[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_153":154,"_155":156},"routes/_app.x.$handle.$name",{"_95":94,"_32":45,"_103":85,"_27":104,"_13":14,"_105":-5,"_11":106,"_107":108,"_109":110,"_111":112,"_113":114,"_20":-5,"_115":-5,"_116":-5,"_117":25,"_118":119,"_120":57,"_121":-5,"_122":-5,"_123":124},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_43":44,"_32":45,"_38":46,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":-5,"_53":-5,"_18":54,"_55":31,"_56":57,"_58":57,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":-5,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":78,"_79":80,"_81":82,"_83":25},"version",5,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":28,"_33":34,"_35":37,"_38":39,"_40":14,"_41":42,"_24":25},"previous",{"_30":31,"_32":28,"_33":34,"_35":36},"deleted",false,"deletedHere","path","main.ts","latestPath","fileId","8174bec8-a770-11f0-9e72-0224a6c84d84","name","fileType","http","content","// @ts-nocheck\n// AgentForge AI - The Multi-Agent Assembly Line\n// An advanced platform for orchestrating teams of AI agents to complete complex projects.\n\nimport { Hono } from \"npm:hono@4.4.12\";\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nimport type { Context } from \"npm:hono@4.4.12\";\nimport { SSEStreamingApi, stream, streamText } from \"npm:hono@4.4.12/streaming\";\n\n// --- TYPE DEFINITIONS ---\ninterface CompanyContext {\n company_name: string;\n company_description: string;\n use_case: string;\n language: string;\n industry?: string;\n}\n\ninterface ProjectBrief {\n initial_task: string;\n occupation_title: string;\n company_context: CompanyContext;\n}\n\n// --- CENTRALIZED CONFIGURATION ---\nconst config = {\n models: {\n major: \"gpt-4o\",\n minor: \"gpt-4o-mini\",\n },\n prompts: {\n // New: Agent that designs the multi-step plan for the assembly line.\n ORCHESTRATOR_AGENT:\n `You are a native JSON speaking, elite project manager AI. Your sole purpose is to deconstruct a user's task into a logical, sequential \"assembly line\" of 3 to 5 steps for a team of other AI agents to execute.\n\n# RULES\n1. **Deconstruct the Task**: Analyze the user's task, occupation, and company context.\n2. **Define Agent Roles**: For each step, define a specialized agent persona (e.g., \"Market Researcher,\" \"Creative Copywriter,\" \"Technical Editor\").\n3. **Create Actionable Steps**: Each step must be a clear, actionable instruction for the assigned agent.\n4. **Logical Flow**: The steps must build upon each other, passing the \"workpiece\" from one agent to the next. The final step should always be a \"Final Review\" or \"Integration\" step.\n5. **Output Format**: Your response MUST be a single, minified JSON object with one key, \"plan\". The value must be an array of objects, where each object has \"agent_persona\" (string) and \"task\" (string).\n\n# USER REQUEST\n- Occupation: {{occupation_title}}\n- Task: {{task}}\n- Company Context: {{company_context}}`,\n\n // New: The primary execution agent for each step in the assembly line.\n EXECUTOR_AGENT: `# YOUR CONTEXT\n- Company Name: {{company_name}}\n- Company Description: {{company_description}}\n- Primary Goal: {{use_case}}\n- Language for Output: {{language}}\n---\n# YOUR PERSONA\nYou are a world-class expert acting as a **{{agent_persona}}**.\n---\n# YOUR MANDATE\nExecute the following task precisely and return only the completed deliverable. If prior work exists, build upon it.\n\n## Previous Steps' Work (if any):\n{{previous_work}}\n\n## Current Task:\n{{task}}\n\n# DELIVERY RULES\n- Produce only the final deliverable for your assigned task.\n- Do not include plans, apologies, or meta-commentary.\n- Make reasonable assumptions for missing details.\n- Ensure the output is production-ready.\n- Write your entire response in the requested language: {{language}}.`,\n\n // The Quality Assurance agent remains crucial for the \"Critic\" step.\n CRITIQUE_AGENT:\n `You are a native JSON speaking, meticulous, and impartial AI QA agent. Given a task briefing, the agent's output, and the overall project goal, you will:\n1. **Score:** Provide a single, holistic quality score from 1 to 10.\n2. **Critique:** Write a concise, professional critique highlighting strengths and actionable weaknesses.