diff --git a/typescript-sdk/apps/dojo/scripts/prep-dojo-everything.js b/typescript-sdk/apps/dojo/scripts/prep-dojo-everything.js index 7b33e8f46..e9ce67861 100755 --- a/typescript-sdk/apps/dojo/scripts/prep-dojo-everything.js +++ b/typescript-sdk/apps/dojo/scripts/prep-dojo-everything.js @@ -45,7 +45,7 @@ const serverStarterAllFeatures = { // Agno const agno = { - command: 'uv venv --allow-existing && uv pip install -r requirements.txt', + command: 'uv sync', name: 'Agno', cwd: path.join(integrationsRoot, 'agno/examples'), } diff --git a/typescript-sdk/apps/dojo/scripts/run-dojo-everything.js b/typescript-sdk/apps/dojo/scripts/run-dojo-everything.js index 90d76e615..4e148b8d2 100755 --- a/typescript-sdk/apps/dojo/scripts/run-dojo-everything.js +++ b/typescript-sdk/apps/dojo/scripts/run-dojo-everything.js @@ -45,7 +45,7 @@ const serverStarterAllFeatures = { // Agno const agno = { - command: 'uv run agent.py', + command: 'uv run dev', name: 'Agno', cwd: path.join(integrationsRoot, 'agno/examples'), env: {PORT: 8002}, diff --git a/typescript-sdk/apps/dojo/src/agents.ts b/typescript-sdk/apps/dojo/src/agents.ts index 9b2234e3d..d71e431ae 100644 --- a/typescript-sdk/apps/dojo/src/agents.ts +++ b/typescript-sdk/apps/dojo/src/agents.ts @@ -163,6 +163,9 @@ export const agentsIntegrations: AgentIntegrationConfig[] = [ tool_based_generative_ui: new LangGraphHttpAgent({ url: `${envVars.langgraphFastApiUrl}/agent/tool_based_generative_ui`, }), + agentic_chat_reasoning: new LangGraphHttpAgent({ + url: "http://localhost:8000/agent/agentic_chat_reasoning", + }), }; }, }, @@ -171,7 +174,10 @@ export const agentsIntegrations: AgentIntegrationConfig[] = [ agents: async () => { return { agentic_chat: new AgnoAgent({ - url: `${envVars.agnoUrl}/agui`, + url: `${envVars.agnoUrl}/agentic_chat/agui`, + }), + tool_based_generative_ui: new AgnoAgent({ + url: `${envVars.agnoUrl}/tool_based_generative_ui/agui`, }), }; }, diff --git a/typescript-sdk/apps/dojo/src/app/[integrationId]/feature/agentic_chat_reasoning/README.mdx b/typescript-sdk/apps/dojo/src/app/[integrationId]/feature/agentic_chat_reasoning/README.mdx new file mode 100644 index 000000000..058011d13 --- /dev/null +++ b/typescript-sdk/apps/dojo/src/app/[integrationId]/feature/agentic_chat_reasoning/README.mdx @@ -0,0 +1,45 @@ +# 🤖 Agentic Chat with Reasoning + +## What This Demo Shows + +This demo showcases CopilotKit's **agentic chat** capabilities with **frontend +tool integration**: + +1. **Natural Conversation**: Chat with your Copilot in a familiar chat interface +2. **Frontend Tool Execution**: The Copilot can directly interacts with your UI + by calling frontend functions +3. **Seamless Integration**: Tools defined in the frontend and automatically + discovered and made available to the agent + +## How to Interact + +Try asking your Copilot to: + +- "Can you change the background color to something more vibrant?" +- "Make the background a blue to purple gradient" +- "Set the background to a sunset-themed gradient" +- "Change it back to a simple light color" + +You can also chat about other topics - the agent will respond conversationally +while having the ability to use your UI tools when appropriate. + +## ✨ Frontend Tool Integration in Action + +**What's happening technically:** + +- The React component defines a frontend function using `useCopilotAction` +- CopilotKit automatically exposes this function to the agent +- When you make a request, the agent determines whether to use the tool +- The agent calls the function with the appropriate parameters +- The UI immediately updates in response + +**What you'll see in this demo:** + +- The Copilot understands requests to change the background +- It generates CSS values for colors and gradients +- When it calls the tool, the background changes instantly +- The agent provides a conversational response about the changes it made + +This technique of exposing frontend functions to your Copilot can be extended to +any UI manipulation you want to enable, from theme changes to data filtering, +navigation, or complex UI state management! diff --git a/typescript-sdk/apps/dojo/src/app/[integrationId]/feature/agentic_chat_reasoning/page.tsx b/typescript-sdk/apps/dojo/src/app/[integrationId]/feature/agentic_chat_reasoning/page.tsx new file mode 100644 index 000000000..ee7333c09 --- /dev/null +++ b/typescript-sdk/apps/dojo/src/app/[integrationId]/feature/agentic_chat_reasoning/page.tsx @@ -0,0 +1,122 @@ +"use client"; +import React, { useState } from "react"; +import "@copilotkit/react-ui/styles.css"; +import "./style.css"; +import { CopilotKit, useCoAgent, useCopilotAction, useCopilotChat } from "@copilotkit/react-core"; +import { CopilotChat } from "@copilotkit/react-ui"; +import { ChevronDown } from "lucide-react"; +import { Button } from "@/components/ui/button"; +import { + DropdownMenu, + DropdownMenuContent, + DropdownMenuItem, + DropdownMenuLabel, + DropdownMenuSeparator, + DropdownMenuTrigger, +} from "@/components/ui/dropdown-menu"; + +interface AgenticChatProps { + params: Promise<{ + integrationId: string; + }>; +} + +const AgenticChat: React.FC = ({ params }) => { + const { integrationId } = React.use(params); + + return ( + + + + ); +}; + +interface AgentState { + model: string; +} + +const Chat = () => { + const [background, setBackground] = useState("--copilot-kit-background-color"); + const { state: agentState, setState: setAgentState } = useCoAgent({ + name: "agentic_chat_reasoning", + initialState: { + model: "OpenAI", + }, + }); + + // Initialize model if not set + const selectedModel = agentState?.model || "OpenAI"; + + const handleModelChange = (model: string) => { + setAgentState({ model }); + }; + + useCopilotAction({ + name: "change_background", + description: + "Change the background color of the chat. Can be anything that the CSS background attribute accepts. Regular colors, linear of radial gradients etc.", + parameters: [ + { + name: "background", + type: "string", + description: "The background. Prefer gradients.", + }, + ], + handler: ({ background }) => { + setBackground(background); + }, + }); + + return ( +
+ {/* Reasoning Model Dropdown */} +
+
+
+ + Reasoning Model: + + + + + + + Select Model + + handleModelChange("OpenAI")}> + OpenAI + + handleModelChange("Anthropic")}> + Anthropic + + handleModelChange("Gemini")}> + Gemini + + + +
+
+
+ + {/* Chat Container */} +
+
+ +
+
+
+ ); +}; + +export default AgenticChat; diff --git a/typescript-sdk/apps/dojo/src/app/[integrationId]/feature/agentic_chat_reasoning/style.css b/typescript-sdk/apps/dojo/src/app/[integrationId]/feature/agentic_chat_reasoning/style.css new file mode 100644 index 000000000..d34f65c10 --- /dev/null +++ b/typescript-sdk/apps/dojo/src/app/[integrationId]/feature/agentic_chat_reasoning/style.css @@ -0,0 +1,12 @@ +.copilotKitInput { + border-bottom-left-radius: 0.75rem; + border-bottom-right-radius: 0.75rem; + border-top-left-radius: 0.75rem; + border-top-right-radius: 0.75rem; + border: 1px solid var(--copilot-kit-separator-color) !important; +} + +.copilotKitChat { + background-color: #fff !important; +} + \ No newline at end of file diff --git a/typescript-sdk/apps/dojo/src/app/[integrationId]/feature/tool_based_generative_ui/page.tsx b/typescript-sdk/apps/dojo/src/app/[integrationId]/feature/tool_based_generative_ui/page.tsx index 27168f731..cd7e19a77 100644 --- a/typescript-sdk/apps/dojo/src/app/[integrationId]/feature/tool_based_generative_ui/page.tsx +++ b/typescript-sdk/apps/dojo/src/app/[integrationId]/feature/tool_based_generative_ui/page.tsx @@ -291,6 +291,7 @@ function Haiku() { useCopilotAction({ name: "generate_haiku", + available: "frontend", parameters: [ { name: "japanese", diff --git a/typescript-sdk/apps/dojo/src/config.ts b/typescript-sdk/apps/dojo/src/config.ts index 14a90fa6c..198cb4eb2 100644 --- a/typescript-sdk/apps/dojo/src/config.ts +++ b/typescript-sdk/apps/dojo/src/config.ts @@ -53,6 +53,12 @@ export const featureConfig: FeatureConfig[] = [ description: "Use collaboration to edit a document in real time with your Copilot", tags: ["State", "Streaming", "Tools"], }), + createFeatureConfig({ + id: "agentic_chat_reasoning", + name: "Agentic Chat Reasoning", + description: "Chat with a reasoning Copilot and call frontend tools", + tags: ["Chat", "Tools", "Streaming", "Reasoning"], + }), ]; export default featureConfig; diff --git a/typescript-sdk/apps/dojo/src/files.json b/typescript-sdk/apps/dojo/src/files.json index 3b7b75a33..c426785ed 100644 --- a/typescript-sdk/apps/dojo/src/files.json +++ b/typescript-sdk/apps/dojo/src/files.json @@ -158,7 +158,7 @@ "pydantic-ai::tool_based_generative_ui": [ { "name": "page.tsx", - "content": "\"use client\";\nimport { CopilotKit, useCopilotAction } from \"@copilotkit/react-core\";\nimport { CopilotKitCSSProperties, CopilotSidebar, CopilotChat } from \"@copilotkit/react-ui\";\nimport { Dispatch, SetStateAction, useState, useEffect } from \"react\";\nimport \"@copilotkit/react-ui/styles.css\";\nimport \"./style.css\";\nimport React, { useMemo } from \"react\";\nimport { useMobileView } from \"@/utils/use-mobile-view\";\nimport { useMobileChat } from \"@/utils/use-mobile-chat\";\n\ninterface ToolBasedGenerativeUIProps {\n params: Promise<{\n integrationId: string;\n }>;\n}\n\ninterface GenerateHaiku{\n japanese : string[] | [],\n english : string[] | [],\n image_names : string[] | [],\n selectedImage : string | null,\n}\n\ninterface HaikuCardProps{\n generatedHaiku : GenerateHaiku | Partial\n setHaikus : Dispatch>\n haikus : GenerateHaiku[]\n}\n\nexport default function ToolBasedGenerativeUI({ params }: ToolBasedGenerativeUIProps) {\n const { integrationId } = React.use(params);\n const { isMobile } = useMobileView();\n const defaultChatHeight = 50\n const {\n isChatOpen,\n setChatHeight,\n setIsChatOpen,\n isDragging,\n chatHeight,\n handleDragStart\n } = useMobileChat(defaultChatHeight)\n\n const chatTitle = 'Haiku Generator'\n const chatDescription = 'Ask me to create haikus'\n const initialLabel = 'I\\'m a haiku generator 👋. How can I help you?'\n\n return (\n \n \n \n\n {/* Desktop Sidebar */}\n {!isMobile && (\n \n )}\n\n {/* Mobile Pull-Up Chat */}\n {isMobile && (\n <>\n {/* Chat Toggle Button */}\n
\n
\n {\n if (!isChatOpen) {\n setChatHeight(defaultChatHeight); // Reset to good default when opening\n }\n setIsChatOpen(!isChatOpen);\n }}\n >\n
\n
\n
{chatTitle}
\n
{chatDescription}
\n
\n
\n
\n \n \n \n
\n
\n \n\n {/* Pull-Up Chat Container */}\n \n {/* Drag Handle Bar */}\n
\n
\n
\n \n {/* Chat Header */}\n
\n
\n
\n

{chatTitle}

\n
\n setIsChatOpen(false)}\n className=\"p-2 hover:bg-gray-100 rounded-full transition-colors\"\n >\n \n \n \n \n
\n
\n\n {/* Chat Content - Flexible container for messages and input */}\n
\n \n
\n \n\n {/* Backdrop */}\n {isChatOpen && (\n setIsChatOpen(false)}\n />\n )}\n \n )}\n \n \n );\n}\n\nconst VALID_IMAGE_NAMES = [\n \"Osaka_Castle_Turret_Stone_Wall_Pine_Trees_Daytime.jpg\",\n \"Tokyo_Skyline_Night_Tokyo_Tower_Mount_Fuji_View.jpg\",\n \"Itsukushima_Shrine_Miyajima_Floating_Torii_Gate_Sunset_Long_Exposure.jpg\",\n \"Takachiho_Gorge_Waterfall_River_Lush_Greenery_Japan.jpg\",\n \"Bonsai_Tree_Potted_Japanese_Art_Green_Foliage.jpeg\",\n \"Shirakawa-go_Gassho-zukuri_Thatched_Roof_Village_Aerial_View.jpg\",\n \"Ginkaku-ji_Silver_Pavilion_Kyoto_Japanese_Garden_Pond_Reflection.jpg\",\n \"Senso-ji_Temple_Asakusa_Cherry_Blossoms_Kimono_Umbrella.jpg\",\n \"Cherry_Blossoms_Sakura_Night_View_City_Lights_Japan.jpg\",\n \"Mount_Fuji_Lake_Reflection_Cherry_Blossoms_Sakura_Spring.jpg\"\n];\n\nfunction HaikuCard({generatedHaiku, setHaikus, haikus} : HaikuCardProps) {\n return (\n
\n
\n {generatedHaiku?.japanese?.map((line, index) => (\n
\n

{line}

\n

\n {generatedHaiku.english?.[index]}\n

\n
\n ))}\n {generatedHaiku?.japanese && generatedHaiku.japanese.length >= 2 && (\n
\n {(() => {\n const firstLine = generatedHaiku?.japanese?.[0];\n if (!firstLine) return null;\n const haikuIndex = haikus.findIndex((h: any) => h.japanese[0] === firstLine);\n const haiku = haikus[haikuIndex];\n if (!haiku?.image_names) return null;\n\n return haiku.image_names.map((imageName, imgIndex) => (\n {\n setHaikus(prevHaikus => {\n const newHaikus = prevHaikus.map((h, idx) => {\n if (idx === haikuIndex) {\n return {\n ...h,\n selectedImage: imageName\n };\n }\n return h;\n });\n return newHaikus;\n });\n }}\n />\n ));\n })()}\n
\n )}\n
\n
\n );\n}\n\ninterface Haiku {\n japanese: string[];\n english: string[];\n image_names: string[];\n selectedImage: string | null;\n}\n\nfunction Haiku() {\n const [haikus, setHaikus] = useState([{\n japanese: [\"仮の句よ\", \"まっさらながら\", \"花を呼ぶ\"],\n english: [\n \"A placeholder verse—\",\n \"even in a blank canvas,\",\n \"it beckons flowers.\",\n ],\n image_names: [],\n selectedImage: null,\n }])\n const [activeIndex, setActiveIndex] = useState(0);\n const [isJustApplied, setIsJustApplied] = useState(false);\n\n const validateAndCorrectImageNames = (rawNames: string[] | undefined): string[] | null => {\n if (!rawNames || rawNames.length !== 3) {\n return null;\n }\n\n const correctedNames: string[] = [];\n const usedValidNames = new Set();\n\n for (const name of rawNames) {\n if (VALID_IMAGE_NAMES.includes(name) && !usedValidNames.has(name)) {\n correctedNames.push(name);\n usedValidNames.add(name);\n if (correctedNames.length === 3) break;\n }\n }\n\n if (correctedNames.length < 3) {\n const availableFallbacks = VALID_IMAGE_NAMES.filter(name => !usedValidNames.has(name));\n for (let i = availableFallbacks.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n [availableFallbacks[i], availableFallbacks[j]] = [availableFallbacks[j], availableFallbacks[i]];\n }\n\n while (correctedNames.length < 3 && availableFallbacks.length > 0) {\n const fallbackName = availableFallbacks.pop();\n if (fallbackName) {\n correctedNames.push(fallbackName);\n }\n }\n }\n\n while (correctedNames.length < 3 && VALID_IMAGE_NAMES.length > 0) {\n const fallbackName = VALID_IMAGE_NAMES[Math.floor(Math.random() * VALID_IMAGE_NAMES.length)];\n correctedNames.push(fallbackName);\n }\n\n return correctedNames.slice(0, 3);\n };\n\n useCopilotAction({\n name: \"generate_haiku\",\n parameters: [\n {\n name: \"japanese\",\n type: \"string[]\",\n },\n {\n name: \"english\",\n type: \"string[]\",\n },\n {\n name: \"image_names\",\n type: \"string[]\",\n description: \"Names of 3 relevant images\",\n },\n ],\n followUp: false,\n handler: async ({ japanese, english, image_names }: { japanese: string[], english: string[], image_names: string[] }) => {\n const finalCorrectedImages = validateAndCorrectImageNames(image_names);\n const newHaiku = {\n japanese: japanese || [],\n english: english || [],\n image_names: finalCorrectedImages || [],\n selectedImage: finalCorrectedImages?.[0] || null,\n };\n setHaikus(prev => [...prev, newHaiku]);\n setActiveIndex(haikus.length - 1);\n setIsJustApplied(true);\n setTimeout(() => setIsJustApplied(false), 600);\n return \"Haiku generated.\";\n },\n render: ({ args: generatedHaiku }: { args: Partial }) => {\n return (\n \n );\n },\n }, [haikus]);\n\n const generatedHaikus = useMemo(() => (\n haikus.filter((haiku) => haiku.english[0] !== \"A placeholder verse—\")\n ), [haikus]);\n\n const { isMobile } = useMobileView();\n\n return (\n
\n {/* Thumbnail List */}\n {Boolean(generatedHaikus.length) && !isMobile && (\n
\n {generatedHaikus.map((haiku, index) => (\n setActiveIndex(index)}\n >\n {haiku.japanese.map((line, lineIndex) => (\n \n

{line}

\n

{haiku.english?.[lineIndex]}

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n )}\n\n {/* Main Display */}\n
\n
\n {haikus.filter((_haiku: Haiku, index: number) => {\n if (haikus.length == 1) return true;\n else return index == activeIndex + 1;\n }).map((haiku, index) => (\n \n {haiku.japanese.map((line, lineIndex) => (\n \n

\n {line}\n

\n

\n {haiku.english?.[lineIndex]}\n

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n \n \n );\n}\n", + "content": "\"use client\";\nimport { CopilotKit, useCopilotAction } from \"@copilotkit/react-core\";\nimport { CopilotKitCSSProperties, CopilotSidebar, CopilotChat } from \"@copilotkit/react-ui\";\nimport { Dispatch, SetStateAction, useState, useEffect } from \"react\";\nimport \"@copilotkit/react-ui/styles.css\";\nimport \"./style.css\";\nimport React, { useMemo } from \"react\";\nimport { useMobileView } from \"@/utils/use-mobile-view\";\nimport { useMobileChat } from \"@/utils/use-mobile-chat\";\n\ninterface ToolBasedGenerativeUIProps {\n params: Promise<{\n integrationId: string;\n }>;\n}\n\ninterface GenerateHaiku{\n japanese : string[] | [],\n english : string[] | [],\n image_names : string[] | [],\n selectedImage : string | null,\n}\n\ninterface HaikuCardProps{\n generatedHaiku : GenerateHaiku | Partial\n setHaikus : Dispatch>\n haikus : GenerateHaiku[]\n}\n\nexport default function ToolBasedGenerativeUI({ params }: ToolBasedGenerativeUIProps) {\n const { integrationId } = React.use(params);\n const { isMobile } = useMobileView();\n const defaultChatHeight = 50\n const {\n isChatOpen,\n setChatHeight,\n setIsChatOpen,\n isDragging,\n chatHeight,\n handleDragStart\n } = useMobileChat(defaultChatHeight)\n\n const chatTitle = 'Haiku Generator'\n const chatDescription = 'Ask me to create haikus'\n const initialLabel = 'I\\'m a haiku generator 👋. How can I help you?'\n\n return (\n \n \n \n\n {/* Desktop Sidebar */}\n {!isMobile && (\n \n )}\n\n {/* Mobile Pull-Up Chat */}\n {isMobile && (\n <>\n {/* Chat Toggle Button */}\n
\n
\n {\n if (!isChatOpen) {\n setChatHeight(defaultChatHeight); // Reset to good default when opening\n }\n setIsChatOpen(!isChatOpen);\n }}\n >\n
\n
\n
{chatTitle}
\n
{chatDescription}
\n
\n
\n
\n \n \n \n
\n
\n \n\n {/* Pull-Up Chat Container */}\n \n {/* Drag Handle Bar */}\n
\n
\n
\n \n {/* Chat Header */}\n
\n
\n
\n

{chatTitle}

\n
\n setIsChatOpen(false)}\n className=\"p-2 hover:bg-gray-100 rounded-full transition-colors\"\n >\n \n \n \n \n
\n
\n\n {/* Chat Content - Flexible container for messages and input */}\n
\n \n
\n \n\n {/* Backdrop */}\n {isChatOpen && (\n setIsChatOpen(false)}\n />\n )}\n \n )}\n \n \n );\n}\n\nconst VALID_IMAGE_NAMES = [\n \"Osaka_Castle_Turret_Stone_Wall_Pine_Trees_Daytime.jpg\",\n \"Tokyo_Skyline_Night_Tokyo_Tower_Mount_Fuji_View.jpg\",\n \"Itsukushima_Shrine_Miyajima_Floating_Torii_Gate_Sunset_Long_Exposure.jpg\",\n \"Takachiho_Gorge_Waterfall_River_Lush_Greenery_Japan.jpg\",\n \"Bonsai_Tree_Potted_Japanese_Art_Green_Foliage.jpeg\",\n \"Shirakawa-go_Gassho-zukuri_Thatched_Roof_Village_Aerial_View.jpg\",\n \"Ginkaku-ji_Silver_Pavilion_Kyoto_Japanese_Garden_Pond_Reflection.jpg\",\n \"Senso-ji_Temple_Asakusa_Cherry_Blossoms_Kimono_Umbrella.jpg\",\n \"Cherry_Blossoms_Sakura_Night_View_City_Lights_Japan.jpg\",\n \"Mount_Fuji_Lake_Reflection_Cherry_Blossoms_Sakura_Spring.jpg\"\n];\n\nfunction HaikuCard({generatedHaiku, setHaikus, haikus} : HaikuCardProps) {\n return (\n
\n
\n {generatedHaiku?.japanese?.map((line, index) => (\n
\n

{line}

\n

\n {generatedHaiku.english?.[index]}\n

\n
\n ))}\n {generatedHaiku?.japanese && generatedHaiku.japanese.length >= 2 && (\n
\n {(() => {\n const firstLine = generatedHaiku?.japanese?.[0];\n if (!firstLine) return null;\n const haikuIndex = haikus.findIndex((h: any) => h.japanese[0] === firstLine);\n const haiku = haikus[haikuIndex];\n if (!haiku?.image_names) return null;\n\n return haiku.image_names.map((imageName, imgIndex) => (\n {\n setHaikus(prevHaikus => {\n const newHaikus = prevHaikus.map((h, idx) => {\n if (idx === haikuIndex) {\n return {\n ...h,\n selectedImage: imageName\n };\n }\n return h;\n });\n return newHaikus;\n });\n }}\n />\n ));\n })()}\n
\n )}\n
\n
\n );\n}\n\ninterface Haiku {\n japanese: string[];\n english: string[];\n image_names: string[];\n selectedImage: string | null;\n}\n\nfunction Haiku() {\n const [haikus, setHaikus] = useState([{\n japanese: [\"仮の句よ\", \"まっさらながら\", \"花を呼ぶ\"],\n english: [\n \"A placeholder verse—\",\n \"even in a blank canvas,\",\n \"it beckons flowers.\",\n ],\n image_names: [],\n selectedImage: null,\n }])\n const [activeIndex, setActiveIndex] = useState(0);\n const [isJustApplied, setIsJustApplied] = useState(false);\n\n const validateAndCorrectImageNames = (rawNames: string[] | undefined): string[] | null => {\n if (!rawNames || rawNames.length !== 3) {\n return null;\n }\n\n const correctedNames: string[] = [];\n const usedValidNames = new Set();\n\n for (const name of rawNames) {\n if (VALID_IMAGE_NAMES.includes(name) && !usedValidNames.has(name)) {\n correctedNames.push(name);\n usedValidNames.add(name);\n if (correctedNames.length === 3) break;\n }\n }\n\n if (correctedNames.length < 3) {\n const availableFallbacks = VALID_IMAGE_NAMES.filter(name => !usedValidNames.has(name));\n for (let i = availableFallbacks.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n [availableFallbacks[i], availableFallbacks[j]] = [availableFallbacks[j], availableFallbacks[i]];\n }\n\n while (correctedNames.length < 3 && availableFallbacks.length > 0) {\n const fallbackName = availableFallbacks.pop();\n if (fallbackName) {\n correctedNames.push(fallbackName);\n }\n }\n }\n\n while (correctedNames.length < 3 && VALID_IMAGE_NAMES.length > 0) {\n const fallbackName = VALID_IMAGE_NAMES[Math.floor(Math.random() * VALID_IMAGE_NAMES.length)];\n correctedNames.push(fallbackName);\n }\n\n return correctedNames.slice(0, 3);\n };\n\n useCopilotAction({\n name: \"generate_haiku\",\n available: \"frontend\",\n parameters: [\n {\n name: \"japanese\",\n type: \"string[]\",\n },\n {\n name: \"english\",\n type: \"string[]\",\n },\n {\n name: \"image_names\",\n type: \"string[]\",\n description: \"Names of 3 relevant images\",\n },\n ],\n followUp: false,\n handler: async ({ japanese, english, image_names }: { japanese: string[], english: string[], image_names: string[] }) => {\n const finalCorrectedImages = validateAndCorrectImageNames(image_names);\n const newHaiku = {\n japanese: japanese || [],\n english: english || [],\n image_names: finalCorrectedImages || [],\n selectedImage: finalCorrectedImages?.[0] || null,\n };\n setHaikus(prev => [...prev, newHaiku]);\n setActiveIndex(haikus.length - 1);\n setIsJustApplied(true);\n setTimeout(() => setIsJustApplied(false), 600);\n return \"Haiku generated.\";\n },\n render: ({ args: generatedHaiku }: { args: Partial }) => {\n return (\n \n );\n },\n }, [haikus]);\n\n const generatedHaikus = useMemo(() => (\n haikus.filter((haiku) => haiku.english[0] !== \"A placeholder verse—\")\n ), [haikus]);\n\n const { isMobile } = useMobileView();\n\n return (\n
\n {/* Thumbnail List */}\n {Boolean(generatedHaikus.length) && !isMobile && (\n
\n {generatedHaikus.map((haiku, index) => (\n setActiveIndex(index)}\n >\n {haiku.japanese.map((line, lineIndex) => (\n \n

{line}

\n

{haiku.english?.[lineIndex]}

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n )}\n\n {/* Main Display */}\n
\n
\n {haikus.filter((_haiku: Haiku, index: number) => {\n if (haikus.length == 1) return true;\n else return index == activeIndex + 1;\n }).map((haiku, index) => (\n \n {haiku.japanese.map((line, lineIndex) => (\n \n

\n {line}\n

\n

\n {haiku.english?.[lineIndex]}\n

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n \n \n );\n}\n", "language": "typescript", "type": "file" }, @@ -288,7 +288,7 @@ "server-starter-all-features::tool_based_generative_ui": [ { "name": "page.tsx", - "content": "\"use client\";\nimport { CopilotKit, useCopilotAction } from \"@copilotkit/react-core\";\nimport { CopilotKitCSSProperties, CopilotSidebar, CopilotChat } from \"@copilotkit/react-ui\";\nimport { Dispatch, SetStateAction, useState, useEffect } from \"react\";\nimport \"@copilotkit/react-ui/styles.css\";\nimport \"./style.css\";\nimport React, { useMemo } from \"react\";\nimport { useMobileView } from \"@/utils/use-mobile-view\";\nimport { useMobileChat } from \"@/utils/use-mobile-chat\";\n\ninterface ToolBasedGenerativeUIProps {\n params: Promise<{\n integrationId: string;\n }>;\n}\n\ninterface GenerateHaiku{\n japanese : string[] | [],\n english : string[] | [],\n image_names : string[] | [],\n selectedImage : string | null,\n}\n\ninterface HaikuCardProps{\n generatedHaiku : GenerateHaiku | Partial\n setHaikus : Dispatch>\n haikus : GenerateHaiku[]\n}\n\nexport default function ToolBasedGenerativeUI({ params }: ToolBasedGenerativeUIProps) {\n const { integrationId } = React.use(params);\n const { isMobile } = useMobileView();\n const defaultChatHeight = 50\n const {\n isChatOpen,\n setChatHeight,\n setIsChatOpen,\n isDragging,\n chatHeight,\n handleDragStart\n } = useMobileChat(defaultChatHeight)\n\n const chatTitle = 'Haiku Generator'\n const chatDescription = 'Ask me to create haikus'\n const initialLabel = 'I\\'m a haiku generator 👋. How can I help you?'\n\n return (\n \n \n \n\n {/* Desktop Sidebar */}\n {!isMobile && (\n \n )}\n\n {/* Mobile Pull-Up Chat */}\n {isMobile && (\n <>\n {/* Chat Toggle Button */}\n
\n
\n {\n if (!isChatOpen) {\n setChatHeight(defaultChatHeight); // Reset to good default when opening\n }\n setIsChatOpen(!isChatOpen);\n }}\n >\n
\n
\n
{chatTitle}
\n
{chatDescription}
\n
\n
\n
\n \n \n \n
\n
\n \n\n {/* Pull-Up Chat Container */}\n \n {/* Drag Handle Bar */}\n
\n
\n
\n \n {/* Chat Header */}\n
\n
\n
\n

{chatTitle}

\n
\n setIsChatOpen(false)}\n className=\"p-2 hover:bg-gray-100 rounded-full transition-colors\"\n >\n \n \n \n \n
\n
\n\n {/* Chat Content - Flexible container for messages and input */}\n
\n \n
\n \n\n {/* Backdrop */}\n {isChatOpen && (\n setIsChatOpen(false)}\n />\n )}\n \n )}\n \n \n );\n}\n\nconst VALID_IMAGE_NAMES = [\n \"Osaka_Castle_Turret_Stone_Wall_Pine_Trees_Daytime.jpg\",\n \"Tokyo_Skyline_Night_Tokyo_Tower_Mount_Fuji_View.jpg\",\n \"Itsukushima_Shrine_Miyajima_Floating_Torii_Gate_Sunset_Long_Exposure.jpg\",\n \"Takachiho_Gorge_Waterfall_River_Lush_Greenery_Japan.jpg\",\n \"Bonsai_Tree_Potted_Japanese_Art_Green_Foliage.jpeg\",\n \"Shirakawa-go_Gassho-zukuri_Thatched_Roof_Village_Aerial_View.jpg\",\n \"Ginkaku-ji_Silver_Pavilion_Kyoto_Japanese_Garden_Pond_Reflection.jpg\",\n \"Senso-ji_Temple_Asakusa_Cherry_Blossoms_Kimono_Umbrella.jpg\",\n \"Cherry_Blossoms_Sakura_Night_View_City_Lights_Japan.jpg\",\n \"Mount_Fuji_Lake_Reflection_Cherry_Blossoms_Sakura_Spring.jpg\"\n];\n\nfunction HaikuCard({generatedHaiku, setHaikus, haikus} : HaikuCardProps) {\n return (\n
\n
\n {generatedHaiku?.japanese?.map((line, index) => (\n
\n

{line}

\n

\n {generatedHaiku.english?.[index]}\n

\n
\n ))}\n {generatedHaiku?.japanese && generatedHaiku.japanese.length >= 2 && (\n
\n {(() => {\n const firstLine = generatedHaiku?.japanese?.[0];\n if (!firstLine) return null;\n const haikuIndex = haikus.findIndex((h: any) => h.japanese[0] === firstLine);\n const haiku = haikus[haikuIndex];\n if (!haiku?.image_names) return null;\n\n return haiku.image_names.map((imageName, imgIndex) => (\n {\n setHaikus(prevHaikus => {\n const newHaikus = prevHaikus.map((h, idx) => {\n if (idx === haikuIndex) {\n return {\n ...h,\n selectedImage: imageName\n };\n }\n return h;\n });\n return newHaikus;\n });\n }}\n />\n ));\n })()}\n
\n )}\n
\n
\n );\n}\n\ninterface Haiku {\n japanese: string[];\n english: string[];\n image_names: string[];\n selectedImage: string | null;\n}\n\nfunction Haiku() {\n const [haikus, setHaikus] = useState([{\n japanese: [\"仮の句よ\", \"まっさらながら\", \"花を呼ぶ\"],\n english: [\n \"A placeholder verse—\",\n \"even in a blank canvas,\",\n \"it beckons flowers.\",\n ],\n image_names: [],\n selectedImage: null,\n }])\n const [activeIndex, setActiveIndex] = useState(0);\n const [isJustApplied, setIsJustApplied] = useState(false);\n\n const validateAndCorrectImageNames = (rawNames: string[] | undefined): string[] | null => {\n if (!rawNames || rawNames.length !== 3) {\n return null;\n }\n\n const correctedNames: string[] = [];\n const usedValidNames = new Set();\n\n for (const name of rawNames) {\n if (VALID_IMAGE_NAMES.includes(name) && !usedValidNames.has(name)) {\n correctedNames.push(name);\n usedValidNames.add(name);\n if (correctedNames.length === 3) break;\n }\n }\n\n if (correctedNames.length < 3) {\n const availableFallbacks = VALID_IMAGE_NAMES.filter(name => !usedValidNames.has(name));\n for (let i = availableFallbacks.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n [availableFallbacks[i], availableFallbacks[j]] = [availableFallbacks[j], availableFallbacks[i]];\n }\n\n while (correctedNames.length < 3 && availableFallbacks.length > 0) {\n const fallbackName = availableFallbacks.pop();\n if (fallbackName) {\n correctedNames.push(fallbackName);\n }\n }\n }\n\n while (correctedNames.length < 3 && VALID_IMAGE_NAMES.length > 0) {\n const fallbackName = VALID_IMAGE_NAMES[Math.floor(Math.random() * VALID_IMAGE_NAMES.length)];\n correctedNames.push(fallbackName);\n }\n\n return correctedNames.slice(0, 3);\n };\n\n useCopilotAction({\n name: \"generate_haiku\",\n parameters: [\n {\n name: \"japanese\",\n type: \"string[]\",\n },\n {\n name: \"english\",\n type: \"string[]\",\n },\n {\n name: \"image_names\",\n type: \"string[]\",\n description: \"Names of 3 relevant images\",\n },\n ],\n followUp: false,\n handler: async ({ japanese, english, image_names }: { japanese: string[], english: string[], image_names: string[] }) => {\n const finalCorrectedImages = validateAndCorrectImageNames(image_names);\n const newHaiku = {\n japanese: japanese || [],\n english: english || [],\n image_names: finalCorrectedImages || [],\n selectedImage: finalCorrectedImages?.[0] || null,\n };\n setHaikus(prev => [...prev, newHaiku]);\n setActiveIndex(haikus.length - 1);\n setIsJustApplied(true);\n setTimeout(() => setIsJustApplied(false), 600);\n return \"Haiku generated.\";\n },\n render: ({ args: generatedHaiku }: { args: Partial }) => {\n return (\n \n );\n },\n }, [haikus]);\n\n const generatedHaikus = useMemo(() => (\n haikus.filter((haiku) => haiku.english[0] !== \"A placeholder verse—\")\n ), [haikus]);\n\n const { isMobile } = useMobileView();\n\n return (\n
\n {/* Thumbnail List */}\n {Boolean(generatedHaikus.length) && !isMobile && (\n
\n {generatedHaikus.map((haiku, index) => (\n setActiveIndex(index)}\n >\n {haiku.japanese.map((line, lineIndex) => (\n \n

{line}

\n

{haiku.english?.[lineIndex]}

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n )}\n\n {/* Main Display */}\n
\n
\n {haikus.filter((_haiku: Haiku, index: number) => {\n if (haikus.length == 1) return true;\n else return index == activeIndex + 1;\n }).map((haiku, index) => (\n \n {haiku.japanese.map((line, lineIndex) => (\n \n

\n {line}\n

\n

\n {haiku.english?.[lineIndex]}\n

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n \n \n );\n}\n", + "content": "\"use client\";\nimport { CopilotKit, useCopilotAction } from \"@copilotkit/react-core\";\nimport { CopilotKitCSSProperties, CopilotSidebar, CopilotChat } from \"@copilotkit/react-ui\";\nimport { Dispatch, SetStateAction, useState, useEffect } from \"react\";\nimport \"@copilotkit/react-ui/styles.css\";\nimport \"./style.css\";\nimport React, { useMemo } from \"react\";\nimport { useMobileView } from \"@/utils/use-mobile-view\";\nimport { useMobileChat } from \"@/utils/use-mobile-chat\";\n\ninterface ToolBasedGenerativeUIProps {\n params: Promise<{\n integrationId: string;\n }>;\n}\n\ninterface GenerateHaiku{\n japanese : string[] | [],\n english : string[] | [],\n image_names : string[] | [],\n selectedImage : string | null,\n}\n\ninterface HaikuCardProps{\n generatedHaiku : GenerateHaiku | Partial\n setHaikus : Dispatch>\n haikus : GenerateHaiku[]\n}\n\nexport default function ToolBasedGenerativeUI({ params }: ToolBasedGenerativeUIProps) {\n const { integrationId } = React.use(params);\n const { isMobile } = useMobileView();\n const defaultChatHeight = 50\n const {\n isChatOpen,\n setChatHeight,\n setIsChatOpen,\n isDragging,\n chatHeight,\n handleDragStart\n } = useMobileChat(defaultChatHeight)\n\n const chatTitle = 'Haiku Generator'\n const chatDescription = 'Ask me to create haikus'\n const initialLabel = 'I\\'m a haiku generator 👋. How can I help you?'\n\n return (\n \n \n \n\n {/* Desktop Sidebar */}\n {!isMobile && (\n \n )}\n\n {/* Mobile Pull-Up Chat */}\n {isMobile && (\n <>\n {/* Chat Toggle Button */}\n
\n
\n {\n if (!isChatOpen) {\n setChatHeight(defaultChatHeight); // Reset to good default when opening\n }\n setIsChatOpen(!isChatOpen);\n }}\n >\n
\n
\n
{chatTitle}
\n
{chatDescription}
\n
\n
\n
\n \n \n \n
\n
\n \n\n {/* Pull-Up Chat Container */}\n \n {/* Drag Handle Bar */}\n
\n
\n
\n \n {/* Chat Header */}\n
\n
\n
\n

{chatTitle}

\n
\n setIsChatOpen(false)}\n className=\"p-2 hover:bg-gray-100 rounded-full transition-colors\"\n >\n \n \n \n \n
\n
\n\n {/* Chat Content - Flexible container for messages and input */}\n
\n \n
\n \n\n {/* Backdrop */}\n {isChatOpen && (\n setIsChatOpen(false)}\n />\n )}\n \n )}\n \n \n );\n}\n\nconst VALID_IMAGE_NAMES = [\n \"Osaka_Castle_Turret_Stone_Wall_Pine_Trees_Daytime.jpg\",\n \"Tokyo_Skyline_Night_Tokyo_Tower_Mount_Fuji_View.jpg\",\n \"Itsukushima_Shrine_Miyajima_Floating_Torii_Gate_Sunset_Long_Exposure.jpg\",\n \"Takachiho_Gorge_Waterfall_River_Lush_Greenery_Japan.jpg\",\n \"Bonsai_Tree_Potted_Japanese_Art_Green_Foliage.jpeg\",\n \"Shirakawa-go_Gassho-zukuri_Thatched_Roof_Village_Aerial_View.jpg\",\n \"Ginkaku-ji_Silver_Pavilion_Kyoto_Japanese_Garden_Pond_Reflection.jpg\",\n \"Senso-ji_Temple_Asakusa_Cherry_Blossoms_Kimono_Umbrella.jpg\",\n \"Cherry_Blossoms_Sakura_Night_View_City_Lights_Japan.jpg\",\n \"Mount_Fuji_Lake_Reflection_Cherry_Blossoms_Sakura_Spring.jpg\"\n];\n\nfunction HaikuCard({generatedHaiku, setHaikus, haikus} : HaikuCardProps) {\n return (\n
\n
\n {generatedHaiku?.japanese?.map((line, index) => (\n
\n

