Foundry AI Agent by axislab-top

<p align="center"> <img src=".github/images/logo.svg" width="150" alt="Foundry Logo"> </p> <h1 align="center">Foundry</h1> <p align="center"> <strong>Open-Source AI Digital Company Platform — Let AI Teams Collaborate Like Real Ones</strong> </p> <p align="center"> <a href="README.zh-CN.md">🇨🇳 中文文档</a> | <a href="https://www.axislab.top">🖥️ Live Demo</a> | <a href="#-quick-start">Quick Start</a> | <a href="#-features">Features</a> | <a href="#-architecture">Architecture</a> | <a href="#-comparison">Comparison</a> | <a href="docs/ARCHITECTURE.md">Docs</a> | <a href="#-contributing">Contributing</a> </p> <p align="center"> <a href="https://github.com/axislab-top/Foundry/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-GPL--3.0-blue" alt="License"></a> <a href="https://github.com/axislab-top/Foundry/stargazers"><img src="https://img.shields.io/github/stars/axislab-top/Foundry?style=social" alt="Stars"></a> <a href="https://github.com/axislab-top/Foundry/network/members"><img src="https://img.shields.io/github/forks/axislab-top/Foundry?style=social" alt="Forks"></a> <a href="https://github.com/axislab-top/Foundry/issues"><img src="https://img.shields.io/github/issues/axislab-top/Foundry" alt="Issues"></a> <img src="https://img.shields.io/badge/TypeScript-5.9-3178c6" alt="TypeScript"> <img src="https://img.shields.io/badge/NestJS-10-e0234e" alt="NestJS"> <img src="https://img.shields.io/badge/React-18-61dafb" alt="React"> <img src="https://img.shields.io/badge/Docker-Compose-2496ED" alt="Docker"> </p>

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🏭 What is Foundry?

Foundry is an open-source AI-powered digital company platform. Unlike agent frameworks where you have to orchestrate everything yourself, Foundry provides a ready-to-use AI company — just input a strategic goal and the AI company runs autonomously.

💡 In one sentence: If CrewAI is "building blocks for agents", Foundry is "the company already built."

Typical scenario: You say "Analyze competitors and create a report" in a group chat. The CEO Agent automatically breaks down the task, assigns it to the analysis department, executes in parallel, and summarizes the results — all you need to do is approve key decisions.

📸 Product Preview

Registration	Organization Chart

Group Chat	Admin Dashboard

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🚀 Quick Start

System Requirements

Requirement	Minimum	Recommended
OS	Windows 10+, macOS 12+, Ubuntu 20.04+	Any 64-bit OS
CPU	2 cores	4+ cores
RAM	8 GB	16 GB
Disk	5 GB free	10 GB+ free
Docker	20.10+	Latest stable
Node.js	20+	22 LTS
pnpm	10+	Latest

💡 Disk breakdown: Project ~1.5 GB + Docker images ~1 GB + build cache ~1 GB. SSD recommended for faster Docker startup.

What's Included (No Separate Installation Needed)

All infrastructure runs in Docker — you don't need to install these separately:

Service	Purpose	Docker Image
PostgreSQL 18	Main database	postgres:18-alpine
Redis 7	Cache & sessions	redis:7-alpine
RabbitMQ 3	Message queue	rabbitmq:3-management
Consul	Service discovery	consul:latest
Nginx	Reverse proxy	nginx:alpine

One Command Setup (Recommended)

git clone https://github.com/axislab-top/Foundry.git && cd Foundry
pnpm setup:dev   # env → install → docker → db → start

Or Step by Step

# 1. Clone the repository
git clone https://github.com/axislab-top/Foundry.git && cd Foundry

# 2. Install dependencies
pnpm install

# 3. Configure environment
node -e "fs.copyFileSync('env.shared.example','.env.shared')"

# 4. Generate service .env files
node scripts/env-manager.js

# 5. Start infrastructure (PostgreSQL, Redis, RabbitMQ)
pnpm infra:start

# 6. Initialize database
node scripts/bootstrap-db.js

# 7. Start development server
pnpm dev

⏱️ First launch takes 5-10 minutes — Docker needs to download all images (~2 GB). Subsequent starts take ~30 seconds.