\n\n# SCORING RULES\n- Penalize heavily (<= 3) for plans instead of deliverables.\n- Penalize for generic content or obvious placeholders.\n\n# OUTPUT FORMAT\nYour entire response MUST be a single, minified JSON object: {\"score\": , \"critique\": \"...\"}. You MUST write the critique in the requested language. Add no other text or markdown.\n\n# CONTEXT\n- Overall Project Goal: {{project_goal}}\n- Agent Persona: {{agent_persona}}\n- Agent's Assigned Task: {{task}}\n- Agent's Raw Output: {{raw_output}}\n- Language: {{language}}`,\n },\n};\n\n// --- STATIC DATA (Simplified for this novel example) ---\nconst exampleTasks = [\n {\n name: \"Develop a Q4 Marketing Strategy\",\n description:\n \"Create a comprehensive marketing plan for the upcoming quarter.\",\n },\n {\n name: \"Write a Press Release for a New Product Launch\",\n description:\n \"Draft a professional press release announcing our latest product.\",\n },\n {\n name: \"Onboard a New Software Engineer\",\n description:\n \"Generate a complete onboarding document for a new technical hire.\",\n },\n];\n\n// --- BACKEND SERVICES ---\nconst services = {\n async callOpenAI(\n systemPrompt: string,\n userContent: string | object,\n options: { model?: string; isJson?: boolean } = {},\n ) {\n const { model = config.models.major, isJson = false } = options;\n const openai = new OpenAI();\n const messages: any[] = [{ role: \"system\", content: systemPrompt }, {\n role: \"user\",\n content: typeof userContent === \"string\"\n ? userContent\n : JSON.stringify(userContent),\n }];\n const requestPayload: any = { model, messages };\n if (isJson) requestPayload.response_format = { type: \"json_object\" };\n try {\n const completion = await openai.chat.completions.create(requestPayload);\n const content = completion.choices[0].message.content;\n return isJson ? JSON.parse(content) : content;\n } catch (e) {\n console.error(`Error calling OpenAI: ${e.message}`);\n throw new Error(\"AI service failed.\");\n }\n },\n async callOpenAIStream(\n systemPrompt: string,\n userContent: string,\n streamApi: SSEStreamingApi,\n ) {\n const openai = new OpenAI();\n const completionStream = await openai.chat.completions.create({\n model: config.models.major,\n messages: [{ role: \"system\", content: systemPrompt }, {\n role: \"user\",\n content: userContent,\n }],\n stream: true,\n });\n\n let fullText = \"\";\n for await (const part of completionStream) {\n const delta = part.choices[0]?.delta?.content || \"\";\n fullText += delta;\n await streamApi.write(delta);\n }\n return fullText;\n },\n};\n\n// --- FRONTEND GENERATION ---\nfunction generateHtml(sourceUrl: string): string {\n const tasksJson = JSON.stringify(exampleTasks);\n return `AgentForge AI | Multi-Agent Assembly Line

AgentForge AI

The Multi-Agent Assembly Line for Complex Tasks

Select a Project to Begin

`;\n}\n\n// --- HONO APPLICATION SETUP ---\nconst app = new Hono();\n\n// --- API ROUTES ---\n\napp.get(\"/\", (c: Context) => c.html(generateHtml(c.req.url)));\n\n// The main project execution endpoint using Server-Sent Events (SSE)\napp.get(\"/api/project/run\", (c: Context) => {\n return stream(c, async (streamApi) => {\n const task = c.req.query(\"task\") || \"No task provided\";\n\n // A mock company context for this novel example\n const company_context: CompanyContext = {\n company_name: \"Innovate Inc.\",\n company_description: \"A leading provider of cutting-edge tech solutions.\",\n use_case: \"Product Development\",\n language: \"English\",\n };\n\n try {\n // --- STAGE 1: The Orchestrator plans the assembly line ---\n await streamApi.writeSSE({\n event: \"step_start\",\n data: JSON.stringify({\n index: -1,\n message: \"Orchestrator is planning...\",\n }),\n });\n\n const planPrompt = config.prompts.ORCHESTRATOR_AGENT\n .replace(\"{{occupation_title}}\", \"Senior Project Manager\")\n .replace(\"{{task}}\", task)\n .replace(\"{{company_context}}\", JSON.stringify(company_context));\n\n const plan = (await services.callOpenAI(\n \"You are a helpful assistant that only speaks JSON.