{line}

\n

\n {generatedHaiku.english?.[index]}\n

\n
\n ))}\n {generatedHaiku?.japanese && generatedHaiku.japanese.length >= 2 && (\n
\n {(() => {\n const firstLine = generatedHaiku?.japanese?.[0];\n if (!firstLine) return null;\n const haikuIndex = haikus.findIndex((h: any) => h.japanese[0] === firstLine);\n const haiku = haikus[haikuIndex];\n if (!haiku?.image_names) return null;\n\n return haiku.image_names.map((imageName, imgIndex) => (\n {\n setHaikus(prevHaikus => {\n const newHaikus = prevHaikus.map((h, idx) => {\n if (idx === haikuIndex) {\n return {\n ...h,\n selectedImage: imageName\n };\n }\n return h;\n });\n return newHaikus;\n });\n }}\n />\n ));\n })()}\n
\n )}\n
\n
\n );\n}\n\ninterface Haiku {\n japanese: string[];\n english: string[];\n image_names: string[];\n selectedImage: string | null;\n}\n\nfunction Haiku() {\n const [haikus, setHaikus] = useState([{\n japanese: [\"仮の句よ\", \"まっさらながら\", \"花を呼ぶ\"],\n english: [\n \"A placeholder verse—\",\n \"even in a blank canvas,\",\n \"it beckons flowers.\",\n ],\n image_names: [],\n selectedImage: null,\n }])\n const [activeIndex, setActiveIndex] = useState(0);\n const [isJustApplied, setIsJustApplied] = useState(false);\n\n const validateAndCorrectImageNames = (rawNames: string[] | undefined): string[] | null => {\n if (!rawNames || rawNames.length !== 3) {\n return null;\n }\n\n const correctedNames: string[] = [];\n const usedValidNames = new Set();\n\n for (const name of rawNames) {\n if (VALID_IMAGE_NAMES.includes(name) && !usedValidNames.has(name)) {\n correctedNames.push(name);\n usedValidNames.add(name);\n if (correctedNames.length === 3) break;\n }\n }\n\n if (correctedNames.length < 3) {\n const availableFallbacks = VALID_IMAGE_NAMES.filter(name => !usedValidNames.has(name));\n for (let i = availableFallbacks.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n [availableFallbacks[i], availableFallbacks[j]] = [availableFallbacks[j], availableFallbacks[i]];\n }\n\n while (correctedNames.length < 3 && availableFallbacks.length > 0) {\n const fallbackName = availableFallbacks.pop();\n if (fallbackName) {\n correctedNames.push(fallbackName);\n }\n }\n }\n\n while (correctedNames.length < 3 && VALID_IMAGE_NAMES.length > 0) {\n const fallbackName = VALID_IMAGE_NAMES[Math.floor(Math.random() * VALID_IMAGE_NAMES.length)];\n correctedNames.push(fallbackName);\n }\n\n return correctedNames.slice(0, 3);\n };\n\n useCopilotAction({\n name: \"generate_haiku\",\n available: \"frontend\",\n parameters: [\n {\n name: \"japanese\",\n type: \"string[]\",\n },\n {\n name: \"english\",\n type: \"string[]\",\n },\n {\n name: \"image_names\",\n type: \"string[]\",\n description: \"Names of 3 relevant images\",\n },\n ],\n followUp: false,\n handler: async ({ japanese, english, image_names }: { japanese: string[], english: string[], image_names: string[] }) => {\n const finalCorrectedImages = validateAndCorrectImageNames(image_names);\n const newHaiku = {\n japanese: japanese || [],\n english: english || [],\n image_names: finalCorrectedImages || [],\n selectedImage: finalCorrectedImages?.[0] || null,\n };\n setHaikus(prev => [...prev, newHaiku]);\n setActiveIndex(haikus.length - 1);\n setIsJustApplied(true);\n setTimeout(() => setIsJustApplied(false), 600);\n return \"Haiku generated.\";\n },\n render: ({ args: generatedHaiku }: { args: Partial }) => {\n return (\n \n );\n },\n }, [haikus]);\n\n const generatedHaikus = useMemo(() => (\n haikus.filter((haiku) => haiku.english[0] !== \"A placeholder verse—\")\n ), [haikus]);\n\n const { isMobile } = useMobileView();\n\n return (\n
\n {/* Thumbnail List */}\n {Boolean(generatedHaikus.length) && !isMobile && (\n
\n {generatedHaikus.map((haiku, index) => (\n setActiveIndex(index)}\n >\n {haiku.japanese.map((line, lineIndex) => (\n \n

{line}

\n

{haiku.english?.[lineIndex]}

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n )}\n\n {/* Main Display */}\n
\n
\n {haikus.filter((_haiku: Haiku, index: number) => {\n if (haikus.length == 1) return true;\n else return index == activeIndex + 1;\n }).map((haiku, index) => (\n \n {haiku.japanese.map((line, lineIndex) => (\n \n

\n {line}\n

\n

\n {haiku.english?.[lineIndex]}\n

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n \n \n );\n}\n", "language": "typescript", "type": "file" }, @@ -306,7 +306,7 @@ }, { "name": "tool_based_generative_ui.py", - "content": "\"\"\"\nTool-based generative UI endpoint for the AG-UI protocol.\n\"\"\"\n\nimport uuid\nimport json\nfrom fastapi import Request\nfrom fastapi.responses import StreamingResponse\nfrom ag_ui.core import (\n RunAgentInput,\n EventType,\n RunStartedEvent,\n RunFinishedEvent,\n MessagesSnapshotEvent\n)\nfrom ag_ui.encoder import EventEncoder\n\nasync def tool_based_generative_ui_endpoint(input_data: RunAgentInput, request: Request):\n \"\"\"Tool-based generative UI endpoint\"\"\"\n # Get the accept header from the request\n accept_header = request.headers.get(\"accept\")\n\n # Create an event encoder to properly format SSE events\n encoder = EventEncoder(accept=accept_header)\n\n async def event_generator():\n # Send run started event\n yield encoder.encode(\n RunStartedEvent(\n type=EventType.RUN_STARTED,\n thread_id=input_data.thread_id,\n run_id=input_data.run_id\n ),\n )\n\n # Check if last message was a tool result\n last_message = None\n if input_data.messages and len(input_data.messages) > 0:\n last_message = input_data.messages[-1]\n\n # Determine what type of message to send\n if last_message and getattr(last_message, 'role', None) == \"tool\":\n # Send text message for tool result\n message_id = str(uuid.uuid4())\n new_message = {\n \"id\": message_id,\n \"role\": \"assistant\",\n \"content\": \"Haiku created\"\n }\n else:\n # Send tool call message\n tool_call_id = str(uuid.uuid4())\n message_id = str(uuid.uuid4())\n \n # Prepare haiku arguments\n haiku_args = {\n \"japanese\": [\"エーアイの\", \"橋つなぐ道\", \"コパキット\"],\n \"english\": [\n \"From AI's realm\",\n \"A bridge-road linking us—\",\n \"CopilotKit.\"\n ]\n }\n\n # Create new assistant message with tool call\n new_message = {\n \"id\": message_id,\n \"role\": \"assistant\",\n \"tool_calls\": [\n {\n \"id\": tool_call_id,\n \"type\": \"function\",\n \"function\": {\n \"name\": \"generate_haiku\",\n \"arguments\": json.dumps(haiku_args)\n }\n }\n ]\n }\n\n # Create messages list with input messages plus the new message\n all_messages = list(input_data.messages) + [new_message]\n\n # Send messages snapshot event\n yield encoder.encode(\n MessagesSnapshotEvent(\n type=EventType.MESSAGES_SNAPSHOT,\n messages=all_messages\n ),\n )\n\n # Send run finished event\n yield encoder.encode(\n RunFinishedEvent(\n type=EventType.RUN_FINISHED,\n thread_id=input_data.thread_id,\n run_id=input_data.run_id\n ),\n )\n\n return StreamingResponse(\n event_generator(),\n media_type=encoder.get_content_type()\n )\n", + "content": "\"\"\"\nTool-based generative UI endpoint for the AG-UI protocol.\n\"\"\"\n\nimport uuid\nimport json\nfrom fastapi import Request\nfrom fastapi.responses import StreamingResponse\nfrom ag_ui.core import (\n RunAgentInput,\n EventType,\n RunStartedEvent,\n RunFinishedEvent,\n MessagesSnapshotEvent\n)\nfrom ag_ui.encoder import EventEncoder\n\nasync def tool_based_generative_ui_endpoint(input_data: RunAgentInput, request: Request):\n \"\"\"Tool-based generative UI endpoint\"\"\"\n # Get the accept header from the request\n accept_header = request.headers.get(\"accept\")\n\n # Create an event encoder to properly format SSE events\n encoder = EventEncoder(accept=accept_header)\n\n async def event_generator():\n # Send run started event\n yield encoder.encode(\n RunStartedEvent(\n type=EventType.RUN_STARTED,\n thread_id=input_data.thread_id,\n run_id=input_data.run_id\n ),\n )\n\n # Check if last message was a tool result\n last_message = None\n if input_data.messages and len(input_data.messages) > 0:\n last_message = input_data.messages[-1]\n\n result_message = None\n\n # Determine what type of message to send\n if last_message and getattr(last_message, 'content', None) == \"thanks\":\n # Send text message for tool result\n message_id = str(uuid.uuid4())\n new_message = {\n \"id\": message_id,\n \"role\": \"assistant\",\n \"content\": \"Haiku created\"\n }\n else:\n # Send tool call message\n tool_call_id = str(uuid.uuid4())\n message_id = str(uuid.uuid4())\n\n # Prepare haiku arguments\n haiku_args = {\n \"japanese\": [\"エーアイの\", \"橋つなぐ道\", \"コパキット\"],\n \"english\": [\n \"From AI's realm\",\n \"A bridge-road linking us—\",\n \"CopilotKit.\"\n ]\n }\n\n # Create new assistant message with tool call\n new_message = {\n \"id\": message_id,\n \"role\": \"assistant\",\n \"tool_calls\": [\n {\n \"id\": tool_call_id,\n \"type\": \"function\",\n \"function\": {\n \"name\": \"generate_haiku\",\n \"arguments\": json.dumps(haiku_args)\n }\n }\n ]\n }\n\n result_message = {\n \"id\": str(uuid.uuid4()),\n \"role\": \"tool\",\n \"tool_call_id\": tool_call_id,\n \"content\": \"Haiku created\"\n }\n\n # Create messages list with input messages plus the new message\n all_messages = list(input_data.messages) + [new_message]\n\n if result_message:\n all_messages.append(result_message)\n\n # Send messages snapshot event\n yield encoder.encode(\n MessagesSnapshotEvent(\n type=EventType.MESSAGES_SNAPSHOT,\n messages=all_messages\n ),\n )\n\n # Send run finished event\n yield encoder.encode(\n RunFinishedEvent(\n type=EventType.RUN_FINISHED,\n thread_id=input_data.thread_id,\n run_id=input_data.run_id\n ),\n )\n\n return StreamingResponse(\n event_generator(),\n media_type=encoder.get_content_type()\n )\n", "language": "python", "type": "file" } @@ -410,7 +410,7 @@ }, { "name": "agent.py", - "content": "\"\"\"\nA simple agentic chat flow using LangGraph instead of CrewAI.\n\"\"\"\n\nfrom typing import List, Any\nimport os\n\n# Updated imports for LangGraph\nfrom langchain_core.runnables import RunnableConfig\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.graph import MessagesState\nfrom langgraph.types import Command\nfrom typing_extensions import Literal\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.messages import SystemMessage\n\nclass AgentState(MessagesState):\n tools: List[Any]\n\nasync def chat_node(state: AgentState, config: RunnableConfig):\n \"\"\"\n Standard chat node based on the ReAct design pattern. It handles:\n - The model to use (and binds in CopilotKit actions and the tools defined above)\n - The system prompt\n - Getting a response from the model\n - Handling tool calls\n\n For more about the ReAct design pattern, see: \n https://www.perplexity.ai/search/react-agents-NcXLQhreS0WDzpVaS4m9Cg\n \"\"\"\n \n # 1. Define the model\n model = ChatOpenAI(model=\"gpt-4o\")\n \n # Define config for the model\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # 2. Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n # your_tool_here\n ],\n\n # 2.1 Disable parallel tool calls to avoid race conditions,\n # enable this for faster performance if you want to manage\n # the complexity of running tool calls in parallel.\n parallel_tool_calls=False,\n )\n\n # 3. Define the system message by which the chat model will be run\n system_message = SystemMessage(\n content=f\"You are a helpful assistant. .\"\n )\n\n # 4. Run the model to generate a response\n response = await model_with_tools.ainvoke([\n system_message,\n *state[\"messages\"],\n ], config)\n\n # 6. We've handled all tool calls, so we can end the graph.\n return Command(\n goto=END,\n update={\n \"messages\": response\n }\n )\n\n# Define a new graph\nworkflow = StateGraph(AgentState)\nworkflow.add_node(\"chat_node\", chat_node)\nworkflow.set_entry_point(\"chat_node\")\n\n# Add explicit edges, matching the pattern in other examples\nworkflow.add_edge(START, \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n", + "content": "\"\"\"\nA simple agentic chat flow using LangGraph instead of CrewAI.\n\"\"\"\n\nfrom typing import List, Any, Optional\nimport os\n\n# Updated imports for LangGraph\nfrom langchain_core.runnables import RunnableConfig\nfrom langchain_core.messages import SystemMessage\nfrom langchain_openai import ChatOpenAI\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.graph import MessagesState\nfrom langgraph.types import Command\n\nclass AgentState(MessagesState):\n \"\"\"\n State of our graph.\n \"\"\"\n tools: List[Any]\n\nasync def chat_node(state: AgentState, config: Optional[RunnableConfig] = None):\n \"\"\"\n Standard chat node based on the ReAct design pattern. It handles:\n - The model to use (and binds in CopilotKit actions and the tools defined above)\n - The system prompt\n - Getting a response from the model\n - Handling tool calls\n\n For more about the ReAct design pattern, see: \n https://www.perplexity.ai/search/react-agents-NcXLQhreS0WDzpVaS4m9Cg\n \"\"\"\n\n # 1. Define the model\n model = ChatOpenAI(model=\"gpt-4o\")\n\n # Define config for the model\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # 2. Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n # your_tool_here\n ],\n\n # 2.1 Disable parallel tool calls to avoid race conditions,\n # enable this for faster performance if you want to manage\n # the complexity of running tool calls in parallel.\n parallel_tool_calls=False,\n )\n\n # 3. Define the system message by which the chat model will be run\n system_message = SystemMessage(\n content=\"You are a helpful assistant.\"\n )\n\n # 4. Run the model to generate a response\n response = await model_with_tools.ainvoke([\n system_message,\n *state[\"messages\"],\n ], config)\n\n # 6. We've handled all tool calls, so we can end the graph.\n return Command(\n goto=END,\n update={\n \"messages\": response\n }\n )\n\n# Define a new graph\nworkflow = StateGraph(AgentState)\nworkflow.add_node(\"chat_node\", chat_node)\nworkflow.set_entry_point(\"chat_node\")\n\n# Add explicit edges, matching the pattern in other examples\nworkflow.add_edge(START, \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n", "language": "python", "type": "file" }, @@ -442,7 +442,7 @@ }, { "name": "agent.py", - "content": "\"\"\"\nAn example demonstrating agentic generative UI using LangGraph.\n\"\"\"\n\nimport asyncio\nfrom typing import List, Any\nimport os\n\n# LangGraph imports\nfrom langchain_core.runnables import RunnableConfig\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command\nfrom langchain_core.callbacks.manager import adispatch_custom_event\nfrom langgraph.graph import MessagesState\n\n# OpenAI imports\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.messages import SystemMessage\n\n# This tool simulates performing a task on the server.\n# The tool call will be streamed to the frontend as it is being generated.\nPERFORM_TASK_TOOL = {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"generate_task_steps_generative_ui\",\n \"description\": \"Make up 10 steps (only a couple of words per step) that are required for a task. The step should be in gerund form (i.e. Digging hole, opening door, ...)\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"steps\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"description\": {\n \"type\": \"string\",\n \"description\": \"The text of the step in gerund form\"\n },\n \"status\": {\n \"type\": \"string\",\n \"enum\": [\"pending\"],\n \"description\": \"The status of the step, always 'pending'\"\n }\n },\n \"required\": [\"description\", \"status\"]\n },\n \"description\": \"An array of 10 step objects, each containing text and status\"\n }\n },\n \"required\": [\"steps\"]\n }\n }\n}\n\n\nclass AgentState(MessagesState):\n steps: List[dict] = []\n tools: List[Any]\n\n\nasync def start_flow(state: AgentState, config: RunnableConfig):\n \"\"\"\n This is the entry point for the flow.\n \"\"\"\n\n if \"steps\" not in state:\n state[\"steps\"] = []\n\n return Command(\n goto=\"chat_node\",\n update={\n \"messages\": state[\"messages\"],\n \"steps\": state[\"steps\"]\n }\n )\n\n\nasync def chat_node(state: AgentState, config: RunnableConfig):\n \"\"\"\n Standard chat node.\n \"\"\"\n system_prompt = \"\"\"\n You are a helpful assistant assisting with any task. \n When asked to do something, you MUST call the function `generate_task_steps_generative_ui`\n that was provided to you.\n If you called the function, you MUST NOT repeat the steps in your next response to the user.\n Just give a very brief summary (one sentence) of what you did with some emojis. \n Always say you actually did the steps, not merely generated them.\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o\")\n \n # Define config for the model with emit_intermediate_state to stream tool calls to frontend\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Use \"predict_state\" metadata to set up streaming for the write_document tool\n config[\"metadata\"][\"predict_state\"] = [{\n \"state_key\": \"steps\",\n \"tool\": \"generate_task_steps_generative_ui\",\n \"tool_argument\": \"steps\",\n }]\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n PERFORM_TASK_TOOL\n ],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model to generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n messages = state[\"messages\"] + [response]\n\n # Extract any tool calls from the response\n if hasattr(response, \"tool_calls\") and response.tool_calls and len(response.tool_calls) > 0:\n tool_call = response.tool_calls[0]\n \n # Handle tool_call as a dictionary rather than an object\n if isinstance(tool_call, dict):\n tool_call_id = tool_call[\"id\"]\n tool_call_name = tool_call[\"name\"]\n tool_call_args = tool_call[\"args\"]\n else:\n # Handle as an object (backward compatibility)\n tool_call_id = tool_call.id\n tool_call_name = tool_call.name\n tool_call_args = tool_call.args\n\n if tool_call_name == \"generate_task_steps_generative_ui\":\n steps = [{\"description\": step[\"description\"], \"status\": step[\"status\"]} for step in tool_call_args[\"steps\"]]\n \n # Add the tool response to messages\n tool_response = {\n \"role\": \"tool\",\n \"content\": \"Steps executed.\",\n \"tool_call_id\": tool_call_id\n }\n\n messages = messages + [tool_response]\n\n # Return Command to route to simulate_task_node\n for i, step in enumerate(steps):\n # simulate executing the step\n await asyncio.sleep(1)\n steps[i][\"status\"] = \"completed\"\n # Update the state with the completed step - using config as first parameter\n state[\"steps\"] = steps\n await adispatch_custom_event(\n \"manually_emit_state\",\n state,\n config=config,\n )\n \n return Command(\n goto='start_flow',\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"]\n }\n )\n\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"]\n }\n )\n\n\n# Define the graph\nworkflow = StateGraph(AgentState)\n\n# Add nodes\nworkflow.add_node(\"start_flow\", start_flow)\nworkflow.add_node(\"chat_node\", chat_node)\n\n# Add edges (equivalent to the routing in CrewAI)\nworkflow.set_entry_point(\"start_flow\")\nworkflow.add_edge(START, \"start_flow\")\nworkflow.add_edge(\"start_flow\", \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n", + "content": "\"\"\"\nAn example demonstrating agentic generative UI using LangGraph.\n\"\"\"\n\nimport asyncio\nfrom typing import List, Any, Optional, Annotated\nimport os\n\n# LangGraph imports\nfrom langchain_core.runnables import RunnableConfig\nfrom langchain_core.callbacks.manager import adispatch_custom_event\nfrom langchain_core.messages import SystemMessage\nfrom langchain_core.tools import tool\nfrom langchain_openai import ChatOpenAI\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command\nfrom langgraph.graph import MessagesState\nfrom pydantic import BaseModel, Field\n\nclass Step(BaseModel):\n \"\"\"\n A step in a task.\n \"\"\"\n description: str = Field(description=\"The text of the step in gerund form\")\n status: str = Field(description=\"The status of the step, always 'pending'\")\n\n\n\n# This tool simulates performing a task on the server.\n# The tool call will be streamed to the frontend as it is being generated.\n@tool\ndef generate_task_steps_generative_ui(\n steps: Annotated[ # pylint: disable=unused-argument\n List[Step],\n \"An array of 10 step objects, each containing text and status\"\n ]\n):\n \"\"\"\n Make up 10 steps (only a couple of words per step) that are required for a task.\n The step should be in gerund form (i.e. Digging hole, opening door, ...).\n \"\"\"\n\n\nclass AgentState(MessagesState):\n \"\"\"\n State of the agent.\n \"\"\"\n steps: List[dict] = []\n tools: List[Any]\n\n\nasync def start_node(state: AgentState, config: RunnableConfig): # pylint: disable=unused-argument\n \"\"\"\n This is the entry point for the flow.\n \"\"\"\n\n if \"steps\" not in state:\n state[\"steps\"] = []\n\n return Command(\n goto=\"chat_node\",\n update={\n \"messages\": state[\"messages\"],\n \"steps\": state[\"steps\"]\n }\n )\n\n\nasync def chat_node(state: AgentState, config: Optional[RunnableConfig] = None):\n \"\"\"\n Standard chat node.\n \"\"\"\n system_prompt = \"\"\"\n You are a helpful assistant assisting with any task. \n When asked to do something, you MUST call the function `generate_task_steps_generative_ui`\n that was provided to you.\n If you called the function, you MUST NOT repeat the steps in your next response to the user.\n Just give a very brief summary (one sentence) of what you did with some emojis. \n Always say you actually did the steps, not merely generated them.\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o\")\n\n # Define config for the model with emit_intermediate_state to stream tool calls to frontend\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Use \"predict_state\" metadata to set up streaming for the write_document tool\n config[\"metadata\"][\"predict_state\"] = [{\n \"state_key\": \"steps\",\n \"tool\": \"generate_task_steps_generative_ui\",\n \"tool_argument\": \"steps\",\n }]\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n generate_task_steps_generative_ui\n ],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model to generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n messages = state[\"messages\"] + [response]\n\n # Extract any tool calls from the response\n if hasattr(response, \"tool_calls\") and response.tool_calls and len(response.tool_calls) > 0:\n # Handle dicts or object (backward compatibility)\n tool_call = (response.tool_calls[0]\n if isinstance(response.tool_calls[0], dict)\n else vars(response.tool_calls[0]))\n\n if tool_call[\"name\"] == \"generate_task_steps_generative_ui\":\n steps = [\n {\"description\": step[\"description\"], \"status\": step[\"status\"]}\n for step in tool_call[\"args\"][\"steps\"]\n ]\n\n # Add the tool response to messages\n tool_response = {\n \"role\": \"tool\",\n \"content\": \"Steps executed.\",\n \"tool_call_id\": tool_call[\"id\"]\n }\n\n messages = messages + [tool_response]\n state[\"steps\"] = steps\n\n # Return Command to route to simulate_task_node\n for i, _ in enumerate(steps):\n # simulate executing the step\n await asyncio.sleep(1)\n steps[i][\"status\"] = \"completed\"\n # Update the state with the completed step using config\n await adispatch_custom_event(\n \"manually_emit_state\",\n state,\n config=config,\n )\n\n return Command(\n goto='start_node',\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"]\n }\n )\n\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"]\n }\n )\n\n\n# Define the graph\nworkflow = StateGraph(AgentState)\n\n# Add nodes\nworkflow.add_node(\"start_node\", start_node)\nworkflow.add_node(\"chat_node\", chat_node)\n\n# Add edges\nworkflow.set_entry_point(\"start_node\")\nworkflow.add_edge(START, \"start_node\")\nworkflow.add_edge(\"start_node\", \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n", "language": "python", "type": "file" }, @@ -474,7 +474,7 @@ }, { "name": "agent.py", - "content": "\"\"\"\nA LangGraph implementation of the human-in-the-loop agent.\n\"\"\"\n\nimport json\nfrom typing import Dict, List, Any\nimport os\n\n# LangGraph imports\nfrom langchain_core.runnables import RunnableConfig\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command, interrupt\nfrom langgraph.graph import MessagesState\n\n# LLM imports\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.messages import SystemMessage\n\nDEFINE_TASK_TOOL = {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"plan_execution_steps\",\n \"description\": \"Make up 10 steps (only a couple of words per step) that are required for a task. The step should be in imperative form (i.e. Dig hole, Open door, ...)\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"steps\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"description\": {\n \"type\": \"string\",\n \"description\": \"The text of the step in imperative form\"\n },\n \"status\": {\n \"type\": \"string\",\n \"enum\": [\"enabled\"],\n \"description\": \"The status of the step, always 'enabled'\"\n }\n },\n \"required\": [\"description\", \"status\"]\n },\n \"description\": \"An array of 10 step objects, each containing text and status\"\n }\n },\n \"required\": [\"steps\"]\n }\n }\n}\n\nclass AgentState(MessagesState):\n steps: List[Dict[str, str]] = []\n tools: List[Any]\n\nasync def start_flow(state: Dict[str, Any], config: RunnableConfig):\n \"\"\"\n This is the entry point for the flow.\n \"\"\"\n\n # Initialize steps list if not exists\n if \"steps\" not in state:\n state[\"steps\"] = []\n\n \n return Command(\n goto=\"chat_node\",\n update={\n \"messages\": state[\"messages\"],\n \"steps\": state[\"steps\"],\n }\n )\n\n\nasync def chat_node(state: Dict[str, Any], config: RunnableConfig):\n \"\"\"\n Standard chat node where the agent processes messages and generates responses.\n If task steps are defined, the user can enable/disable them using interrupts.\n \"\"\"\n system_prompt = \"\"\"\n You are a helpful assistant that can perform any task.\n You MUST call the `plan_execution_steps` function when the user asks you to perform a task.\n Always make sure you will provide tasks based on the user query\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o-mini\")\n \n # Define config for the model\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Use \"predict_state\" metadata to set up streaming for the write_document tool\n config[\"metadata\"][\"predict_state\"] = [{\n \"state_key\": \"steps\",\n \"tool\": \"plan_execution_steps\",\n \"tool_argument\": \"steps\"\n }]\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n DEFINE_TASK_TOOL\n ],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model and generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n # Update messages with the response\n messages = state[\"messages\"] + [response]\n \n # Handle tool calls\n if hasattr(response, \"tool_calls\") and response.tool_calls and len(response.tool_calls) > 0:\n tool_call = response.tool_calls[0]\n # Extract tool call information\n if hasattr(tool_call, \"id\"):\n tool_call_id = tool_call.id\n tool_call_name = tool_call.name\n tool_call_args = tool_call.args if not isinstance(tool_call.args, str) else json.loads(tool_call.args)\n else:\n tool_call_id = tool_call.get(\"id\", \"\")\n tool_call_name = tool_call.get(\"name\", \"\")\n args = tool_call.get(\"args\", {})\n tool_call_args = args if not isinstance(args, str) else json.loads(args)\n\n if tool_call_name == \"plan_execution_steps\":\n # Get the steps from the tool call\n steps_raw = tool_call_args.get(\"steps\", [])\n \n # Set initial status to \"enabled\" for all steps\n steps_data = []\n \n # Handle different potential formats of steps data\n if isinstance(steps_raw, list):\n for step in steps_raw:\n if isinstance(step, dict) and \"description\" in step:\n steps_data.append({\n \"description\": step[\"description\"],\n \"status\": \"enabled\"\n })\n elif isinstance(step, str):\n steps_data.append({\n \"description\": step,\n \"status\": \"enabled\"\n })\n \n # If no steps were processed correctly, return to END with the updated messages\n if not steps_data:\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"],\n }\n )\n # Update steps in state and emit to frontend\n state[\"steps\"] = steps_data\n \n # Add a tool response to satisfy OpenAI's requirements\n tool_response = {\n \"role\": \"tool\",\n \"content\": \"Task steps generated.\",\n \"tool_call_id\": tool_call_id\n }\n \n messages = messages + [tool_response]\n\n # Move to the process_steps_node which will handle the interrupt and final response\n return Command(\n goto=\"process_steps_node\",\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"],\n }\n )\n\n # If no tool calls or not plan_execution_steps, return to END with the updated messages\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"],\n }\n )\n\n\nasync def process_steps_node(state: Dict[str, Any], config: RunnableConfig):\n \"\"\"\n This node handles the user interrupt for step customization and generates the final response.\n \"\"\"\n\n # Check if we already have a user_response in the state\n # This happens when the node restarts after an interrupt\n if \"user_response\" in state and state[\"user_response\"]:\n user_response = state[\"user_response\"]\n else:\n # Use LangGraph interrupt to get user input on steps\n # This will pause execution and wait for user input in the frontend\n user_response = interrupt({\"steps\": state[\"steps\"]})\n # Store the user response in state for when the node restarts\n state[\"user_response\"] = user_response\n\n # Generate the creative completion response\n final_prompt = \"\"\"\n Provide a textual description of how you are performing the task.\n If the user has disabled a step, you are not allowed to perform that step.\n However, you should find a creative workaround to perform the task, and if an essential step is disabled, you can even use\n some humor in the description of how you are performing the task.\n Don't just repeat a list of steps, come up with a creative but short description (3 sentences max) of how you are performing the task.\n \"\"\"\n \n final_response = await ChatOpenAI(model=\"gpt-4o\").ainvoke([\n SystemMessage(content=final_prompt),\n {\"role\": \"user\", \"content\": user_response}\n ], config)\n\n # Add the final response to messages\n messages = state[\"messages\"] + [final_response]\n\n # Clear the user_response from state to prepare for future interactions\n if \"user_response\" in state:\n state.pop(\"user_response\")\n\n # Return to END with the updated messages\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"],\n }\n )\n\n\n# Define the graph\nworkflow = StateGraph(AgentState)\n\n# Add nodes\nworkflow.add_node(\"start_flow\", start_flow)\nworkflow.add_node(\"chat_node\", chat_node)\nworkflow.add_node(\"process_steps_node\", process_steps_node)\n\n# Add edges\nworkflow.set_entry_point(\"start_flow\")\nworkflow.add_edge(START, \"start_flow\")\nworkflow.add_edge(\"start_flow\", \"chat_node\")\n# workflow.add_edge(\"chat_node\", \"process_steps_node\") # Removed unconditional edge\nworkflow.add_edge(\"process_steps_node\", END)\n# workflow.add_edge(\"chat_node\", END) # Removed unconditional edge\n\n# Add conditional edges from chat_node\ndef should_continue(command: Command):\n if command.goto == \"process_steps_node\":\n return \"process_steps_node\"\n else:\n return END\n\nworkflow.add_conditional_edges(\n \"chat_node\",\n should_continue,\n {\n \"process_steps_node\": \"process_steps_node\",\n END: END,\n },\n)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n", + "content": "\"\"\"\nA LangGraph implementation of the human-in-the-loop agent.\n\"\"\"\n\nfrom typing import Dict, List, Any, Annotated, Optional\nimport os\n\n# LangGraph imports\nfrom langchain_core.runnables import RunnableConfig\nfrom langchain_core.messages import SystemMessage\nfrom langchain_core.tools import tool\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command, interrupt\nfrom langgraph.graph import MessagesState\nfrom langchain_openai import ChatOpenAI\nfrom pydantic import BaseModel, Field\n\nclass Step(BaseModel):\n \"\"\"\n A step in a task.\n \"\"\"\n description: str = Field(description=\"The text of the step in imperative form\")\n status: str = Field(description=\"The status of the step, always 'enabled'\")\n\n@tool\ndef plan_execution_steps(\n steps: Annotated[ # pylint: disable=unused-argument\n List[Step],\n \"An array of 10 step objects, each containing text and status\"\n ]\n):\n \"\"\"\n Make up 10 steps (only a couple of words per step) that are required for a task.\n The step should be in imperative form (i.e. Dig hole, Open door, ...).\n \"\"\"\n\nclass AgentState(MessagesState):\n \"\"\"\n State of the agent.\n \"\"\"\n steps: List[Dict[str, str]] = []\n tools: List[Any]\n\nasync def start_node(state: Dict[str, Any], config: RunnableConfig): # pylint: disable=unused-argument\n \"\"\"\n This is the entry point for the flow.\n \"\"\"\n\n # Initialize steps list if not exists\n if \"steps\" not in state:\n state[\"steps\"] = []\n\n # Return command to route to chat_node\n return Command(\n goto=\"chat_node\",\n update={\n \"messages\": state[\"messages\"],\n \"steps\": state[\"steps\"],\n }\n )\n\n\nasync def chat_node(state: AgentState, config: Optional[RunnableConfig] = None):\n \"\"\"\n Standard chat node where the agent processes messages and generates responses.\n If task steps are defined, the user can enable/disable them using interrupts.\n \"\"\"\n system_prompt = \"\"\"\n You are a helpful assistant that can perform any task.\n You MUST call the `plan_execution_steps` function when the user asks you to perform a task.\n Always make sure you will provide tasks based on the user query\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o-mini\")\n\n # Define config for the model\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Use \"predict_state\" metadata to set up streaming for the write_document tool\n config[\"metadata\"][\"predict_state\"] = [{\n \"state_key\": \"steps\",\n \"tool\": \"plan_execution_steps\",\n \"tool_argument\": \"steps\"\n }]\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n plan_execution_steps\n ],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model and generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n # Update messages with the response\n messages = state[\"messages\"] + [response]\n\n # Handle tool calls\n if hasattr(response, \"tool_calls\") and response.tool_calls and len(response.tool_calls) > 0:\n # Handle dicts or object (backward compatibility)\n tool_call = (response.tool_calls[0]\n if isinstance(response.tool_calls[0], dict)\n else vars(response.tool_calls[0]))\n\n if tool_call[\"name\"] == \"plan_execution_steps\":\n # Get the steps from the tool call\n steps_raw = tool_call[\"args\"][\"steps\"]\n\n # Set initial status to \"enabled\" for all steps\n steps_data = []\n\n # Handle different potential formats of steps data\n if isinstance(steps_raw, list):\n for step in steps_raw:\n if isinstance(step, dict) and \"description\" in step:\n steps_data.append({\n \"description\": step[\"description\"],\n \"status\": \"enabled\"\n })\n elif isinstance(step, str):\n steps_data.append({\n \"description\": step,\n \"status\": \"enabled\"\n })\n\n # If no steps were processed correctly, return to END with the updated messages\n if not steps_data:\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"],\n }\n )\n # Update steps in state and emit to frontend\n state[\"steps\"] = steps_data\n\n # Add a tool response to satisfy OpenAI's requirements\n tool_response = {\n \"role\": \"tool\",\n \"content\": \"Task steps generated.\",\n \"tool_call_id\": tool_call[\"id\"]\n }\n\n messages = messages + [tool_response]\n\n # Move to the process_steps_node which will handle the interrupt and final response\n return Command(\n goto=\"process_steps_node\",\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"],\n }\n )\n\n # If no tool calls or not plan_execution_steps, return to END with the updated messages\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"],\n }\n )\n\n\nasync def process_steps_node(state: Dict[str, Any], config: RunnableConfig):\n \"\"\"\n This node handles the user interrupt for step customization and generates the final response.\n \"\"\"\n\n # Check if we already have a user_response in the state\n # This happens when the node restarts after an interrupt\n if \"user_response\" in state and state[\"user_response\"]:\n user_response = state[\"user_response\"]\n else:\n # Use LangGraph interrupt to get user input on steps\n # This will pause execution and wait for user input in the frontend\n user_response = interrupt({\"steps\": state[\"steps\"]})\n # Store the user response in state for when the node restarts\n state[\"user_response\"] = user_response\n\n # Generate the creative completion response\n final_prompt = \"\"\"\n Provide a textual description of how you are performing the task.\n If the user has disabled a step, you are not allowed to perform that step.\n However, you should find a creative workaround to perform the task, and if an essential step is disabled, you can even use\n some humor in the description of how you are performing the task.\n Don't just repeat a list of steps, come up with a creative but short description (3 sentences max) of how you are performing the task.\n \"\"\"\n\n final_response = await ChatOpenAI(model=\"gpt-4o\").ainvoke([\n SystemMessage(content=final_prompt),\n {\"role\": \"user\", \"content\": user_response}\n ], config)\n\n # Add the final response to messages\n messages = state[\"messages\"] + [final_response]\n\n # Clear the user_response from state to prepare for future interactions\n if \"user_response\" in state:\n state.pop(\"user_response\")\n\n # Return to END with the updated messages\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"],\n }\n )\n\n\n# Define the graph\nworkflow = StateGraph(AgentState)\n\n# Add nodes\nworkflow.add_node(\"start_node\", start_node)\nworkflow.add_node(\"chat_node\", chat_node)\nworkflow.add_node(\"process_steps_node\", process_steps_node)\n\n# Add edges\nworkflow.set_entry_point(\"start_node\")\nworkflow.add_edge(START, \"start_node\")\nworkflow.add_edge(\"start_node\", \"chat_node\")\nworkflow.add_edge(\"process_steps_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n", "language": "python", "type": "file" }, @@ -506,7 +506,7 @@ }, { "name": "agent.py", - "content": "\"\"\"\nA demo of predictive state updates using LangGraph.\n\"\"\"\n\nimport uuid\nfrom typing import List, Any, Optional\nimport os\n\n# LangGraph imports\nfrom langchain_core.runnables import RunnableConfig\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command\nfrom langgraph.graph import MessagesState\n# OpenAI imports\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.messages import SystemMessage\n\nWRITE_DOCUMENT_TOOL = {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"write_document_local\",\n \"description\": \" \".join(\"\"\"\n Write a document. Use markdown formatting to format the document.\n It's good to format the document extensively so it's easy to read.\n You can use all kinds of markdown.\n However, do not use italic or strike-through formatting, it's reserved for another purpose.\n You MUST write the full document, even when changing only a few words.\n When making edits to the document, try to make them minimal - do not change every word.\n Keep stories SHORT!\n \"\"\".split()),\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"document\": {\n \"type\": \"string\",\n \"description\": \"The document to write\"\n },\n },\n }\n }\n}\n\n\nclass AgentState(MessagesState):\n \"\"\"\n The state of the agent.\n \"\"\"\n document: Optional[str] = None\n tools: List[Any]\n\n\nasync def start_flow(state: AgentState, config: RunnableConfig):\n \"\"\"\n This is the entry point for the flow.\n \"\"\"\n return Command(\n goto=\"chat_node\"\n )\n\n\nasync def chat_node(state: AgentState, config: RunnableConfig):\n \"\"\"\n Standard chat node.\n \"\"\"\n\n system_prompt = f\"\"\"\n You are a helpful assistant for writing documents.\n To write the document, you MUST use the write_document_local tool.\n You MUST write the full document, even when changing only a few words.\n When you wrote the document, DO NOT repeat it as a message.\n Just briefly summarize the changes you made. 2 sentences max.\n This is the current state of the document: ----\\n {state.get('document')}\\n-----\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o\")\n\n # Define config for the model with emit_intermediate_state to stream tool calls to frontend\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Use \"predict_state\" metadata to set up streaming for the write_document_local tool\n config[\"metadata\"][\"predict_state\"] = [{\n \"state_key\": \"document\",\n \"tool\": \"write_document_local\",\n \"tool_argument\": \"document\"\n }]\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n WRITE_DOCUMENT_TOOL\n ],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model to generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n # Update messages with the response\n messages = state[\"messages\"] + [response]\n\n # Extract any tool calls from the response\n if hasattr(response, \"tool_calls\") and response.tool_calls:\n tool_call = response.tool_calls[0]\n\n # Handle tool_call as a dictionary or an object\n if isinstance(tool_call, dict):\n tool_call_id = tool_call[\"id\"]\n tool_call_name = tool_call[\"name\"]\n tool_call_args = tool_call[\"args\"]\n else:\n # Handle as an object (backward compatibility)\n tool_call_id = tool_call.id\n tool_call_name = tool_call.name\n tool_call_args = tool_call.args\n\n if tool_call_name == \"write_document_local\":\n # Add the tool response to messages\n tool_response = {\n \"role\": \"tool\",\n \"content\": \"Document written.\",\n \"tool_call_id\": tool_call_id\n }\n\n # Add confirmation tool call\n confirm_tool_call = {\n \"role\": \"assistant\",\n \"content\": \"\",\n \"tool_calls\": [{\n \"id\": str(uuid.uuid4()),\n \"function\": {\n \"name\": \"confirm_changes\",\n \"arguments\": \"{}\"\n }\n }]\n }\n\n messages = messages + [tool_response, confirm_tool_call]\n\n # Return Command to route to end\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"document\": tool_call_args[\"document\"]\n }\n )\n\n # If no tool was called, go to end\n return Command(\n goto=END,\n update={\n \"messages\": messages\n }\n )\n\n\n# Define the graph\nworkflow = StateGraph(AgentState)\n\n# Add nodes\nworkflow.add_node(\"start_flow\", start_flow)\nworkflow.add_node(\"chat_node\", chat_node)\n\n# Add edges\nworkflow.set_entry_point(\"start_flow\")\nworkflow.add_edge(START, \"start_flow\")\nworkflow.add_edge(\"start_flow\", \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n\n", + "content": "\"\"\"\nA demo of predictive state updates using LangGraph.\n\"\"\"\n\nimport uuid\nfrom typing import List, Any, Optional\nimport os\n\n# LangGraph imports\nfrom langchain_core.runnables import RunnableConfig\nfrom langchain_core.messages import SystemMessage\nfrom langchain_core.tools import tool\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command\nfrom langgraph.graph import MessagesState\nfrom langgraph.checkpoint.memory import MemorySaver\nfrom langchain_openai import ChatOpenAI\n\n@tool\ndef write_document_local(document: str): # pylint: disable=unused-argument\n \"\"\"\n Write a document. Use markdown formatting to format the document.\n It's good to format the document extensively so it's easy to read.\n You can use all kinds of markdown.\n However, do not use italic or strike-through formatting, it's reserved for another purpose.\n You MUST write the full document, even when changing only a few words.\n When making edits to the document, try to make them minimal - do not change every word.\n Keep stories SHORT!\n \"\"\"\n return document\n\nclass AgentState(MessagesState):\n \"\"\"\n The state of the agent.\n \"\"\"\n document: Optional[str] = None\n tools: List[Any]\n\n\nasync def start_node(state: AgentState, config: RunnableConfig): # pylint: disable=unused-argument\n \"\"\"\n This is the entry point for the flow.\n \"\"\"\n return Command(\n goto=\"chat_node\"\n )\n\n\nasync def chat_node(state: AgentState, config: Optional[RunnableConfig] = None):\n \"\"\"\n Standard chat node.\n \"\"\"\n\n system_prompt = f\"\"\"\n You are a helpful assistant for writing documents.\n To write the document, you MUST use the write_document_local tool.\n You MUST write the full document, even when changing only a few words.\n When you wrote the document, DO NOT repeat it as a message.\n Just briefly summarize the changes you made. 2 sentences max.\n This is the current state of the document: ----\\n {state.get('document')}\\n-----\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o\")\n\n # Define config for the model with emit_intermediate_state to stream tool calls to frontend\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Use \"predict_state\" metadata to set up streaming for the write_document_local tool\n config[\"metadata\"][\"predict_state\"] = [{\n \"state_key\": \"document\",\n \"tool\": \"write_document_local\",\n \"tool_argument\": \"document\"\n }]\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n write_document_local\n ],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model to generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n # Update messages with the response\n messages = state[\"messages\"] + [response]\n\n # Extract any tool calls from the response\n if hasattr(response, \"tool_calls\") and response.tool_calls:\n tool_call = response.tool_calls[0]\n\n # Handle tool_call as a dictionary or an object\n if isinstance(tool_call, dict):\n tool_call_id = tool_call[\"id\"]\n tool_call_name = tool_call[\"name\"]\n tool_call_args = tool_call[\"args\"]\n else:\n # Handle as an object (backward compatibility)\n tool_call_id = tool_call.id\n tool_call_name = tool_call.name\n tool_call_args = tool_call.args\n\n if tool_call_name == \"write_document_local\":\n # Add the tool response to messages\n tool_response = {\n \"role\": \"tool\",\n \"content\": \"Document written.\",\n \"tool_call_id\": tool_call_id\n }\n\n # Add confirmation tool call\n confirm_tool_call = {\n \"role\": \"assistant\",\n \"content\": \"\",\n \"tool_calls\": [{\n \"id\": str(uuid.uuid4()),\n \"function\": {\n \"name\": \"confirm_changes\",\n \"arguments\": \"{}\"\n }\n }]\n }\n\n messages = messages + [tool_response, confirm_tool_call]\n\n # Return Command to route to end\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"document\": tool_call_args[\"document\"]\n }\n )\n\n # If no tool was called, go to end\n return Command(\n goto=END,\n update={\n \"messages\": messages\n }\n )\n\n\n# Define the graph\nworkflow = StateGraph(AgentState)\nworkflow.add_node(\"start_node\", start_node)\nworkflow.add_node(\"chat_node\", chat_node)\nworkflow.set_entry_point(\"start_node\")\nworkflow.add_edge(START, \"start_node\")\nworkflow.add_edge(\"start_node\", \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n\n", "language": "python", "type": "file" }, @@ -538,7 +538,7 @@ }, { "name": "agent.py", - "content": "\"\"\"\nA demo of shared state between the agent and CopilotKit using LangGraph.\n\"\"\"\n\nimport json\nfrom enum import Enum\nfrom typing import Dict, List, Any, Optional\nimport os\n\n# LangGraph imports\nfrom langchain_core.runnables import RunnableConfig\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command\nfrom langchain_core.callbacks.manager import adispatch_custom_event\nfrom langgraph.graph import MessagesState\n# OpenAI imports\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.messages import SystemMessage\n\nclass SkillLevel(str, Enum):\n \"\"\"\n The level of skill required for the recipe.\n \"\"\"\n BEGINNER = \"Beginner\"\n INTERMEDIATE = \"Intermediate\"\n ADVANCED = \"Advanced\"\n\n\nclass SpecialPreferences(str, Enum):\n \"\"\"\n Special preferences for the recipe.\n \"\"\"\n HIGH_PROTEIN = \"High Protein\"\n LOW_CARB = \"Low Carb\"\n SPICY = \"Spicy\"\n BUDGET_FRIENDLY = \"Budget-Friendly\"\n ONE_POT_MEAL = \"One-Pot Meal\"\n VEGETARIAN = \"Vegetarian\"\n VEGAN = \"Vegan\"\n\n\nclass CookingTime(str, Enum):\n \"\"\"\n The cooking time of the recipe.\n \"\"\"\n FIVE_MIN = \"5 min\"\n FIFTEEN_MIN = \"15 min\"\n THIRTY_MIN = \"30 min\"\n FORTY_FIVE_MIN = \"45 min\"\n SIXTY_PLUS_MIN = \"60+ min\"\n\n\nGENERATE_RECIPE_TOOL = {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"generate_recipe\",\n \"description\": \" \".join(\"\"\"Using the existing (if any) ingredients and instructions, proceed with the recipe to finish it.\n Make sure the recipe is complete. ALWAYS provide the entire recipe, not just the changes.\"\"\".split()),\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"recipe\": {\n \"type\": \"object\",\n \"properties\": {\n \"skill_level\": {\n \"type\": \"string\",\n \"enum\": [level.value for level in SkillLevel],\n \"description\": \"The skill level required for the recipe\"\n },\n \"special_preferences\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\",\n \"enum\": [preference.value for preference in SpecialPreferences]\n },\n \"description\": \"A list of special preferences for the recipe\"\n },\n \"cooking_time\": {\n \"type\": \"string\",\n \"enum\": [time.value for time in CookingTime],\n \"description\": \"The cooking time of the recipe\"\n },\n \"ingredients\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"icon\": {\"type\": \"string\", \"description\": \"The icon emoji (not emoji code like '\\x1f35e', but the actual emoji like 🥕) of the ingredient\"},\n \"name\": {\"type\": \"string\"},\n \"amount\": {\"type\": \"string\"}\n }\n },\n \"description\": \"Entire list of ingredients for the recipe, including the new ingredients and the ones that are already in the recipe\"\n },\n \"instructions\": {\n \"type\": \"array\",\n \"items\": {\"type\": \"string\"},\n \"description\": \"Entire list of instructions for the recipe, including the new instructions and the ones that are already there\"\n },\n \"changes\": {\n \"type\": \"string\",\n \"description\": \"A description of the changes made to the recipe\"\n }\n },\n }\n },\n \"required\": [\"recipe\"]\n }\n }\n}\n\n\nclass AgentState(MessagesState):\n \"\"\"\n The state of the recipe.\n \"\"\"\n recipe: Optional[Dict[str, Any]] = None\n tools: List[Any]\n\n\nasync def start_flow(state: Dict[str, Any], config: RunnableConfig):\n \"\"\"\n This is the entry point for the flow.\n \"\"\"\n\n # Initialize recipe if not exists\n if \"recipe\" not in state or state[\"recipe\"] is None:\n state[\"recipe\"] = {\n \"skill_level\": SkillLevel.BEGINNER.value,\n \"special_preferences\": [],\n \"cooking_time\": CookingTime.FIFTEEN_MIN.value,\n \"ingredients\": [{\"icon\": \"🍴\", \"name\": \"Sample Ingredient\", \"amount\": \"1 unit\"}],\n \"instructions\": [\"First step instruction\"]\n }\n # Emit the initial state to ensure it's properly shared with the frontend\n await adispatch_custom_event(\n \"manually_emit_intermediate_state\",\n state,\n config=config,\n )\n \n return Command(\n goto=\"chat_node\",\n update={\n \"messages\": state[\"messages\"],\n \"recipe\": state[\"recipe\"]\n }\n )\n\n\nasync def chat_node(state: Dict[str, Any], config: RunnableConfig):\n \"\"\"\n Standard chat node.\n \"\"\"\n # Create a safer serialization of the recipe\n recipe_json = \"No recipe yet\"\n if \"recipe\" in state and state[\"recipe\"] is not None:\n try:\n recipe_json = json.dumps(state[\"recipe\"], indent=2)\n except Exception as e:\n recipe_json = f\"Error serializing recipe: {str(e)}\"\n \n system_prompt = f\"\"\"You are a helpful assistant for creating recipes. \n This is the current state of the recipe: {recipe_json}\n You can improve the recipe by calling the generate_recipe tool.\n \n IMPORTANT:\n 1. Create a recipe using the existing ingredients and instructions. Make sure the recipe is complete.\n 2. For ingredients, append new ingredients to the existing ones.\n 3. For instructions, append new steps to the existing ones.\n 4. 'ingredients' is always an array of objects with 'icon', 'name', and 'amount' fields\n 5. 'instructions' is always an array of strings\n\n If you have just created or modified the recipe, just answer in one sentence what you did. dont describe the recipe, just say what you did.\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o-mini\")\n \n # Define config for the model\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Use \"predict_state\" metadata to set up streaming for the write_document tool\n config[\"metadata\"][\"predict_state\"] = [{\n \"state_key\": \"recipe\",\n \"tool\": \"generate_recipe\",\n \"tool_argument\": \"recipe\"\n }]\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n GENERATE_RECIPE_TOOL\n ],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model and generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n # Update messages with the response\n messages = state[\"messages\"] + [response]\n \n # Handle tool calls\n if hasattr(response, \"tool_calls\") and response.tool_calls:\n tool_call = response.tool_calls[0]\n \n # Handle tool_call as a dictionary or an object\n if isinstance(tool_call, dict):\n tool_call_id = tool_call[\"id\"]\n tool_call_name = tool_call[\"name\"]\n # Check if args is already a dict or needs to be parsed\n if isinstance(tool_call[\"args\"], dict):\n tool_call_args = tool_call[\"args\"]\n else:\n tool_call_args = json.loads(tool_call[\"args\"])\n else:\n # Handle as an object\n tool_call_id = tool_call.id\n tool_call_name = tool_call.name\n # Check if args is already a dict or needs to be parsed\n if isinstance(tool_call.args, dict):\n tool_call_args = tool_call.args\n else:\n tool_call_args = json.loads(tool_call.args)\n\n if tool_call_name == \"generate_recipe\":\n # Update recipe state with tool_call_args\n recipe_data = tool_call_args[\"recipe\"]\n \n # If we have an existing recipe, update it\n if \"recipe\" in state and state[\"recipe\"] is not None:\n recipe = state[\"recipe\"]\n for key, value in recipe_data.items():\n if value is not None: # Only update fields that were provided\n recipe[key] = value\n else:\n # Create a new recipe\n recipe = {\n \"skill_level\": recipe_data.get(\"skill_level\", SkillLevel.BEGINNER.value),\n \"special_preferences\": recipe_data.get(\"special_preferences\", []),\n \"cooking_time\": recipe_data.get(\"cooking_time\", CookingTime.FIFTEEN_MIN.value),\n \"ingredients\": recipe_data.get(\"ingredients\", []),\n \"instructions\": recipe_data.get(\"instructions\", [])\n }\n \n # Add tool response to messages\n tool_response = {\n \"role\": \"tool\",\n \"content\": \"Recipe generated.\",\n \"tool_call_id\": tool_call_id\n }\n \n messages = messages + [tool_response]\n \n # Explicitly emit the updated state to ensure it's shared with frontend\n state[\"recipe\"] = recipe\n await adispatch_custom_event(\n \"manually_emit_intermediate_state\",\n state,\n config=config,\n )\n \n # Return command with updated recipe\n return Command(\n goto=\"start_flow\",\n update={\n \"messages\": messages,\n \"recipe\": recipe\n }\n )\n\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"recipe\": state[\"recipe\"]\n }\n )\n\n\n# Define the graph\nworkflow = StateGraph(AgentState)\n\n# Add nodes\nworkflow.add_node(\"start_flow\", start_flow)\nworkflow.add_node(\"chat_node\", chat_node)\n\n# Add edges\nworkflow.set_entry_point(\"start_flow\")\nworkflow.add_edge(START, \"start_flow\")\nworkflow.add_edge(\"start_flow\", \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n", + "content": "\"\"\"\nA demo of shared state between the agent and CopilotKit using LangGraph.\n\"\"\"\n\nimport json\nfrom enum import Enum\nfrom typing import Dict, List, Any, Optional\nimport os\n\n# LangGraph imports\nfrom pydantic import BaseModel, Field\nfrom langchain_core.runnables import RunnableConfig\nfrom langchain_core.callbacks.manager import adispatch_custom_event\nfrom langchain_core.messages import SystemMessage\nfrom langchain_core.tools import tool\nfrom langchain_openai import ChatOpenAI\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command\nfrom langgraph.graph import MessagesState\nfrom langgraph.checkpoint.memory import MemorySaver\n\nclass SkillLevel(str, Enum):\n \"\"\"\n The level of skill required for the recipe.\n \"\"\"\n BEGINNER = \"Beginner\"\n INTERMEDIATE = \"Intermediate\"\n ADVANCED = \"Advanced\"\n\nclass SpecialPreferences(str, Enum):\n \"\"\"\n Special preferences for the recipe.\n \"\"\"\n HIGH_PROTEIN = \"High Protein\"\n LOW_CARB = \"Low Carb\"\n SPICY = \"Spicy\"\n BUDGET_FRIENDLY = \"Budget-Friendly\"\n ONE_POT_MEAL = \"One-Pot Meal\"\n VEGETARIAN = \"Vegetarian\"\n VEGAN = \"Vegan\"\n\nclass CookingTime(str, Enum):\n \"\"\"\n The cooking time of the recipe.\n \"\"\"\n FIVE_MIN = \"5 min\"\n FIFTEEN_MIN = \"15 min\"\n THIRTY_MIN = \"30 min\"\n FORTY_FIVE_MIN = \"45 min\"\n SIXTY_PLUS_MIN = \"60+ min\"\n\nclass Ingredient(BaseModel):\n \"\"\"\n An ingredient.\n \"\"\"\n icon: str = Field(\n description=\"Icon: the actual emoji like 🥕\"\n )\n name: str = Field(description=\"The name of the ingredient\")\n amount: str = Field(description=\"The amount of the ingredient\")\n\nclass Recipe(BaseModel):\n \"\"\"\n A recipe.\n \"\"\"\n skill_level: SkillLevel = \\\n Field(description=\"The skill level required for the recipe\")\n special_preferences: List[SpecialPreferences] = \\\n Field(description=\"A list of special preferences for the recipe\")\n cooking_time: CookingTime = \\\n Field(description=\"The cooking time of the recipe\")\n ingredients: List[Ingredient] = \\\n Field(description=\n \"\"\"Entire list of ingredients for the recipe, including the new ingredients\n and the ones that are already in the recipe: Icon: the actual emoji like 🥕,\n name and amount.\n Like so: 🥕 Carrots (250g)\"\"\"\n )\n instructions: List[str] = \\\n Field(description=\n \"\"\"Entire list of instructions for the recipe,\n including the new instructions and the ones that are already there\"\"\"\n )\n changes: str = \\\n Field(description=\"A description of the changes made to the recipe\")\n\nclass GenerateRecipeArgs(BaseModel): # pylint: disable=missing-class-docstring\n recipe: Recipe\n\n@tool(args_schema=GenerateRecipeArgs)\ndef generate_recipe(recipe: Recipe): # pylint: disable=unused-argument\n \"\"\"\n Using the existing (if any) ingredients and instructions, proceed with the recipe to finish it.\n Make sure the recipe is complete. ALWAYS provide the entire recipe, not just the changes.\n \"\"\"\n\nclass AgentState(MessagesState):\n \"\"\"\n The state of the recipe.\n \"\"\"\n recipe: Optional[Dict[str, Any]] = None\n tools: List[Any]\n\n\nasync def start_node(state: Dict[str, Any], config: RunnableConfig):\n \"\"\"\n This is the entry point for the flow.\n \"\"\"\n\n # Initialize recipe if not exists\n if \"recipe\" not in state or state[\"recipe\"] is None:\n state[\"recipe\"] = {\n \"skill_level\": SkillLevel.BEGINNER.value,\n \"special_preferences\": [],\n \"cooking_time\": CookingTime.FIFTEEN_MIN.value,\n \"ingredients\": [{\"icon\": \"🍴\", \"name\": \"Sample Ingredient\", \"amount\": \"1 unit\"}],\n \"instructions\": [\"First step instruction\"]\n }\n # Emit the initial state to ensure it's properly shared with the frontend\n await adispatch_custom_event(\n \"manually_emit_intermediate_state\",\n state,\n config=config,\n )\n\n return Command(\n goto=\"chat_node\",\n update={\n \"messages\": state[\"messages\"],\n \"recipe\": state[\"recipe\"]\n }\n )\n\nasync def chat_node(state: Dict[str, Any], config: RunnableConfig):\n \"\"\"\n Standard chat node.\n \"\"\"\n # Create a safer serialization of the recipe\n recipe_json = \"No recipe yet\"\n if \"recipe\" in state and state[\"recipe\"] is not None:\n try:\n recipe_json = json.dumps(state[\"recipe\"], indent=2)\n except Exception as e: # pylint: disable=broad-exception-caught\n recipe_json = f\"Error serializing recipe: {str(e)}\"\n\n system_prompt = f\"\"\"You are a helpful assistant for creating recipes. \n This is the current state of the recipe: {recipe_json}\n You can improve the recipe by calling the generate_recipe tool.\n \n IMPORTANT:\n 1. Create a recipe using the existing ingredients and instructions. Make sure the recipe is complete.\n 2. For ingredients, append new ingredients to the existing ones.\n 3. For instructions, append new steps to the existing ones.\n 4. 'ingredients' is always an array of objects with 'icon', 'name', and 'amount' fields\n 5. 'instructions' is always an array of strings\n\n If you have just created or modified the recipe, just answer in one sentence what you did. dont describe the recipe, just say what you did.\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o-mini\")\n\n # Define config for the model\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Use \"predict_state\" metadata to set up streaming for the write_document tool\n config[\"metadata\"][\"predict_state\"] = [{\n \"state_key\": \"recipe\",\n \"tool\": \"generate_recipe\",\n \"tool_argument\": \"recipe\"\n }]\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n generate_recipe\n ],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model and generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n # Update messages with the response\n messages = state[\"messages\"] + [response]\n\n # Handle tool calls\n if hasattr(response, \"tool_calls\") and response.tool_calls:\n # Handle dicts or object (backward compatibility)\n tool_call = (response.tool_calls[0]\n if isinstance(response.tool_calls[0], dict)\n else vars(response.tool_calls[0]))\n\n # Check if args is already a dict or needs to be parsed\n tool_call_args = (tool_call[\"args\"]\n if isinstance(tool_call[\"args\"], dict)\n else json.loads(tool_call[\"args\"]))\n\n if tool_call[\"name\"] == \"generate_recipe\":\n # Update recipe state with tool_call_args\n recipe_data = tool_call_args[\"recipe\"]\n\n # If we have an existing recipe, update it\n if \"recipe\" in state and state[\"recipe\"] is not None:\n recipe = state[\"recipe\"]\n for key, value in recipe_data.items():\n if value is not None: # Only update fields that were provided\n recipe[key] = value\n else:\n # Create a new recipe\n recipe = {\n \"skill_level\": recipe_data.get(\"skill_level\", SkillLevel.BEGINNER.value),\n \"special_preferences\": recipe_data.get(\"special_preferences\", []),\n \"cooking_time\": recipe_data.get(\"cooking_time\", CookingTime.FIFTEEN_MIN.value),\n \"ingredients\": recipe_data.get(\"ingredients\", []),\n \"instructions\": recipe_data.get(\"instructions\", [])\n }\n\n # Add tool response to messages\n tool_response = {\n \"role\": \"tool\",\n \"content\": \"Recipe generated.\",\n \"tool_call_id\": tool_call[\"id\"]\n }\n\n messages = messages + [tool_response]\n\n # Explicitly emit the updated state to ensure it's shared with frontend\n state[\"recipe\"] = recipe\n await adispatch_custom_event(\n \"manually_emit_intermediate_state\",\n state,\n config=config,\n )\n\n # Return command with updated recipe\n return Command(\n goto=\"start_node\",\n update={\n \"messages\": messages,\n \"recipe\": recipe\n }\n )\n\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"recipe\": state[\"recipe\"]\n }\n )\n\n\n# Define the graph\nworkflow = StateGraph(AgentState)\nworkflow.add_node(\"start_node\", start_node)\nworkflow.add_node(\"chat_node\", chat_node)\nworkflow.set_entry_point(\"start_node\")\nworkflow.add_edge(START, \"start_node\")\nworkflow.add_edge(\"start_node\", \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n", "language": "python", "type": "file" }, @@ -552,7 +552,7 @@ "langgraph::tool_based_generative_ui": [ { "name": "page.tsx", - "content": "\"use client\";\nimport { CopilotKit, useCopilotAction } from \"@copilotkit/react-core\";\nimport { CopilotKitCSSProperties, CopilotSidebar, CopilotChat } from \"@copilotkit/react-ui\";\nimport { Dispatch, SetStateAction, useState, useEffect } from \"react\";\nimport \"@copilotkit/react-ui/styles.css\";\nimport \"./style.css\";\nimport React, { useMemo } from \"react\";\nimport { useMobileView } from \"@/utils/use-mobile-view\";\nimport { useMobileChat } from \"@/utils/use-mobile-chat\";\n\ninterface ToolBasedGenerativeUIProps {\n params: Promise<{\n integrationId: string;\n }>;\n}\n\ninterface GenerateHaiku{\n japanese : string[] | [],\n english : string[] | [],\n image_names : string[] | [],\n selectedImage : string | null,\n}\n\ninterface HaikuCardProps{\n generatedHaiku : GenerateHaiku | Partial\n setHaikus : Dispatch>\n haikus : GenerateHaiku[]\n}\n\nexport default function ToolBasedGenerativeUI({ params }: ToolBasedGenerativeUIProps) {\n const { integrationId } = React.use(params);\n const { isMobile } = useMobileView();\n const defaultChatHeight = 50\n const {\n isChatOpen,\n setChatHeight,\n setIsChatOpen,\n isDragging,\n chatHeight,\n handleDragStart\n } = useMobileChat(defaultChatHeight)\n\n const chatTitle = 'Haiku Generator'\n const chatDescription = 'Ask me to create haikus'\n const initialLabel = 'I\\'m a haiku generator 👋. How can I help you?'\n\n return (\n \n \n \n\n {/* Desktop Sidebar */}\n {!isMobile && (\n \n )}\n\n {/* Mobile Pull-Up Chat */}\n {isMobile && (\n <>\n {/* Chat Toggle Button */}\n
\n
\n {\n if (!isChatOpen) {\n setChatHeight(defaultChatHeight); // Reset to good default when opening\n }\n setIsChatOpen(!isChatOpen);\n }}\n >\n
\n
\n
{chatTitle}
\n
{chatDescription}
\n
\n
\n
\n \n \n \n
\n
\n \n\n {/* Pull-Up Chat Container */}\n \n {/* Drag Handle Bar */}\n
\n
\n
\n \n {/* Chat Header */}\n
\n
\n
\n

