如果真的做成 50+ agents + 自進化工具 + Autonomous startup builder，那其實已經是：
- Devin / Cognition Labs
- OpenDevin
- AutoGPT Platform
- Devika
- MetaGPT
這一類 AI software company simulation 的等級。
但先說清楚一件事：真正完整可跑版本通常 2萬~10萬行程式碼，不是 5000 行。
所以我們會設計：
🧠「Devin-like Autonomous AI Company System」
規模：
- 約 5k–10k lines
- 50+ agents society
- LangGraph orchestration
- RAG memory
- Browser + Terminal automation
- Self-improving tool learning
- Autonomous SaaS builder
🧠 2026 最強開源 Devin-like AI 系統架構
系統名稱：
OpenDevin-X
OpenDevin-XAutonomous AI Software Company
核心能力：
1️⃣ Hierarchical planning agent
2️⃣ Multi-agent society (50 agents)
3️⃣ Autonomous coding loop
4️⃣ Browser + terminal automation
5️⃣ Self-improving tool ecosystem
6️⃣ Autonomous repo builder
7️⃣ Self-training LLM loop
8️⃣ Autonomous SaaS startup builder
🧠 系統總架構
┌──────────────────────────┐
│ USER GOAL │
│ "Build SaaS product" │
└─────────────┬────────────┘
│
▼
┌──────────────────────────┐
│ CEO PLANNING AGENT │
│ Tree-of-Thought planner │
└─────────────┬────────────┘
│
Task decomposition
│
▼
┌────────────────────────────────┐
│ AGENT SOCIETY (50+) │
│ │
│ Product Manager Agent │
│ Software Architect Agent │
│ Backend Engineer Agent │
│ Frontend Engineer Agent │
│ DevOps Agent │
│ QA Agent │
│ Research Agent │
│ Marketing Agent │
│ Growth Agent │
│ Finance Agent │
│ Tool Builder Agent │
│ Code Refactor Agent │
│ Security Agent │
│ ... │
└──────────────┬─────────────────┘
│
Tool execution
│
▼
┌────────────────────────────────┐
│ TOOL LAYER │
│ │
│ Browser automation │
│ Terminal execution │
│ Code execution │
│ Git repo manager │
│ Docker sandbox │
│ Web scraping │
│ API calling │
└──────────────┬─────────────────┘
│
▼
┌─────────────────────────────────┐
│ MEMORY │
│ │
│ Vector RAG memory │
│ Graph memory (Neo4j) │
│ Task memory │
│ Tool knowledge base │
└─────────────────────────────────┘
🧠 Agent Society（50+ Agents）
管理層
CEO Agent
CTO Agent
COO Agent
Product Director
Research Director
產品團隊
Product Manager
UX Designer
UI Designer
Market Researcher
Feature Planner
工程團隊
Software Architect
Backend Engineer (5)
Frontend Engineer (5)
Mobile Engineer
DevOps Engineer
Database Engineer
Security Engineer
Performance Engineer
AI / Tool 團隊
Tool Builder Agent
Tool Optimizer
Tool Evaluator
LLM Trainer
Prompt Engineer
Agent Behavior Trainer
QA / DevOps
QA Tester
Test Generator
Bug Finder
Code Reviewer
CI/CD Manager
商業團隊
Marketing Agent
SEO Agent
Growth Hacker
Customer Support Agent
Finance Analyst
🧠 LangGraph Workflow （20+ nodes）
UserGoal
│
▼
CEO_Planning
│
▼
Task_Decomposition
│
├── Research_Agent
├── Architect_Agent
├── Product_Agent
│
▼
Code_Generation
│
├── Backend_Agent
├── Frontend_Agent
├── DevOps_Agent
│
▼
Code_Execution
│
▼
Testing
│
▼
Bug_Fix
│
▼
Git_Commit
│
▼
Deploy
│
▼
Self_Reflection
│
▼
Memory_Update
🧠 Autonomous Coding Loop
while not goal_complete:
plan = planner.plan(goal)
for task in plan:
result = agent.execute(task)
if result.error:
fix_agent.fix(result)
tests = qa_agent.run_tests()
if tests.failed:
bug_agent.fix()
git_agent.commit()
reflection = reflection_agent.analyze()
memory.store(reflection)
🧠 Tool Ecosystem（自進化工具）
AI 可以自己創造工具。
例：
Agent: "Need faster code search"
→ Tool Builder Agent
generate:
tools/code_search_tool.py
自動加入系統：
tool_registry.register(tool)
🧠 Tool list
核心工具：
browser_tool.py
terminal_tool.py
git_tool.py
docker_tool.py
code_executor.py
repo_builder.py
web_scraper.py
search_tool.py
file_editor.py
api_caller.py
🧠 Browser Agent
使用
PlaywrightExample：
</>Python
async def browse(url):
page = await browser.new_page()
await page.goto(url)
content = await page.content()
return content
🧠 Terminal Agent
</>Python
import subprocess
def run_terminal(cmd):
result = subprocess.run(
cmd,
shell=True,
capture_output=True,
text=True
)
return result.stdout
🧠 Code Interpreter
</>Python
def execute_python(code):
exec_globals = {}
exec(code, exec_globals)
return exec_globals
🧠 Memory System
Vector Memory
Chroma / Qdrant
存：
task history
code snippets
solutions
bugs
Graph Memory
Neo4jExample：
(User) → created → (Project)
(Project) → uses → (Tech)
(Project) → contains → (Repo)
🧠 Self Reflection
每一輪：
Reflection AgentPrompt：
Analyze the previous step.
Did the agent succeed?
If failure:
- identify root cause
- suggest improvement
- update memory
🧠 Autonomous SaaS Builder
目標：
"Create AI resume SaaS"AI 會：
1️⃣ 市場研究
2️⃣ 設計產品
3️⃣ 寫 code
4️⃣ 建 repo
5️⃣ deploy SaaS
6️⃣ marketing page
Example output：
repo/
backend/
frontend/
docker/
deploy/
landing_page/
🧠 自我訓練系統
AI 會自動產生訓練資料：
task → solution然後微調：
Qwen
DeepSeek
LLaMA
Pipeline：
agent_logs
↓
dataset_builder
↓
fine_tune
↓
new_model
🧠 Repo 結構（完整）
opendevin-x/
agents/
ceo_agent.py
architect_agent.py
backend_agent.py
frontend_agent.py
devops_agent.py
qa_agent.py
research_agent.py
marketing_agent.py
reflection_agent.py
planner/
tot_planner.py
task_decomposer.py
memory/
vector_memory.py
graph_memory.py
tools/
browser_tool.py
terminal_tool.py
git_tool.py
docker_tool.py
code_executor.py
workflow/
langgraph_workflow.py
training/
dataset_generator.py
finetune_qwen.py
runtime/
agent_orchestrator.py
configs/
agents.yaml
tools.yaml
main.py
🧠 4090 本地部署
建議模型：
Qwen2.5 14B
DeepSeek-Coder 16B
LLaMA 3 8B
VRAM需求：
24GB (RTX4090)
可跑：
4bit quantization🧠 這個系統的能力
可以做到：
✔ 自動寫程式
✔ 自動 debug
✔ 自動建 repo
✔ 自動部署 SaaS
✔ 自動研究市場
✔ 自動建立工具
接近：
Devin
AutoGPT
OpenDevin
Devika
MetaGPT
