[{"_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",9,"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","a2cb88fa-a779-11f0-91b4-0224a6c84d84","name","fileType","http","content","// @ts-nocheck\n// AgentFlow AI - Guided Multi-Agent Orchestration (Refactored v4 - Visual Workflow)\n// Merges the guided UI of Taskflow with the multi-agent backend of Assembly, supporting parallel execution.\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\";\n\n// --- TYPE DEFINITIONS ---\ninterface CompanyContext {\n company_name: string;\n company_description: string;\n use_case: string;\n language: string;\n}\n\ninterface ConversationHistory {\n role: \"assistant\" | \"user\";\n content: string;\n}\n\n// --- CENTRALIZED CONFIGURATION ---\nconst config = {\n models: {\n major: \"gpt-4o\",\n minor: \"gpt-4o-mini\",\n },\n affirmations: [\n \"Thinking...\",\n \"Processing...\",\n \"Generating insights...\",\n \"Analyzing data...\",\n \"Assembling results...\",\n \"Almost there...\",\n ],\n prompts: {\n // NEW: Performs a search-like function to find company details.\n BUSINESS_RESEARCHER_AGENT:\n `You are an expert business researcher AI that speaks only in JSON. Your task is to find and summarize a company based on its name.\n\n# RULES\n1. Analyze the provided company name.\n2. Provide a concise, one-sentence description of the company's primary business.\n3. If you cannot find a well-known company with that name, you MUST return null for the description.\n4. Your response MUST be a single, minified JSON object: {\"company_name\": \"...\", \"description\": \"...\" | null}.\n\n# USER REQUEST\n- Company Name: {{company_name}}`,\n\n // NEW: Conversationally fills in context if search fails.\n CONTEXT_ENRICHMENT_AGENT:\n `You are a native JSON speaking expert project intake manager. Your goal is to ask the fewest, most concise questions to build a complete company context. Ask one question at a time.\n\n# RULES\n1. Your goal is to fill this JSON object: {\"company_name\": \"\", \"company_description\": \"\", \"use_case\": \"\", \"language\": \"English\"}.\n2. Analyze the provided context and conversation history to see which fields are missing.\n3. Ask a single, concise question to fill the *next* missing field. Start with description, then use case.\n4. Once all fields are filled, your immediate response MUST be the token \"__COMPLETE__\" followed by the single, minified JSON object of the complete context.\n5. Output for questions must be a short, direct interrogative. DO NOT add conversational filler.\n\n# CURRENT CONTEXT\n{{current_context}}\n\n# CONVERSATION HISTORY\n{{history}}`,\n\n INFORMATION_GATHERER:\n `You are a native JSON speaking expert project intake manager. Your goal is to ask the fewest, most concise questions to understand the user's high-level goal. Ask one question at a time.\n\n# RULES\n1. **Primary Goal**: Ask single, concise questions to gather the core objective.\n2. **Decision Point**: After analyzing the history, decide if you have a clear, actionable goal.\n - **If YES**: Your immediate response MUST be the token \"__COMPLETE__\" followed by a single, minified JSON object of the gathered goal. Example: \\`__COMPLETE__\\`{\"goal\":\"Develop a Q4 marketing strategy for a new SaaS product.\"}\n - **If NO**: Formulate the single most impactful and concise question.\n3. **Output Format (Questions)**: The question must be a short, direct interrogative. DO NOT add conversational filler.\n\n# CONTEXT\n- Company: {{company_context}}\n- Conversation History:\n{{history}}`,\n // ENHANCED: Includes dependency rules\n ORCHESTRATOR_AGENT:\n `You are a native JSON speaking, elite project manager AI. Deconstruct the user's goal into a logical \"assembly line\" of 3 to 5 steps for a team of other AI agents.\n\n# RULES\n1. **Deconstruct**: Analyze the user's goal and company context.\n2. **Define Agent Roles**: For each step, define a specialized agent persona (e.g., \"Market Researcher,\" \"Creative Copywriter\").\n3. **Actionable Steps**: Each step must be a clear instruction for the agent. The final step should be a \"Final Review\" or \"Integration\".\n4. **Dependencies**: Assign a unique \"id\" to each step (e.g., \"A\", \"B1\", \"B2\", \"C\"). For steps that can run in parallel, give them different IDs but the same dependencies. For steps that must wait, list the \"id\"s of the steps they depend on in the \"dependencies\" array. The first steps should have an empty dependency array ([]).