{chatTitle}

\n
\n setIsChatOpen(false)}\n className=\"p-2 hover:bg-gray-100 rounded-full transition-colors\"\n >\n \n \n \n \n
\n
\n\n {/* Chat Content - Flexible container for messages and input */}\n
\n \n
\n \n\n {/* Backdrop */}\n {isChatOpen && (\n setIsChatOpen(false)}\n />\n )}\n \n )}\n \n \n );\n}\n\nconst VALID_IMAGE_NAMES = [\n \"Osaka_Castle_Turret_Stone_Wall_Pine_Trees_Daytime.jpg\",\n \"Tokyo_Skyline_Night_Tokyo_Tower_Mount_Fuji_View.jpg\",\n \"Itsukushima_Shrine_Miyajima_Floating_Torii_Gate_Sunset_Long_Exposure.jpg\",\n \"Takachiho_Gorge_Waterfall_River_Lush_Greenery_Japan.jpg\",\n \"Bonsai_Tree_Potted_Japanese_Art_Green_Foliage.jpeg\",\n \"Shirakawa-go_Gassho-zukuri_Thatched_Roof_Village_Aerial_View.jpg\",\n \"Ginkaku-ji_Silver_Pavilion_Kyoto_Japanese_Garden_Pond_Reflection.jpg\",\n \"Senso-ji_Temple_Asakusa_Cherry_Blossoms_Kimono_Umbrella.jpg\",\n \"Cherry_Blossoms_Sakura_Night_View_City_Lights_Japan.jpg\",\n \"Mount_Fuji_Lake_Reflection_Cherry_Blossoms_Sakura_Spring.jpg\"\n];\n\nfunction HaikuCard({generatedHaiku, setHaikus, haikus} : HaikuCardProps) {\n return (\n
\n
\n {generatedHaiku?.japanese?.map((line, index) => (\n
\n

{line}

\n

\n {generatedHaiku.english?.[index]}\n

\n
\n ))}\n {generatedHaiku?.japanese && generatedHaiku.japanese.length >= 2 && (\n
\n {(() => {\n const firstLine = generatedHaiku?.japanese?.[0];\n if (!firstLine) return null;\n const haikuIndex = haikus.findIndex((h: any) => h.japanese[0] === firstLine);\n const haiku = haikus[haikuIndex];\n if (!haiku?.image_names) return null;\n\n return haiku.image_names.map((imageName, imgIndex) => (\n {\n setHaikus(prevHaikus => {\n const newHaikus = prevHaikus.map((h, idx) => {\n if (idx === haikuIndex) {\n return {\n ...h,\n selectedImage: imageName\n };\n }\n return h;\n });\n return newHaikus;\n });\n }}\n />\n ));\n })()}\n
\n )}\n
\n
\n );\n}\n\ninterface Haiku {\n japanese: string[];\n english: string[];\n image_names: string[];\n selectedImage: string | null;\n}\n\nfunction Haiku() {\n const [haikus, setHaikus] = useState([{\n japanese: [\"仮の句よ\", \"まっさらながら\", \"花を呼ぶ\"],\n english: [\n \"A placeholder verse—\",\n \"even in a blank canvas,\",\n \"it beckons flowers.\",\n ],\n image_names: [],\n selectedImage: null,\n }])\n const [activeIndex, setActiveIndex] = useState(0);\n const [isJustApplied, setIsJustApplied] = useState(false);\n\n const validateAndCorrectImageNames = (rawNames: string[] | undefined): string[] | null => {\n if (!rawNames || rawNames.length !== 3) {\n return null;\n }\n\n const correctedNames: string[] = [];\n const usedValidNames = new Set();\n\n for (const name of rawNames) {\n if (VALID_IMAGE_NAMES.includes(name) && !usedValidNames.has(name)) {\n correctedNames.push(name);\n usedValidNames.add(name);\n if (correctedNames.length === 3) break;\n }\n }\n\n if (correctedNames.length < 3) {\n const availableFallbacks = VALID_IMAGE_NAMES.filter(name => !usedValidNames.has(name));\n for (let i = availableFallbacks.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n [availableFallbacks[i], availableFallbacks[j]] = [availableFallbacks[j], availableFallbacks[i]];\n }\n\n while (correctedNames.length < 3 && availableFallbacks.length > 0) {\n const fallbackName = availableFallbacks.pop();\n if (fallbackName) {\n correctedNames.push(fallbackName);\n }\n }\n }\n\n while (correctedNames.length < 3 && VALID_IMAGE_NAMES.length > 0) {\n const fallbackName = VALID_IMAGE_NAMES[Math.floor(Math.random() * VALID_IMAGE_NAMES.length)];\n correctedNames.push(fallbackName);\n }\n\n return correctedNames.slice(0, 3);\n };\n\n useCopilotAction({\n name: \"generate_haiku\",\n parameters: [\n {\n name: \"japanese\",\n type: \"string[]\",\n },\n {\n name: \"english\",\n type: \"string[]\",\n },\n {\n name: \"image_names\",\n type: \"string[]\",\n description: \"Names of 3 relevant images\",\n },\n ],\n followUp: false,\n handler: async ({ japanese, english, image_names }: { japanese: string[], english: string[], image_names: string[] }) => {\n const finalCorrectedImages = validateAndCorrectImageNames(image_names);\n const newHaiku = {\n japanese: japanese || [],\n english: english || [],\n image_names: finalCorrectedImages || [],\n selectedImage: finalCorrectedImages?.[0] || null,\n };\n setHaikus(prev => [...prev, newHaiku]);\n setActiveIndex(haikus.length - 1);\n setIsJustApplied(true);\n setTimeout(() => setIsJustApplied(false), 600);\n return \"Haiku generated.\";\n },\n render: ({ args: generatedHaiku }: { args: Partial }) => {\n return (\n \n );\n },\n }, [haikus]);\n\n const generatedHaikus = useMemo(() => (\n haikus.filter((haiku) => haiku.english[0] !== \"A placeholder verse—\")\n ), [haikus]);\n\n const { isMobile } = useMobileView();\n\n return (\n
\n {/* Thumbnail List */}\n {Boolean(generatedHaikus.length) && !isMobile && (\n
\n {generatedHaikus.map((haiku, index) => (\n setActiveIndex(index)}\n >\n {haiku.japanese.map((line, lineIndex) => (\n \n

{line}

\n

{haiku.english?.[lineIndex]}

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n )}\n\n {/* Main Display */}\n
\n
\n {haikus.filter((_haiku: Haiku, index: number) => {\n if (haikus.length == 1) return true;\n else return index == activeIndex + 1;\n }).map((haiku, index) => (\n \n {haiku.japanese.map((line, lineIndex) => (\n \n

\n {line}\n

\n

\n {haiku.english?.[lineIndex]}\n

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n \n \n );\n}\n", + "content": "\"use client\";\nimport { CopilotKit, useCopilotAction } from \"@copilotkit/react-core\";\nimport { CopilotKitCSSProperties, CopilotSidebar, CopilotChat } from \"@copilotkit/react-ui\";\nimport { Dispatch, SetStateAction, useState, useEffect } from \"react\";\nimport \"@copilotkit/react-ui/styles.css\";\nimport \"./style.css\";\nimport React, { useMemo } from \"react\";\nimport { useMobileView } from \"@/utils/use-mobile-view\";\nimport { useMobileChat } from \"@/utils/use-mobile-chat\";\n\ninterface ToolBasedGenerativeUIProps {\n params: Promise<{\n integrationId: string;\n }>;\n}\n\ninterface GenerateHaiku{\n japanese : string[] | [],\n english : string[] | [],\n image_names : string[] | [],\n selectedImage : string | null,\n}\n\ninterface HaikuCardProps{\n generatedHaiku : GenerateHaiku | Partial\n setHaikus : Dispatch>\n haikus : GenerateHaiku[]\n}\n\nexport default function ToolBasedGenerativeUI({ params }: ToolBasedGenerativeUIProps) {\n const { integrationId } = React.use(params);\n const { isMobile } = useMobileView();\n const defaultChatHeight = 50\n const {\n isChatOpen,\n setChatHeight,\n setIsChatOpen,\n isDragging,\n chatHeight,\n handleDragStart\n } = useMobileChat(defaultChatHeight)\n\n const chatTitle = 'Haiku Generator'\n const chatDescription = 'Ask me to create haikus'\n const initialLabel = 'I\\'m a haiku generator 👋. How can I help you?'\n\n return (\n \n \n \n\n {/* Desktop Sidebar */}\n {!isMobile && (\n \n )}\n\n {/* Mobile Pull-Up Chat */}\n {isMobile && (\n <>\n {/* Chat Toggle Button */}\n
\n
\n {\n if (!isChatOpen) {\n setChatHeight(defaultChatHeight); // Reset to good default when opening\n }\n setIsChatOpen(!isChatOpen);\n }}\n >\n
\n
\n
{chatTitle}
\n
{chatDescription}
\n
\n
\n
\n \n \n \n
\n
\n \n\n {/* Pull-Up Chat Container */}\n \n {/* Drag Handle Bar */}\n
\n
\n
\n \n {/* Chat Header */}\n
\n
\n
\n

{chatTitle}

\n
\n setIsChatOpen(false)}\n className=\"p-2 hover:bg-gray-100 rounded-full transition-colors\"\n >\n \n \n \n \n
\n
\n\n {/* Chat Content - Flexible container for messages and input */}\n
\n \n
\n \n\n {/* Backdrop */}\n {isChatOpen && (\n setIsChatOpen(false)}\n />\n )}\n \n )}\n \n \n );\n}\n\nconst VALID_IMAGE_NAMES = [\n \"Osaka_Castle_Turret_Stone_Wall_Pine_Trees_Daytime.jpg\",\n \"Tokyo_Skyline_Night_Tokyo_Tower_Mount_Fuji_View.jpg\",\n \"Itsukushima_Shrine_Miyajima_Floating_Torii_Gate_Sunset_Long_Exposure.jpg\",\n \"Takachiho_Gorge_Waterfall_River_Lush_Greenery_Japan.jpg\",\n \"Bonsai_Tree_Potted_Japanese_Art_Green_Foliage.jpeg\",\n \"Shirakawa-go_Gassho-zukuri_Thatched_Roof_Village_Aerial_View.jpg\",\n \"Ginkaku-ji_Silver_Pavilion_Kyoto_Japanese_Garden_Pond_Reflection.jpg\",\n \"Senso-ji_Temple_Asakusa_Cherry_Blossoms_Kimono_Umbrella.jpg\",\n \"Cherry_Blossoms_Sakura_Night_View_City_Lights_Japan.jpg\",\n \"Mount_Fuji_Lake_Reflection_Cherry_Blossoms_Sakura_Spring.jpg\"\n];\n\nfunction HaikuCard({generatedHaiku, setHaikus, haikus} : HaikuCardProps) {\n return (\n
\n
\n {generatedHaiku?.japanese?.map((line, index) => (\n
\n

{line}

\n

\n {generatedHaiku.english?.[index]}\n

\n
\n ))}\n {generatedHaiku?.japanese && generatedHaiku.japanese.length >= 2 && (\n
\n {(() => {\n const firstLine = generatedHaiku?.japanese?.[0];\n if (!firstLine) return null;\n const haikuIndex = haikus.findIndex((h: any) => h.japanese[0] === firstLine);\n const haiku = haikus[haikuIndex];\n if (!haiku?.image_names) return null;\n\n return haiku.image_names.map((imageName, imgIndex) => (\n {\n setHaikus(prevHaikus => {\n const newHaikus = prevHaikus.map((h, idx) => {\n if (idx === haikuIndex) {\n return {\n ...h,\n selectedImage: imageName\n };\n }\n return h;\n });\n return newHaikus;\n });\n }}\n />\n ));\n })()}\n
\n )}\n
\n
\n );\n}\n\ninterface Haiku {\n japanese: string[];\n english: string[];\n image_names: string[];\n selectedImage: string | null;\n}\n\nfunction Haiku() {\n const [haikus, setHaikus] = useState([{\n japanese: [\"仮の句よ\", \"まっさらながら\", \"花を呼ぶ\"],\n english: [\n \"A placeholder verse—\",\n \"even in a blank canvas,\",\n \"it beckons flowers.\",\n ],\n image_names: [],\n selectedImage: null,\n }])\n const [activeIndex, setActiveIndex] = useState(0);\n const [isJustApplied, setIsJustApplied] = useState(false);\n\n const validateAndCorrectImageNames = (rawNames: string[] | undefined): string[] | null => {\n if (!rawNames || rawNames.length !== 3) {\n return null;\n }\n\n const correctedNames: string[] = [];\n const usedValidNames = new Set();\n\n for (const name of rawNames) {\n if (VALID_IMAGE_NAMES.includes(name) && !usedValidNames.has(name)) {\n correctedNames.push(name);\n usedValidNames.add(name);\n if (correctedNames.length === 3) break;\n }\n }\n\n if (correctedNames.length < 3) {\n const availableFallbacks = VALID_IMAGE_NAMES.filter(name => !usedValidNames.has(name));\n for (let i = availableFallbacks.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n [availableFallbacks[i], availableFallbacks[j]] = [availableFallbacks[j], availableFallbacks[i]];\n }\n\n while (correctedNames.length < 3 && availableFallbacks.length > 0) {\n const fallbackName = availableFallbacks.pop();\n if (fallbackName) {\n correctedNames.push(fallbackName);\n }\n }\n }\n\n while (correctedNames.length < 3 && VALID_IMAGE_NAMES.length > 0) {\n const fallbackName = VALID_IMAGE_NAMES[Math.floor(Math.random() * VALID_IMAGE_NAMES.length)];\n correctedNames.push(fallbackName);\n }\n\n return correctedNames.slice(0, 3);\n };\n\n useCopilotAction({\n name: \"generate_haiku\",\n available: \"frontend\",\n parameters: [\n {\n name: \"japanese\",\n type: \"string[]\",\n },\n {\n name: \"english\",\n type: \"string[]\",\n },\n {\n name: \"image_names\",\n type: \"string[]\",\n description: \"Names of 3 relevant images\",\n },\n ],\n followUp: false,\n handler: async ({ japanese, english, image_names }: { japanese: string[], english: string[], image_names: string[] }) => {\n const finalCorrectedImages = validateAndCorrectImageNames(image_names);\n const newHaiku = {\n japanese: japanese || [],\n english: english || [],\n image_names: finalCorrectedImages || [],\n selectedImage: finalCorrectedImages?.[0] || null,\n };\n setHaikus(prev => [...prev, newHaiku]);\n setActiveIndex(haikus.length - 1);\n setIsJustApplied(true);\n setTimeout(() => setIsJustApplied(false), 600);\n return \"Haiku generated.\";\n },\n render: ({ args: generatedHaiku }: { args: Partial }) => {\n return (\n \n );\n },\n }, [haikus]);\n\n const generatedHaikus = useMemo(() => (\n haikus.filter((haiku) => haiku.english[0] !== \"A placeholder verse—\")\n ), [haikus]);\n\n const { isMobile } = useMobileView();\n\n return (\n
\n {/* Thumbnail List */}\n {Boolean(generatedHaikus.length) && !isMobile && (\n
\n {generatedHaikus.map((haiku, index) => (\n setActiveIndex(index)}\n >\n {haiku.japanese.map((line, lineIndex) => (\n \n

