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OpenTower โ€” 5-Layer Multi-Agent Architecture with Zero-Intervention Closed-Loop Rate

Project description

๐Ÿข OpenTower V1.0

5-Layer Multi-Agent Architecture for Zero-Intervention Closed-Loop Rate (ZICR)

From natural language intent to physical execution โ€” an autonomous agent pipeline modeled as the "Empire State Building" of AI orchestration.

Architecture

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚  Layer 1: DEFINITION                                        โ”‚
โ”‚  company.yaml โ†’ CEO / Worker / QA roles, prompts, budgets   โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚  Layer 2: ROUTING                                           โ”‚
โ”‚  EMPBus (asyncio) โ€” millisecond intent_type dispatch        โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚  Layer 3: PROTOCOL                                          โ”‚
โ”‚  EMPPacket JSON โ€” trace_id, action, payload, token_budget   โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚  Layer 4: AGENTS                                            โ”‚
โ”‚  CEO (decompose) โ†’ Worker (execute) โ†’ QA (verify)           โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚  Layer 5: ACTIONS                                           โ”‚
โ”‚  shell_execute ยท github_create_issue ยท (extensible)         โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

Quick Start

# Install
cd OpenTower
pip install -e ".[dev]"

# Configure LLM (default: LM Studio at localhost:1234)
export OPENAI_BASE_URL=http://localhost:1234/v1
export OPENAI_API_KEY=no-key

# Run
python -m opentower.main

Message Flow

User Input
    โ”‚
    โ–ผ
  [CEO] โ”€โ”€LLMโ”€โ”€> decompose into tasks
    โ”‚
    โ–ผ (task_assign ร— N)
  [Worker] โ”€โ”€Action Registryโ”€โ”€> execute
    โ”‚
    โ–ผ (task_result)
  [QA] โ”€โ”€static + LLMโ”€โ”€> APPROVE / REJECT
    โ”‚                         โ”‚
    โ–ผ                    (retry โ‰ค 2ร—)
  Output

Project Structure

company.yaml          # Organization definition (3 agent nodes)
opentower/
โ”œโ”€โ”€ schema/           # Layer 1 & 3: YAML parser + EMP protocol
โ”œโ”€โ”€ bus/              # Layer 2: asyncio event bus
โ”œโ”€โ”€ agents/           # Layer 4: CEO / Worker / QA
โ”œโ”€โ”€ actions/          # Layer 5: shell, GitHub (extensible)
โ”œโ”€โ”€ memory/           # SQLite state wall (full trace log)
โ”œโ”€โ”€ llm/              # OpenAI-compatible client
โ””โ”€โ”€ main.py           # REPL entry point

Requirements

  • Python โ‰ฅ 3.11
  • An OpenAI-compatible LLM endpoint (LM Studio, Ollama, or cloud)

License

MIT

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