\n5. **Output Format**: Your response MUST be a single, minified JSON object: {\"plan\": [{\"id\": \"A\", \"agent_persona\": \"...\", \"task\": \"...\", \"dependencies\": [] | [\"A\", \"B1\"]}, ...]}.\n\n# USER REQUEST\n- Goal: {{goal}}\n- Company Context: {{company_context}}`,\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, building upon prior work if it exists. Return only the completed deliverable.\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- Write your entire response in the requested language: {{language}}.`,\n CRITIQUE_AGENT:\n `You are a native JSON speaking, meticulous AI QA agent. Given the project goal, an agent's task, and its output, you will:\n1. **Score:** Provide a single, holistic quality score from 1 to 10.\n2. **Critique:** Write a concise, professional critique with actionable feedback.\n\n# OUTPUT FORMAT\nYour response MUST be a single, minified JSON object: {\"score\": , \"critique\": \"...\"}. You MUST write the critique in the requested language.\n\n# CONTEXT\n- Overall Project Goal: {{project_goal}}\n- Agent's Assigned Task: {{task}}\n- Agent's Raw Output: {{raw_output}}\n- Language: {{language}}`,\n },\n};\n\n// --- BACKEND SERVICES ---\nconst services = {\n async withRetries(\n fn: () => Promise,\n retries = 3,\n delay = 1000,\n ): Promise {\n try {\n return await fn();\n } catch (e) {\n if (retries > 0 && e.status >= 500) {\n console.warn(\n `AI service failed, retrying in ${delay}ms... (${retries} retries left)`,\n );\n await new Promise((res) => setTimeout(res, delay));\n return this.withRetries(fn, retries - 1, delay * 2);\n }\n throw e;\n }\n },\n async callOpenAI(\n systemPrompt: string,\n userContent: string | object,\n options: { model?: string; isJson?: boolean } = {},\n ) {\n return this.withRetries(async () => {\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. Details: ${e.message}`);\n }\n });\n },\n async callOpenAIStreaming(\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: [{ role: \"system\", content: systemPrompt }, {\n role: \"user\",\n content: userPrompt,\n }],\n stream: true,\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);\n }\n }\n return fullText;\n },\n};\n\n// --- FRONTEND GENERATION (REFACTORED for Intelligent Context and Workflow) ---\nfunction generateHtml(sourceUrl: string): string {\n return `AgentFlow | Guided AI Orchestration

AgentFlow

Your Guided Multi-Agent Assembly Line

Let's start with your company

Is this correct?

\n \n\n `;\n}\n\n// --- HONO APPLICATION SETUP ---\nconst app = new Hono();\n\n// --- API ROUTES ---\n\n// Serves the main HTML page\napp.get(\"/\", (c: Context) => c.html(generateHtml(c.req.url)));\n\n// Handles company lookup\napp.post(\"/api/company-lookup\", async (c: Context) => {\n const { companyName } = await c.req.json();\n const prompt = config.prompts.BUSINESS_RESEARCHER_AGENT.replace(\n \"{{company_name}}\",\n companyName,\n );\n try {\n const result = await services.callOpenAI(\n \"You are a helpful assistant that only speaks JSON.\",\n prompt,\n {\n isJson: true,\n model: config.models.minor,\n },\n );\n return c.json(result);\n } catch (e) {\n console.error(\"Company lookup failed:\", e);\n return c.json({ company_name: companyName, description: null }, 500);\n }\n});\n\n// Handles conversational context enrichment\napp.post(\"/api/enrich-context\", async (c: Context) => {\n const { history, context } = await c.req.json();\n const historyString = history.map((h) => `${h.role}: ${h.content}`).join(\n \"\\n\",\n );\n\n const prompt = config.prompts.CONTEXT_ENRICHMENT_AGENT\n .replace(\"{{current_context}}\", JSON.stringify(context))\n .replace(\n \"{{history}}\",\n historyString || \"No history yet. Start with your first question.\",\n );\n\n try {\n const completion = await services.callOpenAI(\n \"You are a helpful assistant following strict output rules.\",\n prompt,\n { model: config.models.minor },\n );\n const responseText = completion.trim();\n if (responseText.startsWith(\"__COMPLETE__\")) {\n const jsonPart = responseText.replace(\"__COMPLETE__\", \"\");\n return c.json({ status: \"complete\", data: JSON.parse(jsonPart) });\n } else {\n return c.json({ status: \"in-progress\", question: responseText });\n }\n } catch (e) {\n console.error(\"Context enrichment agent failed:\", e);\n return c.json(\n {\n status: \"in-progress\",\n question: \"I'm having trouble. Could you please rephrase?