Multi-agent LLM system architecture

提示詞

Landscape 16:9 high-fidelity systems figure of a multi-agent LLM architecture, in the style of a richly detailed AutoGen / LangGraph / Anthropic Managed Agents Figure 1. Subtle drop-shadows, warm-copper highlights, numbered flow markers ①②③④.

ZONE 1 — "User interface": rounded user box with placeholder task "research question: summarize recent red-teaming attacks and reproduce the top three".

ZONE 2 — "Orchestrator layer": central hexagonal hub "Planner LLM" with warm-copper top edge. Three satellite chips: "Task decomposition", "Agent routing", "Re-plan on failure". Small inset chip "prompt cache hit ~98%".

ZONE 3 — "Specialised workers": 2×2 hexagons "Researcher" / "Coder" / "Critic" / "Writer", each with glyph + status ribbon ("idle", "running step 3/5", "done", "running step 2/4"). Centre labeled "async message bus".

ZONE 4 — "Tools & memory": (a) "Tool registry" panel listing "web_search ×41", "python_exec ×27", "read_file ×18", "write_file ×12", "browser_use ×7"; (b) "Memory" panel with "Short-term scratchpad" and cylinder "Long-term vector store — 1.8M episodes".

Bottom inset "Example trace": 8-step horizontal timeline chips from "User asks" through "Planner decomposes", "Researcher: web_search(...)", "Coder: python_exec(...)", "Critic: verify", "Re-plan" (loop-back arrow), "Writer: compose final answer".

Title: "Agentic LLM system: planner orchestrates specialised workers over a shared tool and memory layer". Subtitle: "adapted from AutoGen (Wu et al., 2023), LangGraph, and Anthropic Managed Agents patterns".

中文翻譯

16:9 橫版高保真系統圖，展示多智能體 LLM 架構，風格參照細節豐富的 AutoGen / LangGraph / Anthropic Managed Agents Figure 1。細膩的投影，暖銅色高光，編號流程標記 ①②③④。

區域 1 —“用戶界面”：圓角用戶框，佔位任務爲“研究問題：總結近期的紅隊攻擊並復現其中排名前三的攻擊”。

區域 2 —“編排層”：中央六邊形樞紐“Planner LLM”，頂部邊緣爲暖銅色。三個衛星標籤塊：“任務分解”、“智能體路由”、“失敗後重新規劃”。小型內嵌標籤“prompt 緩存命中率約 98%”。

區域 3 —“專用執行者”：2×2 六邊形“Researcher”/“Coder”/“Critic”/“Writer”，每個都帶圖標 + 狀態條（“空閒”、“運行第 3/5 步”、“完成”、“運行第 2/4 步”）。中心標註“異步消息總線”。

區域 4 —“工具與記憶”：（a）“工具註冊表”面板，列出“web_search ×41”、“python_exec ×27”、“read_file ×18”、“write_file ×12”、“browser_use ×7”；（b）“記憶”面板，包含“短期草稿區”和圓柱體“長期向量存儲 — 180 萬條 episode”。

底部內嵌“示例軌跡”：8 步橫向時間軸標籤塊，從“用戶提問”到“Planner 分解”、“Researcher: web_search(...)”、“Coder: python_exec(...)”、“Critic: 校驗”、“重新規劃”（迴環箭頭）、“Writer: 撰寫最終答案”。

標題：“智能體式 LLM 系統：規劃器在共享的工具與記憶層之上編排專用執行者”。副標題：“改編自 AutoGen（Wu et al., 2023）、LangGraph 與 Anthropic Managed Agents 的模式”。

溯源

原作者: wuyoscar
收錄來源: wuyoscar/GPT-Image2-Skill · 82 · MIT