💡 Windows users: Run pnpm setup:dev directly in cmd or PowerShell. Run as Administrator if you encounter permission errors.

🇨🇳 China users: Docker Hub may be inaccessible. Configure mirror accelerator in Docker Desktop → Settings → Docker Engine:

{ "registry-mirrors": ["https://docker.1ms.run", "https://docker.xuanyuan.me"] }

Then restart Docker Desktop before running setup.

After containers are healthy and migrations complete, visit:

Service	URL	Description
🖥️ Client UI	http://localhost:5173	Main interface (Vite dev server)
🔧 Admin Panel	http://localhost:3105	Administrator dashboard
📡 API Service	http://localhost:3000/api-docs	API Swagger docs
📡 Gateway	http://localhost:3002/api-docs	Gateway Swagger docs
📊 Grafana	http://localhost:4000	Log visualization (admin/admin)
🐰 RabbitMQ	http://localhost:15672	Message queue management (guest/guest)

Default Admin Account

The admin account is automatically created on first startup:

Field	Value
Email	admin@example.com
Username	admin
Password	changeme

⚠️ Change DEFAULT_ADMIN_PASSWORD in production environments.

Default Service Accounts

Service	URL	Username	Password
🔧 Admin Panel	http://localhost:3105	admin	changeme
📊 Grafana	http://localhost:4000	admin	admin
🐰 RabbitMQ	http://localhost:15672	guest	guest

Useful Commands

pnpm infra:status    # Check container status
pnpm infra:logs      # View container logs
pnpm infra:stop      # Stop all containers
pnpm infra:restart   # Restart all containers

Troubleshooting

Problem	Solution
Port already in use	Run pnpm infra:stop first, or change ports in env.shared.example
Docker not running	Start Docker Desktop and wait for it to be ready
Out of memory	Increase Docker memory limit to 8 GB+ in Docker Desktop settings
Slow on Windows	Enable WSL 2 backend for Docker Desktop
getaddrinfo ENOTFOUND postgres	You have a stale .env file with Docker hostnames. Delete .env and re-run pnpm setup:dev
RabbitMQ ACCESS_REFUSED	RabbitMQ container was created with wrong credentials. Run pnpm infra:stop then pnpm setup:dev again
Database connection timeout	PostgreSQL may be slow to start on Windows. The app will retry automatically (10s timeout)
Docker image pull timeout	Configure mirror accelerator (see below) or pull images manually: node scripts/pull-docker-images.js

Docker Image Pull Issues (China Users)

If you see errors like failed to fetch anonymous token or content not found:

1. Configure Docker Mirror (recommended):

   • Docker Desktop → Settings → Docker Engine
   • Add to JSON:

   {
     "registry-mirrors": [
       "https://docker.1ms.run",
       "https://docker.xuanyuan.me"
     ]
   }
   

   • Click "Apply & restart"

2. Pull images manually (if mirror doesn't work):

   node scripts/pull-docker-images.js
   

3. Pull specific image (if only one fails):

   docker pull node:20-alpine
   docker pull node:20-bookworm-slim
   

💡 Diagnose database issues: Run node scripts/diagnose-db.js in a separate terminal while the project is running.

First Time Setup

After completing the steps above:

# 1. Login to Admin Panel at http://localhost:3105
#    Email: admin@example.com  Password: changeme

# 2. Configure LLM Keys (required for AI agents):
#    Admin Panel → LLM Keys → Add your OpenAI/Claude API key

# 3. Open Client UI at http://localhost:5173
#    Register a new account or login to start using the platform

What's NOT Included (You Need to Configure)

Item	Where to Configure	Why
LLM API Keys	Admin Panel → LLM Keys	Required for AI agents to work
Company Info	Admin Panel → Company	Your company name and industry
Agent Skills	Admin Panel → Skills	Enable/disable and configure skills
Email Settings	Admin Panel → Settings	For notifications (optional)

💡 Tip: After logging into the Admin Panel, go to LLM Keys first and add at least one API key (OpenAI, Claude, etc.) — otherwise AI agents won't be able to respond.