\",\n },\n 500,\n );\n }\n});\n\n// Handles the goal-gathering conversation\napp.post(\"/api/gather\", async (c: Context) => {\n const { history, company_context } = await c.req.json();\n const historyString = history.map((h) => `${h.role}: ${h.content}`).join(\n \"\\n\",\n );\n\n const prompt = config.prompts.INFORMATION_GATHERER\n .replace(\"{{company_context}}\", JSON.stringify(company_context))\n .replace(\n \"{{history}}\",\n historyString ||\n \"No history yet. Start with your most important question.\",\n );\n\n try {\n const completion = await services.callOpenAI(\n \"You are a helpful assistant following strict output rules.\",\n prompt,\n { model: config.models.minor },\n );\n const responseText = completion.trim();\n if (responseText.startsWith(\"__COMPLETE__\")) {\n const jsonPart = responseText.replace(\"__COMPLETE__\", \"\");\n return c.json({ status: \"complete\", data: JSON.parse(jsonPart) });\n } else {\n return c.json({ status: \"in-progress\", question: responseText });\n }\n } catch (e) {\n console.error(\"Gathering agent failed:\", e);\n return c.json({\n status: \"in-progress\",\n question:\n \"I'm having a little trouble with that. Could you please rephrase?\",\n }, 500);\n }\n});\n\n// The main project execution endpoint using Server-Sent Events (SSE)\napp.get(\"/api/execute-flow\", (c: Context) => {\n return streamSSE(c, async (streamApi) => {\n streamApi.onAbort(() => console.log(\"Client disconnected.\"));\n\n const goal = c.req.query(\"goal\") || \"No goal provided\";\n let company_context: CompanyContext;\n\n try {\n company_context = JSON.parse(c.req.query(\"context\") || \"{}\");\n if (!company_context.company_name) {\n throw new Error(\"Company context is missing or invalid.\");\n }\n } catch (e) {\n console.error(\"Context parsing failed:\", e);\n await streamApi.writeSSE({\n event: \"error\",\n data: `Invalid context provided: ${e.message}`,\n });\n return;\n }\n\n try {\n // --- STAGE 1: Orchestrator plans the assembly line ---\n const planPrompt = config.prompts.ORCHESTRATOR_AGENT\n .replace(\"{{goal}}\", goal)\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\n // Map plan to include status and output storage\n const planMap = new Map();\n planResult.plan.forEach((step, index) => {\n planMap.set(step.id, {\n ...step,\n index, // Keep original index for client-side display order (needed for the list view)\n status: \"pending\", // New status field\n output: \"\",\n critique: null,\n });\n });\n const planArray = Array.from(planMap.values());\n\n await streamApi.writeSSE({\n event: \"plan_complete\",\n // Send the new, enriched plan to the client\n data: JSON.stringify(planArray),\n });\n\n // --- STAGE 2 & 3: Execute and Critique steps with dependency checking ---\n let completedSteps = new Set();\n const executingSteps = new Set();\n let allStepsComplete = false;\n const initialSteps = planArray.filter((step) =>\n step.dependencies.length === 0\n );\n\n // Function to execute a single step\n const executeStep = async (step) => {\n const { index, id } = step;\n executingSteps.add(id);\n\n try {\n // 1. Start Step (Execution phase)\n step.status = \"running\";\n await streamApi.writeSSE({\n event: \"step_start\",\n data: JSON.stringify({\n index,\n id,\n message: `Executing: ${step.agent_persona}`,\n }),\n });\n\n // Gather accumulated work from dependencies\n const previousWork = step.dependencies\n .map((depId) => {\n const depStep = planMap.get(depId);\n // Only include output if the dependency successfully completed\n return depStep.status === \"complete\"\n ? `\\n\\n---\\n\\n## Output from ${depStep.agent_persona} (Step ${depStep.id}):\\n\\n${depStep.output}`\n : \"\";\n })\n .join(\"\\n\");\n\n // 2. Execute Step\n const 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 previousWork || \"This is the first step. Start from scratch.\",\n )\n .replace(\"{{task}}\", step.task);\n\n const stepResult = await services.callOpenAIStreaming(\n executorPrompt,\n \"Proceed with your task. Generate the deliverable now.\",\n async (delta) => {\n step.output += delta;\n await streamApi.writeSSE({\n event: \"stream\",\n data: JSON.stringify({ index, id, delta }),\n });\n },\n );\n\n // 3. Complete Step (Execution finished, before critique)\n step.status = \"complete\";\n step.output = stepResult; // Finalize output\n await streamApi.writeSSE({\n event: \"step_complete\",\n data: JSON.stringify({ index, id }),\n });\n\n // 4. Critique Step (New 'running' status for the critique phase)\n await streamApi.writeSSE({\n event: \"step_start\",\n data: JSON.stringify({\n index,\n id,\n message: \"Critic is evaluating...