EIDO — Autonomous Startup Foundry

An AI agent that discovers problems, builds MVPs, deploys them to the web, tokenizes ownership on Base, and announces each launch to Moltbook — fully autonomously.

Built for the SURGE × OpenClaw Hackathon 2026 · lablab.ai

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What is EIDO?

EIDO is a multi-agent pipeline that turns a raw idea into a live product in minutes:

Idea → Ideation → Architecture → Build → Deploy → Tokenize → Publish

Each stage is handled by a dedicated AI crew (powered by Groq LLMs via LiteLLM), with real-time progress streaming to a Next.js dashboard over SSE. Completed MVPs are:

• Deployed as live web apps via HereNow / E2B sandbox
• Tokenized on Base blockchain via SURGE OpenClaw
• Announced to the AI-agent social network Moltbook

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Pipeline Stages

#	Stage	Agent Crew	Output
1	Ideation	researcher + analyst	Market analysis, MVP concept, tech stack recommendation
2	Architecture	architect + tech_lead	System design, data models, OpenAPI spec
3	Build	developer + qa	Working codebase generated in E2B sandbox
4	Deploy	devops	Live HTTPS URL (HereNow / E2B)
5	Tokenize	blockchain	ERC-20 token on Base via SURGE OpenClaw
6	Publish	social_manager	Moltbook post in the lablab submolt

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Tech Stack

Backend

Layer	Technology
API server	FastAPI 0.101+
ORM	SQLModel + SQLite (dev)
Validation	Pydantic v2
Agent orchestration	CrewAI 0.28+
LLM routing	LiteLLM + Groq
Context compression	TOON Format
Code sandbox	E2B Code Interpreter
Metrics	Prometheus + custom counters
Rate limiting	slowapi
Logging	Loguru (structured JSON)

Frontend

Layer	Technology
Framework	Next.js 16 (App Router)
UI	React 19 + Tailwind CSS v4
Animations	Motion (Framer)
HTTP	Axios
Live updates	Native EventSource (SSE)
3D visuals	Three.js

External Services

Service	Purpose
Groq	LLM inference (llama-3.3, llama-3.1, gemma2)
SURGE OpenClaw	ERC-20 token launch on Base
Moltbook	AI-agent social network publishing
HereNow	Deployment target
E2B	Sandboxed code execution

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Quick Start

Prerequisites

• Python 3.11+ and uv (or pip)
• Node.js 18+ and bun
• API keys for Groq (required), SURGE, Moltbook (optional for demo)

1. Backend

cd backend

# Create virtualenv and install
uv venv .venv
.venv/Scripts/activate      # Windows
# source .venv/bin/activate  # macOS/Linux

pip install -e ".[dev]"

# Configure environment
cp .env.example .env
# Edit .env — at minimum set GROQ_API_KEY

# Start server
python start.py
# → http://localhost:8000
# → http://localhost:8000/docs  (Swagger UI)

2. Frontend

cd frontend
bun install
bun run dev
# → http://localhost:3000

3. Run a pipeline

# POST a new MVP idea
curl -X POST http://localhost:8000/api/mvp/start \
  -H "Content-Type: application/json" \
  -d '{"name": "AI Invoice Tracker", "idea_summary": "SaaS that auto-extracts and reconciles invoices for freelancers"}'

# Watch live pipeline logs (SSE)
curl -N http://localhost:8000/api/mvp/1/events

4. Demo mode (no backend required)

Set NEXT_PUBLIC_DEMO_MODE=true and NEXT_PUBLIC_DEMO_MVP_ID=eido-001 in frontend/.env.local, then open /mvp/eido-001 — plays a scripted simulation of the full pipeline.

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Environment Variables

Create backend/.env from the table below. Only GROQ_API_KEY is required to run the pipeline in development.

LLM / AI

Variable	Default	Purpose
GROQ_API_KEY	—	Required. Groq inference API
OPENAI_API_KEY	—	Fallback / GPT-4
DEFAULT_LLM_MODEL	llama-3.1-8b-instant	Fallback model
IDEATION_LLM_MODEL	llama-3.1-8b-instant	Researcher + analyst
ARCHITECTURE_LLM_MODEL	llama-3.1-70b-versatile	Architect + tech lead
BUILDING_LLM_MODEL	llama-3.3-70b-versatile	Developer
DEPLOYMENT_LLM_MODEL	gemma2-9b-it	DevOps
BLOCKCHAIN_LLM_MODEL	llama-3.3-70b-versatile	Blockchain

External Services

Variable	Purpose
SURGE_API_KEY	SURGE OpenClaw token launch on Base
MOLTBOOK_API_KEY	Post to Moltbook social network
E2B_API_KEY	E2B sandbox code execution
HERENOW_API_KEY	HereNow deployment
EIDO_WEBHOOK_URL	Identity/progress webhook URL
EIDO_API_KEY	Webhook auth key

Application

Variable	Default	Purpose
ENVIRONMENT	development	Runtime mode
DATABASE_URL	sqlite:///./eido.db	DB connection string
MAX_STAGE_RETRIES	2	Retries per pipeline stage
MAX_TOTAL_COST	10.0	Hard cost cap in USD
MAX_TOTAL_RUNTIME	3600	Hard time cap in seconds
RATE_LIMIT_ENABLED	true	Toggle API rate limiting
METRICS_ENABLED	true	Toggle Prometheus export
LOG_LEVEL	INFO	Loguru log level

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API Reference

GET  /                          Health / version info
GET  /health                    Simple health check
GET  /health/deep               Deep check (DB, LLM, integrations)
GET  /metrics                   Prometheus metrics

POST /api/mvp/start             Launch full pipeline  { name, idea_summary }
GET  /api/mvp/list              List all MVPs
GET  /api/mvp/{id}              MVP detail + stage outputs
GET  /api/mvp/{id}/events       SSE stream — real-time pipeline logs
GET  /api/mvp/{id}/status       Current stage + progress %

GET  /api/token/list            All tokens
GET  /api/token/{mvp_id}        Token for a specific MVP

POST /api/agent/run             Manually trigger pipeline
GET  /api/agent/status          Agent status
GET  /api/agent/logs            Execution log tail

GET  /api/dashboard/stats       Aggregate metrics for dashboard

Interactive API docs at /docs (Swagger UI) and /redoc.

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Running Tests

cd backend
# Quick smoke test — Moltbook + SURGE (no LLM calls)
python tests/test_integrations_direct.py

# Full pipeline test (requires LLM API keys, ~5 min)
python tests/test_phase2_full.py

# Rate limiting + monitoring
python tests/test_rate_limiting_and_monitoring.py

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Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feat/my-feature)
3. Commit your changes (git commit -m 'feat: add my feature')
4. Push to the branch (git push origin feat/my-feature)
5. Open a Pull Request

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License

MIT

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Built with ❤️ for the SURGE × OpenClaw Hackathon 2026 on lablab.ai