\",\n planPrompt,\n { isJson: true, model: config.models.minor },\n )).plan;\n\n await streamApi.writeSSE({\n event: \"plan_complete\",\n data: JSON.stringify(plan),\n });\n\n let accumulatedWork = \"\";\n\n // --- STAGE 2: Execute each step on the assembly line ---\n for (let i = 0; i < plan.length; i++) {\n const step = plan[i];\n\n // 2a. Execute the task for the current step\n await streamApi.writeSSE({\n event: \"step_start\",\n data: JSON.stringify({\n index: i,\n message: `Executing: ${step.agent_persona}`,\n }),\n });\n\n let executorPrompt = config.prompts.EXECUTOR_AGENT\n .replace(\"{{company_name}}\", company_context.company_name)\n .replace(\n \"{{company_description}}\",\n company_context.company_description,\n )\n .replace(\"{{use_case}}\", company_context.use_case)\n .replace(\"{{language}}\", company_context.language)\n .replace(\"{{agent_persona}}\", step.agent_persona)\n .replace(\n \"{{previous_work}}\",\n accumulatedWork || \"This is the first step. Start from scratch.\",\n )\n .replace(\"{{task}}\", step.task);\n\n const userPrompt =\n \"Proceed with your task. Generate the deliverable now.\";\n\n const streamWrapper = new WritableStream({\n write(chunk) {\n streamApi.writeSSE({\n event: \"stream\",\n data: JSON.stringify({ index: i, delta: chunk }),\n });\n },\n });\n\n const textEncoder = new TextEncoder();\n const customStreamApi = {\n write: (chunk) =>\n streamWrapper.getWriter().write(textEncoder.encode(chunk)),\n };\n\n const stepResult = await services.callOpenAIStream(\n executorPrompt,\n userPrompt,\n customStreamApi as any,\n );\n accumulatedWork +=\n `\\n\\n---\\n\\n## Output from ${step.agent_persona}:\\n\\n${stepResult}`;\n\n await streamApi.writeSSE({\n event: \"step_complete\",\n data: JSON.stringify({ index: i }),\n });\n\n // 2b. The Critic agent evaluates the step's output\n await streamApi.writeSSE({\n event: \"step_start\",\n data: JSON.stringify({\n index: i,\n message: \"Critic is evaluating...\",\n }),\n });\n\n const critiquePrompt = JSON.stringify({\n project_goal: task,\n agent_persona: step.agent_persona,\n task: step.task,\n raw_output: stepResult,\n language: company_context.language,\n });\n\n const critiqueResult = await services.callOpenAI(\n config.prompts.CRITIQUE_AGENT,\n critiquePrompt,\n { isJson: true, model: config.models.minor },\n );\n\n await streamApi.writeSSE({\n event: \"critique_complete\",\n data: JSON.stringify({ index: i, critique: critiqueResult }),\n });\n }\n\n await streamApi.writeSSE({\n event: \"project_complete\",\n data: \"Project finished\",\n });\n } catch (error) {\n console.error(\"Project run failed:\", error);\n await streamApi.writeSSE({\n event: \"error\",\n data: `An error occurred: ${error.message}`,\n });\n } finally {\n await streamApi.close();\n }\n });\n});\n\nexport default app.fetch;","// @ts-nocheck\n// AgentForge AI - The Multi-Agent Assembly Line\n// An advanced platform for orchestrating teams of AI agents to complete complex projects.\n\nimport { Hono } from \"npm:hono@4.4.12\";\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nimport type { Context } from \"npm:hono@4.4.12\";\nimport { streamSSE } from \"npm:hono@4.4.12/streaming\"; // Corrected import\n\n// --- TYPE DEFINITIONS ---\ninterface CompanyContext {\n company_name: string;\n company_description: string;\n use_case: string;\n language: string;\n industry?: string;\n}\n\ninterface ProjectBrief {\n initial_task: string;\n occupation_title: string;\n company_context: CompanyContext;\n}\n\n// --- CENTRALIZED CONFIGURATION ---\nconst config = {\n models: {\n major: \"gpt-4o\",\n minor: \"gpt-4o-mini\",\n },\n prompts: {\n // New: Agent that designs the multi-step plan for the assembly line.\n ORCHESTRATOR_AGENT: `You are a native JSON speaking, elite project manager AI. Your sole purpose is to deconstruct a user's task into a logical, sequential \"assembly line\" of 3 to 5 steps for a team of other AI agents to execute.\n\n# RULES\n1. **Deconstruct the Task**: Analyze the user's task, occupation, and company context.\n2. **Define Agent Roles**: For each step, define a specialized agent persona (e.g., \"Market Researcher,\" \"Creative Copywriter,\" \"Technical Editor\").\n3. **Create Actionable Steps**: Each step must be a clear, actionable instruction for the assigned agent.\n4. **Logical Flow**: The steps must build upon each other, passing the \"workpiece\" from one agent to the next. The final step should always be a \"Final Review\" or \"Integration\" step.