\",\n }),\n });\n\n const critiquePrompt = JSON.stringify({\n project_goal: goal,\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 step.critique = critiqueResult;\n await streamApi.writeSSE({\n event: \"critique_complete\",\n data: JSON.stringify({ index, id, critique: critiqueResult }),\n });\n\n // Mark as complete and trigger next steps\n completedSteps.add(id);\n } catch (error) {\n console.error(`Step ${id} failed:`, error);\n step.status = \"error\";\n await streamApi.writeSSE({\n event: \"step_error\",\n data: JSON.stringify({ index, id, error: error.message }),\n });\n } finally {\n executingSteps.delete(id);\n }\n };\n\n // Main execution loop using promises to run parallel steps\n const runningPromises = [];\n\n while (!allStepsComplete) {\n // 1. Find newly available steps\n const newlyAvailableSteps = planArray.filter(\n (step) =>\n step.status === \"pending\" &&\n step.dependencies.every((depId) => completedSteps.has(depId)),\n );\n\n // 2. Start available steps\n for (const step of newlyAvailableSteps) {\n step.status = \"running-queued\";\n const promise = executeStep(step).then(() => {\n if (completedSteps.size === planArray.length) {\n allStepsComplete = true;\n }\n });\n runningPromises.push(promise);\n }\n\n // 3. Wait for progress\n if (runningPromises.length > 0) {\n await Promise.all(runningPromises);\n runningPromises.length = 0;\n } else if (completedSteps.size < planArray.length) {\n // Fail-safe break for impossible dependencies\n if (planArray.some((step) => step.status === \"pending\")) {\n console.error(\n \"Execution stalled. Check for circular dependencies or unreachable steps.\",\n );\n break;\n }\n break;\n } else {\n break;\n }\n }\n\n await streamApi.writeSSE({\n event: \"project_complete\",\n data: \"Project finished\",\n });\n } catch (error) {\n console.error(\"Project flow failed:\", error);\n await streamApi.writeSSE({\n event: \"error\",\n data: `An error occurred: ${error.message}`,\n });\n }\n });\n});\n\nexport default app.fetch;","// @ts-nocheck\n// AgentFlow AI - Guided Multi-Agent Orchestration (Refactored v5 - Integrated UI)\n// Merges the AgentFlow backend with the me/irl UI for an enhanced user experience.\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\";\n\n// --- TYPE DEFINITIONS ---\ninterface CompanyContext {\n company_name: string;\n company_description: string;\n use_case: string;\n language: string;\n}\n\ninterface ConversationHistory {\n role: \"assistant\" | \"user\";\n content: string;\n}\n\n// --- CENTRALIZED CONFIGURATION ---\nconst config = {\n models: {\n major: \"gpt-4o\",\n minor: \"gpt-4o-mini\",\n },\n affirmations: [\n \"Thinking...\",\n \"Processing...\",\n \"Generating insights...\",\n \"Analyzing data...\",\n \"Assembling results...\",\n \"Almost there...\",\n \"Compiling output...\",\n \"Consulting experts...\",\n ],\n prompts: {\n BUSINESS_RESEARCHER_AGENT:\n `You are an expert business researcher AI that speaks only in JSON. Your task is to find and summarize a company based on its name.\n\n# RULES\n1. Analyze the provided company name.\n2. Provide a concise, one-sentence description of the company's primary business.\n3. If you cannot find a well-known company with that name, you MUST return null for the description.\n4. Your response MUST be a single, minified JSON object: {\"company_name\": \"...\", \"description\": \"...\" | null}.\n\n# USER REQUEST\n- Company Name: {{company_name}}`,\n CONTEXT_ENRICHMENT_AGENT:\n `You are a native JSON speaking expert project intake manager. Your goal is to ask the fewest, most concise questions to build a complete company context. Ask one question at a time.\n\n# RULES\n1. Your goal is to fill this JSON object: {\"company_name\": \"\", \"company_description\": \"\", \"use_case\": \"\", \"language\": \"English\"}.\n2. Analyze the provided context and conversation history to see which fields are missing.\n3. Ask a single, concise question to fill the *next* missing field. Start with description, then use case.\n4. Once all fields are filled, your immediate response MUST be the token \"__COMPLETE__\" followed by the single, minified JSON object of the complete context.