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✨ Features

<details> <summary><strong>🏗️ One-Click Company Creation</strong> — Input name and industry, auto-generate org structure</summary>

• Auto-generates Board → CEO → Department Head → Employee Agent hierarchy
• Built-in industry templates for one-click company initialization
• Drag-and-drop custom organization structure

</details> <details> <summary><strong>🤖 Multi-Agent Collaboration</strong> — Each agent has a role, like a real team</summary>

• CEO Agent handles strategic breakdown and task assignment
• Department Head Agent manages sub-task orchestration
• Employee Agent executes tasks (Skills, APIs, code execution)
• Customizable agent roles and capabilities

</details> <details> <summary><strong>💬 Real-time Group Chat</strong> — Not just conversations, true collaboration</summary>

• Dynamic group chat + streaming output + @mentions
• Human-in-the-loop approval flow (critical decisions need your confirmation)
• Real-time task progress push notifications
• Multi-company, multi-group-chat parallel operation

</details> <details> <summary><strong>🧠 Layered Memory System</strong> — Your AI company "learns"</summary>

• 3-tier memory: Company / Department / Agent level
• RAG intelligent retrieval (powered by pgvector)
• Automatic memory consolidation and decay
• Cross-session context preservation

</details> <details> <summary><strong>🔄 Autonomous Operation</strong> — No need to watch it constantly</summary>

• CEO Agent periodic Heartbeat review of pending tasks
• Task auto-breakdown → assignment → execution → reporting
• Temporal workflow engine ensures reliability
• Scheduled tasks and event-driven support

</details> <details> <summary><strong>💰 Cost & Governance</strong> — Every penny accounted for</summary>

• Real-time token consumption and cost tracking
• Company-level budget control
• Intelligent model routing (auto-select best cost-performance ratio)
• Full audit logging
• LLM Key pool management (multi-key rotation)

</details>

---

🏗️ Architecture

System Overview

graph TB
    subgraph user["👤 User Layer"]
        chat["💬 Chat UI<br/><i>Real-time WebSocket</i>"]
        admin["🖥️ Admin Panel<br/><i>Management Dashboard</i>"]
    end

    nginx["🔀 Nginx Reverse Proxy + SSL"]

    subgraph gateway["🚪 Gateway · port 3002"]
        gw_auth["🔐 JWT Auth + RBAC"]
        gw_ws["🔌 Socket.IO Hub"]
        gw_security["🛡️ HMAC / CSRF / Rate Limit"]
        gw_route["🔀 Dynamic Route Proxy"]
    end

    subgraph services["⚙️ Application Services"]
        api["📡 API Service · port 3000<br/><b>Business Control Plane</b><br/>46 entities · RLS multi-tenancy<br/>Billing · Memory+RAG · Skills"]
        worker["🧠 Worker Service · port 3004<br/><b>AI Orchestration Engine</b><br/>LangGraph Pipelines · Task Scheduler<br/>Tool Registry · Memory Consolidation"]
        webhook["🪝 Webhook Service · port 3003<br/>External event receive/forward/retry"]
    end

    subgraph infra["🏗️ Infrastructure"]
        pg[("🐘 PostgreSQL + pgvector<br/>RLS multi-tenancy · 46 tables<br/>Vector embeddings")]
        redis[("⚡ Redis 7<br/>Cache · Session · Pub/Sub")]
        rabbitmq[("🐇 RabbitMQ<br/>Event Bus · RPC Queues<br/>15 event domains")]
        minio[("📦 MinIO / S3 / OSS<br/>File storage")]
        temporal["⏱️ Temporal<br/>Heartbeat fanout · Approval wait<br/>Supervisor review"]
        grafana["📊 Grafana Stack<br/>Loki · Promtail · Grafana"]
    end

