API Quick Reference

API Quick Reference

Compact tables covering every DSL function in Agents.KT. For in-depth explanations, follow the links to dedicated wiki articles.

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Agent DSL

Defined inside agent<IN, OUT>("name") { ... }:

Function	Signature	Description
prompt	prompt(text: String)	Sets the system prompt sent to the LLM before every agentic call.
model	model { ... }	Configures the LLM backend. See Model Config.
budget	budget { ... }	Sets the agentic loop turn limit. See Budget Config.
tools	tools { ... }	Registers callable tools the LLM can invoke. See Tools.
skills	skills { ... }	Declares the agent's capabilities. At least one skill must produce OUT.
memory	memory(bank: MemoryBank)	Attaches a MemoryBank and auto-injects memory_read, memory_write, memory_search tools.
skillSelection	skillSelection { input -> "skillName" }	Predicate-based skill routing. Runs before LLM routing. See Skill Selection.
onToolUse	onToolUse { name, args, result -> }	Callback fired after every action tool execution.
onKnowledgeUsed	onKnowledgeUsed { name, content -> }	Callback fired when the LLM fetches a knowledge entry.
onSkillChosen	onSkillChosen { name -> }	Callback fired when the agent selects a skill (any routing strategy).

Invocation

val agent = agent<String, String>("myAgent") { /* ... */ }
val result: String = agent("input")  // operator fun invoke(input: IN): OUT

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Skills DSL

Defined inside skills { ... }:

Function	Signature	Description
skill	skill<IN, OUT>("name", "desc") { ... }	Declares a skill inline within the agent.
+ (unaryPlus)	+preDefinedSkill	Adds a pre-defined Skill<IN, OUT> instance to the agent.

Skill Configuration

Defined inside skill<IN, OUT>("name", "desc") { ... }:

Function	Signature	Description
implementedBy	implementedBy { input -> output }	Marks skill as pure Kotlin. No LLM involved.
tools	tools(vararg names: String)	Marks skill as agentic (LLM-driven). Listed tool names become available to the LLM.
knowledge	knowledge("key", "desc") { provider }	Registers a lazy context provider. Loaded on demand in agentic skills, eagerly in non-agentic.
transformOutput	transformOutput { llmString -> parsedType }	Transforms the raw LLM text response into the skill's OUT type.
llmDescription	llmDescription("override text")	Overrides the auto-generated toLlmDescription() markdown.

Skill Introspection

Method	Returns	Description
skill.toLlmDescription()	String	Auto-generated markdown: name, types, description, knowledge index.
skill.toLlmContext()	String	Full context: toLlmDescription() + all knowledge content.
skill.knowledgeTools()	List<KnowledgeTool>	Knowledge entries as callable tools for lazy LLM loading.
skill.execute(input)	OUT	Directly executes the skill's implementedBy lambda.
skill(input)	OUT	Alias for execute(input) via operator fun invoke.

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Model Config

Defined inside model { ... }:

Property/Function	Type	Default	Description
ollama(name)	fun	--	Sets the model name and provider to Ollama.
host	String	"localhost"	Ollama server hostname.
port	Int	11434	Ollama server port.
temperature	Double	0.7	Sampling temperature.
client	ModelClient?	null	Custom ModelClient implementation (overrides Ollama). Used for testing.

model { ollama("qwen2.5:7b"); host = "localhost"; port = 11434; temperature = 0.7 }

ModelClient Interface

fun interface ModelClient {
    fun chat(messages: List<LlmMessage>): LlmResponse
}

Create a mock for testing:

val mock = ModelClient { messages -> LlmResponse.Text("mocked response") }
model { ollama("any"); client = mock }

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Budget Config

Defined inside budget { ... }:

Property	Type	Default	Description
maxTurns	Int	Int.MAX_VALUE	Maximum agentic loop iterations. Throws BudgetExceededException when exceeded.

budget { maxTurns = 10 }

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Tools

Defined inside tools { ... }:

Function	Signature	Description
tool	tool("name", "desc") { args -> result }	Registers a tool. args is Map<String, Any?>. Return value is sent back to the LLM.

tools {
    tool("add", "Add two numbers. Args: a, b") { args ->
        (args["a"] as Number).toDouble() + (args["b"] as Number).toDouble()
    }
}

Auto-Injected Memory Tools

When memory(bank) is called, these tools are auto-registered:

Tool Name	Arguments	Returns	Description
memory_read	--	Full memory content	Reads the agent's memory bank.
memory_write	content: String	"ok"	Overwrites the agent's memory.
memory_search	query: String	Matching lines	Case-insensitive line search.