\n5. Output for questions must be a short, direct interrogative. DO NOT add conversational filler.\n\n# CURRENT CONTEXT\n{{current_context}}\n\n# CONVERSATION HISTORY\n{{history}}`,\n\n INFORMATION_GATHERER:\n `You are a native JSON speaking expert project intake manager. Your goal is to ask the fewest, most concise questions to understand the user's high-level goal. Ask one question at a time.\n\n# RULES\n1. **Primary Goal**: Ask single, concise questions to gather the core objective.\n2. **Decision Point**: After analyzing the history, decide if you have a clear, actionable goal.\n - **If YES**: Your immediate response MUST be the token \"__COMPLETE__\" followed by a single, minified JSON object of the gathered goal. Example: \\`__COMPLETE__\\`{\"goal\":\"Develop a Q4 marketing strategy for a new SaaS product.\"}\n - **If NO**: Formulate the single most impactful and concise question.\n3. **Output Format (Questions)**: The question must be a short, direct interrogative. DO NOT add conversational filler.\n\n# CONTEXT\n- Company: {{company_context}}\n- Conversation History:\n{{history}}`,\n ORCHESTRATOR_AGENT:\n `You are a native JSON speaking, elite project manager AI. Deconstruct the user's goal into a logical \"assembly line\" of 3 to 5 steps for a team of other AI agents.\n\n# RULES\n1. **Deconstruct**: Analyze the user's goal and company context.\n2. **Define Agent Roles**: For each step, define a specialized agent persona (e.g., \"Market Researcher,\" \"Creative Copywriter\").\n3. **Actionable Steps**: Each step must be a clear instruction for the agent. The final step should be a \"Final Review\" or \"Integration\".\n4. **Dependencies**: Assign a unique \"id\" to each step (e.g., \"A\", \"B1\", \"B2\", \"C\"). For steps that can run in parallel, give them different IDs but the same dependencies. For steps that must wait, list the \"id\"s of the steps they depend on in the \"dependencies\" array. The first steps should have an empty dependency array ([]).\n5. **Output Format**: Your response MUST be a single, minified JSON object: {\"plan\": [{\"id\": \"A\", \"agent_persona\": \"...\", \"task\": \"...\", \"dependencies\": [] | [\"A\", \"B1\"]}, ...]}.\n\n# USER REQUEST\n- Goal: {{goal}}\n- Company Context: {{company_context}}`,\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, building upon prior work if it exists. Return only the completed deliverable.\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- Write your entire response in the requested language: {{language}}.`,\n CRITIQUE_AGENT:\n `You are a native JSON speaking, meticulous AI QA agent. Given the project goal, an agent's task, and its output, you will:\n1. **Score:** Provide a single, holistic quality score from 1 to 10.\n2. **Critique:** Write a concise, professional critique with actionable feedback.\n\n# OUTPUT FORMAT\nYour response MUST be a single, minified JSON object: {\"score\": , \"critique\": \"...\"}. You MUST write the critique in the requested language.\n\n# CONTEXT\n- Overall Project Goal: {{project_goal}}\n- Agent's Assigned Task: {{task}}\n- Agent's Raw Output: {{raw_output}}\n- Language: {{language}}`,\n },\n};\n\n// --- BACKEND SERVICES ---\nconst services = {\n async withRetries(\n fn: () => Promise,\n retries = 3,\n delay = 1000,\n ): Promise {\n try {\n return await fn();\n } catch (e) {\n if (retries > 0 && e.status >= 500) {\n console.warn(\n `AI service failed, retrying in ${delay}ms... (${retries} retries left)`,\n );\n await new Promise((res) => setTimeout(res, delay));\n return this.withRetries(fn, retries - 1, delay * 2);\n }\n throw e;\n }\n },\n async callOpenAI(\n systemPrompt: string,\n userContent: string | object,\n options: { model?: string; isJson?: boolean } = {},\n ) {\n return this.withRetries(async () => {\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. Details: ${e.message}`);\n }\n });\n },\n async callOpenAIStreaming(\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: [{ role: \"system\", content: systemPrompt }, {\n role: \"user\",\n content: userPrompt,\n }],\n stream: true,\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);\n }\n }\n return fullText;\n },\n};\n\n// --- FRONTEND GENERATION (REFACTORED for Integrated UI) ---\nfunction generateHtml(sourceUrl: string): string {\n return `AgentFlow | Guided AI Orchestration\n\n\n\n\n\n\n

Let's start with your company

Is this correct?