\n5. **Output Format**: Your response MUST be a single, minified JSON object with one key, \"plan\". The value must be an array of objects, where each object has \"agent_persona\" (string) and \"task\" (string).\n\n# USER REQUEST\n- Occupation: {{occupation_title}}\n- Task: {{task}}\n- Company Context: {{company_context}}`,\n\n // New: The primary execution agent for each step in the assembly line.\n EXECUTOR_AGENT: `# YOUR CONTEXT\n- Company Name: {{company_name}}\n- Company Description: {{company_description}}\n- Primary Goal: {{use_case}}\n- Language for Output: {{language}}\n---\n# YOUR PERSONA\nYou are a world-class expert acting as a **{{agent_persona}}**.\n---\n# YOUR MANDATE\nExecute the following task precisely and return only the completed deliverable. If prior work exists, build upon it.\n\n## Previous Steps' Work (if any):\n{{previous_work}}\n\n## Current Task:\n{{task}}\n\n# DELIVERY RULES\n- Produce only the final deliverable for your assigned task.\n- Do not include plans, apologies, or meta-commentary.\n- Make reasonable assumptions for missing details.\n- Ensure the output is production-ready.\n- Write your entire response in the requested language: {{language}}.`,\n\n // The Quality Assurance agent remains crucial for the \"Critic\" step.\n CRITIQUE_AGENT: `You are a native JSON speaking, meticulous, and impartial AI QA agent. Given a task briefing, the agent's output, and the overall project goal, you will:\n1. **Score:** Provide a single, holistic quality score from 1 to 10.\n2. **Critique:** Write a concise, professional critique highlighting strengths and actionable weaknesses.\n\n# SCORING RULES\n- Penalize heavily (<= 3) for plans instead of deliverables.\n- Penalize for generic content or obvious placeholders.\n\n# OUTPUT FORMAT\nYour entire response MUST be a single, minified JSON object: {\"score\": , \"critique\": \"...\"}. You MUST write the critique in the requested language. Add no other text or markdown.\n\n# CONTEXT\n- Overall Project Goal: {{project_goal}}\n- Agent Persona: {{agent_persona}}\n- Agent's Assigned Task: {{task}}\n- Agent's Raw Output: {{raw_output}}\n- Language: {{language}}`,\n },\n};\n\n// --- STATIC DATA (Simplified for this novel example) ---\nconst exampleTasks = [\n { name: \"Develop a Q4 Marketing Strategy\", description: \"Create a comprehensive marketing plan for the upcoming quarter.\" },\n { name: \"Write a Press Release for a New Product Launch\", description: \"Draft a professional press release announcing our latest product.\" },\n { name: \"Onboard a New Software Engineer\", description: \"Generate a complete onboarding document for a new technical hire.\" },\n];\n\n// --- BACKEND SERVICES ---\nconst services = {\n async callOpenAI(\n systemPrompt: string,\n userContent: string | object,\n options: { model?: string; isJson?: boolean } = {},\n ) {\n const { model = config.models.major, isJson = false } = options;\n const openai = new OpenAI();\n const messages: any[] = [{ role: \"system\", content: systemPrompt }, {\n role: \"user\",\n content: typeof userContent === \"string\"\n ? userContent\n : JSON.stringify(userContent),\n }];\n const requestPayload: any = { model, messages };\n if (isJson) requestPayload.response_format = { type: \"json_object\" };\n try {\n const completion = await openai.chat.completions.create(requestPayload);\n const content = completion.choices[0].message.content;\n return isJson ? JSON.parse(content) : content;\n } catch (e) {\n console.error(`Error calling OpenAI: ${e.message}`);\n throw new Error(\"AI service failed.\");\n }\n },\n};\n\n// --- FRONTEND GENERATION ---\nfunction generateHtml(sourceUrl: string): string {\n const tasksJson = JSON.stringify(exampleTasks);\n return `AgentForge AI | Multi-Agent Assembly Line

AgentForge AI

The Multi-Agent Assembly Line for Complex Tasks

Select a Project to Begin