{line}

\n

{haiku.english?.[lineIndex]}

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n )}\n\n {/* Main Display */}\n
\n
\n {haikus.filter((_haiku: Haiku, index: number) => {\n if (haikus.length == 1) return true;\n else return index == activeIndex + 1;\n }).map((haiku, index) => (\n \n {haiku.japanese.map((line, lineIndex) => (\n \n

\n {line}\n

\n

\n {haiku.english?.[lineIndex]}\n

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n \n \n );\n}\n", "language": "typescript", "type": "file" }, @@ -570,7 +570,7 @@ }, { "name": "agent.py", - "content": "\"\"\"\nAn example demonstrating tool-based generative UI using LangGraph.\n\"\"\"\n\nfrom typing import List, Any\nimport os\n\n# LangGraph imports\nfrom langchain_core.runnables import RunnableConfig\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command\nfrom langgraph.graph import MessagesState\n\n# OpenAI imports\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.messages import SystemMessage\n\n# List of available images (modify path if needed)\nIMAGE_LIST = [\n \"Osaka_Castle_Turret_Stone_Wall_Pine_Trees_Daytime.jpg\",\n \"Tokyo_Skyline_Night_Tokyo_Tower_Mount_Fuji_View.jpg\",\n \"Itsukushima_Shrine_Miyajima_Floating_Torii_Gate_Sunset_Long_Exposure.jpg\",\n \"Takachiho_Gorge_Waterfall_River_Lush_Greenery_Japan.jpg\",\n \"Bonsai_Tree_Potted_Japanese_Art_Green_Foliage.jpeg\",\n \"Shirakawa-go_Gassho-zukuri_Thatched_Roof_Village_Aerial_View.jpg\",\n \"Ginkaku-ji_Silver_Pavilion_Kyoto_Japanese_Garden_Pond_Reflection.jpg\",\n \"Senso-ji_Temple_Asakusa_Cherry_Blossoms_Kimono_Umbrella.jpg\",\n \"Cherry_Blossoms_Sakura_Night_View_City_Lights_Japan.jpg\",\n \"Mount_Fuji_Lake_Reflection_Cherry_Blossoms_Sakura_Spring.jpg\"\n]\n\n# This tool generates a haiku on the server.\n# The tool call will be streamed to the frontend as it is being generated.\nGENERATE_HAIKU_TOOL = {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"generate_haiku\",\n \"description\": \"Generate a haiku in Japanese and its English translation. Also select exactly 3 relevant images from the provided list based on the haiku's theme.\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"japanese\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"An array of three lines of the haiku in Japanese\"\n },\n \"english\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"An array of three lines of the haiku in English\"\n },\n \"image_names\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"An array of EXACTLY THREE image filenames from the provided list that are most relevant to the haiku.\"\n }\n },\n \"required\": [\"japanese\", \"english\", \"image_names\"]\n }\n }\n}\n\nclass AgentState(MessagesState):\n tools: List[Any]\n\nasync def chat_node(state: AgentState, config: RunnableConfig):\n \"\"\"\n The main function handling chat and tool calls.\n \"\"\"\n # Prepare the image list string for the prompt\n image_list_str = \"\\n\".join([f\"- {img}\" for img in IMAGE_LIST])\n\n system_prompt = f\"\"\"\n You assist the user in generating a haiku.\n When generating a haiku using the 'generate_haiku' tool, you MUST also select exactly 3 image filenames from the following list that are most relevant to the haiku's content or theme. Return the filenames in the 'image_names' parameter.\n \n Available images:\n {image_list_str}\n \n Dont provide the relavent image names in your final response to the user.\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o\")\n \n # Define config for the model\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [GENERATE_HAIKU_TOOL],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model to generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n # Return Command to end with updated messages\n return Command(\n goto=END,\n update={\n \"messages\": state[\"messages\"] + [response]\n }\n )\n\n# Define the graph\nworkflow = StateGraph(AgentState)\n\n# Add nodes\nworkflow.add_node(\"chat_node\", chat_node)\n\n# Add edges\nworkflow.set_entry_point(\"chat_node\")\nworkflow.add_edge(START, \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n\n\n", + "content": "\"\"\"\nAn example demonstrating tool-based generative UI using LangGraph.\n\"\"\"\n\nfrom typing import List, Any, Optional, Annotated\nimport os\n\n# LangGraph imports\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.runnables import RunnableConfig\nfrom langchain_core.messages import SystemMessage\nfrom langchain_core.tools import tool\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command\nfrom langgraph.graph import MessagesState\nfrom langgraph.prebuilt import ToolNode\n\n@tool\ndef generate_haiku(\n japanese: Annotated[ # pylint: disable=unused-argument\n List[str],\n \"An array of three lines of the haiku in Japanese\"\n ],\n english: Annotated[ # pylint: disable=unused-argument\n List[str],\n \"An array of three lines of the haiku in English\"\n ]\n):\n \"\"\"\n Generate a haiku in Japanese and its English translation.\n Also select exactly 3 relevant images from the provided list based on the haiku's theme.\n \"\"\"\n\nclass AgentState(MessagesState):\n \"\"\"\n State of the agent.\n \"\"\"\n tools: List[Any]\n\nasync def chat_node(state: AgentState, config: Optional[RunnableConfig] = None):\n \"\"\"\n The main function handling chat and tool calls.\n \"\"\"\n\n system_prompt = \"\"\"\n You assist the user in generating a haiku.\n When generating a haiku using the 'generate_haiku' tool.\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o\")\n\n # Define config for the model\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [generate_haiku],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model to generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n if response.tool_calls:\n return Command(\n goto=\"tool_node\",\n update={\n \"messages\": state[\"messages\"] + [response]\n }\n )\n # Return Command to end with updated messages\n return Command(\n goto=END,\n update={\n \"messages\": state[\"messages\"] + [response]\n }\n )\n\n# Define the graph\nworkflow = StateGraph(AgentState)\n\n# Add nodes\nworkflow.add_node(\"chat_node\", chat_node)\nworkflow.add_node(\"tool_node\", ToolNode([generate_haiku]))\n\n# Add edges\nworkflow.set_entry_point(\"chat_node\")\nworkflow.add_edge(START, \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\nworkflow.add_edge(\"tool_node\", END)\n\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n\n", "language": "python", "type": "file" }, @@ -602,7 +602,7 @@ }, { "name": "agent.py", - "content": "\"\"\"\nA simple agentic chat flow using LangGraph instead of CrewAI.\n\"\"\"\n\nfrom typing import List, Any\nimport os\n\n# Updated imports for LangGraph\nfrom langchain_core.runnables import RunnableConfig\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.graph import MessagesState\nfrom langgraph.types import Command\nfrom typing_extensions import Literal\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.messages import SystemMessage\n\nclass AgentState(MessagesState):\n tools: List[Any]\n\nasync def chat_node(state: AgentState, config: RunnableConfig):\n \"\"\"\n Standard chat node based on the ReAct design pattern. It handles:\n - The model to use (and binds in CopilotKit actions and the tools defined above)\n - The system prompt\n - Getting a response from the model\n - Handling tool calls\n\n For more about the ReAct design pattern, see: \n https://www.perplexity.ai/search/react-agents-NcXLQhreS0WDzpVaS4m9Cg\n \"\"\"\n \n # 1. Define the model\n model = ChatOpenAI(model=\"gpt-4o\")\n \n # Define config for the model\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # 2. Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n # your_tool_here\n ],\n\n # 2.1 Disable parallel tool calls to avoid race conditions,\n # enable this for faster performance if you want to manage\n # the complexity of running tool calls in parallel.\n parallel_tool_calls=False,\n )\n\n # 3. Define the system message by which the chat model will be run\n system_message = SystemMessage(\n content=f\"You are a helpful assistant. .\"\n )\n\n # 4. Run the model to generate a response\n response = await model_with_tools.ainvoke([\n system_message,\n *state[\"messages\"],\n ], config)\n\n # 6. We've handled all tool calls, so we can end the graph.\n return Command(\n goto=END,\n update={\n \"messages\": response\n }\n )\n\n# Define a new graph\nworkflow = StateGraph(AgentState)\nworkflow.add_node(\"chat_node\", chat_node)\nworkflow.set_entry_point(\"chat_node\")\n\n# Add explicit edges, matching the pattern in other examples\nworkflow.add_edge(START, \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n", + "content": "\"\"\"\nA simple agentic chat flow using LangGraph instead of CrewAI.\n\"\"\"\n\nfrom typing import List, Any, Optional\nimport os\n\n# Updated imports for LangGraph\nfrom langchain_core.runnables import RunnableConfig\nfrom langchain_core.messages import SystemMessage\nfrom langchain_openai import ChatOpenAI\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.graph import MessagesState\nfrom langgraph.types import Command\n\nclass AgentState(MessagesState):\n \"\"\"\n State of our graph.\n \"\"\"\n tools: List[Any]\n\nasync def chat_node(state: AgentState, config: Optional[RunnableConfig] = None):\n \"\"\"\n Standard chat node based on the ReAct design pattern. It handles:\n - The model to use (and binds in CopilotKit actions and the tools defined above)\n - The system prompt\n - Getting a response from the model\n - Handling tool calls\n\n For more about the ReAct design pattern, see: \n https://www.perplexity.ai/search/react-agents-NcXLQhreS0WDzpVaS4m9Cg\n \"\"\"\n\n # 1. Define the model\n model = ChatOpenAI(model=\"gpt-4o\")\n\n # Define config for the model\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # 2. Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n # your_tool_here\n ],\n\n # 2.1 Disable parallel tool calls to avoid race conditions,\n # enable this for faster performance if you want to manage\n # the complexity of running tool calls in parallel.\n parallel_tool_calls=False,\n )\n\n # 3. Define the system message by which the chat model will be run\n system_message = SystemMessage(\n content=\"You are a helpful assistant.\"\n )\n\n # 4. Run the model to generate a response\n response = await model_with_tools.ainvoke([\n system_message,\n *state[\"messages\"],\n ], config)\n\n # 6. We've handled all tool calls, so we can end the graph.\n return Command(\n goto=END,\n update={\n \"messages\": response\n }\n )\n\n# Define a new graph\nworkflow = StateGraph(AgentState)\nworkflow.add_node(\"chat_node\", chat_node)\nworkflow.set_entry_point(\"chat_node\")\n\n# Add explicit edges, matching the pattern in other examples\nworkflow.add_edge(START, \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n", "language": "python", "type": "file" } @@ -628,7 +628,7 @@ }, { "name": "agent.py", - "content": "\"\"\"\nAn example demonstrating agentic generative UI using LangGraph.\n\"\"\"\n\nimport asyncio\nfrom typing import List, Any\nimport os\n\n# LangGraph imports\nfrom langchain_core.runnables import RunnableConfig\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command\nfrom langchain_core.callbacks.manager import adispatch_custom_event\nfrom langgraph.graph import MessagesState\n\n# OpenAI imports\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.messages import SystemMessage\n\n# This tool simulates performing a task on the server.\n# The tool call will be streamed to the frontend as it is being generated.\nPERFORM_TASK_TOOL = {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"generate_task_steps_generative_ui\",\n \"description\": \"Make up 10 steps (only a couple of words per step) that are required for a task. The step should be in gerund form (i.e. Digging hole, opening door, ...)\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"steps\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"description\": {\n \"type\": \"string\",\n \"description\": \"The text of the step in gerund form\"\n },\n \"status\": {\n \"type\": \"string\",\n \"enum\": [\"pending\"],\n \"description\": \"The status of the step, always 'pending'\"\n }\n },\n \"required\": [\"description\", \"status\"]\n },\n \"description\": \"An array of 10 step objects, each containing text and status\"\n }\n },\n \"required\": [\"steps\"]\n }\n }\n}\n\n\nclass AgentState(MessagesState):\n steps: List[dict] = []\n tools: List[Any]\n\n\nasync def start_flow(state: AgentState, config: RunnableConfig):\n \"\"\"\n This is the entry point for the flow.\n \"\"\"\n\n if \"steps\" not in state:\n state[\"steps\"] = []\n\n return Command(\n goto=\"chat_node\",\n update={\n \"messages\": state[\"messages\"],\n \"steps\": state[\"steps\"]\n }\n )\n\n\nasync def chat_node(state: AgentState, config: RunnableConfig):\n \"\"\"\n Standard chat node.\n \"\"\"\n system_prompt = \"\"\"\n You are a helpful assistant assisting with any task. \n When asked to do something, you MUST call the function `generate_task_steps_generative_ui`\n that was provided to you.\n If you called the function, you MUST NOT repeat the steps in your next response to the user.\n Just give a very brief summary (one sentence) of what you did with some emojis. \n Always say you actually did the steps, not merely generated them.\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o\")\n \n # Define config for the model with emit_intermediate_state to stream tool calls to frontend\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Use \"predict_state\" metadata to set up streaming for the write_document tool\n config[\"metadata\"][\"predict_state\"] = [{\n \"state_key\": \"steps\",\n \"tool\": \"generate_task_steps_generative_ui\",\n \"tool_argument\": \"steps\",\n }]\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n PERFORM_TASK_TOOL\n ],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model to generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n messages = state[\"messages\"] + [response]\n\n # Extract any tool calls from the response\n if hasattr(response, \"tool_calls\") and response.tool_calls and len(response.tool_calls) > 0:\n tool_call = response.tool_calls[0]\n \n # Handle tool_call as a dictionary rather than an object\n if isinstance(tool_call, dict):\n tool_call_id = tool_call[\"id\"]\n tool_call_name = tool_call[\"name\"]\n tool_call_args = tool_call[\"args\"]\n else:\n # Handle as an object (backward compatibility)\n tool_call_id = tool_call.id\n tool_call_name = tool_call.name\n tool_call_args = tool_call.args\n\n if tool_call_name == \"generate_task_steps_generative_ui\":\n steps = [{\"description\": step[\"description\"], \"status\": step[\"status\"]} for step in tool_call_args[\"steps\"]]\n \n # Add the tool response to messages\n tool_response = {\n \"role\": \"tool\",\n \"content\": \"Steps executed.\",\n \"tool_call_id\": tool_call_id\n }\n\n messages = messages + [tool_response]\n\n # Return Command to route to simulate_task_node\n for i, step in enumerate(steps):\n # simulate executing the step\n await asyncio.sleep(1)\n steps[i][\"status\"] = \"completed\"\n # Update the state with the completed step - using config as first parameter\n state[\"steps\"] = steps\n await adispatch_custom_event(\n \"manually_emit_state\",\n state,\n config=config,\n )\n \n return Command(\n goto='start_flow',\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"]\n }\n )\n\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"]\n }\n )\n\n\n# Define the graph\nworkflow = StateGraph(AgentState)\n\n# Add nodes\nworkflow.add_node(\"start_flow\", start_flow)\nworkflow.add_node(\"chat_node\", chat_node)\n\n# Add edges (equivalent to the routing in CrewAI)\nworkflow.set_entry_point(\"start_flow\")\nworkflow.add_edge(START, \"start_flow\")\nworkflow.add_edge(\"start_flow\", \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n", + "content": "\"\"\"\nAn example demonstrating agentic generative UI using LangGraph.\n\"\"\"\n\nimport asyncio\nfrom typing import List, Any, Optional, Annotated\nimport os\n\n# LangGraph imports\nfrom langchain_core.runnables import RunnableConfig\nfrom langchain_core.callbacks.manager import adispatch_custom_event\nfrom langchain_core.messages import SystemMessage\nfrom langchain_core.tools import tool\nfrom langchain_openai import ChatOpenAI\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command\nfrom langgraph.graph import MessagesState\nfrom pydantic import BaseModel, Field\n\nclass Step(BaseModel):\n \"\"\"\n A step in a task.\n \"\"\"\n description: str = Field(description=\"The text of the step in gerund form\")\n status: str = Field(description=\"The status of the step, always 'pending'\")\n\n\n\n# This tool simulates performing a task on the server.\n# The tool call will be streamed to the frontend as it is being generated.\n@tool\ndef generate_task_steps_generative_ui(\n steps: Annotated[ # pylint: disable=unused-argument\n List[Step],\n \"An array of 10 step objects, each containing text and status\"\n ]\n):\n \"\"\"\n Make up 10 steps (only a couple of words per step) that are required for a task.\n The step should be in gerund form (i.e. Digging hole, opening door, ...).\n \"\"\"\n\n\nclass AgentState(MessagesState):\n \"\"\"\n State of the agent.\n \"\"\"\n steps: List[dict] = []\n tools: List[Any]\n\n\nasync def start_node(state: AgentState, config: RunnableConfig): # pylint: disable=unused-argument\n \"\"\"\n This is the entry point for the flow.\n \"\"\"\n\n if \"steps\" not in state:\n state[\"steps\"] = []\n\n return Command(\n goto=\"chat_node\",\n update={\n \"messages\": state[\"messages\"],\n \"steps\": state[\"steps\"]\n }\n )\n\n\nasync def chat_node(state: AgentState, config: Optional[RunnableConfig] = None):\n \"\"\"\n Standard chat node.\n \"\"\"\n system_prompt = \"\"\"\n You are a helpful assistant assisting with any task. \n When asked to do something, you MUST call the function `generate_task_steps_generative_ui`\n that was provided to you.\n If you called the function, you MUST NOT repeat the steps in your next response to the user.\n Just give a very brief summary (one sentence) of what you did with some emojis. \n Always say you actually did the steps, not merely generated them.\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o\")\n\n # Define config for the model with emit_intermediate_state to stream tool calls to frontend\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Use \"predict_state\" metadata to set up streaming for the write_document tool\n config[\"metadata\"][\"predict_state\"] = [{\n \"state_key\": \"steps\",\n \"tool\": \"generate_task_steps_generative_ui\",\n \"tool_argument\": \"steps\",\n }]\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n generate_task_steps_generative_ui\n ],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model to generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n messages = state[\"messages\"] + [response]\n\n # Extract any tool calls from the response\n if hasattr(response, \"tool_calls\") and response.tool_calls and len(response.tool_calls) > 0:\n # Handle dicts or object (backward compatibility)\n tool_call = (response.tool_calls[0]\n if isinstance(response.tool_calls[0], dict)\n else vars(response.tool_calls[0]))\n\n if tool_call[\"name\"] == \"generate_task_steps_generative_ui\":\n steps = [\n {\"description\": step[\"description\"], \"status\": step[\"status\"]}\n for step in tool_call[\"args\"][\"steps\"]\n ]\n\n # Add the tool response to messages\n tool_response = {\n \"role\": \"tool\",\n \"content\": \"Steps executed.\",\n \"tool_call_id\": tool_call[\"id\"]\n }\n\n messages = messages + [tool_response]\n state[\"steps\"] = steps\n\n # Return Command to route to simulate_task_node\n for i, _ in enumerate(steps):\n # simulate executing the step\n await asyncio.sleep(1)\n steps[i][\"status\"] = \"completed\"\n # Update the state with the completed step using config\n await adispatch_custom_event(\n \"manually_emit_state\",\n state,\n config=config,\n )\n\n return Command(\n goto='start_node',\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"]\n }\n )\n\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"]\n }\n )\n\n\n# Define the graph\nworkflow = StateGraph(AgentState)\n\n# Add nodes\nworkflow.add_node(\"start_node\", start_node)\nworkflow.add_node(\"chat_node\", chat_node)\n\n# Add edges\nworkflow.set_entry_point(\"start_node\")\nworkflow.add_edge(START, \"start_node\")\nworkflow.add_edge(\"start_node\", \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n", "language": "python", "type": "file" } @@ -654,7 +654,7 @@ }, { "name": "agent.py", - "content": "\"\"\"\nA LangGraph implementation of the human-in-the-loop agent.\n\"\"\"\n\nimport json\nfrom typing import Dict, List, Any\nimport os\n\n# LangGraph imports\nfrom langchain_core.runnables import RunnableConfig\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command, interrupt\nfrom langgraph.graph import MessagesState\n\n# LLM imports\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.messages import SystemMessage\n\nDEFINE_TASK_TOOL = {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"plan_execution_steps\",\n \"description\": \"Make up 10 steps (only a couple of words per step) that are required for a task. The step should be in imperative form (i.e. Dig hole, Open door, ...)\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"steps\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"description\": {\n \"type\": \"string\",\n \"description\": \"The text of the step in imperative form\"\n },\n \"status\": {\n \"type\": \"string\",\n \"enum\": [\"enabled\"],\n \"description\": \"The status of the step, always 'enabled'\"\n }\n },\n \"required\": [\"description\", \"status\"]\n },\n \"description\": \"An array of 10 step objects, each containing text and status\"\n }\n },\n \"required\": [\"steps\"]\n }\n }\n}\n\nclass AgentState(MessagesState):\n steps: List[Dict[str, str]] = []\n tools: List[Any]\n\nasync def start_flow(state: Dict[str, Any], config: RunnableConfig):\n \"\"\"\n This is the entry point for the flow.\n \"\"\"\n\n # Initialize steps list if not exists\n if \"steps\" not in state:\n state[\"steps\"] = []\n\n \n return Command(\n goto=\"chat_node\",\n update={\n \"messages\": state[\"messages\"],\n \"steps\": state[\"steps\"],\n }\n )\n\n\nasync def chat_node(state: Dict[str, Any], config: RunnableConfig):\n \"\"\"\n Standard chat node where the agent processes messages and generates responses.\n If task steps are defined, the user can enable/disable them using interrupts.\n \"\"\"\n system_prompt = \"\"\"\n You are a helpful assistant that can perform any task.\n You MUST call the `plan_execution_steps` function when the user asks you to perform a task.\n Always make sure you will provide tasks based on the user query\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o-mini\")\n \n # Define config for the model\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Use \"predict_state\" metadata to set up streaming for the write_document tool\n config[\"metadata\"][\"predict_state\"] = [{\n \"state_key\": \"steps\",\n \"tool\": \"plan_execution_steps\",\n \"tool_argument\": \"steps\"\n }]\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n DEFINE_TASK_TOOL\n ],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model and generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n # Update messages with the response\n messages = state[\"messages\"] + [response]\n \n # Handle tool calls\n if hasattr(response, \"tool_calls\") and response.tool_calls and len(response.tool_calls) > 0:\n tool_call = response.tool_calls[0]\n # Extract tool call information\n if hasattr(tool_call, \"id\"):\n tool_call_id = tool_call.id\n tool_call_name = tool_call.name\n tool_call_args = tool_call.args if not isinstance(tool_call.args, str) else json.loads(tool_call.args)\n else:\n tool_call_id = tool_call.get(\"id\", \"\")\n tool_call_name = tool_call.get(\"name\", \"\")\n args = tool_call.get(\"args\", {})\n tool_call_args = args if not isinstance(args, str) else json.loads(args)\n\n if tool_call_name == \"plan_execution_steps\":\n # Get the steps from the tool call\n steps_raw = tool_call_args.get(\"steps\", [])\n \n # Set initial status to \"enabled\" for all steps\n steps_data = []\n \n # Handle different potential formats of steps data\n if isinstance(steps_raw, list):\n for step in steps_raw:\n if isinstance(step, dict) and \"description\" in step:\n steps_data.append({\n \"description\": step[\"description\"],\n \"status\": \"enabled\"\n })\n elif isinstance(step, str):\n steps_data.append({\n \"description\": step,\n \"status\": \"enabled\"\n })\n \n # If no steps were processed correctly, return to END with the updated messages\n if not steps_data:\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"],\n }\n )\n # Update steps in state and emit to frontend\n state[\"steps\"] = steps_data\n \n # Add a tool response to satisfy OpenAI's requirements\n tool_response = {\n \"role\": \"tool\",\n \"content\": \"Task steps generated.\",\n \"tool_call_id\": tool_call_id\n }\n \n messages = messages + [tool_response]\n\n # Move to the process_steps_node which will handle the interrupt and final response\n return Command(\n goto=\"process_steps_node\",\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"],\n }\n )\n\n # If no tool calls or not plan_execution_steps, return to END with the updated messages\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"],\n }\n )\n\n\nasync def process_steps_node(state: Dict[str, Any], config: RunnableConfig):\n \"\"\"\n This node handles the user interrupt for step customization and generates the final response.\n \"\"\"\n\n # Check if we already have a user_response in the state\n # This happens when the node restarts after an interrupt\n if \"user_response\" in state and state[\"user_response\"]:\n user_response = state[\"user_response\"]\n else:\n # Use LangGraph interrupt to get user input on steps\n # This will pause execution and wait for user input in the frontend\n user_response = interrupt({\"steps\": state[\"steps\"]})\n # Store the user response in state for when the node restarts\n state[\"user_response\"] = user_response\n\n # Generate the creative completion response\n final_prompt = \"\"\"\n Provide a textual description of how you are performing the task.\n If the user has disabled a step, you are not allowed to perform that step.\n However, you should find a creative workaround to perform the task, and if an essential step is disabled, you can even use\n some humor in the description of how you are performing the task.\n Don't just repeat a list of steps, come up with a creative but short description (3 sentences max) of how you are performing the task.\n \"\"\"\n \n final_response = await ChatOpenAI(model=\"gpt-4o\").ainvoke([\n SystemMessage(content=final_prompt),\n {\"role\": \"user\", \"content\": user_response}\n ], config)\n\n # Add the final response to messages\n messages = state[\"messages\"] + [final_response]\n\n # Clear the user_response from state to prepare for future interactions\n if \"user_response\" in state:\n state.pop(\"user_response\")\n\n # Return to END with the updated messages\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"],\n }\n )\n\n\n# Define the graph\nworkflow = StateGraph(AgentState)\n\n# Add nodes\nworkflow.add_node(\"start_flow\", start_flow)\nworkflow.add_node(\"chat_node\", chat_node)\nworkflow.add_node(\"process_steps_node\", process_steps_node)\n\n# Add edges\nworkflow.set_entry_point(\"start_flow\")\nworkflow.add_edge(START, \"start_flow\")\nworkflow.add_edge(\"start_flow\", \"chat_node\")\n# workflow.add_edge(\"chat_node\", \"process_steps_node\") # Removed unconditional edge\nworkflow.add_edge(\"process_steps_node\", END)\n# workflow.add_edge(\"chat_node\", END) # Removed unconditional edge\n\n# Add conditional edges from chat_node\ndef should_continue(command: Command):\n if command.goto == \"process_steps_node\":\n return \"process_steps_node\"\n else:\n return END\n\nworkflow.add_conditional_edges(\n \"chat_node\",\n should_continue,\n {\n \"process_steps_node\": \"process_steps_node\",\n END: END,\n },\n)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n", + "content": "\"\"\"\nA LangGraph implementation of the human-in-the-loop agent.\n\"\"\"\n\nfrom typing import Dict, List, Any, Annotated, Optional\nimport os\n\n# LangGraph imports\nfrom langchain_core.runnables import RunnableConfig\nfrom langchain_core.messages import SystemMessage\nfrom langchain_core.tools import tool\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command, interrupt\nfrom langgraph.graph import MessagesState\nfrom langchain_openai import ChatOpenAI\nfrom pydantic import BaseModel, Field\n\nclass Step(BaseModel):\n \"\"\"\n A step in a task.\n \"\"\"\n description: str = Field(description=\"The text of the step in imperative form\")\n status: str = Field(description=\"The status of the step, always 'enabled'\")\n\n@tool\ndef plan_execution_steps(\n steps: Annotated[ # pylint: disable=unused-argument\n List[Step],\n \"An array of 10 step objects, each containing text and status\"\n ]\n):\n \"\"\"\n Make up 10 steps (only a couple of words per step) that are required for a task.\n The step should be in imperative form (i.e. Dig hole, Open door, ...).\n \"\"\"\n\nclass AgentState(MessagesState):\n \"\"\"\n State of the agent.\n \"\"\"\n steps: List[Dict[str, str]] = []\n tools: List[Any]\n\nasync def start_node(state: Dict[str, Any], config: RunnableConfig): # pylint: disable=unused-argument\n \"\"\"\n This is the entry point for the flow.\n \"\"\"\n\n # Initialize steps list if not exists\n if \"steps\" not in state:\n state[\"steps\"] = []\n\n # Return command to route to chat_node\n return Command(\n goto=\"chat_node\",\n update={\n \"messages\": state[\"messages\"],\n \"steps\": state[\"steps\"],\n }\n )\n\n\nasync def chat_node(state: AgentState, config: Optional[RunnableConfig] = None):\n \"\"\"\n Standard chat node where the agent processes messages and generates responses.\n If task steps are defined, the user can enable/disable them using interrupts.\n \"\"\"\n system_prompt = \"\"\"\n You are a helpful assistant that can perform any task.\n You MUST call the `plan_execution_steps` function when the user asks you to perform a task.\n Always make sure you will provide tasks based on the user query\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o-mini\")\n\n # Define config for the model\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Use \"predict_state\" metadata to set up streaming for the write_document tool\n config[\"metadata\"][\"predict_state\"] = [{\n \"state_key\": \"steps\",\n \"tool\": \"plan_execution_steps\",\n \"tool_argument\": \"steps\"\n }]\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n plan_execution_steps\n ],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model and generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n # Update messages with the response\n messages = state[\"messages\"] + [response]\n\n # Handle tool calls\n if hasattr(response, \"tool_calls\") and response.tool_calls and len(response.tool_calls) > 0:\n # Handle dicts or object (backward compatibility)\n tool_call = (response.tool_calls[0]\n if isinstance(response.tool_calls[0], dict)\n else vars(response.tool_calls[0]))\n\n if tool_call[\"name\"] == \"plan_execution_steps\":\n # Get the steps from the tool call\n steps_raw = tool_call[\"args\"][\"steps\"]\n\n # Set initial status to \"enabled\" for all steps\n steps_data = []\n\n # Handle different potential formats of steps data\n if isinstance(steps_raw, list):\n for step in steps_raw:\n if isinstance(step, dict) and \"description\" in step:\n steps_data.append({\n \"description\": step[\"description\"],\n \"status\": \"enabled\"\n })\n elif isinstance(step, str):\n steps_data.append({\n \"description\": step,\n \"status\": \"enabled\"\n })\n\n # If no steps were processed correctly, return to END with the updated messages\n if not steps_data:\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"],\n }\n )\n # Update steps in state and emit to frontend\n state[\"steps\"] = steps_data\n\n # Add a tool response to satisfy OpenAI's requirements\n tool_response = {\n \"role\": \"tool\",\n \"content\": \"Task steps generated.\",\n \"tool_call_id\": tool_call[\"id\"]\n }\n\n messages = messages + [tool_response]\n\n # Move to the process_steps_node which will handle the interrupt and final response\n return Command(\n goto=\"process_steps_node\",\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"],\n }\n )\n\n # If no tool calls or not plan_execution_steps, return to END with the updated messages\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"],\n }\n )\n\n\nasync def process_steps_node(state: Dict[str, Any], config: RunnableConfig):\n \"\"\"\n This node handles the user interrupt for step customization and generates the final response.\n \"\"\"\n\n # Check if we already have a user_response in the state\n # This happens when the node restarts after an interrupt\n if \"user_response\" in state and state[\"user_response\"]:\n user_response = state[\"user_response\"]\n else:\n # Use LangGraph interrupt to get user input on steps\n # This will pause execution and wait for user input in the frontend\n user_response = interrupt({\"steps\": state[\"steps\"]})\n # Store the user response in state for when the node restarts\n state[\"user_response\"] = user_response\n\n # Generate the creative completion response\n final_prompt = \"\"\"\n Provide a textual description of how you are performing the task.\n If the user has disabled a step, you are not allowed to perform that step.\n However, you should find a creative workaround to perform the task, and if an essential step is disabled, you can even use\n some humor in the description of how you are performing the task.\n Don't just repeat a list of steps, come up with a creative but short description (3 sentences max) of how you are performing the task.\n \"\"\"\n\n final_response = await ChatOpenAI(model=\"gpt-4o\").ainvoke([\n SystemMessage(content=final_prompt),\n {\"role\": \"user\", \"content\": user_response}\n ], config)\n\n # Add the final response to messages\n messages = state[\"messages\"] + [final_response]\n\n # Clear the user_response from state to prepare for future interactions\n if \"user_response\" in state:\n state.pop(\"user_response\")\n\n # Return to END with the updated messages\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"steps\": state[\"steps\"],\n }\n )\n\n\n# Define the graph\nworkflow = StateGraph(AgentState)\n\n# Add nodes\nworkflow.add_node(\"start_node\", start_node)\nworkflow.add_node(\"chat_node\", chat_node)\nworkflow.add_node(\"process_steps_node\", process_steps_node)\n\n# Add edges\nworkflow.set_entry_point(\"start_node\")\nworkflow.add_edge(START, \"start_node\")\nworkflow.add_edge(\"start_node\", \"chat_node\")\nworkflow.add_edge(\"process_steps_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n", "language": "python", "type": "file" } @@ -680,7 +680,7 @@ }, { "name": "agent.py", - "content": "\"\"\"\nA demo of predictive state updates using LangGraph.\n\"\"\"\n\nimport uuid\nfrom typing import List, Any, Optional\nimport os\n\n# LangGraph imports\nfrom langchain_core.runnables import RunnableConfig\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command\nfrom langgraph.graph import MessagesState\n# OpenAI imports\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.messages import SystemMessage\n\nWRITE_DOCUMENT_TOOL = {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"write_document_local\",\n \"description\": \" \".join(\"\"\"\n Write a document. Use markdown formatting to format the document.\n It's good to format the document extensively so it's easy to read.\n You can use all kinds of markdown.\n However, do not use italic or strike-through formatting, it's reserved for another purpose.\n You MUST write the full document, even when changing only a few words.\n When making edits to the document, try to make them minimal - do not change every word.\n Keep stories SHORT!\n \"\"\".split()),\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"document\": {\n \"type\": \"string\",\n \"description\": \"The document to write\"\n },\n },\n }\n }\n}\n\n\nclass AgentState(MessagesState):\n \"\"\"\n The state of the agent.\n \"\"\"\n document: Optional[str] = None\n tools: List[Any]\n\n\nasync def start_flow(state: AgentState, config: RunnableConfig):\n \"\"\"\n This is the entry point for the flow.\n \"\"\"\n return Command(\n goto=\"chat_node\"\n )\n\n\nasync def chat_node(state: AgentState, config: RunnableConfig):\n \"\"\"\n Standard chat node.\n \"\"\"\n\n system_prompt = f\"\"\"\n You are a helpful assistant for writing documents.\n To write the document, you MUST use the write_document_local tool.\n You MUST write the full document, even when changing only a few words.\n When you wrote the document, DO NOT repeat it as a message.\n Just briefly summarize the changes you made. 2 sentences max.\n This is the current state of the document: ----\\n {state.get('document')}\\n-----\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o\")\n\n # Define config for the model with emit_intermediate_state to stream tool calls to frontend\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Use \"predict_state\" metadata to set up streaming for the write_document_local tool\n config[\"metadata\"][\"predict_state\"] = [{\n \"state_key\": \"document\",\n \"tool\": \"write_document_local\",\n \"tool_argument\": \"document\"\n }]\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n WRITE_DOCUMENT_TOOL\n ],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model to generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n # Update messages with the response\n messages = state[\"messages\"] + [response]\n\n # Extract any tool calls from the response\n if hasattr(response, \"tool_calls\") and response.tool_calls:\n tool_call = response.tool_calls[0]\n\n # Handle tool_call as a dictionary or an object\n if isinstance(tool_call, dict):\n tool_call_id = tool_call[\"id\"]\n tool_call_name = tool_call[\"name\"]\n tool_call_args = tool_call[\"args\"]\n else:\n # Handle as an object (backward compatibility)\n tool_call_id = tool_call.id\n tool_call_name = tool_call.name\n tool_call_args = tool_call.args\n\n if tool_call_name == \"write_document_local\":\n # Add the tool response to messages\n tool_response = {\n \"role\": \"tool\",\n \"content\": \"Document written.\",\n \"tool_call_id\": tool_call_id\n }\n\n # Add confirmation tool call\n confirm_tool_call = {\n \"role\": \"assistant\",\n \"content\": \"\",\n \"tool_calls\": [{\n \"id\": str(uuid.uuid4()),\n \"function\": {\n \"name\": \"confirm_changes\",\n \"arguments\": \"{}\"\n }\n }]\n }\n\n messages = messages + [tool_response, confirm_tool_call]\n\n # Return Command to route to end\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"document\": tool_call_args[\"document\"]\n }\n )\n\n # If no tool was called, go to end\n return Command(\n goto=END,\n update={\n \"messages\": messages\n }\n )\n\n\n# Define the graph\nworkflow = StateGraph(AgentState)\n\n# Add nodes\nworkflow.add_node(\"start_flow\", start_flow)\nworkflow.add_node(\"chat_node\", chat_node)\n\n# Add edges\nworkflow.set_entry_point(\"start_flow\")\nworkflow.add_edge(START, \"start_flow\")\nworkflow.add_edge(\"start_flow\", \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n\n", + "content": "\"\"\"\nA demo of predictive state updates using LangGraph.\n\"\"\"\n\nimport uuid\nfrom typing import List, Any, Optional\nimport os\n\n# LangGraph imports\nfrom langchain_core.runnables import RunnableConfig\nfrom langchain_core.messages import SystemMessage\nfrom langchain_core.tools import tool\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command\nfrom langgraph.graph import MessagesState\nfrom langgraph.checkpoint.memory import MemorySaver\nfrom langchain_openai import ChatOpenAI\n\n@tool\ndef write_document_local(document: str): # pylint: disable=unused-argument\n \"\"\"\n Write a document. Use markdown formatting to format the document.\n It's good to format the document extensively so it's easy to read.\n You can use all kinds of markdown.\n However, do not use italic or strike-through formatting, it's reserved for another purpose.\n You MUST write the full document, even when changing only a few words.\n When making edits to the document, try to make them minimal - do not change every word.\n Keep stories SHORT!\n \"\"\"\n return document\n\nclass AgentState(MessagesState):\n \"\"\"\n The state of the agent.\n \"\"\"\n document: Optional[str] = None\n tools: List[Any]\n\n\nasync def start_node(state: AgentState, config: RunnableConfig): # pylint: disable=unused-argument\n \"\"\"\n This is the entry point for the flow.\n \"\"\"\n return Command(\n goto=\"chat_node\"\n )\n\n\nasync def chat_node(state: AgentState, config: Optional[RunnableConfig] = None):\n \"\"\"\n Standard chat node.\n \"\"\"\n\n system_prompt = f\"\"\"\n You are a helpful assistant for writing documents.\n To write the document, you MUST use the write_document_local tool.\n You MUST write the full document, even when changing only a few words.\n When you wrote the document, DO NOT repeat it as a message.\n Just briefly summarize the changes you made. 2 sentences max.\n This is the current state of the document: ----\\n {state.get('document')}\\n-----\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o\")\n\n # Define config for the model with emit_intermediate_state to stream tool calls to frontend\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Use \"predict_state\" metadata to set up streaming for the write_document_local tool\n config[\"metadata\"][\"predict_state\"] = [{\n \"state_key\": \"document\",\n \"tool\": \"write_document_local\",\n \"tool_argument\": \"document\"\n }]\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n write_document_local\n ],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model to generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n # Update messages with the response\n messages = state[\"messages\"] + [response]\n\n # Extract any tool calls from the response\n if hasattr(response, \"tool_calls\") and response.tool_calls:\n tool_call = response.tool_calls[0]\n\n # Handle tool_call as a dictionary or an object\n if isinstance(tool_call, dict):\n tool_call_id = tool_call[\"id\"]\n tool_call_name = tool_call[\"name\"]\n tool_call_args = tool_call[\"args\"]\n else:\n # Handle as an object (backward compatibility)\n tool_call_id = tool_call.id\n tool_call_name = tool_call.name\n tool_call_args = tool_call.args\n\n if tool_call_name == \"write_document_local\":\n # Add the tool response to messages\n tool_response = {\n \"role\": \"tool\",\n \"content\": \"Document written.\",\n \"tool_call_id\": tool_call_id\n }\n\n # Add confirmation tool call\n confirm_tool_call = {\n \"role\": \"assistant\",\n \"content\": \"\",\n \"tool_calls\": [{\n \"id\": str(uuid.uuid4()),\n \"function\": {\n \"name\": \"confirm_changes\",\n \"arguments\": \"{}\"\n }\n }]\n }\n\n messages = messages + [tool_response, confirm_tool_call]\n\n # Return Command to route to end\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"document\": tool_call_args[\"document\"]\n }\n )\n\n # If no tool was called, go to end\n return Command(\n goto=END,\n update={\n \"messages\": messages\n }\n )\n\n\n# Define the graph\nworkflow = StateGraph(AgentState)\nworkflow.add_node(\"start_node\", start_node)\nworkflow.add_node(\"chat_node\", chat_node)\nworkflow.set_entry_point(\"start_node\")\nworkflow.add_edge(START, \"start_node\")\nworkflow.add_edge(\"start_node\", \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n\n", "language": "python", "type": "file" } @@ -706,7 +706,7 @@ }, { "name": "agent.py", - "content": "\"\"\"\nA demo of shared state between the agent and CopilotKit using LangGraph.\n\"\"\"\n\nimport json\nfrom enum import Enum\nfrom typing import Dict, List, Any, Optional\nimport os\n\n# LangGraph imports\nfrom langchain_core.runnables import RunnableConfig\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command\nfrom langchain_core.callbacks.manager import adispatch_custom_event\nfrom langgraph.graph import MessagesState\n# OpenAI imports\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.messages import SystemMessage\n\nclass SkillLevel(str, Enum):\n \"\"\"\n The level of skill required for the recipe.\n \"\"\"\n BEGINNER = \"Beginner\"\n INTERMEDIATE = \"Intermediate\"\n ADVANCED = \"Advanced\"\n\n\nclass SpecialPreferences(str, Enum):\n \"\"\"\n Special preferences for the recipe.\n \"\"\"\n HIGH_PROTEIN = \"High Protein\"\n LOW_CARB = \"Low Carb\"\n SPICY = \"Spicy\"\n BUDGET_FRIENDLY = \"Budget-Friendly\"\n ONE_POT_MEAL = \"One-Pot Meal\"\n VEGETARIAN = \"Vegetarian\"\n VEGAN = \"Vegan\"\n\n\nclass CookingTime(str, Enum):\n \"\"\"\n The cooking time of the recipe.\n \"\"\"\n FIVE_MIN = \"5 min\"\n FIFTEEN_MIN = \"15 min\"\n THIRTY_MIN = \"30 min\"\n FORTY_FIVE_MIN = \"45 min\"\n SIXTY_PLUS_MIN = \"60+ min\"\n\n\nGENERATE_RECIPE_TOOL = {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"generate_recipe\",\n \"description\": \" \".join(\"\"\"Using the existing (if any) ingredients and instructions, proceed with the recipe to finish it.\n Make sure the recipe is complete. ALWAYS provide the entire recipe, not just the changes.\"\"\".split()),\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"recipe\": {\n \"type\": \"object\",\n \"properties\": {\n \"skill_level\": {\n \"type\": \"string\",\n \"enum\": [level.value for level in SkillLevel],\n \"description\": \"The skill level required for the recipe\"\n },\n \"special_preferences\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\",\n \"enum\": [preference.value for preference in SpecialPreferences]\n },\n \"description\": \"A list of special preferences for the recipe\"\n },\n \"cooking_time\": {\n \"type\": \"string\",\n \"enum\": [time.value for time in CookingTime],\n \"description\": \"The cooking time of the recipe\"\n },\n \"ingredients\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"icon\": {\"type\": \"string\", \"description\": \"The icon emoji (not emoji code like '\\x1f35e', but the actual emoji like 🥕) of the ingredient\"},\n \"name\": {\"type\": \"string\"},\n \"amount\": {\"type\": \"string\"}\n }\n },\n \"description\": \"Entire list of ingredients for the recipe, including the new ingredients and the ones that are already in the recipe\"\n },\n \"instructions\": {\n \"type\": \"array\",\n \"items\": {\"type\": \"string\"},\n \"description\": \"Entire list of instructions for the recipe, including the new instructions and the ones that are already there\"\n },\n \"changes\": {\n \"type\": \"string\",\n \"description\": \"A description of the changes made to the recipe\"\n }\n },\n }\n },\n \"required\": [\"recipe\"]\n }\n }\n}\n\n\nclass AgentState(MessagesState):\n \"\"\"\n The state of the recipe.\n \"\"\"\n recipe: Optional[Dict[str, Any]] = None\n tools: List[Any]\n\n\nasync def start_flow(state: Dict[str, Any], config: RunnableConfig):\n \"\"\"\n This is the entry point for the flow.\n \"\"\"\n\n # Initialize recipe if not exists\n if \"recipe\" not in state or state[\"recipe\"] is None:\n state[\"recipe\"] = {\n \"skill_level\": SkillLevel.BEGINNER.value,\n \"special_preferences\": [],\n \"cooking_time\": CookingTime.FIFTEEN_MIN.value,\n \"ingredients\": [{\"icon\": \"🍴\", \"name\": \"Sample Ingredient\", \"amount\": \"1 unit\"}],\n \"instructions\": [\"First step instruction\"]\n }\n # Emit the initial state to ensure it's properly shared with the frontend\n await adispatch_custom_event(\n \"manually_emit_intermediate_state\",\n state,\n config=config,\n )\n \n return Command(\n goto=\"chat_node\",\n update={\n \"messages\": state[\"messages\"],\n \"recipe\": state[\"recipe\"]\n }\n )\n\n\nasync def chat_node(state: Dict[str, Any], config: RunnableConfig):\n \"\"\"\n Standard chat node.\n \"\"\"\n # Create a safer serialization of the recipe\n recipe_json = \"No recipe yet\"\n if \"recipe\" in state and state[\"recipe\"] is not None:\n try:\n recipe_json = json.dumps(state[\"recipe\"], indent=2)\n except Exception as e:\n recipe_json = f\"Error serializing recipe: {str(e)}\"\n \n system_prompt = f\"\"\"You are a helpful assistant for creating recipes. \n This is the current state of the recipe: {recipe_json}\n You can improve the recipe by calling the generate_recipe tool.\n \n IMPORTANT:\n 1. Create a recipe using the existing ingredients and instructions. Make sure the recipe is complete.\n 2. For ingredients, append new ingredients to the existing ones.\n 3. For instructions, append new steps to the existing ones.\n 4. 'ingredients' is always an array of objects with 'icon', 'name', and 'amount' fields\n 5. 'instructions' is always an array of strings\n\n If you have just created or modified the recipe, just answer in one sentence what you did. dont describe the recipe, just say what you did.\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o-mini\")\n \n # Define config for the model\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Use \"predict_state\" metadata to set up streaming for the write_document tool\n config[\"metadata\"][\"predict_state\"] = [{\n \"state_key\": \"recipe\",\n \"tool\": \"generate_recipe\",\n \"tool_argument\": \"recipe\"\n }]\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n GENERATE_RECIPE_TOOL\n ],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model and generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n # Update messages with the response\n messages = state[\"messages\"] + [response]\n \n # Handle tool calls\n if hasattr(response, \"tool_calls\") and response.tool_calls:\n tool_call = response.tool_calls[0]\n \n # Handle tool_call as a dictionary or an object\n if isinstance(tool_call, dict):\n tool_call_id = tool_call[\"id\"]\n tool_call_name = tool_call[\"name\"]\n # Check if args is already a dict or needs to be parsed\n if isinstance(tool_call[\"args\"], dict):\n tool_call_args = tool_call[\"args\"]\n else:\n tool_call_args = json.loads(tool_call[\"args\"])\n else:\n # Handle as an object\n tool_call_id = tool_call.id\n tool_call_name = tool_call.name\n # Check if args is already a dict or needs to be parsed\n if isinstance(tool_call.args, dict):\n tool_call_args = tool_call.args\n else:\n tool_call_args = json.loads(tool_call.args)\n\n if tool_call_name == \"generate_recipe\":\n # Update recipe state with tool_call_args\n recipe_data = tool_call_args[\"recipe\"]\n \n # If we have an existing recipe, update it\n if \"recipe\" in state and state[\"recipe\"] is not None:\n recipe = state[\"recipe\"]\n for key, value in recipe_data.items():\n if value is not None: # Only update fields that were provided\n recipe[key] = value\n else:\n # Create a new recipe\n recipe = {\n \"skill_level\": recipe_data.get(\"skill_level\", SkillLevel.BEGINNER.value),\n \"special_preferences\": recipe_data.get(\"special_preferences\", []),\n \"cooking_time\": recipe_data.get(\"cooking_time\", CookingTime.FIFTEEN_MIN.value),\n \"ingredients\": recipe_data.get(\"ingredients\", []),\n \"instructions\": recipe_data.get(\"instructions\", [])\n }\n \n # Add tool response to messages\n tool_response = {\n \"role\": \"tool\",\n \"content\": \"Recipe generated.\",\n \"tool_call_id\": tool_call_id\n }\n \n messages = messages + [tool_response]\n \n # Explicitly emit the updated state to ensure it's shared with frontend\n state[\"recipe\"] = recipe\n await adispatch_custom_event(\n \"manually_emit_intermediate_state\",\n state,\n config=config,\n )\n \n # Return command with updated recipe\n return Command(\n goto=\"start_flow\",\n update={\n \"messages\": messages,\n \"recipe\": recipe\n }\n )\n\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"recipe\": state[\"recipe\"]\n }\n )\n\n\n# Define the graph\nworkflow = StateGraph(AgentState)\n\n# Add nodes\nworkflow.add_node(\"start_flow\", start_flow)\nworkflow.add_node(\"chat_node\", chat_node)\n\n# Add edges\nworkflow.set_entry_point(\"start_flow\")\nworkflow.add_edge(START, \"start_flow\")\nworkflow.add_edge(\"start_flow\", \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n", + "content": "\"\"\"\nA demo of shared state between the agent and CopilotKit using LangGraph.\n\"\"\"\n\nimport json\nfrom enum import Enum\nfrom typing import Dict, List, Any, Optional\nimport os\n\n# LangGraph imports\nfrom pydantic import BaseModel, Field\nfrom langchain_core.runnables import RunnableConfig\nfrom langchain_core.callbacks.manager import adispatch_custom_event\nfrom langchain_core.messages import SystemMessage\nfrom langchain_core.tools import tool\nfrom langchain_openai import ChatOpenAI\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command\nfrom langgraph.graph import MessagesState\nfrom langgraph.checkpoint.memory import MemorySaver\n\nclass SkillLevel(str, Enum):\n \"\"\"\n The level of skill required for the recipe.\n \"\"\"\n BEGINNER = \"Beginner\"\n INTERMEDIATE = \"Intermediate\"\n ADVANCED = \"Advanced\"\n\nclass SpecialPreferences(str, Enum):\n \"\"\"\n Special preferences for the recipe.\n \"\"\"\n HIGH_PROTEIN = \"High Protein\"\n LOW_CARB = \"Low Carb\"\n SPICY = \"Spicy\"\n BUDGET_FRIENDLY = \"Budget-Friendly\"\n ONE_POT_MEAL = \"One-Pot Meal\"\n VEGETARIAN = \"Vegetarian\"\n VEGAN = \"Vegan\"\n\nclass CookingTime(str, Enum):\n \"\"\"\n The cooking time of the recipe.\n \"\"\"\n FIVE_MIN = \"5 min\"\n FIFTEEN_MIN = \"15 min\"\n THIRTY_MIN = \"30 min\"\n FORTY_FIVE_MIN = \"45 min\"\n SIXTY_PLUS_MIN = \"60+ min\"\n\nclass Ingredient(BaseModel):\n \"\"\"\n An ingredient.\n \"\"\"\n icon: str = Field(\n description=\"Icon: the actual emoji like 🥕\"\n )\n name: str = Field(description=\"The name of the ingredient\")\n amount: str = Field(description=\"The amount of the ingredient\")\n\nclass Recipe(BaseModel):\n \"\"\"\n A recipe.\n \"\"\"\n skill_level: SkillLevel = \\\n Field(description=\"The skill level required for the recipe\")\n special_preferences: List[SpecialPreferences] = \\\n Field(description=\"A list of special preferences for the recipe\")\n cooking_time: CookingTime = \\\n Field(description=\"The cooking time of the recipe\")\n ingredients: List[Ingredient] = \\\n Field(description=\n \"\"\"Entire list of ingredients for the recipe, including the new ingredients\n and the ones that are already in the recipe: Icon: the actual emoji like 🥕,\n name and amount.\n Like so: 🥕 Carrots (250g)\"\"\"\n )\n instructions: List[str] = \\\n Field(description=\n \"\"\"Entire list of instructions for the recipe,\n including the new instructions and the ones that are already there\"\"\"\n )\n changes: str = \\\n Field(description=\"A description of the changes made to the recipe\")\n\nclass GenerateRecipeArgs(BaseModel): # pylint: disable=missing-class-docstring\n recipe: Recipe\n\n@tool(args_schema=GenerateRecipeArgs)\ndef generate_recipe(recipe: Recipe): # pylint: disable=unused-argument\n \"\"\"\n Using the existing (if any) ingredients and instructions, proceed with the recipe to finish it.\n Make sure the recipe is complete. ALWAYS provide the entire recipe, not just the changes.\n \"\"\"\n\nclass AgentState(MessagesState):\n \"\"\"\n The state of the recipe.\n \"\"\"\n recipe: Optional[Dict[str, Any]] = None\n tools: List[Any]\n\n\nasync def start_node(state: Dict[str, Any], config: RunnableConfig):\n \"\"\"\n This is the entry point for the flow.\n \"\"\"\n\n # Initialize recipe if not exists\n if \"recipe\" not in state or state[\"recipe\"] is None:\n state[\"recipe\"] = {\n \"skill_level\": SkillLevel.BEGINNER.value,\n \"special_preferences\": [],\n \"cooking_time\": CookingTime.FIFTEEN_MIN.value,\n \"ingredients\": [{\"icon\": \"🍴\", \"name\": \"Sample Ingredient\", \"amount\": \"1 unit\"}],\n \"instructions\": [\"First step instruction\"]\n }\n # Emit the initial state to ensure it's properly shared with the frontend\n await adispatch_custom_event(\n \"manually_emit_intermediate_state\",\n state,\n config=config,\n )\n\n return Command(\n goto=\"chat_node\",\n update={\n \"messages\": state[\"messages\"],\n \"recipe\": state[\"recipe\"]\n }\n )\n\nasync def chat_node(state: Dict[str, Any], config: RunnableConfig):\n \"\"\"\n Standard chat node.\n \"\"\"\n # Create a safer serialization of the recipe\n recipe_json = \"No recipe yet\"\n if \"recipe\" in state and state[\"recipe\"] is not None:\n try:\n recipe_json = json.dumps(state[\"recipe\"], indent=2)\n except Exception as e: # pylint: disable=broad-exception-caught\n recipe_json = f\"Error serializing recipe: {str(e)}\"\n\n system_prompt = f\"\"\"You are a helpful assistant for creating recipes. \n This is the current state of the recipe: {recipe_json}\n You can improve the recipe by calling the generate_recipe tool.\n \n IMPORTANT:\n 1. Create a recipe using the existing ingredients and instructions. Make sure the recipe is complete.\n 2. For ingredients, append new ingredients to the existing ones.\n 3. For instructions, append new steps to the existing ones.\n 4. 'ingredients' is always an array of objects with 'icon', 'name', and 'amount' fields\n 5. 'instructions' is always an array of strings\n\n If you have just created or modified the recipe, just answer in one sentence what you did. dont describe the recipe, just say what you did.\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o-mini\")\n\n # Define config for the model\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Use \"predict_state\" metadata to set up streaming for the write_document tool\n config[\"metadata\"][\"predict_state\"] = [{\n \"state_key\": \"recipe\",\n \"tool\": \"generate_recipe\",\n \"tool_argument\": \"recipe\"\n }]\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n generate_recipe\n ],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model and generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n # Update messages with the response\n messages = state[\"messages\"] + [response]\n\n # Handle tool calls\n if hasattr(response, \"tool_calls\") and response.tool_calls:\n # Handle dicts or object (backward compatibility)\n tool_call = (response.tool_calls[0]\n if isinstance(response.tool_calls[0], dict)\n else vars(response.tool_calls[0]))\n\n # Check if args is already a dict or needs to be parsed\n tool_call_args = (tool_call[\"args\"]\n if isinstance(tool_call[\"args\"], dict)\n else json.loads(tool_call[\"args\"]))\n\n if tool_call[\"name\"] == \"generate_recipe\":\n # Update recipe state with tool_call_args\n recipe_data = tool_call_args[\"recipe\"]\n\n # If we have an existing recipe, update it\n if \"recipe\" in state and state[\"recipe\"] is not None:\n recipe = state[\"recipe\"]\n for key, value in recipe_data.items():\n if value is not None: # Only update fields that were provided\n recipe[key] = value\n else:\n # Create a new recipe\n recipe = {\n \"skill_level\": recipe_data.get(\"skill_level\", SkillLevel.BEGINNER.value),\n \"special_preferences\": recipe_data.get(\"special_preferences\", []),\n \"cooking_time\": recipe_data.get(\"cooking_time\", CookingTime.FIFTEEN_MIN.value),\n \"ingredients\": recipe_data.get(\"ingredients\", []),\n \"instructions\": recipe_data.get(\"instructions\", [])\n }\n\n # Add tool response to messages\n tool_response = {\n \"role\": \"tool\",\n \"content\": \"Recipe generated.\",\n \"tool_call_id\": tool_call[\"id\"]\n }\n\n messages = messages + [tool_response]\n\n # Explicitly emit the updated state to ensure it's shared with frontend\n state[\"recipe\"] = recipe\n await adispatch_custom_event(\n \"manually_emit_intermediate_state\",\n state,\n config=config,\n )\n\n # Return command with updated recipe\n return Command(\n goto=\"start_node\",\n update={\n \"messages\": messages,\n \"recipe\": recipe\n }\n )\n\n return Command(\n goto=END,\n update={\n \"messages\": messages,\n \"recipe\": state[\"recipe\"]\n }\n )\n\n\n# Define the graph\nworkflow = StateGraph(AgentState)\nworkflow.add_node(\"start_node\", start_node)\nworkflow.add_node(\"chat_node\", chat_node)\nworkflow.set_entry_point(\"start_node\")\nworkflow.add_edge(START, \"start_node\")\nworkflow.add_edge(\"start_node\", \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n", "language": "python", "type": "file" } @@ -714,7 +714,7 @@ "langgraph-fastapi::tool_based_generative_ui": [ { "name": "page.tsx", - "content": "\"use client\";\nimport { CopilotKit, useCopilotAction } from \"@copilotkit/react-core\";\nimport { CopilotKitCSSProperties, CopilotSidebar, CopilotChat } from \"@copilotkit/react-ui\";\nimport { Dispatch, SetStateAction, useState, useEffect } from \"react\";\nimport \"@copilotkit/react-ui/styles.css\";\nimport \"./style.css\";\nimport React, { useMemo } from \"react\";\nimport { useMobileView } from \"@/utils/use-mobile-view\";\nimport { useMobileChat } from \"@/utils/use-mobile-chat\";\n\ninterface ToolBasedGenerativeUIProps {\n params: Promise<{\n integrationId: string;\n }>;\n}\n\ninterface GenerateHaiku{\n japanese : string[] | [],\n english : string[] | [],\n image_names : string[] | [],\n selectedImage : string | null,\n}\n\ninterface HaikuCardProps{\n generatedHaiku : GenerateHaiku | Partial\n setHaikus : Dispatch>\n haikus : GenerateHaiku[]\n}\n\nexport default function ToolBasedGenerativeUI({ params }: ToolBasedGenerativeUIProps) {\n const { integrationId } = React.use(params);\n const { isMobile } = useMobileView();\n const defaultChatHeight = 50\n const {\n isChatOpen,\n setChatHeight,\n setIsChatOpen,\n isDragging,\n chatHeight,\n handleDragStart\n } = useMobileChat(defaultChatHeight)\n\n const chatTitle = 'Haiku Generator'\n const chatDescription = 'Ask me to create haikus'\n const initialLabel = 'I\\'m a haiku generator 👋. How can I help you?'\n\n return (\n \n \n \n\n {/* Desktop Sidebar */}\n {!isMobile && (\n \n )}\n\n {/* Mobile Pull-Up Chat */}\n {isMobile && (\n <>\n {/* Chat Toggle Button */}\n
\n
\n {\n if (!isChatOpen) {\n setChatHeight(defaultChatHeight); // Reset to good default when opening\n }\n setIsChatOpen(!isChatOpen);\n }}\n >\n
\n
\n
{chatTitle}
\n
{chatDescription}
\n
\n
\n
\n \n \n \n
\n
\n \n\n {/* Pull-Up Chat Container */}\n \n {/* Drag Handle Bar */}\n
\n
\n
\n \n {/* Chat Header */}\n
\n
\n
\n