\n \n\n `;\n}\n\n// --- HONO APPLICATION SETUP ---\nconst app = new Hono();\n\n// --- API ROUTES ---\n\n// Serves the main HTML page\napp.get(\"/\", (c: Context) => c.html(generateHtml(c.req.url)));\n\n// Handles company lookup\napp.post(\"/api/company-lookup\", async (c: Context) => {\n const { companyName } = await c.req.json();\n const prompt = config.prompts.BUSINESS_RESEARCHER_AGENT.replace(\n \"{{company_name}}\",\n companyName,\n );\n try {\n const result = await services.callOpenAI(\n \"You are a helpful assistant that only speaks JSON.\",\n prompt,\n {\n isJson: true,\n model: config.models.minor,\n },\n );\n return c.json(result);\n } catch (e) {\n console.error(\"Company lookup failed:\", e);\n return c.json({ company_name: companyName, description: null }, 500);\n }\n});\n\n// Handles conversational context enrichment\napp.post(\"/api/enrich-context\", async (c: Context) => {\n const { history, context } = await c.req.json();\n const historyString = history.map((h) => `${h.role}: ${h.content}`).join(\n \"\\n\",\n );\n\n const prompt = config.prompts.CONTEXT_ENRICHMENT_AGENT\n .replace(\"{{current_context}}\", JSON.stringify(context))\n .replace(\n \"{{history}}\",\n historyString || \"No history yet. Start with your first question.\",\n );\n\n try {\n const completion = await services.callOpenAI(\n \"You are a helpful assistant following strict output rules.\",\n prompt,\n { model: config.models.minor },\n );\n const responseText = completion.trim();\n if (responseText.startsWith(\"__COMPLETE__\")) {\n const jsonPart = responseText.replace(\"__COMPLETE__\", \"\");\n return c.json({ status: \"complete\", data: JSON.parse(jsonPart) });\n } else {\n return c.json({ status: \"in-progress\", question: responseText });\n }\n } catch (e) {\n console.error(\"Context enrichment agent failed:\", e);\n return c.json(\n {\n status: \"in-progress\",\n question: \"I'm having trouble. Could you please rephrase?\",\n },\n 500,\n );\n }\n});\n\n// Handles the goal-gathering conversation\napp.post(\"/api/gather\", async (c: Context) => {\n const { history, company_context } = await c.req.json();\n const historyString = history.map((h) => `${h.role}: ${h.content}`).join(\n \"\\n\",\n );\n\n const prompt = config.prompts.INFORMATION_GATHERER\n .replace(\"{{company_context}}\", JSON.stringify(company_context))\n .replace(\n \"{{history}}\",\n historyString ||\n \"No history yet. Start with your most important question.\",\n );\n\n try {\n const completion = await services.callOpenAI(\n \"You are a helpful assistant following strict output rules.\",\n prompt,\n { model: config.models.minor },\n );\n const responseText = completion.trim();\n if (responseText.startsWith(\"__COMPLETE__\")) {\n const jsonPart = responseText.replace(\"__COMPLETE__\", \"\");\n return c.json({ status: \"complete\", data: JSON.parse(jsonPart) });\n } else {\n return c.json({ status: \"in-progress\", question: responseText });\n }\n } catch (e) {\n console.error(\"Gathering agent failed:\", e);\n return c.json({\n status: \"in-progress\",\n question:\n \"I'm having a little trouble with that. Could you please rephrase?\",\n }, 500);\n }\n});\n\n// The main project execution endpoint using Server-Sent Events (SSE)\napp.get(\"/api/execute-flow\", (c: Context) => {\n return streamSSE(c, async (streamApi) => {\n streamApi.onAbort(() => console.log(\"Client disconnected.