diff --git a/packages/components/nodes/agents/ConversationalRetrievalToolAgent/ConversationalRetrievalToolAgent.ts b/packages/components/nodes/agents/ConversationalRetrievalToolAgent/ConversationalRetrievalToolAgent.ts
index a850db307..7a8966e14 100644
--- a/packages/components/nodes/agents/ConversationalRetrievalToolAgent/ConversationalRetrievalToolAgent.ts
+++ b/packages/components/nodes/agents/ConversationalRetrievalToolAgent/ConversationalRetrievalToolAgent.ts
@@ -5,7 +5,7 @@ import { RunnableSequence } from '@langchain/core/runnables'
 import { BaseChatModel } from '@langchain/core/language_models/chat_models'
 import { ChatPromptTemplate, MessagesPlaceholder, HumanMessagePromptTemplate, PromptTemplate } from '@langchain/core/prompts'
 import { formatToOpenAIToolMessages } from 'langchain/agents/format_scratchpad/openai_tools'
-import { getBaseClasses, transformBracesWithColon } from '../../../src/utils'
+import { getBaseClasses, transformBracesWithColon, convertChatHistoryToText, convertBaseMessagetoIMessage } from '../../../src/utils'
 import { type ToolsAgentStep } from 'langchain/agents/openai/output_parser'
 import {
     FlowiseMemory,
@@ -23,8 +23,10 @@ import { Moderation, checkInputs, streamResponse } from '../../moderation/Modera
 import { formatResponse } from '../../outputparsers/OutputParserHelpers'
 import type { Document } from '@langchain/core/documents'
 import { BaseRetriever } from '@langchain/core/retrievers'
-import { RESPONSE_TEMPLATE } from '../../chains/ConversationalRetrievalQAChain/prompts'
+import { RESPONSE_TEMPLATE, REPHRASE_TEMPLATE } from '../../chains/ConversationalRetrievalQAChain/prompts'
 import { addImagesToMessages, llmSupportsVision } from '../../../src/multiModalUtils'
+import { StringOutputParser } from '@langchain/core/output_parsers'
+import { Tool } from '@langchain/core/tools'
 
 class ConversationalRetrievalToolAgent_Agents implements INode {
     label: string
@@ -42,7 +44,7 @@ class ConversationalRetrievalToolAgent_Agents implements INode {
     constructor(fields?: { sessionId?: string }) {
         this.label = 'Conversational Retrieval Tool Agent'
         this.name = 'conversationalRetrievalToolAgent'
-        this.author = 'niztal(falkor)'
+        this.author = 'niztal(falkor) and nikitas-novatix'
         this.version = 1.0
         this.type = 'AgentExecutor'
         this.category = 'Agents'
@@ -79,6 +81,26 @@ class ConversationalRetrievalToolAgent_Agents implements INode {
                 optional: true,
                 default: RESPONSE_TEMPLATE
             },
+            {
+                label: 'Rephrase Prompt',
+                name: 'rephrasePrompt',
+                type: 'string',
+                description: 'Using previous chat history, rephrase question into a standalone question',
+                warning: 'Prompt must include input variables: {chat_history} and {question}',
+                rows: 4,
+                additionalParams: true,
+                optional: true,
+                default: REPHRASE_TEMPLATE
+            },
+            {
+                label: 'Rephrase Model',
+                name: 'rephraseModel',
+                type: 'BaseChatModel',
+                description:
+                    'Optional: Use a different (faster/cheaper) model for rephrasing. If not specified, uses the main Tool Calling Chat Model.',
+                optional: true,
+                additionalParams: true
+            },
             {
                 label: 'Input Moderation',
                 description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
@@ -103,8 +125,9 @@ class ConversationalRetrievalToolAgent_Agents implements INode {
         this.sessionId = fields?.sessionId
     }
 