{chatTitle}

\n
\n setIsChatOpen(false)}\n className=\"p-2 hover:bg-gray-100 rounded-full transition-colors\"\n >\n \n \n \n \n
\n
\n\n {/* Chat Content - Flexible container for messages and input */}\n
\n \n
\n \n\n {/* Backdrop */}\n {isChatOpen && (\n setIsChatOpen(false)}\n />\n )}\n \n )}\n \n \n );\n}\n\nconst VALID_IMAGE_NAMES = [\n \"Osaka_Castle_Turret_Stone_Wall_Pine_Trees_Daytime.jpg\",\n \"Tokyo_Skyline_Night_Tokyo_Tower_Mount_Fuji_View.jpg\",\n \"Itsukushima_Shrine_Miyajima_Floating_Torii_Gate_Sunset_Long_Exposure.jpg\",\n \"Takachiho_Gorge_Waterfall_River_Lush_Greenery_Japan.jpg\",\n \"Bonsai_Tree_Potted_Japanese_Art_Green_Foliage.jpeg\",\n \"Shirakawa-go_Gassho-zukuri_Thatched_Roof_Village_Aerial_View.jpg\",\n \"Ginkaku-ji_Silver_Pavilion_Kyoto_Japanese_Garden_Pond_Reflection.jpg\",\n \"Senso-ji_Temple_Asakusa_Cherry_Blossoms_Kimono_Umbrella.jpg\",\n \"Cherry_Blossoms_Sakura_Night_View_City_Lights_Japan.jpg\",\n \"Mount_Fuji_Lake_Reflection_Cherry_Blossoms_Sakura_Spring.jpg\"\n];\n\nfunction HaikuCard({generatedHaiku, setHaikus, haikus} : HaikuCardProps) {\n return (\n
\n
\n {generatedHaiku?.japanese?.map((line, index) => (\n
\n

{line}

\n

\n {generatedHaiku.english?.[index]}\n

\n
\n ))}\n {generatedHaiku?.japanese && generatedHaiku.japanese.length >= 2 && (\n
\n {(() => {\n const firstLine = generatedHaiku?.japanese?.[0];\n if (!firstLine) return null;\n const haikuIndex = haikus.findIndex((h: any) => h.japanese[0] === firstLine);\n const haiku = haikus[haikuIndex];\n if (!haiku?.image_names) return null;\n\n return haiku.image_names.map((imageName, imgIndex) => (\n {\n setHaikus(prevHaikus => {\n const newHaikus = prevHaikus.map((h, idx) => {\n if (idx === haikuIndex) {\n return {\n ...h,\n selectedImage: imageName\n };\n }\n return h;\n });\n return newHaikus;\n });\n }}\n />\n ));\n })()}\n
\n )}\n
\n
\n );\n}\n\ninterface Haiku {\n japanese: string[];\n english: string[];\n image_names: string[];\n selectedImage: string | null;\n}\n\nfunction Haiku() {\n const [haikus, setHaikus] = useState([{\n japanese: [\"仮の句よ\", \"まっさらながら\", \"花を呼ぶ\"],\n english: [\n \"A placeholder verse—\",\n \"even in a blank canvas,\",\n \"it beckons flowers.\",\n ],\n image_names: [],\n selectedImage: null,\n }])\n const [activeIndex, setActiveIndex] = useState(0);\n const [isJustApplied, setIsJustApplied] = useState(false);\n\n const validateAndCorrectImageNames = (rawNames: string[] | undefined): string[] | null => {\n if (!rawNames || rawNames.length !== 3) {\n return null;\n }\n\n const correctedNames: string[] = [];\n const usedValidNames = new Set();\n\n for (const name of rawNames) {\n if (VALID_IMAGE_NAMES.includes(name) && !usedValidNames.has(name)) {\n correctedNames.push(name);\n usedValidNames.add(name);\n if (correctedNames.length === 3) break;\n }\n }\n\n if (correctedNames.length < 3) {\n const availableFallbacks = VALID_IMAGE_NAMES.filter(name => !usedValidNames.has(name));\n for (let i = availableFallbacks.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n [availableFallbacks[i], availableFallbacks[j]] = [availableFallbacks[j], availableFallbacks[i]];\n }\n\n while (correctedNames.length < 3 && availableFallbacks.length > 0) {\n const fallbackName = availableFallbacks.pop();\n if (fallbackName) {\n correctedNames.push(fallbackName);\n }\n }\n }\n\n while (correctedNames.length < 3 && VALID_IMAGE_NAMES.length > 0) {\n const fallbackName = VALID_IMAGE_NAMES[Math.floor(Math.random() * VALID_IMAGE_NAMES.length)];\n correctedNames.push(fallbackName);\n }\n\n return correctedNames.slice(0, 3);\n };\n\n useCopilotAction({\n name: \"generate_haiku\",\n parameters: [\n {\n name: \"japanese\",\n type: \"string[]\",\n },\n {\n name: \"english\",\n type: \"string[]\",\n },\n {\n name: \"image_names\",\n type: \"string[]\",\n description: \"Names of 3 relevant images\",\n },\n ],\n followUp: false,\n handler: async ({ japanese, english, image_names }: { japanese: string[], english: string[], image_names: string[] }) => {\n const finalCorrectedImages = validateAndCorrectImageNames(image_names);\n const newHaiku = {\n japanese: japanese || [],\n english: english || [],\n image_names: finalCorrectedImages || [],\n selectedImage: finalCorrectedImages?.[0] || null,\n };\n setHaikus(prev => [...prev, newHaiku]);\n setActiveIndex(haikus.length - 1);\n setIsJustApplied(true);\n setTimeout(() => setIsJustApplied(false), 600);\n return \"Haiku generated.\";\n },\n render: ({ args: generatedHaiku }: { args: Partial }) => {\n return (\n \n );\n },\n }, [haikus]);\n\n const generatedHaikus = useMemo(() => (\n haikus.filter((haiku) => haiku.english[0] !== \"A placeholder verse—\")\n ), [haikus]);\n\n const { isMobile } = useMobileView();\n\n return (\n
\n {/* Thumbnail List */}\n {Boolean(generatedHaikus.length) && !isMobile && (\n
\n {generatedHaikus.map((haiku, index) => (\n setActiveIndex(index)}\n >\n {haiku.japanese.map((line, lineIndex) => (\n \n

{line}

\n

{haiku.english?.[lineIndex]}

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n )}\n\n {/* Main Display */}\n
\n
\n {haikus.filter((_haiku: Haiku, index: number) => {\n if (haikus.length == 1) return true;\n else return index == activeIndex + 1;\n }).map((haiku, index) => (\n \n {haiku.japanese.map((line, lineIndex) => (\n \n

\n {line}\n

\n

\n {haiku.english?.[lineIndex]}\n

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n \n \n );\n}\n", + "content": "\"use client\";\nimport { CopilotKit, useCopilotAction } from \"@copilotkit/react-core\";\nimport { CopilotKitCSSProperties, CopilotSidebar, CopilotChat } from \"@copilotkit/react-ui\";\nimport { Dispatch, SetStateAction, useState, useEffect } from \"react\";\nimport \"@copilotkit/react-ui/styles.css\";\nimport \"./style.css\";\nimport React, { useMemo } from \"react\";\nimport { useMobileView } from \"@/utils/use-mobile-view\";\nimport { useMobileChat } from \"@/utils/use-mobile-chat\";\n\ninterface ToolBasedGenerativeUIProps {\n params: Promise<{\n integrationId: string;\n }>;\n}\n\ninterface GenerateHaiku{\n japanese : string[] | [],\n english : string[] | [],\n image_names : string[] | [],\n selectedImage : string | null,\n}\n\ninterface HaikuCardProps{\n generatedHaiku : GenerateHaiku | Partial\n setHaikus : Dispatch>\n haikus : GenerateHaiku[]\n}\n\nexport default function ToolBasedGenerativeUI({ params }: ToolBasedGenerativeUIProps) {\n const { integrationId } = React.use(params);\n const { isMobile } = useMobileView();\n const defaultChatHeight = 50\n const {\n isChatOpen,\n setChatHeight,\n setIsChatOpen,\n isDragging,\n chatHeight,\n handleDragStart\n } = useMobileChat(defaultChatHeight)\n\n const chatTitle = 'Haiku Generator'\n const chatDescription = 'Ask me to create haikus'\n const initialLabel = 'I\\'m a haiku generator 👋. How can I help you?'\n\n return (\n \n \n \n\n {/* Desktop Sidebar */}\n {!isMobile && (\n \n )}\n\n {/* Mobile Pull-Up Chat */}\n {isMobile && (\n <>\n {/* Chat Toggle Button */}\n
\n
\n {\n if (!isChatOpen) {\n setChatHeight(defaultChatHeight); // Reset to good default when opening\n }\n setIsChatOpen(!isChatOpen);\n }}\n >\n
\n
\n
{chatTitle}
\n
{chatDescription}
\n
\n
\n
\n \n \n \n
\n
\n \n\n {/* Pull-Up Chat Container */}\n \n {/* Drag Handle Bar */}\n
\n
\n
\n \n {/* Chat Header */}\n
\n
\n
\n

{chatTitle}

\n
\n setIsChatOpen(false)}\n className=\"p-2 hover:bg-gray-100 rounded-full transition-colors\"\n >\n \n \n \n \n
\n
\n\n {/* Chat Content - Flexible container for messages and input */}\n
\n \n
\n \n\n {/* Backdrop */}\n {isChatOpen && (\n setIsChatOpen(false)}\n />\n )}\n \n )}\n \n \n );\n}\n\nconst VALID_IMAGE_NAMES = [\n \"Osaka_Castle_Turret_Stone_Wall_Pine_Trees_Daytime.jpg\",\n \"Tokyo_Skyline_Night_Tokyo_Tower_Mount_Fuji_View.jpg\",\n \"Itsukushima_Shrine_Miyajima_Floating_Torii_Gate_Sunset_Long_Exposure.jpg\",\n \"Takachiho_Gorge_Waterfall_River_Lush_Greenery_Japan.jpg\",\n \"Bonsai_Tree_Potted_Japanese_Art_Green_Foliage.jpeg\",\n \"Shirakawa-go_Gassho-zukuri_Thatched_Roof_Village_Aerial_View.jpg\",\n \"Ginkaku-ji_Silver_Pavilion_Kyoto_Japanese_Garden_Pond_Reflection.jpg\",\n \"Senso-ji_Temple_Asakusa_Cherry_Blossoms_Kimono_Umbrella.jpg\",\n \"Cherry_Blossoms_Sakura_Night_View_City_Lights_Japan.jpg\",\n \"Mount_Fuji_Lake_Reflection_Cherry_Blossoms_Sakura_Spring.jpg\"\n];\n\nfunction HaikuCard({generatedHaiku, setHaikus, haikus} : HaikuCardProps) {\n return (\n
\n
\n {generatedHaiku?.japanese?.map((line, index) => (\n
\n

{line}

\n

\n {generatedHaiku.english?.[index]}\n

\n
\n ))}\n {generatedHaiku?.japanese && generatedHaiku.japanese.length >= 2 && (\n
\n {(() => {\n const firstLine = generatedHaiku?.japanese?.[0];\n if (!firstLine) return null;\n const haikuIndex = haikus.findIndex((h: any) => h.japanese[0] === firstLine);\n const haiku = haikus[haikuIndex];\n if (!haiku?.image_names) return null;\n\n return haiku.image_names.map((imageName, imgIndex) => (\n {\n setHaikus(prevHaikus => {\n const newHaikus = prevHaikus.map((h, idx) => {\n if (idx === haikuIndex) {\n return {\n ...h,\n selectedImage: imageName\n };\n }\n return h;\n });\n return newHaikus;\n });\n }}\n />\n ));\n })()}\n
\n )}\n
\n
\n );\n}\n\ninterface Haiku {\n japanese: string[];\n english: string[];\n image_names: string[];\n selectedImage: string | null;\n}\n\nfunction Haiku() {\n const [haikus, setHaikus] = useState([{\n japanese: [\"仮の句よ\", \"まっさらながら\", \"花を呼ぶ\"],\n english: [\n \"A placeholder verse—\",\n \"even in a blank canvas,\",\n \"it beckons flowers.\",\n ],\n image_names: [],\n selectedImage: null,\n }])\n const [activeIndex, setActiveIndex] = useState(0);\n const [isJustApplied, setIsJustApplied] = useState(false);\n\n const validateAndCorrectImageNames = (rawNames: string[] | undefined): string[] | null => {\n if (!rawNames || rawNames.length !== 3) {\n return null;\n }\n\n const correctedNames: string[] = [];\n const usedValidNames = new Set();\n\n for (const name of rawNames) {\n if (VALID_IMAGE_NAMES.includes(name) && !usedValidNames.has(name)) {\n correctedNames.push(name);\n usedValidNames.add(name);\n if (correctedNames.length === 3) break;\n }\n }\n\n if (correctedNames.length < 3) {\n const availableFallbacks = VALID_IMAGE_NAMES.filter(name => !usedValidNames.has(name));\n for (let i = availableFallbacks.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n [availableFallbacks[i], availableFallbacks[j]] = [availableFallbacks[j], availableFallbacks[i]];\n }\n\n while (correctedNames.length < 3 && availableFallbacks.length > 0) {\n const fallbackName = availableFallbacks.pop();\n if (fallbackName) {\n correctedNames.push(fallbackName);\n }\n }\n }\n\n while (correctedNames.length < 3 && VALID_IMAGE_NAMES.length > 0) {\n const fallbackName = VALID_IMAGE_NAMES[Math.floor(Math.random() * VALID_IMAGE_NAMES.length)];\n correctedNames.push(fallbackName);\n }\n\n return correctedNames.slice(0, 3);\n };\n\n useCopilotAction({\n name: \"generate_haiku\",\n available: \"frontend\",\n parameters: [\n {\n name: \"japanese\",\n type: \"string[]\",\n },\n {\n name: \"english\",\n type: \"string[]\",\n },\n {\n name: \"image_names\",\n type: \"string[]\",\n description: \"Names of 3 relevant images\",\n },\n ],\n followUp: false,\n handler: async ({ japanese, english, image_names }: { japanese: string[], english: string[], image_names: string[] }) => {\n const finalCorrectedImages = validateAndCorrectImageNames(image_names);\n const newHaiku = {\n japanese: japanese || [],\n english: english || [],\n image_names: finalCorrectedImages || [],\n selectedImage: finalCorrectedImages?.[0] || null,\n };\n setHaikus(prev => [...prev, newHaiku]);\n setActiveIndex(haikus.length - 1);\n setIsJustApplied(true);\n setTimeout(() => setIsJustApplied(false), 600);\n return \"Haiku generated.\";\n },\n render: ({ args: generatedHaiku }: { args: Partial }) => {\n return (\n \n );\n },\n }, [haikus]);\n\n const generatedHaikus = useMemo(() => (\n haikus.filter((haiku) => haiku.english[0] !== \"A placeholder verse—\")\n ), [haikus]);\n\n const { isMobile } = useMobileView();\n\n return (\n
\n {/* Thumbnail List */}\n {Boolean(generatedHaikus.length) && !isMobile && (\n
\n {generatedHaikus.map((haiku, index) => (\n setActiveIndex(index)}\n >\n {haiku.japanese.map((line, lineIndex) => (\n \n

{line}

\n

{haiku.english?.[lineIndex]}

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n )}\n\n {/* Main Display */}\n
\n
\n {haikus.filter((_haiku: Haiku, index: number) => {\n if (haikus.length == 1) return true;\n else return index == activeIndex + 1;\n }).map((haiku, index) => (\n \n {haiku.japanese.map((line, lineIndex) => (\n \n