\"));\n\n const goal = c.req.query(\"goal\") || \"No goal provided\";\n let company_context: CompanyContext;\n\n try {\n company_context = JSON.parse(c.req.query(\"context\") || \"{}\");\n if (!company_context.company_name) {\n throw new Error(\"Company context is missing or invalid.\");\n }\n } catch (e) {\n console.error(\"Context parsing failed:\", e);\n await streamApi.writeSSE({\n event: \"error\",\n data: `Invalid context provided: ${e.message}`,\n });\n return;\n }\n\n try {\n // --- STAGE 1: Orchestrator plans the assembly line ---\n const planPrompt = config.prompts.ORCHESTRATOR_AGENT\n .replace(\"{{goal}}\", goal)\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\n const planMap = new Map();\n planResult.plan.forEach((step, index) => {\n planMap.set(step.id, {\n ...step,\n index,\n status: \"pending\",\n output: \"\",\n critique: null,\n });\n });\n const planArray = Array.from(planMap.values());\n\n await streamApi.writeSSE({\n event: \"plan_complete\",\n data: JSON.stringify(planArray),\n });\n\n // --- STAGE 2 & 3: Execute and Critique steps with dependency checking ---\n const completedSteps = new Set();\n const executingSteps = new Set();\n let allStepsComplete = false;\n\n const executeStep = async (step) => {\n const { index, id } = step;\n executingSteps.add(id);\n\n try {\n // 1. Start Step\n step.status = \"running\";\n await streamApi.writeSSE({\n event: \"step_start\",\n data: JSON.stringify({ index, id }),\n });\n\n // Gather accumulated work from dependencies\n const previousWork = step.dependencies\n .map((depId) => {\n const depStep = planMap.get(depId);\n return depStep.status === \"complete\"\n ? `\\n\\n---\\n\\n## Output from ${depStep.agent_persona} (Step ${depStep.id}):\\n\\n${depStep.output}`\n : \"\";\n })\n .join(\"\\n\");\n\n // 2. Execute Step\n const 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 previousWork || \"This is the first step. Start from scratch.\",\n )\n .replace(\"{{task}}\", step.task);\n\n const stepResult = await services.callOpenAIStreaming(\n executorPrompt,\n \"Proceed with your task. Generate the deliverable now.\",\n async (delta) => {\n step.output += delta;\n await streamApi.writeSSE({\n event: \"stream\",\n data: JSON.stringify({ index, id, delta }),\n });\n },\n );\n\n // 3. Complete Step\n step.status = \"complete\";\n step.output = stepResult;\n await streamApi.writeSSE({\n event: \"step_complete\",\n data: JSON.stringify({ index, id }),\n });\n\n // 4. Critique Step\n const critiquePrompt = JSON.stringify({\n project_goal: goal,\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 step.critique = critiqueResult;\n await streamApi.writeSSE({\n event: \"critique_complete\",\n data: JSON.stringify({ index, id, critique: critiqueResult }),\n });\n\n completedSteps.add(id);\n } catch (error) {\n console.error(`Step ${id} failed:`, error);\n step.status = \"error\";\n await streamApi.writeSSE({\n event: \"step_error\",\n data: JSON.stringify({ index, id, error: error.message }),\n });\n } finally {\n executingSteps.delete(id);\n }\n };\n\n // Main execution loop\n const runningPromises = [];\n\n while (!allStepsComplete) {\n const newlyAvailableSteps = planArray.filter(\n (step) =>\n step.status === \"pending\" &&\n step.dependencies.every((depId) => completedSteps.has(depId)),\n );\n\n for (const step of newlyAvailableSteps) {\n step.status = \"running-queued\";\n const promise = executeStep(step).then(() => {\n if (completedSteps.size === planArray.length) {\n allStepsComplete = true;\n }\n });\n runningPromises.push(promise);\n }\n\n if (runningPromises.length > 0) {\n await Promise.all(runningPromises);\n runningPromises.length = 0;\n } else if (completedSteps.size < planArray.length) {\n if (\n !planArray.some((step) =>\n step.status === \"pending\" || step.status.startsWith(\"running\")\n )\n ) {\n console.error(\n \"Execution stalled. No more steps can run, but project is not complete.