    subgraph runners["🔒 Execution"]
        runner["🏃 Runner · gVisor Sandbox<br/>Code execution · Command policy<br/>K8s Jobs isolation"]
    end

    chat --> nginx
    admin --> nginx
    nginx --> gateway

    gw_ws -- "Redis Pub/Sub<br/>collab:notify" --> redis
    gw_route -- "RPC proxy" --> api
    gw_route -- "RPC proxy" --> webhook

    api <== "RPC<br/>api-rpc-queue" ==> worker
    worker ==>|"RPC sandbox"| runner
    runner ==>|"RPC token"| api

    api --> pg
    api --> redis
    api --> rabbitmq
    worker --> rabbitmq
    webhook --> rabbitmq
    api --> minio
    worker --> pg

    style user fill:#e3f2fd,stroke:#1565c0,color:#000
    style gateway fill:#fff3e0,stroke:#e65100,color:#000
    style services fill:#e8f5e9,stroke:#2e7d32,color:#000
    style infra fill:#f3e5f5,stroke:#6a1b9a,color:#000
    style runners fill:#fce4ec,stroke:#b71c1c,color:#000

Core Differentiator: Autonomous Heartbeat Loop

The CEO Agent runs on a ~30min autonomous cycle — no human prompt needed. Each cycle gathers context from 7 sources, plans via LLM, creates tasks, and streams a report back to chat.

graph LR
    subgraph cycle["⏰ Autonomous Heartbeat Cycle"]
        A["📋 TaskHeartbeatScheduler<br/><i>Round-robin across companies</i>"]
        B["📥 Ingest Context<br/>Dashboard · Memory RAG<br/>Budgets · Tasks · Lessons<br/>Org tree · Model routing"]
        C["🧠 CEO LLM Plan<br/><i>Structured output + Zod repair</i><br/>nextStep: generate_tasks | summary_only"]
        D["✅ Validate & Persist<br/>Create tasks via API RPC<br/>Pull agents into rooms"]
        E["📝 Summarize & Notify<br/>Stream report to chat room<br/>Store in memory · Request approval"]
        F["💾 Memory + Approval"]
    end

    A -- "~30min" --> B
    B --> C
    C --> D
    D --> E
    E --> F
    F -.->|"repeat"| A

    style cycle fill:#fffde7,stroke:#f57f17,color:#000

Core Differentiator: Collaboration Pipeline

User messages trigger a LangGraph state machine with 4 intent paths and a human-in-the-loop approval gate (interrupt/resume).

graph TB
    msg["💬 User message in chat<br/><i>@CEO or @Agent</i>"]

    subgraph pipeline["CollaborationRoomPipeline · LangGraph"]
        classify["🎯 CEO LLM<br/>Classify Intent"]

        discuss["💬💬💬 Discussion<br/><i>Multi-agent moderated</i>"]
        direct["🤖 Single Reply<br/><i>@Agent direct answer</i>"]
        execute["📋 Task Execution<br/><i>CEO breaks down into tasks</i>"]
        gate["⏸️ Approval Gate<br/><i>LangGraph interrupt()</i><br/>Waits for human decision"]

        approve{"👤 Human<br/>Approve?"}
    end

    out["Agent responses → API persist<br/>→ Redis Pub/Sub → Socket.IO<br/>→ Client (200-char streaming chunks)"]

    msg --> classify
    classify -->|"discussion"| discuss
    classify -->|"direct"| direct
    classify -->|"execution"| execute
    classify -->|"approval"| gate

    gate --> approve
    approve -- "✅ 批准/approve" --> execute
    approve -- "❌ 拒绝/reject" --> out

    discuss --> out
    direct --> out
    execute --> out

    style pipeline fill:#e8eaf6,stroke:#283593,color:#000
    style gate fill:#fff9c4,stroke:#f9a825,color:#000

End-to-End Core Flow

The full lifecycle: from user goal to agent execution, cost tracking, failure review, and memory feedback — forming a self-improving loop.