\n {line}\n

\n

\n {haiku.english?.[lineIndex]}\n

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n \n \n );\n}\n", "language": "typescript", "type": "file" }, @@ -732,7 +732,33 @@ }, { "name": "agent.py", - "content": "\"\"\"\nAn example demonstrating tool-based generative UI using LangGraph.\n\"\"\"\n\nfrom typing import List, Any\nimport os\n\n# LangGraph imports\nfrom langchain_core.runnables import RunnableConfig\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command\nfrom langgraph.graph import MessagesState\n\n# OpenAI imports\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.messages import SystemMessage\n\n# List of available images (modify path if needed)\nIMAGE_LIST = [\n \"Osaka_Castle_Turret_Stone_Wall_Pine_Trees_Daytime.jpg\",\n \"Tokyo_Skyline_Night_Tokyo_Tower_Mount_Fuji_View.jpg\",\n \"Itsukushima_Shrine_Miyajima_Floating_Torii_Gate_Sunset_Long_Exposure.jpg\",\n \"Takachiho_Gorge_Waterfall_River_Lush_Greenery_Japan.jpg\",\n \"Bonsai_Tree_Potted_Japanese_Art_Green_Foliage.jpeg\",\n \"Shirakawa-go_Gassho-zukuri_Thatched_Roof_Village_Aerial_View.jpg\",\n \"Ginkaku-ji_Silver_Pavilion_Kyoto_Japanese_Garden_Pond_Reflection.jpg\",\n \"Senso-ji_Temple_Asakusa_Cherry_Blossoms_Kimono_Umbrella.jpg\",\n \"Cherry_Blossoms_Sakura_Night_View_City_Lights_Japan.jpg\",\n \"Mount_Fuji_Lake_Reflection_Cherry_Blossoms_Sakura_Spring.jpg\"\n]\n\n# This tool generates a haiku on the server.\n# The tool call will be streamed to the frontend as it is being generated.\nGENERATE_HAIKU_TOOL = {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"generate_haiku\",\n \"description\": \"Generate a haiku in Japanese and its English translation. Also select exactly 3 relevant images from the provided list based on the haiku's theme.\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"japanese\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"An array of three lines of the haiku in Japanese\"\n },\n \"english\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"An array of three lines of the haiku in English\"\n },\n \"image_names\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"An array of EXACTLY THREE image filenames from the provided list that are most relevant to the haiku.\"\n }\n },\n \"required\": [\"japanese\", \"english\", \"image_names\"]\n }\n }\n}\n\nclass AgentState(MessagesState):\n tools: List[Any]\n\nasync def chat_node(state: AgentState, config: RunnableConfig):\n \"\"\"\n The main function handling chat and tool calls.\n \"\"\"\n # Prepare the image list string for the prompt\n image_list_str = \"\\n\".join([f\"- {img}\" for img in IMAGE_LIST])\n\n system_prompt = f\"\"\"\n You assist the user in generating a haiku.\n When generating a haiku using the 'generate_haiku' tool, you MUST also select exactly 3 image filenames from the following list that are most relevant to the haiku's content or theme. Return the filenames in the 'image_names' parameter.\n \n Available images:\n {image_list_str}\n \n Dont provide the relavent image names in your final response to the user.\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o\")\n \n # Define config for the model\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [GENERATE_HAIKU_TOOL],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model to generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n # Return Command to end with updated messages\n return Command(\n goto=END,\n update={\n \"messages\": state[\"messages\"] + [response]\n }\n )\n\n# Define the graph\nworkflow = StateGraph(AgentState)\n\n# Add nodes\nworkflow.add_node(\"chat_node\", chat_node)\n\n# Add edges\nworkflow.set_entry_point(\"chat_node\")\nworkflow.add_edge(START, \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n\n\n", + "content": "\"\"\"\nAn example demonstrating tool-based generative UI using LangGraph.\n\"\"\"\n\nfrom typing import List, Any, Optional, Annotated\nimport os\n\n# LangGraph imports\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.runnables import RunnableConfig\nfrom langchain_core.messages import SystemMessage\nfrom langchain_core.tools import tool\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.types import Command\nfrom langgraph.graph import MessagesState\nfrom langgraph.prebuilt import ToolNode\n\n@tool\ndef generate_haiku(\n japanese: Annotated[ # pylint: disable=unused-argument\n List[str],\n \"An array of three lines of the haiku in Japanese\"\n ],\n english: Annotated[ # pylint: disable=unused-argument\n List[str],\n \"An array of three lines of the haiku in English\"\n ]\n):\n \"\"\"\n Generate a haiku in Japanese and its English translation.\n Also select exactly 3 relevant images from the provided list based on the haiku's theme.\n \"\"\"\n\nclass AgentState(MessagesState):\n \"\"\"\n State of the agent.\n \"\"\"\n tools: List[Any]\n\nasync def chat_node(state: AgentState, config: Optional[RunnableConfig] = None):\n \"\"\"\n The main function handling chat and tool calls.\n \"\"\"\n\n system_prompt = \"\"\"\n You assist the user in generating a haiku.\n When generating a haiku using the 'generate_haiku' tool.\n \"\"\"\n\n # Define the model\n model = ChatOpenAI(model=\"gpt-4o\")\n\n # Define config for the model\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # Bind the tools to the model\n model_with_tools = model.bind_tools(\n [generate_haiku],\n # Disable parallel tool calls to avoid race conditions\n parallel_tool_calls=False,\n )\n\n # Run the model to generate a response\n response = await model_with_tools.ainvoke([\n SystemMessage(content=system_prompt),\n *state[\"messages\"],\n ], config)\n\n if response.tool_calls:\n return Command(\n goto=\"tool_node\",\n update={\n \"messages\": state[\"messages\"] + [response]\n }\n )\n # Return Command to end with updated messages\n return Command(\n goto=END,\n update={\n \"messages\": state[\"messages\"] + [response]\n }\n )\n\n# Define the graph\nworkflow = StateGraph(AgentState)\n\n# Add nodes\nworkflow.add_node(\"chat_node\", chat_node)\nworkflow.add_node(\"tool_node\", ToolNode([generate_haiku]))\n\n# Add edges\nworkflow.set_entry_point(\"chat_node\")\nworkflow.add_edge(START, \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\nworkflow.add_edge(\"tool_node\", END)\n\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()\n\n", + "language": "python", + "type": "file" + } + ], + "langgraph-fastapi::agentic_chat_reasoning": [ + { + "name": "page.tsx", + "content": "\"use client\";\nimport React, { useState } from \"react\";\nimport \"@copilotkit/react-ui/styles.css\";\nimport \"./style.css\";\nimport { CopilotKit, useCoAgent, useCopilotAction, useCopilotChat } from \"@copilotkit/react-core\";\nimport { CopilotChat } from \"@copilotkit/react-ui\";\nimport { ChevronDown } from \"lucide-react\";\nimport { Button } from \"@/components/ui/button\";\nimport {\n DropdownMenu,\n DropdownMenuContent,\n DropdownMenuItem,\n DropdownMenuLabel,\n DropdownMenuSeparator,\n DropdownMenuTrigger,\n} from \"@/components/ui/dropdown-menu\";\n\ninterface AgenticChatProps {\n params: Promise<{\n integrationId: string;\n }>;\n}\n\nconst AgenticChat: React.FC = ({ params }) => {\n const { integrationId } = React.use(params);\n\n return (\n \n \n \n );\n};\n\ninterface AgentState {\n model: string;\n}\n\nconst Chat = () => {\n const [background, setBackground] = useState(\"--copilot-kit-background-color\");\n const { state: agentState, setState: setAgentState } = useCoAgent({\n name: \"agentic_chat_reasoning\",\n initialState: {\n model: \"OpenAI\",\n },\n });\n\n // Initialize model if not set\n const selectedModel = agentState?.model || \"OpenAI\";\n\n const handleModelChange = (model: string) => {\n setAgentState({ model });\n };\n\n useCopilotAction({\n name: \"change_background\",\n description:\n \"Change the background color of the chat. Can be anything that the CSS background attribute accepts. Regular colors, linear of radial gradients etc.\",\n parameters: [\n {\n name: \"background\",\n type: \"string\",\n description: \"The background. Prefer gradients.\",\n },\n ],\n handler: ({ background }) => {\n setBackground(background);\n },\n });\n\n return (\n
\n {/* Reasoning Model Dropdown */}\n
\n
\n
\n \n Reasoning Model:\n \n \n \n \n \n \n Select Model\n \n handleModelChange(\"OpenAI\")}>\n OpenAI\n \n handleModelChange(\"Anthropic\")}>\n Anthropic\n \n handleModelChange(\"Gemini\")}>\n Gemini\n \n \n \n
\n
\n
\n\n {/* Chat Container */}\n
\n
\n \n
\n
\n
\n );\n};\n\nexport default AgenticChat;\n", + "language": "typescript", + "type": "file" + }, + { + "name": "style.css", + "content": ".copilotKitInput {\n border-bottom-left-radius: 0.75rem;\n border-bottom-right-radius: 0.75rem;\n border-top-left-radius: 0.75rem;\n border-top-right-radius: 0.75rem;\n border: 1px solid var(--copilot-kit-separator-color) !important;\n}\n \n.copilotKitChat {\n background-color: #fff !important;\n}\n ", + "language": "css", + "type": "file" + }, + { + "name": "README.mdx", + "content": "# 🤖 Agentic Chat with Reasoning\n\n## What This Demo Shows\n\nThis demo showcases CopilotKit's **agentic chat** capabilities with **frontend\ntool integration**:\n\n1. **Natural Conversation**: Chat with your Copilot in a familiar chat interface\n2. **Frontend Tool Execution**: The Copilot can directly interacts with your UI\n by calling frontend functions\n3. **Seamless Integration**: Tools defined in the frontend and automatically\n discovered and made available to the agent\n\n## How to Interact\n\nTry asking your Copilot to:\n\n- \"Can you change the background color to something more vibrant?\"\n- \"Make the background a blue to purple gradient\"\n- \"Set the background to a sunset-themed gradient\"\n- \"Change it back to a simple light color\"\n\nYou can also chat about other topics - the agent will respond conversationally\nwhile having the ability to use your UI tools when appropriate.\n\n## ✨ Frontend Tool Integration in Action\n\n**What's happening technically:**\n\n- The React component defines a frontend function using `useCopilotAction`\n- CopilotKit automatically exposes this function to the agent\n- When you make a request, the agent determines whether to use the tool\n- The agent calls the function with the appropriate parameters\n- The UI immediately updates in response\n\n**What you'll see in this demo:**\n\n- The Copilot understands requests to change the background\n- It generates CSS values for colors and gradients\n- When it calls the tool, the background changes instantly\n- The agent provides a conversational response about the changes it made\n\nThis technique of exposing frontend functions to your Copilot can be extended to\nany UI manipulation you want to enable, from theme changes to data filtering,\nnavigation, or complex UI state management!\n", + "language": "markdown", + "type": "file" + }, + { + "name": "agent.py", + "content": "\"\"\"\nA simple agentic chat flow using LangGraph instead of CrewAI.\n\"\"\"\n\nfrom typing import List, Any, Optional\nimport os\n\nfrom langchain_core.runnables import RunnableConfig\nfrom langchain_core.messages import SystemMessage\nfrom langchain_openai import ChatOpenAI\nfrom langchain_anthropic import ChatAnthropic\nfrom langchain_google_genai import ChatGoogleGenerativeAI\nfrom langgraph.graph import StateGraph, END, START\nfrom langgraph.graph import MessagesState\nfrom langgraph.types import Command\nfrom langgraph.checkpoint.memory import MemorySaver\n\nclass AgentState(MessagesState):\n \"\"\"\n State of our graph.\n \"\"\"\n tools: List[Any]\n model: str\n\nasync def chat_node(state: AgentState, config: Optional[RunnableConfig] = None):\n \"\"\"\n Standard chat node based on the ReAct design pattern. It handles:\n - The model to use (and binds in CopilotKit actions and the tools defined above)\n - The system prompt\n - Getting a response from the model\n - Handling tool calls\n\n For more about the ReAct design pattern, see:\n https://www.perplexity.ai/search/react-agents-NcXLQhreS0WDzpVaS4m9Cg\n \"\"\"\n\n\n # 1. Define the model\n model = ChatOpenAI(model=\"o3\")\n if state[\"model\"] == \"Anthropic\":\n model = ChatAnthropic(\n model=\"claude-sonnet-4-20250514\",\n thinking={\"type\": \"enabled\", \"budget_tokens\": 2000}\n )\n elif state[\"model\"] == \"Gemini\":\n model = ChatGoogleGenerativeAI(model=\"gemini-2.5-pro\", thinking_budget=1024)\n\n # Define config for the model\n if config is None:\n config = RunnableConfig(recursion_limit=25)\n\n # 2. Bind the tools to the model\n model_with_tools = model.bind_tools(\n [\n *state[\"tools\"],\n # your_tool_here\n ],\n )\n\n # 3. Define the system message by which the chat model will be run\n system_message = SystemMessage(\n content=\"You are a helpful assistant.\"\n )\n\n # 4. Run the model to generate a response\n response = await model_with_tools.ainvoke([\n system_message,\n *state[\"messages\"],\n ], config)\n\n # 6. We've handled all tool calls, so we can end the graph.\n return Command(\n goto=END,\n update={\n \"messages\": response\n }\n )\n\n# Define a new graph\nworkflow = StateGraph(AgentState)\nworkflow.add_node(\"chat_node\", chat_node)\nworkflow.set_entry_point(\"chat_node\")\n\n# Add explicit edges, matching the pattern in other examples\nworkflow.add_edge(START, \"chat_node\")\nworkflow.add_edge(\"chat_node\", END)\n\n# Conditionally use a checkpointer based on the environment\n# Check for multiple indicators that we're running in LangGraph dev/API mode\nis_fast_api = os.environ.get(\"LANGGRAPH_FAST_API\", \"false\").lower() == \"true\"\n\n# Compile the graph\nif is_fast_api:\n # For CopilotKit and other contexts, use MemorySaver\n from langgraph.checkpoint.memory import MemorySaver\n memory = MemorySaver()\n graph = workflow.compile(checkpointer=memory)\nelse:\n # When running in LangGraph API/dev, don't use a custom checkpointer\n graph = workflow.compile()", "language": "python", "type": "file" } @@ -757,6 +783,26 @@ "type": "file" } ], + "agno::tool_based_generative_ui": [ + { + "name": "page.tsx", + "content": "\"use client\";\nimport { CopilotKit, useCopilotAction } from \"@copilotkit/react-core\";\nimport { CopilotKitCSSProperties, CopilotSidebar, CopilotChat } from \"@copilotkit/react-ui\";\nimport { Dispatch, SetStateAction, useState, useEffect } from \"react\";\nimport \"@copilotkit/react-ui/styles.css\";\nimport \"./style.css\";\nimport React, { useMemo } from \"react\";\nimport { useMobileView } from \"@/utils/use-mobile-view\";\nimport { useMobileChat } from \"@/utils/use-mobile-chat\";\n\ninterface ToolBasedGenerativeUIProps {\n params: Promise<{\n integrationId: string;\n }>;\n}\n\ninterface GenerateHaiku{\n japanese : string[] | [],\n english : string[] | [],\n image_names : string[] | [],\n selectedImage : string | null,\n}\n\ninterface HaikuCardProps{\n generatedHaiku : GenerateHaiku | Partial\n setHaikus : Dispatch>\n haikus : GenerateHaiku[]\n}\n\nexport default function ToolBasedGenerativeUI({ params }: ToolBasedGenerativeUIProps) {\n const { integrationId } = React.use(params);\n const { isMobile } = useMobileView();\n const defaultChatHeight = 50\n const {\n isChatOpen,\n setChatHeight,\n setIsChatOpen,\n isDragging,\n chatHeight,\n handleDragStart\n } = useMobileChat(defaultChatHeight)\n\n const chatTitle = 'Haiku Generator'\n const chatDescription = 'Ask me to create haikus'\n const initialLabel = 'I\\'m a haiku generator 👋. How can I help you?'\n\n return (\n \n \n \n\n {/* Desktop Sidebar */}\n {!isMobile && (\n \n )}\n\n {/* Mobile Pull-Up Chat */}\n {isMobile && (\n <>\n {/* Chat Toggle Button */}\n
\n
\n {\n if (!isChatOpen) {\n setChatHeight(defaultChatHeight); // Reset to good default when opening\n }\n setIsChatOpen(!isChatOpen);\n }}\n >\n
\n
\n
{chatTitle}
\n
{chatDescription}
\n
\n
\n
\n \n \n \n
\n
\n \n\n {/* Pull-Up Chat Container */}\n \n {/* Drag Handle Bar */}\n
\n
\n
\n \n {/* Chat Header */}\n
\n
\n
\n

{chatTitle}

\n
\n setIsChatOpen(false)}\n className=\"p-2 hover:bg-gray-100 rounded-full transition-colors\"\n >\n \n \n \n \n
\n
\n\n {/* Chat Content - Flexible container for messages and input */}\n
\n \n
\n \n\n {/* Backdrop */}\n {isChatOpen && (\n setIsChatOpen(false)}\n />\n )}\n \n )}\n \n \n );\n}\n\nconst VALID_IMAGE_NAMES = [\n \"Osaka_Castle_Turret_Stone_Wall_Pine_Trees_Daytime.jpg\",\n \"Tokyo_Skyline_Night_Tokyo_Tower_Mount_Fuji_View.jpg\",\n \"Itsukushima_Shrine_Miyajima_Floating_Torii_Gate_Sunset_Long_Exposure.jpg\",\n \"Takachiho_Gorge_Waterfall_River_Lush_Greenery_Japan.jpg\",\n \"Bonsai_Tree_Potted_Japanese_Art_Green_Foliage.jpeg\",\n \"Shirakawa-go_Gassho-zukuri_Thatched_Roof_Village_Aerial_View.jpg\",\n \"Ginkaku-ji_Silver_Pavilion_Kyoto_Japanese_Garden_Pond_Reflection.jpg\",\n \"Senso-ji_Temple_Asakusa_Cherry_Blossoms_Kimono_Umbrella.jpg\",\n \"Cherry_Blossoms_Sakura_Night_View_City_Lights_Japan.jpg\",\n \"Mount_Fuji_Lake_Reflection_Cherry_Blossoms_Sakura_Spring.jpg\"\n];\n\nfunction HaikuCard({generatedHaiku, setHaikus, haikus} : HaikuCardProps) {\n return (\n
\n
\n {generatedHaiku?.japanese?.map((line, index) => (\n
\n

{line}

\n

\n {generatedHaiku.english?.[index]}\n

\n
\n ))}\n {generatedHaiku?.japanese && generatedHaiku.japanese.length >= 2 && (\n
\n {(() => {\n const firstLine = generatedHaiku?.japanese?.[0];\n if (!firstLine) return null;\n const haikuIndex = haikus.findIndex((h: any) => h.japanese[0] === firstLine);\n const haiku = haikus[haikuIndex];\n if (!haiku?.image_names) return null;\n\n return haiku.image_names.map((imageName, imgIndex) => (\n {\n setHaikus(prevHaikus => {\n const newHaikus = prevHaikus.map((h, idx) => {\n if (idx === haikuIndex) {\n return {\n ...h,\n selectedImage: imageName\n };\n }\n return h;\n });\n return newHaikus;\n });\n }}\n />\n ));\n })()}\n
\n )}\n
\n
\n );\n}\n\ninterface Haiku {\n japanese: string[];\n english: string[];\n image_names: string[];\n selectedImage: string | null;\n}\n\nfunction Haiku() {\n const [haikus, setHaikus] = useState([{\n japanese: [\"仮の句よ\", \"まっさらながら\", \"花を呼ぶ\"],\n english: [\n \"A placeholder verse—\",\n \"even in a blank canvas,\",\n \"it beckons flowers.\",\n ],\n image_names: [],\n selectedImage: null,\n }])\n const [activeIndex, setActiveIndex] = useState(0);\n const [isJustApplied, setIsJustApplied] = useState(false);\n\n const validateAndCorrectImageNames = (rawNames: string[] | undefined): string[] | null => {\n if (!rawNames || rawNames.length !== 3) {\n return null;\n }\n\n const correctedNames: string[] = [];\n const usedValidNames = new Set();\n\n for (const name of rawNames) {\n if (VALID_IMAGE_NAMES.includes(name) && !usedValidNames.has(name)) {\n correctedNames.push(name);\n usedValidNames.add(name);\n if (correctedNames.length === 3) break;\n }\n }\n\n if (correctedNames.length < 3) {\n const availableFallbacks = VALID_IMAGE_NAMES.filter(name => !usedValidNames.has(name));\n for (let i = availableFallbacks.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n [availableFallbacks[i], availableFallbacks[j]] = [availableFallbacks[j], availableFallbacks[i]];\n }\n\n while (correctedNames.length < 3 && availableFallbacks.length > 0) {\n const fallbackName = availableFallbacks.pop();\n if (fallbackName) {\n correctedNames.push(fallbackName);\n }\n }\n }\n\n while (correctedNames.length < 3 && VALID_IMAGE_NAMES.length > 0) {\n const fallbackName = VALID_IMAGE_NAMES[Math.floor(Math.random() * VALID_IMAGE_NAMES.length)];\n correctedNames.push(fallbackName);\n }\n\n return correctedNames.slice(0, 3);\n };\n\n useCopilotAction({\n name: \"generate_haiku\",\n available: \"frontend\",\n parameters: [\n {\n name: \"japanese\",\n type: \"string[]\",\n },\n {\n name: \"english\",\n type: \"string[]\",\n },\n {\n name: \"image_names\",\n type: \"string[]\",\n description: \"Names of 3 relevant images\",\n },\n ],\n followUp: false,\n handler: async ({ japanese, english, image_names }: { japanese: string[], english: string[], image_names: string[] }) => {\n const finalCorrectedImages = validateAndCorrectImageNames(image_names);\n const newHaiku = {\n japanese: japanese || [],\n english: english || [],\n image_names: finalCorrectedImages || [],\n selectedImage: finalCorrectedImages?.[0] || null,\n };\n setHaikus(prev => [...prev, newHaiku]);\n setActiveIndex(haikus.length - 1);\n setIsJustApplied(true);\n setTimeout(() => setIsJustApplied(false), 600);\n return \"Haiku generated.\";\n },\n render: ({ args: generatedHaiku }: { args: Partial }) => {\n return (\n \n );\n },\n }, [haikus]);\n\n const generatedHaikus = useMemo(() => (\n haikus.filter((haiku) => haiku.english[0] !== \"A placeholder verse—\")\n ), [haikus]);\n\n const { isMobile } = useMobileView();\n\n return (\n
\n {/* Thumbnail List */}\n {Boolean(generatedHaikus.length) && !isMobile && (\n
\n {generatedHaikus.map((haiku, index) => (\n setActiveIndex(index)}\n >\n {haiku.japanese.map((line, lineIndex) => (\n \n

{line}

\n

{haiku.english?.[lineIndex]}

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n )}\n\n {/* Main Display */}\n
\n
\n {haikus.filter((_haiku: Haiku, index: number) => {\n if (haikus.length == 1) return true;\n else return index == activeIndex + 1;\n }).map((haiku, index) => (\n \n {haiku.japanese.map((line, lineIndex) => (\n \n

\n {line}\n

\n

\n {haiku.english?.[lineIndex]}\n

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n \n \n );\n}\n", + "language": "typescript", + "type": "file" + }, + { + "name": "style.css", + "content": ".copilotKitWindow {\n box-shadow: 0 2px 10px rgba(0, 0, 0, 0.1);\n}\n\n.copilotKitHeader {\n border-top-left-radius: 5px !important;\n}\n\n.page-background {\n /* Darker gradient background */\n background: linear-gradient(170deg, #e9ecef 0%, #ced4da 100%);\n}\n\n@keyframes fade-scale-in {\n from {\n opacity: 0;\n transform: translateY(10px) scale(0.98);\n }\n to {\n opacity: 1;\n transform: translateY(0) scale(1);\n }\n}\n\n/* Updated card entry animation */\n@keyframes pop-in {\n 0% {\n opacity: 0;\n transform: translateY(15px) scale(0.95);\n }\n 70% {\n opacity: 1;\n transform: translateY(-2px) scale(1.02);\n }\n 100% {\n opacity: 1;\n transform: translateY(0) scale(1);\n }\n}\n\n/* Animation for subtle background gradient movement */\n@keyframes animated-gradient {\n 0% {\n background-position: 0% 50%;\n }\n 50% {\n background-position: 100% 50%;\n }\n 100% {\n background-position: 0% 50%;\n }\n}\n\n/* Animation for flash effect on apply */\n@keyframes flash-border-glow {\n 0% {\n /* Start slightly intensified */\n border-top-color: #ff5b4a !important;\n box-shadow: 0 10px 30px rgba(0, 0, 0, 0.07),\n inset 0 1px 2px rgba(0, 0, 0, 0.01),\n 0 0 25px rgba(255, 91, 74, 0.5);\n }\n 50% {\n /* Peak intensity */\n border-top-color: #ff4733 !important;\n box-shadow: 0 10px 30px rgba(0, 0, 0, 0.08),\n inset 0 1px 2px rgba(0, 0, 0, 0.01),\n 0 0 35px rgba(255, 71, 51, 0.7);\n }\n 100% {\n /* Return to default state appearance */\n border-top-color: #ff6f61 !important;\n box-shadow: 0 10px 30px rgba(0, 0, 0, 0.07),\n inset 0 1px 2px rgba(0, 0, 0, 0.01),\n 0 0 10px rgba(255, 111, 97, 0.15);\n }\n}\n\n/* Existing animation for haiku lines */\n@keyframes fade-slide-in {\n from {\n opacity: 0;\n transform: translateX(-15px);\n }\n to {\n opacity: 1;\n transform: translateX(0);\n }\n}\n\n.animated-fade-in {\n /* Use the new pop-in animation */\n animation: pop-in 0.6s ease-out forwards;\n}\n\n.haiku-card {\n /* Subtle animated gradient background */\n background: linear-gradient(120deg, #ffffff 0%, #fdfdfd 50%, #ffffff 100%);\n background-size: 200% 200%;\n animation: animated-gradient 10s ease infinite;\n\n /* === Explicit Border Override Attempt === */\n /* 1. Set the default grey border for all sides */\n border: 1px solid #dee2e6;\n\n /* 2. Explicitly override the top border immediately after */\n border-top: 10px solid #ff6f61 !important; /* Orange top - Added !important */\n /* === End Explicit Border Override Attempt === */\n\n padding: 2.5rem 3rem;\n border-radius: 20px;\n\n /* Default glow intensity */\n box-shadow: 0 10px 30px rgba(0, 0, 0, 0.07),\n inset 0 1px 2px rgba(0, 0, 0, 0.01),\n 0 0 15px rgba(255, 111, 97, 0.25);\n text-align: left;\n max-width: 745px;\n margin: 3rem auto;\n min-width: 600px;\n\n /* Transition */\n transition: transform 0.35s ease, box-shadow 0.35s ease, border-top-width 0.35s ease, border-top-color 0.35s ease;\n}\n\n.haiku-card:hover {\n transform: translateY(-8px) scale(1.03);\n /* Enhanced shadow + Glow */\n box-shadow: 0 15px 35px rgba(0, 0, 0, 0.1),\n inset 0 1px 2px rgba(0, 0, 0, 0.01),\n 0 0 25px rgba(255, 91, 74, 0.5);\n /* Modify only top border properties */\n border-top-width: 14px !important; /* Added !important */\n border-top-color: #ff5b4a !important; /* Added !important */\n}\n\n.haiku-card .flex {\n margin-bottom: 1.5rem;\n}\n\n.haiku-card .flex.haiku-line { /* Target the lines specifically */\n margin-bottom: 1.5rem;\n opacity: 0; /* Start hidden for animation */\n animation: fade-slide-in 0.5s ease-out forwards;\n /* animation-delay is set inline in page.tsx */\n}\n\n/* Remove previous explicit color overrides - rely on Tailwind */\n/* .haiku-card p.text-4xl {\n color: #212529;\n}\n\n.haiku-card p.text-base {\n color: #495057;\n} */\n\n.haiku-card.applied-flash {\n /* Apply the flash animation once */\n /* Note: animation itself has !important on border-top-color */\n animation: flash-border-glow 0.6s ease-out forwards;\n}\n\n/* Styling for images within the main haiku card */\n.haiku-card-image {\n width: 9.5rem; /* Increased size (approx w-48) */\n height: 9.5rem; /* Increased size (approx h-48) */\n object-fit: cover;\n border-radius: 1.5rem; /* rounded-xl */\n border: 1px solid #e5e7eb;\n /* Enhanced shadow with subtle orange hint */\n box-shadow: 0 8px 15px rgba(0, 0, 0, 0.1),\n 0 3px 6px rgba(0, 0, 0, 0.08),\n 0 0 10px rgba(255, 111, 97, 0.2);\n /* Inherit animation delay from inline style */\n animation-name: fadeIn;\n animation-duration: 0.5s;\n animation-fill-mode: both;\n}\n\n/* Styling for images within the suggestion card */\n.suggestion-card-image {\n width: 6.5rem; /* Increased slightly (w-20) */\n height: 6.5rem; /* Increased slightly (h-20) */\n object-fit: cover;\n border-radius: 1rem; /* Equivalent to rounded-md */\n border: 1px solid #d1d5db; /* Equivalent to border (using Tailwind gray-300) */\n margin-top: 0.5rem;\n /* Added shadow for suggestion images */\n box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1),\n 0 2px 4px rgba(0, 0, 0, 0.06);\n transition: all 0.2s ease-in-out; /* Added for smooth deselection */\n}\n\n/* Styling for the focused suggestion card image */\n.suggestion-card-image-focus {\n width: 6.5rem;\n height: 6.5rem;\n object-fit: cover;\n border-radius: 1rem;\n margin-top: 0.5rem;\n /* Highlight styles */\n border: 2px solid #ff6f61; /* Thicker, themed border */\n box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1), /* Base shadow for depth */\n 0 0 12px rgba(255, 111, 97, 0.6); /* Orange glow */\n transform: scale(1.05); /* Slightly scale up */\n transition: all 0.2s ease-in-out; /* Smooth transition for focus */\n}\n\n/* Styling for the suggestion card container in the sidebar */\n.suggestion-card {\n border: 1px solid #dee2e6; /* Same default border as haiku-card */\n border-top: 10px solid #ff6f61; /* Same orange top border */\n border-radius: 0.375rem; /* Default rounded-md */\n /* Note: background-color is set by Tailwind bg-gray-100 */\n /* Other styles like padding, margin, flex are handled by Tailwind */\n}\n\n.suggestion-image-container {\n display: flex;\n gap: 1rem;\n justify-content: space-between;\n width: 100%;\n height: 6.5rem;\n}\n\n/* Mobile responsive styles - matches useMobileView hook breakpoint */\n@media (max-width: 767px) {\n .haiku-card {\n padding: 1rem 1.5rem; /* Reduced from 2.5rem 3rem */\n min-width: auto; /* Remove min-width constraint */\n max-width: 100%; /* Full width on mobile */\n margin: 1rem auto; /* Reduced margin */\n }\n\n .haiku-card-image {\n width: 5.625rem; /* 90px - smaller on mobile */\n height: 5.625rem; /* 90px - smaller on mobile */\n }\n\n .suggestion-card-image {\n width: 5rem; /* Slightly smaller on mobile */\n height: 5rem; /* Slightly smaller on mobile */\n }\n\n .suggestion-card-image-focus {\n width: 5rem; /* Slightly smaller on mobile */\n height: 5rem; /* Slightly smaller on mobile */\n }\n}\n", + "language": "css", + "type": "file" + }, + { + "name": "README.mdx", + "content": "# 🪶 Tool-Based Generative UI Haiku Creator\n\n## What This Demo Shows\n\nThis demo showcases CopilotKit's **tool-based generative UI** capabilities:\n\n1. **Frontend Rendering of Tool Calls**: Backend tool calls are automatically\n rendered in the UI\n2. **Dynamic UI Generation**: The UI updates in real-time as the agent generates\n content\n3. **Elegant Content Presentation**: Complex structured data (haikus) are\n beautifully displayed\n\n## How to Interact\n\nChat with your Copilot and ask for haikus about different topics:\n\n- \"Create a haiku about nature\"\n- \"Write a haiku about technology\"\n- \"Generate a haiku about the changing seasons\"\n- \"Make a humorous haiku about programming\"\n\nEach request will trigger the agent to generate a haiku and display it in a\nvisually appealing card format in the UI.\n\n## ✨ Tool-Based Generative UI in Action\n\n**What's happening technically:**\n\n- The agent processes your request and determines it should create a haiku\n- It calls a backend tool that returns structured haiku data\n- CopilotKit automatically renders this tool call in the frontend\n- The rendering is handled by the registered tool component in your React app\n- No manual state management is required to display the results\n\n**What you'll see in this demo:**\n\n- As you request a haiku, a beautifully formatted card appears in the UI\n- The haiku follows the traditional 5-7-5 syllable structure\n- Each haiku is presented with consistent styling\n- Multiple haikus can be generated in sequence\n- The UI adapts to display each new piece of content\n\nThis pattern of tool-based generative UI can be extended to create any kind of\ndynamic content - from data visualizations to interactive components, all driven\nby your Copilot's tool calls!\n", + "language": "markdown", + "type": "file" + } + ], "llama-index::agentic_chat": [ { "name": "page.tsx", @@ -916,7 +962,7 @@ "crewai::tool_based_generative_ui": [ { "name": "page.tsx", - "content": "\"use client\";\nimport { CopilotKit, useCopilotAction } from \"@copilotkit/react-core\";\nimport { CopilotKitCSSProperties, CopilotSidebar, CopilotChat } from \"@copilotkit/react-ui\";\nimport { Dispatch, SetStateAction, useState, useEffect } from \"react\";\nimport \"@copilotkit/react-ui/styles.css\";\nimport \"./style.css\";\nimport React, { useMemo } from \"react\";\nimport { useMobileView } from \"@/utils/use-mobile-view\";\nimport { useMobileChat } from \"@/utils/use-mobile-chat\";\n\ninterface ToolBasedGenerativeUIProps {\n params: Promise<{\n integrationId: string;\n }>;\n}\n\ninterface GenerateHaiku{\n japanese : string[] | [],\n english : string[] | [],\n image_names : string[] | [],\n selectedImage : string | null,\n}\n\ninterface HaikuCardProps{\n generatedHaiku : GenerateHaiku | Partial\n setHaikus : Dispatch>\n haikus : GenerateHaiku[]\n}\n\nexport default function ToolBasedGenerativeUI({ params }: ToolBasedGenerativeUIProps) {\n const { integrationId } = React.use(params);\n const { isMobile } = useMobileView();\n const defaultChatHeight = 50\n const {\n isChatOpen,\n setChatHeight,\n setIsChatOpen,\n isDragging,\n chatHeight,\n handleDragStart\n } = useMobileChat(defaultChatHeight)\n\n const chatTitle = 'Haiku Generator'\n const chatDescription = 'Ask me to create haikus'\n const initialLabel = 'I\\'m a haiku generator 👋. How can I help you?'\n\n return (\n \n \n \n\n {/* Desktop Sidebar */}\n {!isMobile && (\n \n )}\n\n {/* Mobile Pull-Up Chat */}\n {isMobile && (\n <>\n {/* Chat Toggle Button */}\n
\n
\n {\n if (!isChatOpen) {\n setChatHeight(defaultChatHeight); // Reset to good default when opening\n }\n setIsChatOpen(!isChatOpen);\n }}\n >\n
\n
\n
{chatTitle}
\n
{chatDescription}
\n
\n
\n
\n \n \n \n
\n
\n \n\n {/* Pull-Up Chat Container */}\n \n {/* Drag Handle Bar */}\n
\n
\n
\n \n {/* Chat Header */}\n
\n
\n
\n

{chatTitle}

\n
\n setIsChatOpen(false)}\n className=\"p-2 hover:bg-gray-100 rounded-full transition-colors\"\n >\n \n \n \n \n
\n
\n\n {/* Chat Content - Flexible container for messages and input */}\n
\n \n
\n \n\n {/* Backdrop */}\n {isChatOpen && (\n setIsChatOpen(false)}\n />\n )}\n \n )}\n \n \n );\n}\n\nconst VALID_IMAGE_NAMES = [\n \"Osaka_Castle_Turret_Stone_Wall_Pine_Trees_Daytime.jpg\",\n \"Tokyo_Skyline_Night_Tokyo_Tower_Mount_Fuji_View.jpg\",\n \"Itsukushima_Shrine_Miyajima_Floating_Torii_Gate_Sunset_Long_Exposure.jpg\",\n \"Takachiho_Gorge_Waterfall_River_Lush_Greenery_Japan.jpg\",\n \"Bonsai_Tree_Potted_Japanese_Art_Green_Foliage.jpeg\",\n \"Shirakawa-go_Gassho-zukuri_Thatched_Roof_Village_Aerial_View.jpg\",\n \"Ginkaku-ji_Silver_Pavilion_Kyoto_Japanese_Garden_Pond_Reflection.jpg\",\n \"Senso-ji_Temple_Asakusa_Cherry_Blossoms_Kimono_Umbrella.jpg\",\n \"Cherry_Blossoms_Sakura_Night_View_City_Lights_Japan.jpg\",\n \"Mount_Fuji_Lake_Reflection_Cherry_Blossoms_Sakura_Spring.jpg\"\n];\n\nfunction HaikuCard({generatedHaiku, setHaikus, haikus} : HaikuCardProps) {\n return (\n
\n
\n {generatedHaiku?.japanese?.map((line, index) => (\n
\n

{line}

\n

\n {generatedHaiku.english?.[index]}\n

\n
\n ))}\n {generatedHaiku?.japanese && generatedHaiku.japanese.length >= 2 && (\n
\n {(() => {\n const firstLine = generatedHaiku?.japanese?.[0];\n if (!firstLine) return null;\n const haikuIndex = haikus.findIndex((h: any) => h.japanese[0] === firstLine);\n const haiku = haikus[haikuIndex];\n if (!haiku?.image_names) return null;\n\n return haiku.image_names.map((imageName, imgIndex) => (\n {\n setHaikus(prevHaikus => {\n const newHaikus = prevHaikus.map((h, idx) => {\n if (idx === haikuIndex) {\n return {\n ...h,\n selectedImage: imageName\n };\n }\n return h;\n });\n return newHaikus;\n });\n }}\n />\n ));\n })()}\n
\n )}\n
\n
\n );\n}\n\ninterface Haiku {\n japanese: string[];\n english: string[];\n image_names: string[];\n selectedImage: string | null;\n}\n\nfunction Haiku() {\n const [haikus, setHaikus] = useState([{\n japanese: [\"仮の句よ\", \"まっさらながら\", \"花を呼ぶ\"],\n english: [\n \"A placeholder verse—\",\n \"even in a blank canvas,\",\n \"it beckons flowers.\",\n ],\n image_names: [],\n selectedImage: null,\n }])\n const [activeIndex, setActiveIndex] = useState(0);\n const [isJustApplied, setIsJustApplied] = useState(false);\n\n const validateAndCorrectImageNames = (rawNames: string[] | undefined): string[] | null => {\n if (!rawNames || rawNames.length !== 3) {\n return null;\n }\n\n const correctedNames: string[] = [];\n const usedValidNames = new Set();\n\n for (const name of rawNames) {\n if (VALID_IMAGE_NAMES.includes(name) && !usedValidNames.has(name)) {\n correctedNames.push(name);\n usedValidNames.add(name);\n if (correctedNames.length === 3) break;\n }\n }\n\n if (correctedNames.length < 3) {\n const availableFallbacks = VALID_IMAGE_NAMES.filter(name => !usedValidNames.has(name));\n for (let i = availableFallbacks.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n [availableFallbacks[i], availableFallbacks[j]] = [availableFallbacks[j], availableFallbacks[i]];\n }\n\n while (correctedNames.length < 3 && availableFallbacks.length > 0) {\n const fallbackName = availableFallbacks.pop();\n if (fallbackName) {\n correctedNames.push(fallbackName);\n }\n }\n }\n\n while (correctedNames.length < 3 && VALID_IMAGE_NAMES.length > 0) {\n const fallbackName = VALID_IMAGE_NAMES[Math.floor(Math.random() * VALID_IMAGE_NAMES.length)];\n correctedNames.push(fallbackName);\n }\n\n return correctedNames.slice(0, 3);\n };\n\n useCopilotAction({\n name: \"generate_haiku\",\n parameters: [\n {\n name: \"japanese\",\n type: \"string[]\",\n },\n {\n name: \"english\",\n type: \"string[]\",\n },\n {\n name: \"image_names\",\n type: \"string[]\",\n description: \"Names of 3 relevant images\",\n },\n ],\n followUp: false,\n handler: async ({ japanese, english, image_names }: { japanese: string[], english: string[], image_names: string[] }) => {\n const finalCorrectedImages = validateAndCorrectImageNames(image_names);\n const newHaiku = {\n japanese: japanese || [],\n english: english || [],\n image_names: finalCorrectedImages || [],\n selectedImage: finalCorrectedImages?.[0] || null,\n };\n setHaikus(prev => [...prev, newHaiku]);\n setActiveIndex(haikus.length - 1);\n setIsJustApplied(true);\n setTimeout(() => setIsJustApplied(false), 600);\n return \"Haiku generated.\";\n },\n render: ({ args: generatedHaiku }: { args: Partial }) => {\n return (\n \n );\n },\n }, [haikus]);\n\n const generatedHaikus = useMemo(() => (\n haikus.filter((haiku) => haiku.english[0] !== \"A placeholder verse—\")\n ), [haikus]);\n\n const { isMobile } = useMobileView();\n\n return (\n
\n {/* Thumbnail List */}\n {Boolean(generatedHaikus.length) && !isMobile && (\n
\n {generatedHaikus.map((haiku, index) => (\n setActiveIndex(index)}\n >\n {haiku.japanese.map((line, lineIndex) => (\n \n

{line}

\n

{haiku.english?.[lineIndex]}

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n )}\n\n {/* Main Display */}\n
\n
\n {haikus.filter((_haiku: Haiku, index: number) => {\n if (haikus.length == 1) return true;\n else return index == activeIndex + 1;\n }).map((haiku, index) => (\n \n {haiku.japanese.map((line, lineIndex) => (\n \n

\n {line}\n

\n

\n {haiku.english?.[lineIndex]}\n

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n \n \n );\n}\n", + "content": "\"use client\";\nimport { CopilotKit, useCopilotAction } from \"@copilotkit/react-core\";\nimport { CopilotKitCSSProperties, CopilotSidebar, CopilotChat } from \"@copilotkit/react-ui\";\nimport { Dispatch, SetStateAction, useState, useEffect } from \"react\";\nimport \"@copilotkit/react-ui/styles.css\";\nimport \"./style.css\";\nimport React, { useMemo } from \"react\";\nimport { useMobileView } from \"@/utils/use-mobile-view\";\nimport { useMobileChat } from \"@/utils/use-mobile-chat\";\n\ninterface ToolBasedGenerativeUIProps {\n params: Promise<{\n integrationId: string;\n }>;\n}\n\ninterface GenerateHaiku{\n japanese : string[] | [],\n english : string[] | [],\n image_names : string[] | [],\n selectedImage : string | null,\n}\n\ninterface HaikuCardProps{\n generatedHaiku : GenerateHaiku | Partial\n setHaikus : Dispatch>\n haikus : GenerateHaiku[]\n}\n\nexport default function ToolBasedGenerativeUI({ params }: ToolBasedGenerativeUIProps) {\n const { integrationId } = React.use(params);\n const { isMobile } = useMobileView();\n const defaultChatHeight = 50\n const {\n isChatOpen,\n setChatHeight,\n setIsChatOpen,\n isDragging,\n chatHeight,\n handleDragStart\n } = useMobileChat(defaultChatHeight)\n\n const chatTitle = 'Haiku Generator'\n const chatDescription = 'Ask me to create haikus'\n const initialLabel = 'I\\'m a haiku generator 👋. How can I help you?'\n\n return (\n \n \n \n\n {/* Desktop Sidebar */}\n {!isMobile && (\n \n )}\n\n {/* Mobile Pull-Up Chat */}\n {isMobile && (\n <>\n {/* Chat Toggle Button */}\n
\n
\n {\n if (!isChatOpen) {\n setChatHeight(defaultChatHeight); // Reset to good default when opening\n }\n setIsChatOpen(!isChatOpen);\n }}\n >\n
\n
\n
{chatTitle}
\n
{chatDescription}
\n
\n
\n
\n \n \n \n
\n
\n \n\n {/* Pull-Up Chat Container */}\n \n {/* Drag Handle Bar */}\n
\n
\n
\n \n {/* Chat Header */}\n
\n
\n
\n

