Step 8.

Step 8 — Hand Off

• Write the strategy definition and the modified agent construction to disk with explicit Path: labels (same convention as scaffold-agent):
  • Path: src/main/kotlin/com/example/Strategy.kt — strategy definition with the graph DSL
  • Path: src/main/kotlin/com/example/Main.kt — modified agent construction passing strategy = ...
  • Create files if they don't exist. Do not respond with prose only
• Pass the strategy into AIAgent(strategy = myStrategy("name"), ...). If the strategy isn't singleRunStrategy (the default), the strategy = parameter must be named explicitly
• Run ./gradlew build to confirm it compiles. The most common failure is a node-type mismatch — go back to Step 3
• If the strategy needs runtime-decided ordering rather than compile-time topology, this is the wrong primitive — invoke Skill(skill: "use-planner") instead

Finish here.