graph TB
    subgraph trigger["🎯 Triggers"]
        user["💬 User @CEO in chat"]
        heartbeat["⏰ Heartbeat ~30min"]
        webhook_trig["🪝 External Webhook"]
    end

    plan["🧠 CEO LLM<br/>Break down goal into tasks<br/><i>Structured output + Zod repair</i>"]

    assign["📋 Task Assignment<br/>DAG: parent → sub-tasks<br/>Assign to department agents"]

    subgraph exec["⚙️ Agent Execution"]
        agent["🤖 Agent LLM<br/>Execute with skills + context"]
        skills["🔧 Skill Engine<br/>Tool calling (function schema)"]
        runner["🏃 Runner Sandbox<br/>gVisor K8s Job"]
    end

    result{{"✅❌ Task Result"}}

    subgraph cost["💰 Cost Control"]
        billing["📊 Token Billing<br/>per LLM call"]
        budget["💵 Budget Check<br/>company / dept / agent"]
        degrade["📉 Model Degradation<br/>fallback to cheaper model"]
        pause["🚫 Budget Exceeded<br/>pause autonomous ops"]
    end

    subgraph feedback["🔄 Memory Feedback Loop"]
        sup["🔍 Supervisor Review<br/>Post-mortem on failures"]
        lessons["📝 Lessons Learned<br/>LLM-generated insights"]
        memory["🧠 Memory RAG<br/>pgvector embeddings<br/>company → dept → agent"]
    end

    chat_out["💬 Streaming Report<br/>→ Chat Room (200-char chunks)<br/>→ Socket.IO → Client"]

    trigger --> plan
    plan --> assign
    assign --> exec
    agent --> skills
    skills -->|"code/commands"| runner
    runner --> result
    agent --> result

    result -->|"completed"| chat_out
    result -->|"failed"| sup
    sup --> lessons
    lessons --> memory
    memory -.->|"RAG search<br/>next heartbeat"| plan

    agent --> billing
    billing --> budget
    budget -->|"warning"| degrade
    budget -->|"exceeded"| pause
    degrade -.->|"cheaper model"| agent

    style trigger fill:#e3f2fd,stroke:#1565c0,color:#000
    style cost fill:#fff3e0,stroke:#e65100,color:#000
    style feedback fill:#e8f5e9,stroke:#2e7d32,color:#000
    style exec fill:#f3e5f5,stroke:#6a1b9a,color:#000

📄 Full architecture documentation → docs/ARCHITECTURE.md

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🆚 Comparison

Feature	Foundry	CrewAI	MetaGPT	ChatDev	AutoGen
Positioning	AI Digital Company Platform	Agent Orchestration Framework	AI Software Company	Zero-code Dev Platform	Agent Programming Framework
Ready to Use	✅ Complete platform	❌ Needs orchestration	⚠️ Code generation only	⚠️ Code generation only	❌ Needs coding
Real-time Chat	✅ WebSocket	❌	❌	❌	❌
Org Chart Visualization	✅ Drag-and-drop	❌	⚠️ Fixed roles	⚠️ Fixed roles	❌
Layered Memory	✅ 3-tier + RAG	❌	❌	❌	❌
Cost Control	✅ Budget + Routing	❌	❌	❌	❌
Multi-tenancy	✅ RLS isolation	❌	❌	❌	❌
Approval Flow	✅ Human-in-loop	❌	❌	❌	❌
Admin Panel	✅ Separate frontend	❌	❌	❌	✅ Studio
Tech Stack	NestJS + React	Python	Python	Python	Python + .NET
License	GPL-3.0	MIT	MIT	Apache-2.0	MIT

💡 Key difference: Competitors are "frameworks" — you write code to orchestrate agents. Foundry is a "platform" — register and use it. Like the difference between Slack and IRC.