{chatTitle}

\n
\n setIsChatOpen(false)}\n className=\"p-2 hover:bg-gray-100 rounded-full transition-colors\"\n >\n \n \n \n \n
\n
\n\n {/* Chat Content - Flexible container for messages and input */}\n
\n \n
\n \n\n {/* Backdrop */}\n {isChatOpen && (\n setIsChatOpen(false)}\n />\n )}\n \n )}\n \n \n );\n}\n\nconst VALID_IMAGE_NAMES = [\n \"Osaka_Castle_Turret_Stone_Wall_Pine_Trees_Daytime.jpg\",\n \"Tokyo_Skyline_Night_Tokyo_Tower_Mount_Fuji_View.jpg\",\n \"Itsukushima_Shrine_Miyajima_Floating_Torii_Gate_Sunset_Long_Exposure.jpg\",\n \"Takachiho_Gorge_Waterfall_River_Lush_Greenery_Japan.jpg\",\n \"Bonsai_Tree_Potted_Japanese_Art_Green_Foliage.jpeg\",\n \"Shirakawa-go_Gassho-zukuri_Thatched_Roof_Village_Aerial_View.jpg\",\n \"Ginkaku-ji_Silver_Pavilion_Kyoto_Japanese_Garden_Pond_Reflection.jpg\",\n \"Senso-ji_Temple_Asakusa_Cherry_Blossoms_Kimono_Umbrella.jpg\",\n \"Cherry_Blossoms_Sakura_Night_View_City_Lights_Japan.jpg\",\n \"Mount_Fuji_Lake_Reflection_Cherry_Blossoms_Sakura_Spring.jpg\"\n];\n\nfunction HaikuCard({generatedHaiku, setHaikus, haikus} : HaikuCardProps) {\n return (\n
\n
\n {generatedHaiku?.japanese?.map((line, index) => (\n
\n

{line}

\n

\n {generatedHaiku.english?.[index]}\n

\n
\n ))}\n {generatedHaiku?.japanese && generatedHaiku.japanese.length >= 2 && (\n
\n {(() => {\n const firstLine = generatedHaiku?.japanese?.[0];\n if (!firstLine) return null;\n const haikuIndex = haikus.findIndex((h: any) => h.japanese[0] === firstLine);\n const haiku = haikus[haikuIndex];\n if (!haiku?.image_names) return null;\n\n return haiku.image_names.map((imageName, imgIndex) => (\n {\n setHaikus(prevHaikus => {\n const newHaikus = prevHaikus.map((h, idx) => {\n if (idx === haikuIndex) {\n return {\n ...h,\n selectedImage: imageName\n };\n }\n return h;\n });\n return newHaikus;\n });\n }}\n />\n ));\n })()}\n
\n )}\n
\n
\n );\n}\n\ninterface Haiku {\n japanese: string[];\n english: string[];\n image_names: string[];\n selectedImage: string | null;\n}\n\nfunction Haiku() {\n const [haikus, setHaikus] = useState([{\n japanese: [\"仮の句よ\", \"まっさらながら\", \"花を呼ぶ\"],\n english: [\n \"A placeholder verse—\",\n \"even in a blank canvas,\",\n \"it beckons flowers.\",\n ],\n image_names: [],\n selectedImage: null,\n }])\n const [activeIndex, setActiveIndex] = useState(0);\n const [isJustApplied, setIsJustApplied] = useState(false);\n\n const validateAndCorrectImageNames = (rawNames: string[] | undefined): string[] | null => {\n if (!rawNames || rawNames.length !== 3) {\n return null;\n }\n\n const correctedNames: string[] = [];\n const usedValidNames = new Set();\n\n for (const name of rawNames) {\n if (VALID_IMAGE_NAMES.includes(name) && !usedValidNames.has(name)) {\n correctedNames.push(name);\n usedValidNames.add(name);\n if (correctedNames.length === 3) break;\n }\n }\n\n if (correctedNames.length < 3) {\n const availableFallbacks = VALID_IMAGE_NAMES.filter(name => !usedValidNames.has(name));\n for (let i = availableFallbacks.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n [availableFallbacks[i], availableFallbacks[j]] = [availableFallbacks[j], availableFallbacks[i]];\n }\n\n while (correctedNames.length < 3 && availableFallbacks.length > 0) {\n const fallbackName = availableFallbacks.pop();\n if (fallbackName) {\n correctedNames.push(fallbackName);\n }\n }\n }\n\n while (correctedNames.length < 3 && VALID_IMAGE_NAMES.length > 0) {\n const fallbackName = VALID_IMAGE_NAMES[Math.floor(Math.random() * VALID_IMAGE_NAMES.length)];\n correctedNames.push(fallbackName);\n }\n\n return correctedNames.slice(0, 3);\n };\n\n useCopilotAction({\n name: \"generate_haiku\",\n available: \"frontend\",\n parameters: [\n {\n name: \"japanese\",\n type: \"string[]\",\n },\n {\n name: \"english\",\n type: \"string[]\",\n },\n {\n name: \"image_names\",\n type: \"string[]\",\n description: \"Names of 3 relevant images\",\n },\n ],\n followUp: false,\n handler: async ({ japanese, english, image_names }: { japanese: string[], english: string[], image_names: string[] }) => {\n const finalCorrectedImages = validateAndCorrectImageNames(image_names);\n const newHaiku = {\n japanese: japanese || [],\n english: english || [],\n image_names: finalCorrectedImages || [],\n selectedImage: finalCorrectedImages?.[0] || null,\n };\n setHaikus(prev => [...prev, newHaiku]);\n setActiveIndex(haikus.length - 1);\n setIsJustApplied(true);\n setTimeout(() => setIsJustApplied(false), 600);\n return \"Haiku generated.\";\n },\n render: ({ args: generatedHaiku }: { args: Partial }) => {\n return (\n \n );\n },\n }, [haikus]);\n\n const generatedHaikus = useMemo(() => (\n haikus.filter((haiku) => haiku.english[0] !== \"A placeholder verse—\")\n ), [haikus]);\n\n const { isMobile } = useMobileView();\n\n return (\n
\n {/* Thumbnail List */}\n {Boolean(generatedHaikus.length) && !isMobile && (\n
\n {generatedHaikus.map((haiku, index) => (\n setActiveIndex(index)}\n >\n {haiku.japanese.map((line, lineIndex) => (\n \n

{line}

\n

{haiku.english?.[lineIndex]}

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n )}\n\n {/* Main Display */}\n
\n
\n {haikus.filter((_haiku: Haiku, index: number) => {\n if (haikus.length == 1) return true;\n else return index == activeIndex + 1;\n }).map((haiku, index) => (\n \n {haiku.japanese.map((line, lineIndex) => (\n \n