-    async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
-        return prepareAgent(nodeData, options, { sessionId: this.sessionId, chatId: options.chatId, input })
+    // The agent will be prepared in run() with the correct user message - it needs the actual runtime input for rephrasing
+    async init(_nodeData: INodeData, _input: string, _options: ICommonObject): Promise<any> {
+        return null
     }
 
     async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
@@ -148,6 +171,23 @@ class ConversationalRetrievalToolAgent_Agents implements INode {
                 sseStreamer.streamUsedToolsEvent(chatId, res.usedTools)
                 usedTools = res.usedTools
             }
+
+            // If the tool is set to returnDirect, stream the output to the client
+            if (res.usedTools && res.usedTools.length) {
+                let inputTools = nodeData.inputs?.tools
+                inputTools = flatten(inputTools)
+                for (const tool of res.usedTools) {
+                    const inputTool = inputTools.find((inputTool: Tool) => inputTool.name === tool.tool)
+                    if (inputTool && (inputTool as any).returnDirect && shouldStreamResponse) {
+                        sseStreamer.streamTokenEvent(chatId, tool.toolOutput)
+                        // Prevent CustomChainHandler from streaming the same output again
+                        if (res.output === tool.toolOutput) {
+                            res.output = ''
+                        }
+                    }
+                }
+            }
+            // The CustomChainHandler will send the stream end event
         } else {
             res = await executor.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
             if (res.sourceDocuments) {
@@ -210,9 +250,11 @@ const prepareAgent = async (
     flowObj: { sessionId?: string; chatId?: string; input?: string }
 ) => {
     const model = nodeData.inputs?.model as BaseChatModel
+    const rephraseModel = (nodeData.inputs?.rephraseModel as BaseChatModel) || model // Use main model if not specified
     const maxIterations = nodeData.inputs?.maxIterations as string
     const memory = nodeData.inputs?.memory as FlowiseMemory
     let systemMessage = nodeData.inputs?.systemMessage as string
+    let rephrasePrompt = nodeData.inputs?.rephrasePrompt as string
     let tools = nodeData.inputs?.tools
     tools = flatten(tools)
     const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
@@ -220,6 +262,9 @@ const prepareAgent = async (
     const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever as BaseRetriever
 
     systemMessage = transformBracesWithColon(systemMessage)
+    if (rephrasePrompt) {
+        rephrasePrompt = transformBracesWithColon(rephrasePrompt)
+    }
 
     const prompt = ChatPromptTemplate.fromMessages([
         ['system', systemMessage ? systemMessage : `You are a helpful AI assistant.`],
@@ -263,6 +308,37 @@ const prepareAgent = async (
 
     const modelWithTools = model.bindTools(tools)
 
+    // Function to get standalone question (either rephrased or original)
+    const getStandaloneQuestion = async (input: string): Promise<string> => {
+        // If no rephrase prompt, return the original input
+        if (!rephrasePrompt) {
+            return input
+        }
+
+        // Get chat history (use empty string if none)
+        const messages = (await memory.getChatMessages(flowObj?.sessionId, true)) as BaseMessage[]
+        const iMessages = convertBaseMessagetoIMessage(messages)
+        const chatHistoryString = convertChatHistoryToText(iMessages)
+
+        // Always rephrase to normalize/expand user queries for better retrieval
+        try {
+            const CONDENSE_QUESTION_PROMPT = PromptTemplate.fromTemplate(rephrasePrompt)
+            const condenseQuestionChain = RunnableSequence.from([CONDENSE_QUESTION_PROMPT, rephraseModel, new StringOutputParser()])
+            const res = await condenseQuestionChain.invoke({
+                question: input,
+                chat_history: chatHistoryString
+            })
+            return res
+        } catch (error) {
+            console.error('Error rephrasing question:', error)
+            // On error, fall back to original input
+            return input
+        }
+    }
+
+    // Get standalone question before creating runnable
+    const standaloneQuestion = await getStandaloneQuestion(flowObj?.input || '')
+
     const runnableAgent = RunnableSequence.from([
         {
             [inputKey]: (i: { input: string; steps: ToolsAgentStep[] }) => i.input,
@@ -272,7 +348,9 @@ const prepareAgent = async (
                 return messages ?? []
             },
             context: async (i: { input: string; chatHistory?: string }) => {
-                const relevantDocs = await vectorStoreRetriever.invoke(i.input)
+                // Use the standalone question (rephrased or original) for retrieval
+                const retrievalQuery = standaloneQuestion || i.input
+                const relevantDocs = await vectorStoreRetriever.invoke(retrievalQuery)
                 const formattedDocs = formatDocs(relevantDocs)
                 return formattedDocs
             }
@@ -295,4 +373,6 @@ const prepareAgent = async (
     return executor
 }
 
-module.exports = { nodeClass: ConversationalRetrievalToolAgent_Agents }
+module.exports = {
+    nodeClass: ConversationalRetrievalToolAgent_Agents
+}