---

🛠️ Tech Stack

Layer	Technology
Backend	NestJS (TypeScript) · 7 microservices
Frontend	React 18 · Vite · TypeScript
Build	pnpm workspace · Turborepo
Database	PostgreSQL (TypeORM + RLS multi-tenancy) · pgvector
Message Queue	RabbitMQ
Cache	Redis
AI Orchestration	LangChain · LangGraph
Real-time	Socket.IO (WebSocket)
Object Storage	MinIO / S3 / OSS / Local
Workflow	Temporal (optional)
Containerization	Docker Compose

---

📁 Project Structure

Foundry/
├── apps/                    # Microservices
│   ├── api/                 #   API service (core business)
│   ├── gateway/             #   Gateway (auth, rate limiting, routing)
│   ├── worker/              #   Background tasks
│   ├── webhooks/            #   Webhook handling
│   ├── runner/              #   Code execution sandbox
│   ├── temporal-worker/     #   Temporal workflows
│   └── logging/             #   Logging service
├── admin-system/            # Admin panel frontend
├── client-frontend/         # Client frontend
├── packages/                # Shared packages (messaging, security, tenant...)
├── infrastructure/          # Infrastructure configs
├── contracts/               # Event contracts & OpenAPI
├── deployment/              # Docker Compose deployment
└── docs/                    # Documentation

---

⚙️ Environment Variables

Core configuration is documented in env.shared.example. Key variables:

# 🔴 Required (production)
JWT_SECRET=<openssl rand -base64 32>
DB_PASSWORD=<strong-password>
DEFAULT_ADMIN_PASSWORD=<strong-password>

# 🟡 Optional
TEST_AUTH_ENABLED=false          # Test user injection (dev only)
FILE_UPLOAD_MAX_SIZE=52428800    # File upload limit 50MB
KIBANA_ENCRYPTION_KEY=<key>      # Kibana (if using ELK)

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❓ FAQ

<details> <summary><strong>Q: How is Foundry different from CrewAI/AutoGen?</strong></summary>

CrewAI/AutoGen are agent orchestration frameworks — you write Python code to define agents, set up tools, and orchestrate workflows.

Foundry is an AI digital company platform — you register, create a company, and the AI runs automatically. No coding required.

Analogy: CrewAI is like building a PC from parts. Foundry is like buying a ready-to-use computer.

</details> <details> <summary><strong>Q: Which AI models are supported?</strong></summary>

Through LLM Key pool management, all major models are supported: OpenAI, Anthropic Claude, Azure OpenAI, and Chinese models (Qwen, Wenxin, etc.). Features multi-key rotation and intelligent routing.

</details> <details> <summary><strong>Q: Can I use it commercially?</strong></summary>

Yes. This project is open-sourced under GPL-3.0. Commercial use requires compliance with GPL-3.0 terms (derivative works must also be open-sourced). Contact us for commercial licensing.

</details> <details> <summary><strong>Q: How is data security ensured?</strong></summary>

• Multi-tenant RLS (Row-Level Security) isolation
• LLM Key encrypted storage (AES-256-GCM)
• JWT + RBAC access control
• Full audit logging
• All credentials managed via environment variables, never hardcoded

</details> <details> <summary><strong>Q: What are the minimum hardware requirements?</strong></summary>

• Development: 4GB RAM + 2 CPU (Docker)
• Production: 8GB RAM + 4 CPU (recommended)
• Storage: PostgreSQL + Redis + RabbitMQ + MinIO

</details>

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🤝 Contributing

We welcome all forms of contribution!

Type	How
🐛 Bug Report	Open an Issue
💡 Feature Request	Open an Issue
📝 Documentation	Submit a PR directly
🔧 Code	Fork → Branch → PR

See CONTRIBUTING.md for details.

Contributors

<a href="https://github.com/axislab-top/Foundry/graphs/contributors"> <img src="https://contrib.rocks/image?repo=axislab-top/Foundry" /> </a>

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📜 License

This project is licensed under GPL-3.0.

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⭐ Star History

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<p align="center"> If you find this useful, please give us a ⭐ Star!<br> <a href="https://github.com/axislab-top/Foundry/stargazers">⭐ Star</a> • <a href="https://github.com/axislab-top/Foundry/fork">🍴 Fork</a> • <a href="https://github.com/axislab-top/Foundry/issues/new?template=bug_report.yml">🐛 Report Bug</a> </p>