fixup! Updated docs

Author: sdubov

docs/docs/agents/functional-agents.md

@@ -34,14 +34,15 @@ In Kotlin, the most convenient way is to use the `functionalStrategy {...}` DSL
     <!--- INCLUDE
     import ai.koog.agents.core.agent.AIAgent
     import ai.koog.agents.core.agent.functionalStrategy
+    import ai.koog.prompt.message.MessagePart
     import ai.koog.prompt.executor.llms.all.simpleOllamaAIExecutor
     import ai.koog.prompt.executor.ollama.client.OllamaModels
     import kotlinx.coroutines.runBlocking
     -->
     ```kotlin
     val strategy = functionalStrategy<String, String> { input ->
         val response = requestLLM(input)
-        response.asAssistantMessage().content
+        response.parts.filterIsInstance<MessagePart.Text>().joinToString("\n") { it.text }
     }
 
     val mathAgent = AIAgent(
@@ -98,15 +99,20 @@ You can extend the previous strategy to make multiple sequential LLM calls:
 
     <!--- INCLUDE
     import ai.koog.agents.core.agent.functionalStrategy
+    import ai.koog.prompt.message.Message
+    import ai.koog.prompt.message.MessagePart
     -->
     ```kotlin
+    fun Message.Assistant.text(): String =
+        parts.filterIsInstance<MessagePart.Text>().joinToString("\n") { it.text }
+
     val strategy = functionalStrategy<String, String> { input ->
         // The first LLM call produces an initial draft based on the user input
-        val draft = requestLLM("Draft: $input").asAssistantMessage().content
+        val draft = requestLLM("Draft: $input").text()
         // The second LLM call improves the initial draft
-        val improved = requestLLM("Improve and clarify.").asAssistantMessage().content
+        val improved = requestLLM("Improve and clarify.").text()
         // The final LLM call formats the improved text and returns the result
-        requestLLM("Format the result as bold.").asAssistantMessage().content
+        requestLLM("Format the result as bold.").text()
     }
     ```
     <!--- KNIT example-functional-agent-02.kt -->
@@ -181,6 +187,7 @@ Here is what you need to do:
     import ai.koog.agents.core.tools.annotations.LLMDescription
     import ai.koog.agents.core.tools.annotations.Tool
     import ai.koog.agents.core.tools.reflect.ToolSet
+    import ai.koog.prompt.message.MessagePart
     import ai.koog.prompt.executor.llms.all.simpleOllamaAIExecutor
     import ai.koog.prompt.executor.ollama.client.OllamaModels
     import kotlinx.coroutines.runBlocking
@@ -203,20 +210,20 @@ Here is what you need to do:
 
     val strategy = functionalStrategy<String, String> { input ->
         // Send the user input to the LLM
-        var responses = requestLLMMultiple(input)
+        var response = requestLLM(input)
 
         // Only loop while the LLM requests tools
-        while (responses.containsToolCalls()) {
-            // Extract tool calls from the response
-            val pendingCalls = extractToolCalls(responses)
+        var toolCalls = response.parts.filterIsInstance<MessagePart.Tool.Call>()
+        while (toolCalls.isNotEmpty()) {
             // Execute the tools and return the results
-            val results = executeMultipleTools(pendingCalls)
+            val results = executeTools(toolCalls)
             // Send the tool results back to the LLM. The LLM may call more tools or return a final output
-            responses = sendMultipleToolResults(results)
+            response = sendToolResults(results)
+            toolCalls = response.parts.filterIsInstance<MessagePart.Tool.Call>()
         }
 
         // When no tool calls remain, extract and return the assistant message content from the response
-        responses.single().asAssistantMessage().content
+        response.parts.filterIsInstance<MessagePart.Text>().joinToString("\n") { it.text }
     }
 
     val mathAgentWithTools = AIAgent(

docs/docs/agents/graph-based-agents.md

@@ -50,6 +50,7 @@ while the LLM performs the actual content generation within each action.
     import ai.koog.agents.planner.goap
     import ai.koog.agents.planner.goap.GoapAgentState
     import ai.koog.prompt.dsl.prompt
+    import ai.koog.prompt.message.MessagePart
     import ai.koog.prompt.executor.clients.openai.OpenAIModels
     import ai.koog.prompt.executor.llms.all.simpleOpenAIExecutor
     -->
@@ -84,7 +85,7 @@ while the LLM performs the actual content generation within each action.
                 }
                 requestLLM()
             }
-            state.copy(hasOutline = true, outline = response.content)
+            state.copy(hasOutline = true, outline = response.parts.filterIsInstance<MessagePart.Text>().joinToString("\n") { it.text })
         }
 
         action(
@@ -100,7 +101,7 @@ while the LLM performs the actual content generation within each action.
                 }
                 requestLLM()
             }
-            state.copy(hasDraft = true, draft = response.content)
+            state.copy(hasDraft = true, draft = response.parts.filterIsInstance<MessagePart.Text>().joinToString("\n") { it.text })
         }
 
         action(
@@ -116,7 +117,7 @@ while the LLM performs the actual content generation within each action.
                 }
                 requestLLM()
             }
-            println("Review feedback: ${response.content}")
+            println("Review feedback: ${response.parts.filterIsInstance<MessagePart.Text>().joinToString("\n") { it.text }}")
             state.copy(hasReview = true)
         }
 