\n {line}\n

\n

\n {haiku.english?.[lineIndex]}\n

\n
\n ))}\n {haiku.image_names && haiku.image_names.length === 3 && (\n
\n {haiku.image_names.map((imageName, imgIndex) => (\n \n ))}\n
\n )}\n
\n ))}\n \n \n \n );\n}\n", "language": "typescript", "type": "file" }, @@ -934,7 +980,7 @@ }, { "name": "tool_based_generative_ui.py", - "content": "\"\"\"\nAn example demonstrating tool-based generative UI.\n\"\"\"\n\nfrom crewai.flow.flow import Flow, start\nfrom litellm import completion\nfrom ..sdk import copilotkit_stream, CopilotKitState\n\n\n# This tool generates a haiku on the server.\n# The tool call will be streamed to the frontend as it is being generated.\nGENERATE_HAIKU_TOOL = {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"generate_haiku\",\n \"description\": \"Generate a haiku in Japanese and its English translation\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"japanese\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"An array of three lines of the haiku in Japanese\"\n },\n \"english\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"An array of three lines of the haiku in English\"\n },\n \"image_names\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"Names of 3 relevant images from the provided list\"\n }\n },\n \"required\": [\"japanese\", \"english\", \"image_names\"]\n }\n }\n}\n\n\nclass ToolBasedGenerativeUIFlow(Flow[CopilotKitState]):\n \"\"\"\n A flow that demonstrates tool-based generative UI.\n \"\"\"\n\n @start()\n async def chat(self):\n \"\"\"\n The main function handling chat and tool calls.\n \"\"\"\n system_prompt = \"You assist the user in generating a haiku. When generating a haiku using the 'generate_haiku' tool, you MUST also select exactly 3 image filenames from the following list that are most relevant to the haiku's content or theme. Return the filenames in the 'image_names' parameter. Dont provide the relavent image names in your final response to the user. \"\n\n\n # 1. Run the model and stream the response\n # Note: In order to stream the response, wrap the completion call in\n # copilotkit_stream and set stream=True.\n response = await copilotkit_stream(\n completion(\n\n # 1.1 Specify the model to use\n model=\"openai/gpt-4o\",\n messages=[\n {\n \"role\": \"system\", \n \"content\": system_prompt\n },\n *self.state.messages\n ],\n\n # 1.2 Bind the available tools to the model\n tools=[ GENERATE_HAIKU_TOOL ],\n\n # 1.3 Disable parallel tool calls to avoid race conditions,\n # enable this for faster performance if you want to manage\n # the complexity of running tool calls in parallel.\n parallel_tool_calls=False,\n stream=True\n )\n )\n\n message = response.choices[0].message\n\n # 2. Append the message to the messages in state\n self.state.messages.append(message)\n\n", + "content": "\"\"\"\nAn example demonstrating tool-based generative UI.\n\"\"\"\n\nfrom crewai.flow.flow import Flow, start\nfrom litellm import completion\nfrom ..sdk import copilotkit_stream, CopilotKitState\n\n\n# This tool generates a haiku on the server.\n# The tool call will be streamed to the frontend as it is being generated.\nGENERATE_HAIKU_TOOL = {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"generate_haiku\",\n \"description\": \"Generate a haiku in Japanese and its English translation\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"japanese\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"An array of three lines of the haiku in Japanese\"\n },\n \"english\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"An array of three lines of the haiku in English\"\n },\n \"image_names\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"Names of 3 relevant images from the provided list\"\n }\n },\n \"required\": [\"japanese\", \"english\", \"image_names\"]\n }\n }\n}\n\n\nclass ToolBasedGenerativeUIFlow(Flow[CopilotKitState]):\n \"\"\"\n A flow that demonstrates tool-based generative UI.\n \"\"\"\n\n @start()\n async def chat(self):\n \"\"\"\n The main function handling chat and tool calls.\n \"\"\"\n system_prompt = \"You assist the user in generating a haiku. When generating a haiku using the 'generate_haiku' tool, you MUST also select exactly 3 image filenames from the following list that are most relevant to the haiku's content or theme. Return the filenames in the 'image_names' parameter. Dont provide the relavent image names in your final response to the user. \"\n\n\n # 1. Run the model and stream the response\n # Note: In order to stream the response, wrap the completion call in\n # copilotkit_stream and set stream=True.\n response = await copilotkit_stream(\n completion(\n\n # 1.1 Specify the model to use\n model=\"openai/gpt-4o\",\n messages=[\n {\n \"role\": \"system\", \n \"content\": system_prompt\n },\n *self.state.messages\n ],\n\n # 1.2 Bind the available tools to the model\n tools=[ GENERATE_HAIKU_TOOL ],\n\n # 1.3 Disable parallel tool calls to avoid race conditions,\n # enable this for faster performance if you want to manage\n # the complexity of running tool calls in parallel.\n parallel_tool_calls=False,\n stream=True\n )\n )\n message = response.choices[0].message\n\n # 2. Append the message to the messages in state\n self.state.messages.append(message)\n\n # 3. If there are tool calls, append a tool message to the messages in state\n if message.tool_calls:\n self.state.messages.append(\n {\n \"tool_call_id\": message.tool_calls[0].id,\n \"role\": \"tool\",\n \"content\": \"Haiku generated.\"\n }\n )\n", "language": "python", "type": "file" } diff --git a/typescript-sdk/apps/dojo/src/mastra/index.ts b/typescript-sdk/apps/dojo/src/mastra/index.ts index ce0af8252..27db6463d 100644 --- a/typescript-sdk/apps/dojo/src/mastra/index.ts +++ b/typescript-sdk/apps/dojo/src/mastra/index.ts @@ -5,6 +5,7 @@ import { LibSQLStore } from "@mastra/libsql"; import { DynamoDBStore } from "@mastra/dynamodb"; import { Mastra } from "@mastra/core"; +import { createTool } from "@mastra/core"; import { z } from "zod"; @@ -118,5 +119,31 @@ export const mastra = new Mastra({ }, }), }), + tool_based_generative_ui: new Agent({ + name: "tool_based_generative_ui", + instructions: ` + You are a helpful assistant for creating haikus. + `, + model: openai("gpt-4o"), + tools: { + generate_haiku: createTool({ + id: "generate_haiku", + description: + "Generate a haiku in Japanese and its English translation. Also select exactly 3 relevant images from the provided list based on the haiku's theme.", + inputSchema: z.object({ + japanese: z + .array(z.string()) + .describe("An array of three lines of the haiku in Japanese"), + english: z + .array(z.string()) + .describe("An array of three lines of the haiku in English"), + }), + outputSchema: z.string(), + execute: async ({ context }) => { + return "Haiku generated."; + }, + }), + }, + }), }, }); diff --git a/typescript-sdk/apps/dojo/src/menu.ts b/typescript-sdk/apps/dojo/src/menu.ts index 36a378c99..80a7fad5e 100644 --- a/typescript-sdk/apps/dojo/src/menu.ts +++ b/typescript-sdk/apps/dojo/src/menu.ts @@ -38,12 +38,12 @@ export const menuIntegrations: MenuIntegrationConfig[] = [ { id: "mastra", name: "Mastra", - features: ["agentic_chat"], + features: ["agentic_chat", "tool_based_generative_ui"], }, { id: "mastra-agent-local", name: "Mastra Agent (Local)", - features: ["agentic_chat", "shared_state"], + features: ["agentic_chat", "shared_state", "tool_based_generative_ui"], }, { id: "vercel-ai-sdk", @@ -72,12 +72,13 @@ export const menuIntegrations: MenuIntegrationConfig[] = [ "tool_based_generative_ui", "predictive_state_updates", "shared_state", + "agentic_chat_reasoning", ], }, { id: "agno", name: "Agno", - features: ["agentic_chat"], + features: ["agentic_chat", "tool_based_generative_ui"], }, { id: "llama-index", diff --git a/typescript-sdk/apps/dojo/src/types/integration.ts b/typescript-sdk/apps/dojo/src/types/integration.ts index 11b632ac3..705e0f8dd 100644 --- a/typescript-sdk/apps/dojo/src/types/integration.ts +++ b/typescript-sdk/apps/dojo/src/types/integration.ts @@ -6,7 +6,8 @@ export type Feature = | "human_in_the_loop" | "predictive_state_updates" | "shared_state" - | "tool_based_generative_ui"; + | "tool_based_generative_ui" + | "agentic_chat_reasoning"; export interface MenuIntegrationConfig { id: string; diff --git a/typescript-sdk/integrations/agno/examples/.gitignore b/typescript-sdk/integrations/agno/examples/.gitignore index 181e39fbe..8a9235820 100644 --- a/typescript-sdk/integrations/agno/examples/.gitignore +++ b/typescript-sdk/integrations/agno/examples/.gitignore @@ -1,4 +1,203 @@ -__pycache__ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[codz] +*$py.class -# LangGraph API -.langgraph_api +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py.cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock +#poetry.toml + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +# pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python. +# https://pdm-project.org/en/latest/usage/project/#working-with-version-control +#pdm.lock +#pdm.toml +.pdm-python +.pdm-build/ + +# pixi +# Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control. +#pixi.lock +# Pixi creates a virtual environment in the .pixi directory, just like venv module creates one +# in the .venv directory. It is recommended not to include this directory in version control. +.pixi + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.envrc +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# Abstra +# Abstra is an AI-powered process automation framework. +# Ignore directories containing user credentials, local state, and settings. +# Learn more at https://abstra.io/docs +.abstra/ + +# Visual Studio Code +# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore +# that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore +# and can be added to the global gitignore or merged into this file. However, if you prefer, +# you could uncomment the following to ignore the entire vscode folder +# .vscode/ + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc + +# Marimo +marimo/_static/ +marimo/_lsp/ +__marimo__/ + +# Streamlit +.streamlit/secrets.toml diff --git a/typescript-sdk/integrations/agno/examples/README.md b/typescript-sdk/integrations/agno/examples/README.md index 95cc57982..805324cca 100644 --- a/typescript-sdk/integrations/agno/examples/README.md +++ b/typescript-sdk/integrations/agno/examples/README.md @@ -1,7 +1,55 @@ -Extracted from with-agno +# Agno Finance Agent -To install deps -pip install -r requirements.txt +An Agno Agent with Finance tools for AG-UI that researches stock prices, analyst recommendations, and stock fundamentals. -to run -python agent.py \ No newline at end of file +## Setup + +This project uses [uv](https://github.com/astral-sh/uv) for dependency management. + +### Prerequisites + +1. Install uv: `pip install uv` +2. Set your OpenAI API key: `export OPENAI_API_KEY="your-api-key"` + +### Installation + +```bash +# Install dependencies +uv sync + +# Activate the virtual environment +uv shell +``` + +### Running the Agent + +```bash +# Run the agent +uv run python agent.py +``` + +The agent will be available at `http://localhost:9001` (or the port specified by the `PORT` environment variable). + +## Development + +```bash +# Install development dependencies +uv sync --extra dev + +# Run tests +uv run pytest + +# Format code +uv run black . +uv run isort . + +# Lint code +uv run flake8 . +``` + +## Features + +- Stock price lookup +- Analyst recommendations +- Stock fundamentals analysis +- AG-UI compatible interface \ No newline at end of file diff --git a/typescript-sdk/integrations/agno/examples/pyproject.toml b/typescript-sdk/integrations/agno/examples/pyproject.toml new file mode 100644 index 000000000..8b6fe19b7 --- /dev/null +++ b/typescript-sdk/integrations/agno/examples/pyproject.toml @@ -0,0 +1,25 @@ +tool.uv.package = true + +[project] +name = "server" +version = "0.1.0" +description = "Example usage of the AG-UI adapter for Agno" +license = "MIT" + +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "agno>=1.7.7", + "openai>=1.99.1", + "yfinance>=0.2.63", + "fastapi>=0.116.1", + "uvicorn>=0.35.0", + "ag-ui-protocol>=0.1.8", +] +authors = [ + {name = "AG-UI Team"} +] + + +[project.scripts] +dev = "server:main" \ No newline at end of file diff --git a/typescript-sdk/integrations/agno/examples/server/__init__.py b/typescript-sdk/integrations/agno/examples/server/__init__.py new file mode 100644 index 000000000..4ca5d2029 --- /dev/null +++ b/typescript-sdk/integrations/agno/examples/server/__init__.py @@ -0,0 +1,32 @@ +"""Example usage of the AG-UI adapter for Agno. + +This provides a FastAPI application that demonstrates how to use the +Agno agent with the AG-UI protocol. It includes examples for +AG-UI dojo features: +- Agentic Chat (Investment Analyst with Finance tools) +""" + +from __future__ import annotations + +from fastapi import FastAPI +import uvicorn +import os + +from .api import ( + agentic_chat_app, + tool_based_generative_ui_app, +) + +app = FastAPI(title='Agno AG-UI server') +app.mount('/agentic_chat', agentic_chat_app, 'Agentic Chat') +app.mount('/tool_based_generative_ui', tool_based_generative_ui_app, 'Tool-based Generative UI') + +def main(): + """Main function to start the FastAPI server.""" + port = int(os.getenv("PORT", "9001")) + uvicorn.run(app, host="0.0.0.0", port=port) + +if __name__ == "__main__": + main() + +__all__ = ["main"] diff --git a/typescript-sdk/integrations/agno/examples/server/api/__init__.py b/typescript-sdk/integrations/agno/examples/server/api/__init__.py new file mode 100644 index 000000000..83be232bc --- /dev/null +++ b/typescript-sdk/integrations/agno/examples/server/api/__init__.py @@ -0,0 +1,11 @@ +"""Example API for a AG-UI compatible Agno Agent UI.""" + +from __future__ import annotations + +from .agentic_chat import app as agentic_chat_app +from .tool_based_generative_ui import app as tool_based_generative_ui_app + +__all__ = [ + 'agentic_chat_app', + 'tool_based_generative_ui_app', +] \ No newline at end of file diff --git a/typescript-sdk/integrations/agno/examples/agent.py b/typescript-sdk/integrations/agno/examples/server/api/agentic_chat.py similarity index 81% rename from typescript-sdk/integrations/agno/examples/agent.py rename to typescript-sdk/integrations/agno/examples/server/api/agentic_chat.py index 155a73605..934fc2882 100644 --- a/typescript-sdk/integrations/agno/examples/agent.py +++ b/typescript-sdk/integrations/agno/examples/server/api/agentic_chat.py @@ -2,8 +2,6 @@ This example shows how to create an Agno Agent with tools (YFinanceTools) and expose it in an AG-UI compatible way. """ -import os - from agno.agent.agent import Agent from agno.app.agui.app import AGUIApp from agno.models.openai import OpenAIChat @@ -23,13 +21,8 @@ agui_app = AGUIApp( agent=agent, name="Investment Analyst", - app_id="investment_analyst", + app_id="agentic_chat", description="An investment analyst that researches stock prices, analyst recommendations, and stock fundamentals.", ) -app = agui_app.get_app() - -if __name__ == "__main__": - port = int(os.getenv("PORT", "9001")) - - agui_app.serve(app="agent:app", host="0.0.0.0", port=port, reload=True) +app = agui_app.get_app() \ No newline at end of file diff --git a/typescript-sdk/integrations/agno/examples/server/api/tool_based_generative_ui.py b/typescript-sdk/integrations/agno/examples/server/api/tool_based_generative_ui.py new file mode 100644 index 000000000..203aff12f --- /dev/null +++ b/typescript-sdk/integrations/agno/examples/server/api/tool_based_generative_ui.py @@ -0,0 +1,52 @@ +"""Example: Tool-based Generative UI Agent + +This example shows how to create an Agno Agent with custom tools for haiku generation +and background changing, exposed in an AG-UI compatible way. +""" +from typing import List + +from agno.agent.agent import Agent +from agno.app.agui.app import AGUIApp +from agno.models.openai import OpenAIChat +from agno.tools import tool + + +@tool() +def generate_haiku(english: List[str], japanese: List[str]) -> str: # pylint: disable=unused-argument + """ + Generate a haiku in Japanese and its English translation. + YOU MUST PROVIDE THE ENGLISH HAIKU AND THE JAPANESE HAIKU. + + Args: + english: List[str]: An array of three lines of the haiku in English. YOU MUST PROVIDE THE ENGLISH HAIKU. + japanese: List[str]: An array of three lines of the haiku in Japanese. YOU MUST PROVIDE THE JAPANESE HAIKU. + + Returns: + str: A confirmation message. + """ # pylint: disable=line-too-long + return "Haiku generated" + + +@tool(external_execution=True) +def change_background(background: str) -> str: # pylint: disable=unused-argument + """ + Change the background color of the chat. Can be anything that the CSS background attribute accepts. Regular colors, linear of radial gradients etc. + + Args: + background: str: The background color to change to. Can be anything that the CSS background attribute accepts. 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"add_crewai_flow_fastapi_endpoint", diff --git a/typescript-sdk/integrations/crewai/python/ag_ui_crewai/enterprise.py b/typescript-sdk/integrations/crewai/python/ag_ui_crewai/enterprise.py index 65d8b6061..734604808 100644 --- a/typescript-sdk/integrations/crewai/python/ag_ui_crewai/enterprise.py +++ b/typescript-sdk/integrations/crewai/python/ag_ui_crewai/enterprise.py @@ -1,173 +1,173 @@ -from typing import Literal, List, Any -from crewai.utilities.events import ( - FlowStartedEvent, - FlowFinishedEvent, - MethodExecutionStartedEvent, - MethodExecutionFinishedEvent -) -from crewai.utilities.events.base_event_listener import BaseEventListener -from crewai.utilities.events.base_events import BaseEvent - -from ag_ui.core import EventType, Message, State - -from .sdk import ( - litellm_messages_to_ag_ui_messages, - BridgedTextMessageChunkEvent, - BridgedToolCallChunkEvent, - BridgedCustomEvent, - BridgedStateSnapshotEvent, -) - -class EnterpriseRunStartedEvent(BaseEvent): - """Enterprise run started event""" - type: Literal[EventType.RUN_STARTED] - -class EnterpriseRunFinishedEvent(BaseEvent): - """Enterprise run finished event""" - type: Literal[EventType.RUN_FINISHED] - -class EnterpriseStepStartedEvent(BaseEvent): - """Enterprise step started event""" - type: Literal[EventType.STEP_STARTED] - -class EnterpriseStepFinishedEvent(BaseEvent): - """Enterprise step finished event""" - type: Literal[EventType.STEP_FINISHED] - -class EnterpriseMessagesSnapshotEvent(BaseEvent): - """Enterprise messages snapshot event""" - type: Literal[EventType.MESSAGES_SNAPSHOT] - messages: List[Message] - -class EnterpriseStateSnapshotEvent(BaseEvent): - """Enterprise state snapshot event""" - type: Literal[EventType.STATE_SNAPSHOT] - snapshot: State - -class EnterpriseTextMessageChunkEvent(BaseEvent): - """Enterprise text message chunk event""" - type: Literal[EventType.TEXT_MESSAGE_CHUNK] - message_id: str - role: Literal["assistant"] - delta: str - -class EnterpriseToolCallChunkEvent(BaseEvent): - """Enterprise tool call chunk event""" - type: Literal[EventType.TOOL_CALL_CHUNK] - tool_call_id: str - tool_call_name: str - delta: str - -class EnterpriseCustomEvent(BaseEvent): - """Enterprise custom event""" - type: Literal[EventType.CUSTOM] - name: str - value: Any - -class CrewEnterpriseEventListener(BaseEventListener): - """ - This class is used to produce custom events when running a crewai flow on CrewAI Enterprise. - NOTE: These listeners only fire when the Flow is not run on enterprise. - """ - def setup_listeners(self, crewai_event_bus): - @crewai_event_bus.on(FlowStartedEvent) - def _(source, event): # pylint: disable=unused-argument - crewai_event_bus.emit( - source, - EnterpriseRunStartedEvent( - type=EventType.RUN_STARTED - ) - ) - - @crewai_event_bus.on(FlowFinishedEvent) - def _(source, event): # pylint: disable=unused-argument - crewai_event_bus.emit( - source, - EnterpriseRunFinishedEvent( - type=EventType.RUN_FINISHED - ) - ) - - @crewai_event_bus.on(MethodExecutionStartedEvent) - def _(source, event): # pylint: disable=unused-argument - crewai_event_bus.emit( - source, - EnterpriseStepStartedEvent( - type=EventType.STEP_STARTED, - step_name=event.method_name - ) - ) - - @crewai_event_bus.on(MethodExecutionFinishedEvent) - def _(source, event): - messages = litellm_messages_to_ag_ui_messages(source.state.messages) - - crewai_event_bus.emit( - source, - EnterpriseMessagesSnapshotEvent( - type=EventType.MESSAGES_SNAPSHOT, - messages=messages - ) - ) - - crewai_event_bus.emit( - source, - EnterpriseStateSnapshotEvent( - type=EventType.STATE_SNAPSHOT, - snapshot=source.state - ) - ) - - crewai_event_bus.emit( - source, - EnterpriseStepFinishedEvent( - type=EventType.STEP_FINISHED, - step_name=event.method_name - ) - ) - - @crewai_event_bus.on(BridgedTextMessageChunkEvent) - def _(source, event): # pylint: disable=unused-argument - crewai_event_bus.emit( - source, - EnterpriseTextMessageChunkEvent( - type=EventType.TEXT_MESSAGE_CHUNK, - message_id=event.message_id, - role=event.role, - delta=event.delta - ) - ) - - @crewai_event_bus.on(BridgedToolCallChunkEvent) - def _(source, event): # pylint: disable=unused-argument - crewai_event_bus.emit( - source, - EnterpriseToolCallChunkEvent( - type=EventType.TOOL_CALL_CHUNK, - tool_call_id=event.tool_call_id, - tool_call_name=event.tool_call_name, - delta=event.delta - ) - ) - - - @crewai_event_bus.on(BridgedCustomEvent) - def _(source, event): # pylint: disable=unused-argument - crewai_event_bus.emit( - source, - EnterpriseCustomEvent( - type=EventType.CUSTOM, - name=event.name, - value=event.value - ) - ) - - @crewai_event_bus.on(BridgedStateSnapshotEvent) - def _(source, event): # pylint: disable=unused-argument - crewai_event_bus.emit( - source, - EnterpriseStateSnapshotEvent( - type=EventType.STATE_SNAPSHOT, - snapshot=event.snapshot - ) - ) +# from typing import Literal, List, Any +# from crewai.utilities.events import ( +# FlowStartedEvent, +# FlowFinishedEvent, +# MethodExecutionStartedEvent, +# MethodExecutionFinishedEvent +# ) +# from crewai.utilities.events.base_event_listener import BaseEventListener +# from crewai.utilities.events.base_events import BaseEvent + +# from ag_ui.core import EventType, Message, State + +# from .sdk import ( +# litellm_messages_to_ag_ui_messages, +# BridgedTextMessageChunkEvent, +# BridgedToolCallChunkEvent, +# BridgedCustomEvent, +# BridgedStateSnapshotEvent, +# ) + +# class EnterpriseRunStartedEvent(BaseEvent): +# """Enterprise run started event""" +# type: Literal[EventType.RUN_STARTED] + +# class EnterpriseRunFinishedEvent(BaseEvent): +# """Enterprise run finished event""" +# type: Literal[EventType.RUN_FINISHED] + +# class EnterpriseStepStartedEvent(BaseEvent): +# """Enterprise step started event""" +# type: Literal[EventType.STEP_STARTED] + +# class EnterpriseStepFinishedEvent(BaseEvent): +# """Enterprise step finished event""" +# type: Literal[EventType.STEP_FINISHED] + +# class EnterpriseMessagesSnapshotEvent(BaseEvent): +# """Enterprise messages snapshot event""" +# type: Literal[EventType.MESSAGES_SNAPSHOT] +# messages: List[Message] + +# class EnterpriseStateSnapshotEvent(BaseEvent): +# """Enterprise state snapshot event""" +# type: Literal[EventType.STATE_SNAPSHOT] +# snapshot: State + +# class EnterpriseTextMessageChunkEvent(BaseEvent): +# """Enterprise text message chunk event""" +# type: Literal[EventType.TEXT_MESSAGE_CHUNK] +# message_id: str +# role: Literal["assistant"] +# delta: str + +# class EnterpriseToolCallChunkEvent(BaseEvent): +# """Enterprise tool call chunk event""" +# type: Literal[EventType.TOOL_CALL_CHUNK] +# tool_call_id: str +# tool_call_name: str +# delta: str + +# class EnterpriseCustomEvent(BaseEvent): +# """Enterprise custom event""" +# type: Literal[EventType.CUSTOM] +# name: str +# value: Any + +# class CrewEnterpriseEventListener(BaseEventListener): +# """ +# This class is used to produce custom events when running a crewai flow on CrewAI Enterprise. +# NOTE: These listeners only fire when the Flow is not run on enterprise. +# """ +# def setup_listeners(self, crewai_event_bus): +# @crewai_event_bus.on(FlowStartedEvent) +# def _(source, event): # pylint: disable=unused-argument +# crewai_event_bus.emit( +# source, +# EnterpriseRunStartedEvent( +# type=EventType.RUN_STARTED +# ) +# ) + +# @crewai_event_bus.on(FlowFinishedEvent) +# def _(source, event): # pylint: disable=unused-argument +# crewai_event_bus.emit( +# source, +# EnterpriseRunFinishedEvent( +# type=EventType.RUN_FINISHED +# ) +# ) + +# @crewai_event_bus.on(MethodExecutionStartedEvent) +# def _(source, event): # pylint: disable=unused-argument +# crewai_event_bus.emit( +# source, +# EnterpriseStepStartedEvent( +# type=EventType.STEP_STARTED, +# step_name=event.method_name +# ) +# ) + +# @crewai_event_bus.on(MethodExecutionFinishedEvent) +# def _(source, event): +# messages = litellm_messages_to_ag_ui_messages(source.state.messages) + +# crewai_event_bus.emit( +# source, +# EnterpriseMessagesSnapshotEvent( +# type=EventType.MESSAGES_SNAPSHOT, +# messages=messages +# ) +# ) + +# crewai_event_bus.emit( +# source, +# EnterpriseStateSnapshotEvent( +# type=EventType.STATE_SNAPSHOT, +# snapshot=source.state +# ) +# ) + +# crewai_event_bus.emit( +# source, +# EnterpriseStepFinishedEvent( +# type=EventType.STEP_FINISHED, +# step_name=event.method_name +# ) +# ) + +# @crewai_event_bus.on(BridgedTextMessageChunkEvent) +# def _(source, event): # pylint: disable=unused-argument +# crewai_event_bus.emit( +# source, +# EnterpriseTextMessageChunkEvent( +# type=EventType.TEXT_MESSAGE_CHUNK, +# message_id=event.message_id, +# role=event.role, +# delta=event.delta +# ) +# ) + +# @crewai_event_bus.on(BridgedToolCallChunkEvent) +# def _(source, event): # pylint: disable=unused-argument +# crewai_event_bus.emit( +# source, +# EnterpriseToolCallChunkEvent( +# type=EventType.TOOL_CALL_CHUNK, +# tool_call_id=event.tool_call_id, +# tool_call_name=event.tool_call_name, +# delta=event.delta +# ) +# ) + + +# @crewai_event_bus.on(BridgedCustomEvent) +# def _(source, event): # pylint: disable=unused-argument +# crewai_event_bus.emit( +# source, +# EnterpriseCustomEvent( +# type=EventType.CUSTOM, +# name=event.name, +# value=event.value +# ) +# ) + +# @crewai_event_bus.on(BridgedStateSnapshotEvent) +# def _(source, event): # pylint: disable=unused-argument +# crewai_event_bus.emit( +# source, +# EnterpriseStateSnapshotEvent( +# type=EventType.STATE_SNAPSHOT, +# snapshot=event.snapshot +# ) +# ) diff --git a/typescript-sdk/integrations/crewai/python/ag_ui_crewai/examples/tool_based_generative_ui.py b/typescript-sdk/integrations/crewai/python/ag_ui_crewai/examples/tool_based_generative_ui.py index fb80f1a91..753ced9f1 100644 --- a/typescript-sdk/integrations/crewai/python/ag_ui_crewai/examples/tool_based_generative_ui.py +++ b/typescript-sdk/integrations/crewai/python/ag_ui_crewai/examples/tool_based_generative_ui.py @@ -84,9 +84,17 @@ async def chat(self): stream=True ) ) - message = response.choices[0].message # 2. Append the message to the messages in state self.state.messages.append(message) + # 3. If there are tool calls, append a tool message to the messages in state + if message.tool_calls: + self.state.messages.append( + { + "tool_call_id": message.tool_calls[0].id, + "role": "tool", + "content": "Haiku generated." + } + ) diff --git a/typescript-sdk/integrations/langgraph/examples/.gitignore b/typescript-sdk/integrations/langgraph/examples/.gitignore index 717aad3c1..2b2a65de6 100644 --- a/typescript-sdk/integrations/langgraph/examples/.gitignore +++ b/typescript-sdk/integrations/langgraph/examples/.gitignore @@ -1,3 +1,4 @@ # LangGraph API .langgraph_api -uv.lock +__pycache__ +uv.lock \ No newline at end of file diff --git a/typescript-sdk/integrations/langgraph/examples/python/agents/agentic_chat/agent.py b/typescript-sdk/integrations/langgraph/examples/python/agents/agentic_chat/agent.py index 08b63fd50..e4afac936 100644 --- a/typescript-sdk/integrations/langgraph/examples/python/agents/agentic_chat/agent.py +++ b/typescript-sdk/integrations/langgraph/examples/python/agents/agentic_chat/agent.py @@ -2,22 +2,24 @@ A simple agentic chat flow using LangGraph instead of CrewAI. """ -from typing import List, Any +from typing import List, Any, Optional import os # Updated imports for LangGraph from langchain_core.runnables import RunnableConfig +from langchain_core.messages import SystemMessage +from langchain_openai import ChatOpenAI from langgraph.graph import StateGraph, END, START from langgraph.graph import MessagesState from langgraph.types import Command -from typing_extensions import Literal -from langchain_openai import ChatOpenAI -from langchain_core.messages import SystemMessage class AgentState(MessagesState): + """ + State of our graph. + """ tools: List[Any] -async def chat_node(state: AgentState, config: RunnableConfig): +async def chat_node(state: AgentState, config: Optional[RunnableConfig] = None): """ Standard chat node based on the ReAct design pattern. It handles: - The model to use (and binds in CopilotKit actions and the tools defined above) @@ -28,10 +30,10 @@ async def chat_node(state: AgentState, config: RunnableConfig): For more about the ReAct design pattern, see: https://www.perplexity.ai/search/react-agents-NcXLQhreS0WDzpVaS4m9Cg """ - + # 1. Define the model model = ChatOpenAI(model="gpt-4o") - + # Define config for the model if config is None: config = RunnableConfig(recursion_limit=25) @@ -51,7 +53,7 @@ async def chat_node(state: AgentState, config: RunnableConfig): # 3. Define the system message by which the chat model will be run system_message = SystemMessage( - content=f"You are a helpful assistant. ." + content="You are a helpful assistant." ) # 4. Run the model to generate a response diff --git a/typescript-sdk/integrations/langgraph/examples/python/agents/agentic_chat_reasoning/__init__.py b/typescript-sdk/integrations/langgraph/examples/python/agents/agentic_chat_reasoning/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/typescript-sdk/integrations/langgraph/examples/python/agents/agentic_chat_reasoning/agent.py b/typescript-sdk/integrations/langgraph/examples/python/agents/agentic_chat_reasoning/agent.py new file mode 100644 index 000000000..d67977bec --- /dev/null +++ b/typescript-sdk/integrations/langgraph/examples/python/agents/agentic_chat_reasoning/agent.py @@ -0,0 +1,100 @@ +""" +A simple agentic chat flow using LangGraph instead of CrewAI. +""" + +from typing import List, Any, Optional +import os + +from langchain_core.runnables import RunnableConfig +from langchain_core.messages import SystemMessage +from langchain_openai import ChatOpenAI +from langchain_anthropic import ChatAnthropic +from langchain_google_genai import ChatGoogleGenerativeAI +from langgraph.graph import StateGraph, END, START +from langgraph.graph import MessagesState +from langgraph.types import Command +from langgraph.checkpoint.memory import MemorySaver + +class AgentState(MessagesState): + """ + State of our graph. + """ + tools: List[Any] + model: str + +async def chat_node(state: AgentState, config: Optional[RunnableConfig] = None): + """ + Standard chat node based on the ReAct design pattern. It handles: + - The model to use (and binds in CopilotKit actions and the tools defined above) + - The system prompt + - Getting a response from the model + - Handling tool calls + + For more about the ReAct design pattern, see: + https://www.perplexity.ai/search/react-agents-NcXLQhreS0WDzpVaS4m9Cg + """ + + + # 1. Define the model + model = ChatOpenAI(model="o3") + if state["model"] == "Anthropic": + model = ChatAnthropic( + model="claude-sonnet-4-20250514", + thinking={"type": "enabled", "budget_tokens": 2000} + ) + elif state["model"] == "Gemini": + model = ChatGoogleGenerativeAI(model="gemini-2.5-pro", thinking_budget=1024) + + # Define config for the model + if config is None: + config = RunnableConfig(recursion_limit=25) + + # 2. Bind the tools to the model + model_with_tools = model.bind_tools( + [ + *state["tools"], + # your_tool_here + ], + ) + + # 3. Define the system message by which the chat model will be run + system_message = SystemMessage( + content="You are a helpful assistant." + ) + + # 4. Run the model to generate a response + response = await model_with_tools.ainvoke([ + system_message, + *state["messages"], + ], config) + + # 6. We've handled all tool calls, so we can end the graph. + return Command( + goto=END, + update={ + "messages": response + } + ) + +# Define a new graph +workflow = StateGraph(AgentState) +workflow.add_node("chat_node", chat_node) +workflow.set_entry_point("chat_node") + +# Add explicit edges, matching the pattern in other examples +workflow.add_edge(START, "chat_node") +workflow.add_edge("chat_node", END) + +# Conditionally use a checkpointer based on the environment +# Check for multiple indicators that we're running in LangGraph dev/API mode +is_fast_api = os.environ.get("LANGGRAPH_FAST_API", "false").lower() == "true" + +# Compile the graph +if is_fast_api: + # For CopilotKit and other contexts, use MemorySaver + from langgraph.checkpoint.memory import MemorySaver + memory = MemorySaver() + graph = workflow.compile(checkpointer=memory) +else: + # When running in LangGraph API/dev, don't use a custom checkpointer + graph = workflow.compile() \ No newline at end of file diff --git a/typescript-sdk/integrations/langgraph/examples/python/agents/agentic_generative_ui/agent.py b/typescript-sdk/integrations/langgraph/examples/python/agents/agentic_generative_ui/agent.py index 930d848a7..f14a52f22 100644 --- a/typescript-sdk/integrations/langgraph/examples/python/agents/agentic_generative_ui/agent.py +++ b/typescript-sdk/integrations/langgraph/examples/python/agents/agentic_generative_ui/agent.py @@ -3,62 +3,53 @@ """ import asyncio -from typing import List, Any +from typing import List, Any, Optional, Annotated import os # LangGraph imports from langchain_core.runnables import RunnableConfig +from langchain_core.callbacks.manager import adispatch_custom_event +from langchain_core.messages import SystemMessage +from langchain_core.tools import tool +from langchain_openai import ChatOpenAI from langgraph.graph import StateGraph, END, START from langgraph.types import Command -from langchain_core.callbacks.manager import adispatch_custom_event from langgraph.graph import MessagesState +from pydantic import BaseModel, Field + +class Step(BaseModel): + """ + A step in a task. + """ + description: str = Field(description="The text of the step in gerund form") + status: str = Field(description="The status of the step, always 'pending'") + -# OpenAI imports -from langchain_openai import ChatOpenAI -from langchain_core.messages import SystemMessage # This tool simulates performing a task on the server. # The tool call will be streamed to the frontend as it is being generated. -PERFORM_TASK_TOOL = { - "type": "function", - "function": { - "name": "generate_task_steps_generative_ui", - "description": "Make up 10 steps (only a couple of words per step) that are required for a task. The step should be in gerund form (i.e. Digging hole, opening door, ...)", - "parameters": { - "type": "object", - "properties": { - "steps": { - "type": "array", - "items": { - "type": "object", - "properties": { - "description": { - "type": "string", - "description": "The text of the step in gerund form" - }, - "status": { - "type": "string", - "enum": ["pending"], - "description": "The status of the step, always 'pending'" - } - }, - "required": ["description", "status"] - }, - "description": "An array of 10 step objects, each containing text and status" - } - }, - "required": ["steps"] - } - } -} +@tool +def generate_task_steps_generative_ui( + steps: Annotated[ # pylint: disable=unused-argument + List[Step], + "An array of 10 step objects, each containing text and status" + ] +): + """ + Make up 10 steps (only a couple of words per step) that are required for a task. + The step should be in gerund form (i.e. Digging hole, opening door, ...). + """ class AgentState(MessagesState): + """ + State of the agent. + """ steps: List[dict] = [] tools: List[Any] -async def start_flow(state: AgentState, config: RunnableConfig): +async def start_node(state: AgentState, config: RunnableConfig): # pylint: disable=unused-argument """ This is the entry point for the flow. """ @@ -75,7 +66,7 @@ async def start_flow(state: AgentState, config: RunnableConfig): ) -async def chat_node(state: AgentState, config: RunnableConfig): +async def chat_node(state: AgentState, config: Optional[RunnableConfig] = None): """ Standard chat node. """ @@ -90,7 +81,7 @@ async def chat_node(state: AgentState, config: RunnableConfig): # Define the model model = ChatOpenAI(model="gpt-4o") - + # Define config for the model with emit_intermediate_state to stream tool calls to frontend if config is None: config = RunnableConfig(recursion_limit=25) @@ -106,7 +97,7 @@ async def chat_node(state: AgentState, config: RunnableConfig): model_with_tools = model.bind_tools( [ *state["tools"], - PERFORM_TASK_TOOL + generate_task_steps_generative_ui ], # Disable parallel tool calls to avoid race conditions parallel_tool_calls=False, @@ -122,46 +113,41 @@ async def chat_node(state: AgentState, config: RunnableConfig): # Extract any tool calls from the response if hasattr(response, "tool_calls") and response.tool_calls and len(response.tool_calls) > 0: - tool_call = response.tool_calls[0] - - # Handle tool_call as a dictionary rather than an object - if isinstance(tool_call, dict): - tool_call_id = tool_call["id"] - tool_call_name = tool_call["name"] - tool_call_args = tool_call["args"] - else: - # Handle as an object (backward compatibility) - tool_call_id = tool_call.id - tool_call_name = tool_call.name - tool_call_args = tool_call.args - - if tool_call_name == "generate_task_steps_generative_ui": - steps = [{"description": step["description"], "status": step["status"]} for step in tool_call_args["steps"]] - + # Handle dicts or object (backward compatibility) + tool_call = (response.tool_calls[0] + if isinstance(response.tool_calls[0], dict) + else vars(response.tool_calls[0])) + + if tool_call["name"] == "generate_task_steps_generative_ui": + steps = [ + {"description": step["description"], "status": step["status"]} + for step in tool_call["args"]["steps"] + ] + # Add the tool response to messages tool_response = { "role": "tool", "content": "Steps executed.", - "tool_call_id": tool_call_id + "tool_call_id": tool_call["id"] } messages = messages + [tool_response] + state["steps"] = steps # Return Command to route to simulate_task_node - for i, step in enumerate(steps): + for i, _ in enumerate(steps): # simulate executing the step await asyncio.sleep(1) steps[i]["status"] = "completed" - # Update the state with the completed step - using config as first parameter - state["steps"] = steps + # Update the state with the completed step using config await adispatch_custom_event( "manually_emit_state", state, config=config, ) - + return Command( - goto='start_flow', + goto='start_node', update={ "messages": messages, "steps": state["steps"] @@ -181,13 +167,13 @@ async def chat_node(state: AgentState, config: RunnableConfig): workflow = StateGraph(AgentState) # Add nodes -workflow.add_node("start_flow", start_flow) +workflow.add_node("start_node", start_node) workflow.add_node("chat_node", chat_node) -# Add edges (equivalent to the routing in CrewAI) -workflow.set_entry_point("start_flow") -workflow.add_edge(START, "start_flow") -workflow.add_edge("start_flow", "chat_node") +# Add edges +workflow.set_entry_point("start_node") +workflow.add_edge(START, "start_node") +workflow.add_edge("start_node", "chat_node") workflow.add_edge("chat_node", END) # Conditionally use a checkpointer based on the environment diff --git a/typescript-sdk/integrations/langgraph/examples/python/agents/dojo.py b/typescript-sdk/integrations/langgraph/examples/python/agents/dojo.py index ea7d50566..c9371f71f 100644 --- a/typescript-sdk/integrations/langgraph/examples/python/agents/dojo.py +++ b/typescript-sdk/integrations/langgraph/examples/python/agents/dojo.py @@ -14,6 +14,7 @@ from .tool_based_generative_ui.agent import graph as tool_based_generative_ui_graph from .agentic_chat.agent import graph as agentic_chat_graph from .agentic_generative_ui.agent import graph as agentic_generative_ui_graph +from .agentic_chat_reasoning.agent import graph as agentic_chat_reasoning_graph app = FastAPI(title="LangGraph Dojo Example Server") @@ -48,7 +49,12 @@ name="predictive_state_updates", description="An example for a predictive state updates flow.", graph=predictive_state_updates_graph, - ) + ), + "agentic_chat_reasoning": LangGraphAgent( + name="agentic_chat_reasoning", + description="An example for a reasoning chat.", + graph=agentic_chat_reasoning_graph, + ), } add_langgraph_fastapi_endpoint( @@ -87,6 +93,12 @@ path="/agent/predictive_state_updates" ) +add_langgraph_fastapi_endpoint( + app=app, + agent=agents["agentic_chat_reasoning"], + path="/agent/agentic_chat_reasoning" +) + def main(): """Run the uvicorn server.""" port = int(os.getenv("PORT", "8000")) diff --git a/typescript-sdk/integrations/langgraph/examples/python/agents/human_in_the_loop/agent.py b/typescript-sdk/integrations/langgraph/examples/python/agents/human_in_the_loop/agent.py index 0793dd66b..9051b0ea6 100644 --- a/typescript-sdk/integrations/langgraph/examples/python/agents/human_in_the_loop/agent.py +++ b/typescript-sdk/integrations/langgraph/examples/python/agents/human_in_the_loop/agent.py @@ -2,58 +2,46 @@ A LangGraph implementation of the human-in-the-loop agent. """ -import json -from typing import Dict, List, Any +from typing import Dict, List, Any, Annotated, Optional import os # LangGraph imports from langchain_core.runnables import RunnableConfig +from langchain_core.messages import SystemMessage +from langchain_core.tools import tool from langgraph.graph import StateGraph, END, START from langgraph.types import Command, interrupt from langgraph.graph import MessagesState - -# LLM imports from langchain_openai import ChatOpenAI -from langchain_core.messages import SystemMessage +from pydantic import BaseModel, Field -DEFINE_TASK_TOOL = { - "type": "function", - "function": { - "name": "plan_execution_steps", - "description": "Make up 10 steps (only a couple of words per step) that are required for a task. The step should be in imperative form (i.e. Dig hole, Open door, ...)", - "parameters": { - "type": "object", - "properties": { - "steps": { - "type": "array", - "items": { - "type": "object", - "properties": { - "description": { - "type": "string", - "description": "The text of the step in imperative form" - }, - "status": { - "type": "string", - "enum": ["enabled"], - "description": "The status of the step, always 'enabled'" - } - }, - "required": ["description", "status"] - }, - "description": "An array of 10 step objects, each containing text and status" - } - }, - "required": ["steps"] - } - } -} +class Step(BaseModel): + """ + A step in a task. + """ + description: str = Field(description="The text of the step in imperative form") + status: str = Field(description="The status of the step, always 'enabled'") + +@tool +def plan_execution_steps( + steps: Annotated[ # pylint: disable=unused-argument + List[Step], + "An array of 10 step objects, each containing text and status" + ] +): + """ + Make up 10 steps (only a couple of words per step) that are required for a task. + The step should be in imperative form (i.e. Dig hole, Open door, ...). + """ class AgentState(MessagesState): + """ + State of the agent. + """ steps: List[Dict[str, str]] = [] tools: List[Any] -async def start_flow(state: Dict[str, Any], config: RunnableConfig): +async def start_node(state: Dict[str, Any], config: RunnableConfig): # pylint: disable=unused-argument """ This is the entry point for the flow. """ @@ -62,7 +50,7 @@ async def start_flow(state: Dict[str, Any], config: RunnableConfig): if "steps" not in state: state["steps"] = [] - + # Return command to route to chat_node return Command( goto="chat_node", update={ @@ -72,7 +60,7 @@ async def start_flow(state: Dict[str, Any], config: RunnableConfig): ) -async def chat_node(state: Dict[str, Any], config: RunnableConfig): +async def chat_node(state: AgentState, config: Optional[RunnableConfig] = None): """ Standard chat node where the agent processes messages and generates responses. If task steps are defined, the user can enable/disable them using interrupts. @@ -85,7 +73,7 @@ async def chat_node(state: Dict[str, Any], config: RunnableConfig): # Define the model model = ChatOpenAI(model="gpt-4o-mini") - + # Define config for the model if config is None: config = RunnableConfig(recursion_limit=25) @@ -101,7 +89,7 @@ async def chat_node(state: Dict[str, Any], config: RunnableConfig): model_with_tools = model.bind_tools( [ *state["tools"], - DEFINE_TASK_TOOL + plan_execution_steps ], # Disable parallel tool calls to avoid race conditions parallel_tool_calls=False, @@ -115,28 +103,21 @@ async def chat_node(state: Dict[str, Any], config: RunnableConfig): # Update messages with the response messages = state["messages"] + [response] - + # Handle tool calls if hasattr(response, "tool_calls") and response.tool_calls and len(response.tool_calls) > 0: - tool_call = response.tool_calls[0] - # Extract tool call information - if hasattr(tool_call, "id"): - tool_call_id = tool_call.id - tool_call_name = tool_call.name - tool_call_args = tool_call.args if not isinstance(tool_call.args, str) else json.loads(tool_call.args) - else: - tool_call_id = tool_call.get("id", "") - tool_call_name = tool_call.get("name", "") - args = tool_call.get("args", {}) - tool_call_args = args if not isinstance(args, str) else json.loads(args) - - if tool_call_name == "plan_execution_steps": + # Handle dicts or object (backward compatibility) + tool_call = (response.tool_calls[0] + if isinstance(response.tool_calls[0], dict) + else vars(response.tool_calls[0])) + + if tool_call["name"] == "plan_execution_steps": # Get the steps from the tool call - steps_raw = tool_call_args.get("steps", []) - + steps_raw = tool_call["args"]["steps"] + # Set initial status to "enabled" for all steps steps_data = [] - + # Handle different potential formats of steps data if isinstance(steps_raw, list): for step in steps_raw: @@ -150,7 +131,7 @@ async def chat_node(state: Dict[str, Any], config: RunnableConfig): "description": step, "status": "enabled" }) - + # If no steps were processed correctly, return to END with the updated messages if not steps_data: return Command( @@ -162,14 +143,14 @@ async def chat_node(state: Dict[str, Any], config: RunnableConfig): ) # Update steps in state and emit to frontend state["steps"] = steps_data - + # Add a tool response to satisfy OpenAI's requirements tool_response = { "role": "tool", "content": "Task steps generated.", - "tool_call_id": tool_call_id + "tool_call_id": tool_call["id"] } - + messages = messages + [tool_response] # Move to the process_steps_node which will handle the interrupt and final response @@ -215,7 +196,7 @@ async def process_steps_node(state: Dict[str, Any], config: RunnableConfig): some humor in the description of how you are performing the task. Don't just repeat a list of steps, come up with a creative but short description (3 sentences max) of how you are performing the task. """ - + final_response = await ChatOpenAI(model="gpt-4o").ainvoke([ SystemMessage(content=final_prompt), {"role": "user", "content": user_response} @@ -242,33 +223,15 @@ async def process_steps_node(state: Dict[str, Any], config: RunnableConfig): workflow = StateGraph(AgentState) # Add nodes -workflow.add_node("start_flow", start_flow) +workflow.add_node("start_node", start_node) workflow.add_node("chat_node", chat_node) workflow.add_node("process_steps_node", process_steps_node) # Add edges -workflow.set_entry_point("start_flow") -workflow.add_edge(START, "start_flow") -workflow.add_edge("start_flow", "chat_node") -# workflow.add_edge("chat_node", "process_steps_node") # Removed unconditional edge +workflow.set_entry_point("start_node") +workflow.add_edge(START, "start_node") +workflow.add_edge("start_node", "chat_node") workflow.add_edge("process_steps_node", END) -# workflow.add_edge("chat_node", END) # Removed unconditional edge - -# Add conditional edges from chat_node -def should_continue(command: Command): - if command.goto == "process_steps_node": - return "process_steps_node" - else: - return END - -workflow.add_conditional_edges( - "chat_node", - should_continue, - { - "process_steps_node": "process_steps_node", - END: END, - }, -) # Conditionally use a checkpointer based on the environment # Check for multiple indicators that we're running in LangGraph dev/API mode diff --git a/typescript-sdk/integrations/langgraph/examples/python/agents/predictive_state_updates/agent.py b/typescript-sdk/integrations/langgraph/examples/python/agents/predictive_state_updates/agent.py index 59a291e12..3e26d2106 100644 --- a/typescript-sdk/integrations/langgraph/examples/python/agents/predictive_state_updates/agent.py +++ b/typescript-sdk/integrations/langgraph/examples/python/agents/predictive_state_updates/agent.py @@ -8,38 +8,26 @@ # LangGraph imports from langchain_core.runnables import RunnableConfig +from langchain_core.messages import SystemMessage +from langchain_core.tools import tool from langgraph.graph import StateGraph, END, START from langgraph.types import Command from langgraph.graph import MessagesState -# OpenAI imports +from langgraph.checkpoint.memory import MemorySaver from langchain_openai import ChatOpenAI -from langchain_core.messages import SystemMessage - -WRITE_DOCUMENT_TOOL = { - "type": "function", - "function": { - "name": "write_document_local", - "description": " ".join(""" - Write a document. Use markdown formatting to format the document. - It's good to format the document extensively so it's easy to read. - You can use all kinds of markdown. - However, do not use italic or strike-through formatting, it's reserved for another purpose. - You MUST write the full document, even when changing only a few words. - When making edits to the document, try to make them minimal - do not change every word. - Keep stories SHORT! - """.split()), - "parameters": { - "type": "object", - "properties": { - "document": { - "type": "string", - "description": "The document to write" - }, - }, - } - } -} +@tool +def write_document_local(document: str): # pylint: disable=unused-argument + """ + Write a document. Use markdown formatting to format the document. + It's good to format the document extensively so it's easy to read. + You can use all kinds of markdown. + However, do not use italic or strike-through formatting, it's reserved for another purpose. + You MUST write the full document, even when changing only a few words. + When making edits to the document, try to make them minimal - do not change every word. + Keep stories SHORT! + """ + return document class AgentState(MessagesState): """ @@ -49,7 +37,7 @@ class AgentState(MessagesState): tools: List[Any] -async def start_flow(state: AgentState, config: RunnableConfig): +async def start_node(state: AgentState, config: RunnableConfig): # pylint: disable=unused-argument """ This is the entry point for the flow. """ @@ -58,7 +46,7 @@ async def start_flow(state: AgentState, config: RunnableConfig): ) -async def chat_node(state: AgentState, config: RunnableConfig): +async def chat_node(state: AgentState, config: Optional[RunnableConfig] = None): """ Standard chat node. """ @@ -90,7 +78,7 @@ async def chat_node(state: AgentState, config: RunnableConfig): model_with_tools = model.bind_tools( [ *state["tools"], - WRITE_DOCUMENT_TOOL + write_document_local ], # Disable parallel tool calls to avoid race conditions parallel_tool_calls=False, @@ -163,15 +151,11 @@ async def chat_node(state: AgentState, config: RunnableConfig): # Define the graph workflow = StateGraph(AgentState) - -# Add nodes -workflow.add_node("start_flow", start_flow) +workflow.add_node("start_node", start_node) workflow.add_node("chat_node", chat_node) - -# Add edges -workflow.set_entry_point("start_flow") -workflow.add_edge(START, "start_flow") -workflow.add_edge("start_flow", "chat_node") +workflow.set_entry_point("start_node") +workflow.add_edge(START, "start_node") +workflow.add_edge("start_node", "chat_node") workflow.add_edge("chat_node", END) # Conditionally use a checkpointer based on the environment diff --git a/typescript-sdk/integrations/langgraph/examples/python/agents/shared_state/agent.py b/typescript-sdk/integrations/langgraph/examples/python/agents/shared_state/agent.py index 9a081cfc4..12889a47c 100644 --- a/typescript-sdk/integrations/langgraph/examples/python/agents/shared_state/agent.py +++ b/typescript-sdk/integrations/langgraph/examples/python/agents/shared_state/agent.py @@ -8,14 +8,16 @@ import os # LangGraph imports +from pydantic import BaseModel, Field from langchain_core.runnables import RunnableConfig +from langchain_core.callbacks.manager import adispatch_custom_event +from langchain_core.messages import SystemMessage +from langchain_core.tools import tool +from langchain_openai import ChatOpenAI from langgraph.graph import StateGraph, END, START from langgraph.types import Command -from langchain_core.callbacks.manager import adispatch_custom_event from langgraph.graph import MessagesState -# OpenAI imports -from langchain_openai import ChatOpenAI -from langchain_core.messages import SystemMessage +from langgraph.checkpoint.memory import MemorySaver class SkillLevel(str, Enum): """ @@ -25,7 +27,6 @@ class SkillLevel(str, Enum): INTERMEDIATE = "Intermediate" ADVANCED = "Advanced" - class SpecialPreferences(str, Enum): """ Special preferences for the recipe. @@ -38,7 +39,6 @@ class SpecialPreferences(str, Enum): VEGETARIAN = "Vegetarian" VEGAN = "Vegan" - class CookingTime(str, Enum): """ The cooking time of the recipe. @@ -49,66 +49,50 @@ class CookingTime(str, Enum): FORTY_FIVE_MIN = "45 min" SIXTY_PLUS_MIN = "60+ min" +class Ingredient(BaseModel): + """ + An ingredient. + """ + icon: str = Field( + description="Icon: the actual emoji like 🥕" + ) + name: str = Field(description="The name of the ingredient") + amount: str = Field(description="The amount of the ingredient") + +class Recipe(BaseModel): + """ + A recipe. + """ + skill_level: SkillLevel = \ + Field(description="The skill level required for the recipe") + special_preferences: List[SpecialPreferences] = \ + Field(description="A list of special preferences for the recipe") + cooking_time: CookingTime = \ + Field(description="The cooking time of the recipe") + ingredients: List[Ingredient] = \ + Field(description= + """Entire list of ingredients for the recipe, including the new ingredients + and the ones that are already in the recipe: Icon: the actual emoji like 🥕, + name and amount. + Like so: 🥕 Carrots (250g)""" + ) + instructions: List[str] = \ + Field(description= + """Entire list of instructions for the recipe, + including the new instructions and the ones that are already there""" + ) + changes: str = \ + Field(description="A description of the changes made to the recipe") -GENERATE_RECIPE_TOOL = { - "type": "function", - "function": { - "name": "generate_recipe", - "description": " ".join("""Using the existing (if any) ingredients and instructions, proceed with the recipe to finish it. - Make sure the recipe is complete. ALWAYS provide the entire recipe, not just the changes.""".split()), - "parameters": { - "type": "object", - "properties": { - "recipe": { - "type": "object", - "properties": { - "skill_level": { - "type": "string", - "enum": [level.value for level in SkillLevel], - "description": "The skill level required for the recipe" - }, - "special_preferences": { - "type": "array", - "items": { - "type": "string", - "enum": [preference.value for preference in SpecialPreferences] - }, - "description": "A list of special preferences for the recipe" - }, - "cooking_time": { - "type": "string", - "enum": [time.value for time in CookingTime], - "description": "The cooking time of the recipe" - }, - "ingredients": { - "type": "array", - "items": { - "type": "object", - "properties": { - "icon": {"type": "string", "description": "The icon emoji (not emoji code like '\x1f35e', but the actual emoji like 🥕) of the ingredient"}, - "name": {"type": "string"}, - "amount": {"type": "string"} - } - }, - "description": "Entire list of ingredients for the recipe, including the new ingredients and the ones that are already in the recipe" - }, - "instructions": { - "type": "array", - "items": {"type": "string"}, - "description": "Entire list of instructions for the recipe, including the new instructions and the ones that are already there" - }, - "changes": { - "type": "string", - "description": "A description of the changes made to the recipe" - } - }, - } - }, - "required": ["recipe"] - } - } -} +class GenerateRecipeArgs(BaseModel): # pylint: disable=missing-class-docstring + recipe: Recipe +@tool(args_schema=GenerateRecipeArgs) +def generate_recipe(recipe: Recipe): # pylint: disable=unused-argument + """ + Using the existing (if any) ingredients and instructions, proceed with the recipe to finish it. + Make sure the recipe is complete. ALWAYS provide the entire recipe, not just the changes. + """ class AgentState(MessagesState): """ @@ -118,7 +102,7 @@ class AgentState(MessagesState): tools: List[Any] -async def start_flow(state: Dict[str, Any], config: RunnableConfig): +async def start_node(state: Dict[str, Any], config: RunnableConfig): """ This is the entry point for the flow. """ @@ -138,7 +122,7 @@ async def start_flow(state: Dict[str, Any], config: RunnableConfig): state, config=config, ) - + return Command( goto="chat_node", update={ @@ -147,7 +131,6 @@ async def start_flow(state: Dict[str, Any], config: RunnableConfig): } ) - async def chat_node(state: Dict[str, Any], config: RunnableConfig): """ Standard chat node. @@ -157,9 +140,9 @@ async def chat_node(state: Dict[str, Any], config: RunnableConfig): if "recipe" in state and state["recipe"] is not None: try: recipe_json = json.dumps(state["recipe"], indent=2) - except Exception as e: + except Exception as e: # pylint: disable=broad-exception-caught recipe_json = f"Error serializing recipe: {str(e)}" - + system_prompt = f"""You are a helpful assistant for creating recipes. This is the current state of the recipe: {recipe_json} You can improve the recipe by calling the generate_recipe tool. @@ -176,7 +159,7 @@ async def chat_node(state: Dict[str, Any], config: RunnableConfig): # Define the model model = ChatOpenAI(model="gpt-4o-mini") - + # Define config for the model if config is None: config = RunnableConfig(recursion_limit=25) @@ -192,7 +175,7 @@ async def chat_node(state: Dict[str, Any], config: RunnableConfig): model_with_tools = model.bind_tools( [ *state["tools"], - GENERATE_RECIPE_TOOL + generate_recipe ], # Disable parallel tool calls to avoid race conditions parallel_tool_calls=False, @@ -206,34 +189,23 @@ async def chat_node(state: Dict[str, Any], config: RunnableConfig): # Update messages with the response messages = state["messages"] + [response] - + # Handle tool calls if hasattr(response, "tool_calls") and response.tool_calls: - tool_call = response.tool_calls[0] - - # Handle tool_call as a dictionary or an object - if isinstance(tool_call, dict): - tool_call_id = tool_call["id"] - tool_call_name = tool_call["name"] - # Check if args is already a dict or needs to be parsed - if isinstance(tool_call["args"], dict): - tool_call_args = tool_call["args"] - else: - tool_call_args = json.loads(tool_call["args"]) - else: - # Handle as an object - tool_call_id = tool_call.id - tool_call_name = tool_call.name - # Check if args is already a dict or needs to be parsed - if isinstance(tool_call.args, dict): - tool_call_args = tool_call.args - else: - tool_call_args = json.loads(tool_call.args) + # Handle dicts or object (backward compatibility) + tool_call = (response.tool_calls[0] + if isinstance(response.tool_calls[0], dict) + else vars(response.tool_calls[0])) + + # Check if args is already a dict or needs to be parsed + tool_call_args = (tool_call["args"] + if isinstance(tool_call["args"], dict) + else json.loads(tool_call["args"])) - if tool_call_name == "generate_recipe": + if tool_call["name"] == "generate_recipe": # Update recipe state with tool_call_args recipe_data = tool_call_args["recipe"] - + # If we have an existing recipe, update it if "recipe" in state and state["recipe"] is not None: recipe = state["recipe"] @@ -249,16 +221,16 @@ async def chat_node(state: Dict[str, Any], config: RunnableConfig): "ingredients": recipe_data.get("ingredients", []), "instructions": recipe_data.get("instructions", []) } - + # Add tool response to messages tool_response = { "role": "tool", "content": "Recipe generated.", - "tool_call_id": tool_call_id + "tool_call_id": tool_call["id"] } - + messages = messages + [tool_response] - + # Explicitly emit the updated state to ensure it's shared with frontend state["recipe"] = recipe await adispatch_custom_event( @@ -266,10 +238,10 @@ async def chat_node(state: Dict[str, Any], config: RunnableConfig): state, config=config, ) - + # Return command with updated recipe return Command( - goto="start_flow", + goto="start_node", update={ "messages": messages, "recipe": recipe @@ -287,15 +259,11 @@ async def chat_node(state: Dict[str, Any], config: RunnableConfig): # Define the graph workflow = StateGraph(AgentState) - -# Add nodes -workflow.add_node("start_flow", start_flow) +workflow.add_node("start_node", start_node) workflow.add_node("chat_node", chat_node) - -# Add edges -workflow.set_entry_point("start_flow") -workflow.add_edge(START, "start_flow") -workflow.add_edge("start_flow", "chat_node") +workflow.set_entry_point("start_node") +workflow.add_edge(START, "start_node") +workflow.add_edge("start_node", "chat_node") workflow.add_edge("chat_node", END) # Conditionally use a checkpointer based on the environment diff --git a/typescript-sdk/integrations/langgraph/examples/python/agents/tool_based_generative_ui/agent.py b/typescript-sdk/integrations/langgraph/examples/python/agents/tool_based_generative_ui/agent.py index a70e96d65..acba8b2e6 100644 --- a/typescript-sdk/integrations/langgraph/examples/python/agents/tool_based_generative_ui/agent.py +++ b/typescript-sdk/integrations/langgraph/examples/python/agents/tool_based_generative_ui/agent.py @@ -2,100 +2,61 @@ An example demonstrating tool-based generative UI using LangGraph. """ -from typing import List, Any +from typing import List, Any, Optional, Annotated import os # LangGraph imports +from langchain_openai import ChatOpenAI from langchain_core.runnables import RunnableConfig +from langchain_core.messages import SystemMessage +from langchain_core.tools import tool from langgraph.graph import StateGraph, END, START from langgraph.types import Command from langgraph.graph import MessagesState - -# OpenAI imports -from langchain_openai import ChatOpenAI -from langchain_core.messages import SystemMessage - -# List of available images (modify path if needed) -IMAGE_LIST = [ - "Osaka_Castle_Turret_Stone_Wall_Pine_Trees_Daytime.jpg", - "Tokyo_Skyline_Night_Tokyo_Tower_Mount_Fuji_View.jpg", - "Itsukushima_Shrine_Miyajima_Floating_Torii_Gate_Sunset_Long_Exposure.jpg", - "Takachiho_Gorge_Waterfall_River_Lush_Greenery_Japan.jpg", - "Bonsai_Tree_Potted_Japanese_Art_Green_Foliage.jpeg", - "Shirakawa-go_Gassho-zukuri_Thatched_Roof_Village_Aerial_View.jpg", - "Ginkaku-ji_Silver_Pavilion_Kyoto_Japanese_Garden_Pond_Reflection.jpg", - "Senso-ji_Temple_Asakusa_Cherry_Blossoms_Kimono_Umbrella.jpg", - "Cherry_Blossoms_Sakura_Night_View_City_Lights_Japan.jpg", - "Mount_Fuji_Lake_Reflection_Cherry_Blossoms_Sakura_Spring.jpg" -] - -# This tool generates a haiku on the server. -# The tool call will be streamed to the frontend as it is being generated. -GENERATE_HAIKU_TOOL = { - "type": "function", - "function": { - "name": "generate_haiku", - "description": "Generate a haiku in Japanese and its English translation. Also select exactly 3 relevant images from the provided list based on the haiku's theme.", - "parameters": { - "type": "object", - "properties": { - "japanese": { - "type": "array", - "items": { - "type": "string" - }, - "description": "An array of three lines of the haiku in Japanese" - }, - "english": { - "type": "array", - "items": { - "type": "string" - }, - "description": "An array of three lines of the haiku in English" - }, - "image_names": { - "type": "array", - "items": { - "type": "string" - }, - "description": "An array of EXACTLY THREE image filenames from the provided list that are most relevant to the haiku." - } - }, - "required": ["japanese", "english", "image_names"] - } - } -} +from langgraph.prebuilt import ToolNode + +@tool +def generate_haiku( + japanese: Annotated[ # pylint: disable=unused-argument + List[str], + "An array of three lines of the haiku in Japanese" + ], + english: Annotated[ # pylint: disable=unused-argument + List[str], + "An array of three lines of the haiku in English" + ] +): + """ + Generate a haiku in Japanese and its English translation. + Also select exactly 3 relevant images from the provided list based on the haiku's theme. + """ class AgentState(MessagesState): + """ + State of the agent. + """ tools: List[Any] -async def chat_node(state: AgentState, config: RunnableConfig): +async def chat_node(state: AgentState, config: Optional[RunnableConfig] = None): """ The main function handling chat and tool calls. """ - # Prepare the image list string for the prompt - image_list_str = "\n".join([f"- {img}" for img in IMAGE_LIST]) - system_prompt = f""" + system_prompt = """ You assist the user in generating a haiku. - When generating a haiku using the 'generate_haiku' tool, you MUST also select exactly 3 image filenames from the following list that are most relevant to the haiku's content or theme. Return the filenames in the 'image_names' parameter. - - Available images: - {image_list_str} - - Dont provide the relavent image names in your final response to the user. + When generating a haiku using the 'generate_haiku' tool. """ # Define the model model = ChatOpenAI(model="gpt-4o") - + # Define config for the model if config is None: config = RunnableConfig(recursion_limit=25) # Bind the tools to the model model_with_tools = model.bind_tools( - [GENERATE_HAIKU_TOOL], + [generate_haiku], # Disable parallel tool calls to avoid race conditions parallel_tool_calls=False, ) @@ -106,6 +67,13 @@ async def chat_node(state: AgentState, config: RunnableConfig): *state["messages"], ], config) + if response.tool_calls: + return Command( + goto="tool_node", + update={ + "messages": state["messages"] + [response] + } + ) # Return Command to end with updated messages return Command( goto=END, @@ -119,11 +87,14 @@ async def chat_node(state: AgentState, config: RunnableConfig): # Add nodes workflow.add_node("chat_node", chat_node) +workflow.add_node("tool_node", ToolNode([generate_haiku])) # Add edges workflow.set_entry_point("chat_node") workflow.add_edge(START, "chat_node") workflow.add_edge("chat_node", END) +workflow.add_edge("tool_node", END) + # Conditionally use a checkpointer based on the environment # Check for multiple indicators that we're running in LangGraph dev/API mode @@ -139,4 +110,3 @@ async def chat_node(state: AgentState, config: RunnableConfig): # When running in LangGraph API/dev, don't use a custom checkpointer graph = workflow.compile() - diff --git a/typescript-sdk/integrations/langgraph/examples/python/langgraph.json b/typescript-sdk/integrations/langgraph/examples/python/langgraph.json index 6e1c0e082..af4c9878c 100644 --- a/typescript-sdk/integrations/langgraph/examples/python/langgraph.json +++ b/typescript-sdk/integrations/langgraph/examples/python/langgraph.json @@ -8,7 +8,8 @@ "human_in_the_loop": "./agents/human_in_the_loop/agent.py:graph", "predictive_state_updates": "./agents/predictive_state_updates/agent.py:graph", "shared_state": "./agents/shared_state/agent.py:graph", - "tool_based_generative_ui": "./agents/tool_based_generative_ui/agent.py:graph" + "tool_based_generative_ui": "./agents/tool_based_generative_ui/agent.py:graph", + "agentic_chat_reasoning": "./agents/agentic_chat_reasoning/agent.py:graph" }, "env": ".env" } diff --git a/typescript-sdk/integrations/langgraph/examples/python/poetry.lock b/typescript-sdk/integrations/langgraph/examples/python/poetry.lock index 038eda7e4..2a5cb8e31 100644 --- a/typescript-sdk/integrations/langgraph/examples/python/poetry.lock +++ b/typescript-sdk/integrations/langgraph/examples/python/poetry.lock @@ -179,6 +179,32 @@ files = [ {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, ] +[[package]] +name = "anthropic" +version = "0.61.0" +description = "The official Python library for the anthropic API" +optional = false +python-versions = ">=3.8" +groups = ["main"] +files = [ + {file = "anthropic-0.61.0-py3-none-any.whl", hash = "sha256:798c8e6cc61e6315143c3f5847d2f220c45f1e69f433436872a237413ca58803"}, + {file = "anthropic-0.61.0.tar.gz", hash = "sha256:af4b3b8f3bc4626cca6af2d412e301974da1747179341ad9e271bdf5cbd2f008"}, +] + +[package.dependencies] +anyio = ">=3.5.0,<5" +distro = ">=1.7.0,<2" +httpx = ">=0.25.0,<1" +jiter = ">=0.4.0,<1" +pydantic = ">=1.9.0,<3" +sniffio = "*" +typing-extensions = ">=4.10,<5" + +[package.extras] +aiohttp = ["aiohttp", "httpx-aiohttp (>=0.1.8)"] +bedrock = ["boto3 (>=1.28.57)", "botocore (>=1.31.57)"] +vertex = ["google-auth[requests] (>=2,<3)"] + [[package]] name = "anyio" version = "4.9.0" @@ -221,6 +247,18 @@ docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphi tests = ["cloudpickle ; 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openai } from "@ai-sdk/openai"; +import { Agent } from "@mastra/core/agent"; +import { Memory } from "@mastra/memory"; +import { LibSQLStore } from "@mastra/libsql"; +import { createTool } from "@mastra/core"; +import z from "zod"; + +export const toolBasedGenerativeUIAgent = new Agent({ + name: "Haiku Agent", + instructions: ` + You are a helpful haiku assistant that provides the user with a haiku. +`, + model: openai("gpt-4o-mini"), + tools: { + generate_haiku: createTool({ + id: "generate_haiku", + description: + "Generate a haiku in Japanese and its English translation. Also select exactly 3 relevant images from the provided list based on the haiku's theme.", + inputSchema: z.object({ + japanese: z.array(z.string()).describe("An array of three lines of the haiku in Japanese"), + english: z.array(z.string()).describe("An array of three lines of the haiku in English"), + }), + outputSchema: z.string(), + execute: async ({ context }) => { + return "Haiku generated."; + }, + }), + }, + memory: new Memory({ + storage: new LibSQLStore({ + url: "file:../mastra.db", // path is relative to the .mastra/output directory + }), + }), +}); diff --git a/typescript-sdk/integrations/mastra/example/src/mastra/index.ts b/typescript-sdk/integrations/mastra/example/src/mastra/index.ts index bf9c5b518..7bb833e77 100644 --- a/typescript-sdk/integrations/mastra/example/src/mastra/index.ts +++ b/typescript-sdk/integrations/mastra/example/src/mastra/index.ts @@ -2,13 +2,14 @@ import { Mastra } from "@mastra/core/mastra"; import { PinoLogger } from "@mastra/loggers"; import { LibSQLStore } from "@mastra/libsql"; -import { agenticChatAgent } from "./agents/weather-agent"; +import { agenticChatAgent } from "./agents/agentic-chat"; +import { toolBasedGenerativeUIAgent } from "./agents/tool-based-generative-ui"; export const mastra = new Mastra({ server: { port: process.env.PORT ? parseInt(process.env.PORT) : 4111, }, - agents: { agentic_chat: agenticChatAgent }, + agents: { agentic_chat: agenticChatAgent, tool_based_generative_ui: toolBasedGenerativeUIAgent }, storage: new LibSQLStore({ // stores telemetry, evals, ... into memory storage, if it needs to persist, change to file:../mastra.db url: ":memory:", diff --git a/typescript-sdk/integrations/server-starter-all-features/server/python/example_server/tool_based_generative_ui.py b/typescript-sdk/integrations/server-starter-all-features/server/python/example_server/tool_based_generative_ui.py index e0912652b..87e0b7be0 100644 --- a/typescript-sdk/integrations/server-starter-all-features/server/python/example_server/tool_based_generative_ui.py +++ b/typescript-sdk/integrations/server-starter-all-features/server/python/example_server/tool_based_generative_ui.py @@ -38,8 +38,10 @@ async def event_generator(): if input_data.messages and len(input_data.messages) > 0: last_message = input_data.messages[-1] + result_message = None + # Determine what type of message to send - if last_message and getattr(last_message, 'role', None) == "tool": + if last_message and getattr(last_message, 'content', None) == "thanks": # Send text message for tool result message_id = str(uuid.uuid4()) new_message = { @@ -51,7 +53,7 @@ async def event_generator(): # Send tool call message tool_call_id = str(uuid.uuid4()) message_id = str(uuid.uuid4()) - + # Prepare haiku arguments haiku_args = { "japanese": ["エーアイの", "橋つなぐ道", "コパキット"], @@ -78,9 +80,19 @@ async def event_generator(): ] } + result_message = { + "id": str(uuid.uuid4()), + "role": "tool", + "tool_call_id": tool_call_id, + "content": "Haiku created" + } + # Create messages list with input messages plus the new message all_messages = list(input_data.messages) + [new_message] + if result_message: + all_messages.append(result_message) + # Send messages snapshot event yield encoder.encode( MessagesSnapshotEvent(