agentrylab 0.2.0

Lightweight, hackable multi-agent orchestration lab (CLI + Python) with transcripts, checkpoints, budgets, and pluggable providers/tools.

🧪 AgentryLab

Multi-agent orchestration made simple. Drop in agents, watch the magic happen.

🚀 Quick Start

pip install agentrylab

# Comedy gold
agentrylab run standup_club.yaml --objective "remote work" --max-iters 4

# Real debates with evidence  
agentrylab run debates.yaml --objective "Should we colonize Mars?" --max-iters 4

# Facebook Marketplace deals finder
agentrylab run marketplace_deals.yaml --objective "MacBook Pro deals"

🎭 What You Get

5 killer presets that actually work:

Preset	What It Does	Cool Factor
🎤 Stand-Up Club	Two comedians + MC	Comedy gold, pure entertainment
🏛️ Debates	Pro/con + evidence search	Real web research, actual citations
🔬 Research	Scientists collaborate	Academic rigor meets AI
🤖 Research Assistant	Interactive research chat	Human-in-the-loop web research
🛒 Marketplace Deals	Facebook Marketplace finder	Real listings, real URLs, real deals

🧠 Core Concepts

• Agents: Roles that speak (comedian, scientist, debater — no limits!)
• Tools: Real integrations (DuckDuckGo search, Facebook Marketplace, Wolfram Alpha)
• Providers: LLM backends (OpenAI, Ollama)
• Schedulers: Who talks when (round-robin, every-N)

🛠️ Installation & Setup

# Install
pip install agentrylab

# Optional: Local models with Ollama
curl -fsSL https://ollama.ai/install.sh | sh
ollama pull llama3

# Optional: API keys in .env
echo "OPENAI_API_KEY=sk-..." >> .env
echo "APIFY_API_TOKEN=apify_..." >> .env

🎯 Examples

Comedy Club

agentrylab run standup_club.yaml --objective "AI taking over the world" --max-iters 6

Real Research

agentrylab run research.yaml --objective "quantum biology breakthroughs"

Interactive Research

# Start conversation
agentrylab run research_assistant.yaml --objective "latest AI developments"

# Jump in anytime
agentrylab say research_assistant.yaml demo "What about quantum computing?"
agentrylab run research_assistant.yaml --thread-id demo --resume --max-iters 1

Marketplace Deals

agentrylab run marketplace_deals.yaml --objective "iPhone 15 Pro deals in NYC"

# With structured inputs (user_inputs) non-interactively
agentrylab run marketplace_deals.yaml \
  --params '{"query":"MacBook Pro 14 M3","location":"Tel Aviv","min_price":5000,"max_price":12000}'

Telegram-style parameter collection (concept)

• Presets may declare a user_inputs section. If required inputs are missing when starting a conversation via the Telegram adapter, the conversation enters COLLECTING status until inputs are provided.
• Adapter helpers:
  • provide_user_param(conversation_id, key, value): supply a single input; returns remaining keys
  • finalize_params_and_start(conversation_id): substitute values, initialize the lab, transition to ACTIVE

This enables progressive, chat-like forms for complex scenarios (e.g., location, radius, min/max price) while still supporting one-shot runs with --params.

🐍 Python API

from agentrylab import init

# Start a comedy show
lab = init("standup_club.yaml", experiment_id="comedy-night")
lab.run(rounds=6)

# Check out the show
for msg in lab.state.history:
    print(f"[{msg['role']}]: {msg['content']}")

🔧 Advanced Features

• Real-time streaming: Watch agents work live
• Resume anywhere: Pick up where you left off
• Tool budgets: Prevent runaway API costs
• Human-in-the-loop: Jump into conversations anytime
• Persistence: Everything saved to SQLite + JSONL

📚 Documentation

• CLI Reference - All commands
• Configuration - YAML preset format
• Architecture - How it works
• Persistence - Data storage format

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for details.

Quick wins:

• New presets (comedy, debates, research, etc.)
• New tools (APIs, databases, etc.)
• New providers (Claude, Gemini, etc.)

📄 License

MIT - Go build something amazing!

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Made with ❤️ for the AI community. Because single agents are boring. 🤖