Autonomous LLM Agent Simulation

Visualizing the agent architecture from \"Fundamentals of Building Autonomous LLM Agents\"

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1. Perception System

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4. Execution System

\n Action:\n

\n Justification:\n

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2. Reasoning System (AI Core)

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3. Memory System

\n Short-Term Memory (Context):\n

\n Long-Term Memory (Facts):\n

\n\n\n\n`;\n}\n\n// --- VAL TOWN BACKEND ---\n\n/**\n * This is the main exported function for the Val Town val.\n * It serves the HTML UI for GET requests and handles the AI\n * reasoning simulation for POST requests.\n */\nexport default async function (req: Request) {\n const url = new URL(req.url);\n\n // --- GET Request: Serve the HTML User Interface ---\n if (req.method === \"GET\") {\n // Adheres to \"URL Formatting\" principle by ensuring trailing slash.\n const sourceUrl = `https://${url.hostname}${url.pathname}`;\n return new Response(generateHtml(sourceUrl), {\n headers: { \"Content-Type\": \"text/html\" },\n });\n }\n\n // --- POST Request: Run the AI Reasoning Step ---\n if (req.method === \"POST\") {\n try {\n const openai = new OpenAI();\n\n const body = await req.json();\n\n // Construct the prompt for the AI simulation.\n const messages = [\n {\n role: \"system\",\n content: AGENT_SIMULATOR_PROMPT,\n },\n {\n role: \"user\",\n content: JSON.stringify(body, null, 2),\n },\n ];\n\n // Call OpenAI to get the agent's next step.\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: messages,\n temperature: 0.2,\n response_format: { type: \"json_object\" },\n });\n\n const aiResponse = completion.choices[0].message.content;\n\n // Return the raw JSON string from the AI.\n // The frontend is responsible for parsing this trusted structure.\n return new Response(aiResponse, {\n headers: { \"Content-Type\": \"application/json\" },\n });\n } catch (error) {\n console.error(\"AI simulation error:\", error);\n return new Response(\n JSON.stringify({\n error: error.message || \"An unknown error occurred.\",\n }),\n {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n },\n );\n }\n }\n\n // --- Other HTTP Methods ---\n return new Response(\"Method Not Allowed\", { status: 405 });\n}","deleted","label","children",[],[150],"files",[148],"counts",{"_173":32,"_174":175,"_176":32,"_177":178},"pulls","branches",1,"forks","envVars",{"_179":32,"_180":32},"count","emptyCount","contents",[183],{"_102":149,"_95":150,"_151":152,"_153":-5,"_154":136,"_155":135,"_156":157,"_104":184,"_99":159,"_160":161,"_162":163,"_164":22,"_165":-5,"_98":185},["D",1762265895964],[150],"file","upstreamProject","upstreamBranch","emptyEnvVars","isNew","flags",[],"routes/_app.x.$handle.$name._index",{"_93":94,"_95":96,"_97":-7,"_99":-7,"_100":195,"_181":200,"_203":-7,"_204":-5,"_205":-5,"_169":206,"_210":211,"_212":-5,"_213":-5},{"_102":103,"_95":96,"_104":196,"_106":-5,"_107":22,"_108":109,"_110":-5,"_111":-5,"_14":112,"_113":-5,"_114":22,"_115":-5,"_116":32,"_16":117,"_118":119,"_120":119,"_121":122,"_123":122,"_124":122,"_125":119,"_126":119,"_23":197,"_130":198,"_133":199,"_145":22},["D",1762262498952],{"_93":94,"_128":129},{"_93":94,"_132":129},{"_99":135,"_95":43,"_102":136,"_137":103,"_138":-5,"_139":-5,"_140":-5,"_141":-5,"_142":-5,"_143":96,"_144":94},[201],{"_102":149,"_95":150,"_151":152,"_153":-5,"_154":136,"_155":135,"_156":157,"_104":184,"_99":159,"_160":161,"_162":163,"_164":22,"_165":-5,"_98":202},[150],"readme","upstream","pull",[207],{"_102":149,"_95":150,"_151":152,"_153":-5,"_154":136,"_155":135,"_156":157,"_104":158,"_99":159,"_160":161,"_162":163,"_164":22,"_165":-5,"_166":208,"_98":209},[],[150],"previews",[207],"ahead","behind","actionData","errors"]

bomb

epistemology