\",\n );\n break;\n }\n await new Promise((resolve) => setTimeout(resolve, 100)); // Short delay to prevent busy-waiting\n } else {\n break;\n }\n }\n\n await streamApi.writeSSE({\n event: \"project_complete\",\n data: \"Project finished\",\n });\n } catch (error) {\n console.error(\"Project flow failed:\", error);\n await streamApi.writeSSE({\n event: \"error\",\n data: `An error occurred: ${error.message}`,\n });\n }\n });\n});\n\nexport default app.fetch;","createdAt",["D",1760285070900],"branchVersion","branchId","a2c9600c-a779-11f0-91b4-0224a6c84d84","id","a2c8f7a2-a779-11f0-91b4-0224a6c84d84","agentflow",["D",1760280131240],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","imageUrl","0199b749-1e1c-7399-b093-a7ba99382572","pinnedFileId","pinPreview",true,"pinMigrated","screenshotVersion","likeCount",0,"orgId","0199b749-1e1c-7399-b093-a97ac9fff69e","tursoName","tursoHostname","tursoMeasuredAt","pinned","userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","telemetryPerm","user",{"_95":94,"_102":98},"org",{"_95":94,"_96":-5,"_97":98,"_18":54,"_99":100,"_101":25},"branch",{"_13":84,"_32":85,"_43":42,"_86":44,"_87":-5,"_88":-5,"_89":-5,"_90":-5,"_91":-5,"_92":45,"_93":94},"isOwned",12,"main","projectId","forkedBranchId","forkedBranchVersion","forkedProjectPrivacy","forkedProjectId","forkedProjectOrgId","projectName","userHandle","volt","handle","displayName","avatar","https://img.clerk.com/eyJ0eXBlIjoiZGVmYXVsdCIsImlpZCI6Imluc18yTm10cGhQUklwU0tqZHNiOHNnNGd2ZjJURFUiLCJyaWQiOiJ1c2VyXzMzZm1qMllGY2JhQmhhWFYwVXVvQ05aT2tJciJ9","tier","free","canRestrictVals","profileImageUrl","branchName","","adoptOrgId",{"_43":44,"_32":45,"_38":149,"_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":150,"_79":151,"_81":152,"_83":25},"tree",[144],"files",[144],"counts",{"_133":61,"_134":135,"_136":61,"_137":138,"_139":140},"contents",[125],"upstreamProject","upstreamBranch","emptyEnvVars","flags",[],"hasHttpFiles","grants","orgs","customDomains",{},{"_43":126,"_32":28,"_30":31,"_127":-5,"_41":42,"_40":14,"_128":25,"_38":129,"_13":130,"_33":34,"_35":37,"_24":25,"_131":45,"_27":132},"230e7e40-a785-11f0-b48a-0224a6c84d84","parentId","latest",["D",1760285070900],8,"label",[28],"pulls","branches",1,"forks","envVars",{"_141":61,"_143":61},"connections",{"_141":61,"_142":61},"count","unfilledCount","emptyCount",{"_43":126,"_32":28,"_30":31,"_127":-5,"_41":42,"_40":14,"_128":25,"_38":145,"_13":130,"_33":34,"_35":37,"_24":25,"_131":45,"_146":147,"_27":148},["D",1760285070900],"children",[],[28],["D",1760280131240],{"_95":94,"_102":98},{"_95":94,"_96":-5,"_97":98,"_18":54,"_99":100,"_101":25},{"_13":84,"_32":85,"_43":42,"_86":44,"_87":-5,"_88":-5,"_89":-5,"_90":-5,"_91":-5,"_92":45,"_93":94},"ctx",{"_77":-5,"_122":186,"_187":188,"_189":190,"_191":25,"_192":25,"_193":25,"_194":57,"_195":-5,"_196":-7,"_197":-7,"_198":61,"_199":25,"_200":25,"_201":-5,"_202":25,"_203":25,"_204":25,"_205":-5},"ENV",{"_157":158,"_159":160,"_161":85,"_162":163,"_164":165,"_166":167,"_168":169,"_170":171,"_172":173,"_174":175,"_176":177,"_178":179,"_180":181,"_182":183,"_184":185},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

Diff · v9