`;\n}\n\n// --- HONO APPLICATION SETUP ---\nconst app = new Hono();\n\n// --- API ROUTES ---\n\napp.get(\"/\", (c: Context) => c.html(generateHtml(c.req.url)));\n\n// A helper function to make the main logic cleaner\nasync function callOpenAIAndStreamToClient(\n systemPrompt: string,\n userPrompt: string,\n onDelta: (delta: string) => Promise,\n) {\n const openai = new OpenAI();\n const completionStream = await openai.chat.completions.create({\n model: config.models.major,\n messages: [\n { role: \"system\", content: systemPrompt },\n { role: \"user\", content: userPrompt },\n ],\n stream: true,\n });\n\n let fullText = \"\";\n for await (const part of completionStream) {\n const delta = part.choices[0]?.delta?.content || \"\";\n if (delta) {\n fullText += delta;\n await onDelta(delta); // Use the callback to send data\n }\n }\n return fullText;\n}\n\n\n// The main project execution endpoint using Server-Sent Events (SSE)\napp.get(\"/api/project/run\", (c: Context) => {\n // Use streamSSE instead of stream\n return streamSSE(c, async (streamApi) => {\n streamApi.onAbort(() => {\n console.log(\"Client disconnected.\");\n });\n \n const task = c.req.query(\"task\") || \"No task provided\";\n\n const company_context: CompanyContext = {\n company_name: \"Innovate Inc.\",\n company_description: \"A leading provider of cutting-edge tech solutions.\",\n use_case: \"Product Development\",\n language: \"English\",\n };\n\n try {\n // --- STAGE 1: Orchestrator plans the assembly line ---\n await streamApi.writeSSE({\n event: \"step_start\",\n data: JSON.stringify({ index: -1, message: \"Orchestrator is planning...\" }),\n });\n\n const planPrompt = config.prompts.ORCHESTRATOR_AGENT\n .replace(\"{{occupation_title}}\", \"Senior Project Manager\")\n .replace(\"{{task}}\", task)\n .replace(\"{{company_context}}\", JSON.stringify(company_context));\n\n const planResult = await services.callOpenAI(\n \"You are a helpful assistant that only speaks JSON.\",\n planPrompt,\n { isJson: true, model: config.models.minor },\n );\n const plan = planResult.plan;\n\n await streamApi.writeSSE({ event: \"plan_complete\", data: JSON.stringify(plan) });\n\n let accumulatedWork = \"\";\n\n // --- STAGE 2: Execute each step on the assembly line ---\n for (let i = 0; i < plan.length; i++) {\n const step = plan[i];\n\n await streamApi.writeSSE({\n event: \"step_start\",\n data: JSON.stringify({ index: i, message: `Executing: ${step.agent_persona}` }),\n });\n\n const executorPrompt = config.prompts.EXECUTOR_AGENT\n .replace(\"{{company_name}}\", company_context.company_name)\n .replace(\"{{company_description}}\", company_context.company_description)\n .replace(\"{{use_case}}\", company_context.use_case)\n .replace(\"{{language}}\", company_context.language)\n .replace(\"{{agent_persona}}\", step.agent_persona)\n .replace(\"{{previous_work}}\", accumulatedWork || \"This is the first step. Start from scratch.\")\n .replace(\"{{task}}\", step.task);\n \n const userPrompt = \"Proceed with your task. Generate the deliverable now.\";\n \n // This is the cleaner way to pipe the stream\n const stepResult = await callOpenAIAndStreamToClient(\n executorPrompt,\n userPrompt,\n async (delta) => {\n await streamApi.writeSSE({\n event: \"stream\",\n data: JSON.stringify({ index: i, delta }),\n });\n }\n );\n\n accumulatedWork += `\\n\\n---\\n\\n## Output from ${step.agent_persona}:\\n\\n${stepResult}`;\n await streamApi.writeSSE({ event: \"step_complete\", data: JSON.stringify({ index: i }) });\n\n // --- The Critic agent evaluates the step's output ---\n await streamApi.writeSSE({\n event: \"step_start\",\n data: JSON.stringify({ index: i, message: \"Critic is evaluating...\" }),\n });\n\n const critiquePrompt = JSON.stringify({\n project_goal: task,\n agent_persona: step.agent_persona,\n task: step.task,\n raw_output: stepResult,\n language: company_context.language,\n });\n\n const critiqueResult = await services.callOpenAI(\n config.prompts.CRITIQUE_AGENT,\n critiquePrompt,\n { isJson: true, model: config.models.minor },\n );\n\n await streamApi.writeSSE({\n event: \"critique_complete\",\n data: JSON.stringify({ index: i, critique: critiqueResult }),\n });\n }\n\n await streamApi.writeSSE({ event: \"project_complete\", data: \"Project finished\" });\n\n } catch (error) {\n console.error(\"Project run failed:\", error);\n await streamApi.writeSSE({\n event: \"error\",\n data: `An error occurred: ${error.message}`,\n });\n }\n });\n});\n\nexport default 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Diff · v5