@@ -168,6 +169,8 @@ while the LLM performs the actual content generation within each action.
     import ai.koog.agents.planner.goap.GoapAgentState;
     import ai.koog.prompt.executor.clients.openai.OpenAIModels;
     import ai.koog.prompt.executor.model.PromptExecutor;
+    import ai.koog.prompt.message.MessagePart;
+    import java.util.stream.Collectors;
     class exampleGoapAgents01 {
     -->
     <!--- SUFFIX
@@ -227,7 +230,10 @@ while the LLM performs the actual content generation within each action.
                             prompt.user("Create a detailed outline for an article about: " + state.topic);
                             return null;
                         });
-                        return session.requestLLM().getContent();
+                        return session.requestLLM().getParts().stream()
+                            .filter(p -> p instanceof MessagePart.Text)
+                            .map(p -> ((MessagePart.Text) p).getText())
+                            .collect(Collectors.joining());
                     });
                     return state.copy(true, response, state.hasDraft, state.draft,
                                     state.hasReview, state.isPublished);
@@ -243,7 +249,10 @@ while the LLM performs the actual content generation within each action.
                             prompt.user("Write an article based on this outline:\n" + state.outline);
                             return null;
                         });
-                        return session.requestLLM().getContent();
+                        return session.requestLLM().getParts().stream()
+                            .filter(p -> p instanceof MessagePart.Text)
+                            .map(p -> ((MessagePart.Text) p).getText())
+                            .collect(Collectors.joining());
                     });
                     return state.copy(state.hasOutline, state.outline, true, response,
                                     state.hasReview, state.isPublished);
@@ -260,7 +269,10 @@ while the LLM performs the actual content generation within each action.
                             prompt.user("Review this article and suggest improvements:\n" + state.draft);
                             return null;
                         });
-                        return session.requestLLM().getContent();
+                        return session.requestLLM().getParts().stream()
+                            .filter(p -> p instanceof MessagePart.Text)
+                            .map(p -> ((MessagePart.Text) p).getText())
+                            .collect(Collectors.joining());
                     });
                     System.out.println("Review feedback: " + response);
                     return state.copy(state.hasOutline, state.outline, state.hasDraft,

docs/docs/agents/planner-agents/goap-agents.md

@@ -506,6 +506,7 @@ use pre-built factory methods like `AIAgentNode.llmRequest()` that handle prompt
     <!--- INCLUDE
     import ai.koog.agents.core.dsl.builder.strategy
     import ai.koog.agents.core.dsl.builder.node
+    import ai.koog.prompt.message.MessagePart
     val strategy = strategy<String, String>("strategy_name") {
     -->
     <!--- SUFFIX
@@ -519,7 +520,7 @@ use pre-built factory methods like `AIAgentNode.llmRequest()` that handle prompt
             }
 
             val response = requestLLMWithoutTools()
-            response.content
+            response.parts.filterIsInstance<MessagePart.Text>().joinToString("\n") { it.text }
         }
     }
     ```
@@ -530,6 +531,8 @@ use pre-built factory methods like `AIAgentNode.llmRequest()` that handle prompt
     <!--- INCLUDE
     import ai.koog.agents.core.agent.entity.AIAgentNode;
     import ai.koog.prompt.message.Message;
+    import ai.koog.prompt.message.MessagePart;
+    import java.util.stream.Collectors;
     class exampleCustomNodesJava10 {
         public static void main(String[] args) {
     -->
@@ -542,13 +545,16 @@ use pre-built factory methods like `AIAgentNode.llmRequest()` that handle prompt
     // AIAgentNode.llmRequest() creates a node that sends the input string as a user
     // message to the LLM and returns the response. The prompt text is provided as
     // the node's input when it is executed in the graph.
-    var summarizeTextNode = AIAgentNode.llmRequest(true, "node_name");
+    var summarizeTextNode = AIAgentNode.llmRequest("node_name");
 
     // To extract the text content from the LLM response, chain a separate node:
     var extractContent = AIAgentNode.builder("extract-content")
-        .withInput(Message.Response.class)
+        .withInput(Message.Assistant.class)
         .withOutput(String.class)
-        .withAction((response, ctx) -> response.getContent())
+        .withAction((response, ctx) -> response.getParts().stream()
+            .filter(p -> p instanceof MessagePart.Text)
+            .map(p -> ((MessagePart.Text) p).getText())
+            .collect(Collectors.joining()))
         .build();
     ```
     <!--- KNIT exampleCustomNodesJava10.java -->
@@ -566,9 +572,7 @@ typically use subgraphs that delegate tool orchestration to the LLM.
     <!--- INCLUDE
     import ai.koog.agents.core.dsl.builder.strategy
     import ai.koog.agents.core.dsl.builder.node
-    import ai.koog.prompt.message.Message
-    import ai.koog.prompt.message.ResponseMetaInfo
-    import ai.koog.utils.time.KoogClock
+    import ai.koog.prompt.message.MessagePart
     import kotlinx.serialization.Serializable
     import kotlinx.serialization.json.Json
     import java.util.*
@@ -585,12 +589,11 @@ typically use subgraphs that delegate tool orchestration to the LLM.
         val toolCall = MessagePart.Tool.Call(
             id = UUID.randomUUID().toString(),
             tool = toolName,
-            metaInfo = ResponseMetaInfo.create(KoogClock.System),
-            content = Json.encodeToString(ToolArgs(arg1 = input, arg2 = 42)) // Use the input as tool arguments
+            args = Json.encodeToString(ToolArgs(arg1 = input, arg2 = 42)) // Use the input as tool arguments
         )
 
         val result = environment.executeTool(toolCall)
-        result.content
+        result.output
     }
     ```
     <!--- KNIT example-custom-nodes-11.kt -->