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Composition Operators

All composition results are callable via operator fun invoke. See Type Algebra for full overload table.

Operator	Syntax	Result Type	Description
Pipeline	A then B	Pipeline<A.IN, B.OUT>	Sequential execution. Requires A.OUT == B.IN.
Parallel	A / B	Parallel<A.IN, A.OUT>	Concurrent fan-out. Both agents must share <IN, OUT>. Returns List<OUT>.
Forum	A * B	Forum<A.IN, B.OUT>	Multi-agent deliberation.
Loop	A.loop { out -> nextInput? }	Loop<A.IN, A.OUT>	Feedback loop. Returns null to stop, IN to continue.
Branch	A.branch { on<V>() then handler }	Branch<A.IN, handler.OUT>	Conditional routing on sealed type variants.

import agents_engine.composition.pipeline.then
import agents_engine.composition.parallel.div
import agents_engine.composition.forum.times
import agents_engine.composition.loop.loop
import agents_engine.composition.branch.branch

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Generation API

See Generable and Guide for detailed usage.

Annotations

Annotation	Target	Description
@Generable("desc")	Class	Marks a data class or sealed interface as an LLM generation target.
@Guide("desc")	Constructor param, sealed subclass	Per-field or per-variant guidance for the LLM.
@LlmDescription("text")	Class	Overrides auto-generated toLlmDescription() verbatim.

KClass Extension Functions

Function	Returns	Description
KClass<*>.toLlmDescription()	String	Markdown description with fields, types, and @Guide texts.
KClass<*>.jsonSchema()	String	JSON Schema string for constrained decoding.
KClass<*>.promptFragment()	String	Natural-language prompt fragment with JSON template.
KClass<T>.fromLlmOutput(json)	T?	Lenient deserialization. Handles markdown fences, trailing commas. Returns null on failure.

PartiallyGenerated

Method	Description
PartiallyGenerated.empty<T>()	Creates an empty partial instance.
partial.withField("name", value)	Returns a new partial with the field set.
partial.toComplete()	Attempts full construction. Returns T?.
partial.has("name")	Checks if a field has arrived.
partial["name"]	Gets a field value (or null).
partial.arrivedFieldNames	Set<String> of fields received so far.

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MemoryBank

See Agent Memory for detailed usage.

Method	Signature	Description
Constructor	MemoryBank(maxLines: Int = Int.MAX_VALUE)	Creates a bank with optional line cap.
read	read(key: String): String	Returns stored content or empty string.
write	write(key: String, content: String)	Writes content, auto-truncating to maxLines.
entries	entries(): Map<String, String>	Returns a snapshot of all key-value pairs.

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Error Recovery DSL

See Tool Error Recovery for detailed usage.

ToolError Types

Type	Fields	Description
ToolError.InvalidArgs	rawArgs, parseError, expectedSchema	Malformed tool call arguments.
ToolError.DeserializationError	rawValue, targetType, cause	Type coercion failure.
ToolError.ExecutionError	args, cause	Runtime error during tool execution.
ToolError.EscalationError	source, reason, severity, originalError, attempts	Repair agent escalated the error.

Severity Levels

LOW | MEDIUM | HIGH | CRITICAL

onError DSL

onError {
    invalidArgs { rawArgs, error ->
        fix { rawArgs.replace(",}", "}") }          // deterministic fix
        fix(agent = jsonFixer, retries = 3)           // LLM-driven repair
    }
    deserializationError { rawValue, error ->
        sanitize { rawValue.replace("\\", "/") }
    }
    executionError { cause ->
        retry(maxAttempts = 3, backoff = exponential())
    }
}

Tool-Level Defaults

tools {
    defaults {
        onError { invalidArgs { _, _ -> fix(agent = jsonFixer, retries = 3) } }
    }
    tool("write_file") { /* inherits defaults */ }
    tool("compile")    { onError { /* override */ } }
}

Built-in Repair Tools

Tool	Description
escalate()	Soft failure -- parent agent decides what to do.
throwException()	Hard failure -- propagates through the pipeline.

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LLM Message Types

Type	Description
LlmMessage(role, content, toolCalls?)	A message in the conversation. Roles: "system", "user", "assistant", "tool".
LlmResponse.Text(content)	LLM returned a text response.
LlmResponse.ToolCalls(calls)	LLM returned one or more tool calls.
ToolCall(name, arguments)	A single tool invocation. arguments is Map<String, Any?>.

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See also: Type Algebra | Glossary | Best Practices | Cookbook | Troubleshooting