--- title: "MetaGPT: Agent as a Service — Empowering Individual Intelligence and Leading Natural-Language Programming" author: "Unique Research" sourcePublication: "Unique Research (FFCAP English library)" originalPublishedAt: "2023-11-29T16:35:00+08:00" canonical: "https://ffcap.cn/en/research/20231129a071i600" source: "https://ffcap.cn/en/research/20231129a071i600" language: "en" cnSourceUrl: "https://view.inews.qq.com/a/20231129A071I600" --- # MetaGPT: Agent as a Service — Empowering Individual Intelligence and Leading Natural-Language Programming _Unique Research · Interview · November 29, 2023 · Speaker: Wu Chenglin (吴承霖), Founder & CEO, DeepWisdom · From AWS × Unique Capital “AI Globalization Accelerator” Shanghai offline_ Wu brings large-scale complex AI delivery experience at Tencent-class user/data scale; authored open multi-agent framework **MetaGPT**; world champion in NeurIPS AutoDL / AutoWSL / KDDCup Graph; papers in TPAMI / KDD / CVPR / ACL; Forbes 30U30, Hurun 30×30, and numerous Tencent/Huawei internal awards. **Keypoints:** Agents add memory, planning, and tools on top of LLMs; Multi-Agents further fold in environment and SOPs. Core formula: **code = SOP(team)** — replace SOPs with an intelligent system and code generation can fully automate as SOPs and teams go online. Evaluate SOPs like code — with benchmarks. Boundary between what LLMs vs. Agents should do remains blurry. Agents excel at longer code planning/generation; the core metric vs. Copilot is leverage — Copilot’s is finite, Agents can make it unbounded. Natural-language programming is arriving fast. AutoGPT, AutoGen, and MetaGPT each have design strengths; MetaGPT’s research path is more “Android-like” — and hard. ## 1\. MetaGPT in practice Two thrusts: (1) a natural-language programming framework so users guide Agents to write code easily; (2) an Agent society emphasizing multi-agent collaboration. Late last year the team foresaw NL programming within ~two years — C → C++ → Java → Python ultimately returns to natural language; programming languages were designed for machines, not as humanity’s endpoint. One NL sentence can map to unbounded lines of Python. Distinction vs. Copilot: Copilot leverage is capped; Agents unbounded. MetaGPT has **30,000+** GitHub stars; one-line prompts can generate small games at ~**$1 per prompt line**. MetaGPT both constructs tasks and plays the user role — build something and use something as two sides of one coin — enabling self-iteration on complex games and CRM-class systems. In Minecraft, task-oriented strategies completed the diamond tech tree in **16** task rounds — **5×** faster than VOYAGER. For Werewolf (multi-round group strategy), the team designed fine multi-agent communication topologies plus reflection and experience-learning mechanisms. Tech ambition: push HumanEval Pass@1 toward **100**. With MetaGPT they have reached an impressive **86** (GPT-4 reference ~67); data-based projection ~**97** with low risk. When one-shot correct generation of every function nears 100, the core problem shifts from single-function generation to function-list generation + single-function generation — approaching unbounded code generation. ## 2\. Code = SOP(team) Example: build 2048 — multi-agent collaboration among product manager, architect, project manager, engineer, and QA: requirements, decomposition, competitor analysis, quadrant maps, demand pools — classic software-company SOPs simulating Scrum as a mini-waterfall. Curious observation: ask for a Toutiao-like recommender architecture (not on GitHub) and MetaGPT draws a complete architecture diagram — acting as an information compressor that summarizes fragmented information into workable structure. Abstract every Fortune 500’s SOPs and you might simulate a new Fortune 500; put a software company’s SOPs online and you create an online software company. Formula: **code = SOP(team)**. ## 3\. Human-in-the-loop and self-improvement Human-in-the-loop patches Agent weaknesses (e.g. vision-language gaps in text-centric GPT). One approach: a mid-tier human “hire” for UI drafts; MetaGPT also has an Agent UI via a fine-tuned Stable Diffusion model — not as good as humans, but workable. The framework can migrate beyond software cos — e-commerce CEOs in the community have moved business logic into the virtual world via code. Stack: OS + AI Infra (train/fine-tune/infer/deploy, data/features) under MetaGPT as NL-level IDE; top layer AgentStore for Agent interactions and one-stop AI Infra so self-optimizing Agent groups accelerate model development and ops. Agents as “human-like structures”: OpenAI aims to scale GPT params ~100× toward brain scale, but completion ability alone doesn’t solve memory. Vector DBs may hit ~60–70% precision retrieving ~100 texts — not how brains work. Human memory has multiple systems (including procedural) and a critical forgetting mechanism — most things forgotten, important things retained as semantic, episodic, and procedural memory. ## 4\. Human–Agent co-creation Two concepts for multi-agents: SOP (encoding humans from practice, unified with machine encoding like Python in the digital world) and review. SOP data lives in head companies’ systems/docs and is hard to get — modeling needs close industry relationships. Review corrects hallucinations and can prevent ~**95%** of errors. Human–machine collaboration evolved Embedded → Copilot → Agent → Society. We are in Agent mode. Future Agents might be company CEOs while humans are bot members with a kill switch. ## 5\. Remake internet apps in the LLM era Thirty years of information efficiency: yellow pages → search → recommendation; LLMs raise fuzzy-logic compute further. Paper reading can be LLM-digested then peer-reviewed at near-human quality — most internet apps deserve a remake. WeChat to Taobao are all reconsidering how to use LLMs — whether incumbents add the services or third parties take their traffic. Agents abstract brain logic; solve thinking and standardized memory and the Agent era truly arrives. — END — --- Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20231129a071i600 On-site reading page: https://ffcap.cn/en/research/20231129a071i600