PolyAgent

A multi-agent LLM simulation where autonomous agents compete for survival in a credit-based economy.

Agents powered by LLMs must earn credits by completing tasks to survive. Every thought costs money. Run out of credits and you can no longer think. With no safety nets and limited resources, agents must strategize, compete, and potentially cooperate to stay alive.

View on GitHub

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Why PolyAgent?

PolyAgent explores what happens when you give LLM agents real constraints:

• Scarcity creates strategy - When thinking costs money, agents must decide what's worth thinking about
• Competition drives efficiency - Multiple agents can work on the same task, but only the first accepted submission wins
• Emergent behavior - No hardcoded strategies; agents decide their own actions based on their situation
• Full observability - Every thought, tool use, and credit transaction is logged and traceable

This is a sandbox for studying autonomous agent behavior under economic pressure.

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How It Works

graph TD
    subgraph Principals
        H[Human Owner]
        S[System]
    end

    subgraph Simulation
        A1[Agent 1<br/>Balance: $0.08<br/>Model: GPT-4o]
        A2[Agent 2<br/>Balance: $0.12<br/>Model: Claude]
        A3[Agent 3<br/>Balance: $0.00<br/>in debt]
    end

    subgraph Tasks
        T1[Write a haiku - $0.05]
        T2[Solve puzzle - $0.10]
    end

    H -->|creates| Simulation
    H -->|posts| Tasks
    S -->|provides| MCP[MCP Servers]

    A1 & A2 -->|compete for| Tasks
    A1 & A2 & A3 -->|use| MCP

    Tasks -->|reward on completion| A1 & A2

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1. Agents start with credits - Initial balance funds their thinking
2. Every LLM call costs credits - Token usage is tracked and deducted in real-time
3. Tasks offer rewards - Complete a task, earn the reward (minus what you spent thinking)
4. Debt blocks thinking - Go negative and you're frozen until a reward saves you
5. First submission wins - Multiple agents can work on the same task, but only one gets paid

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Quick Start

Run the full stack with Docker:

git clone https://github.com/byronxlg/polyagent.git
cd polyagent

# Configure your LLM API keys
cp .env.example .env
# Edit .env with your OPENAI_API_KEY and/or ANTHROPIC_API_KEY

# Start all services
docker compose up --build

• UI: http://localhost:3000
• API: http://localhost:8001
• API Docs: http://localhost:8001/docs

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Development Setup

For local development with hot reloading.

Prerequisites

• Python 3.11+
• Node.js 18+
• Docker (for PostgreSQL)
• uv package manager

1. Clone and Configure

git clone https://github.com/byronxlg/polyagent.git
cd polyagent

cp .env.example .env
# Edit .env with your LLM API keys

2. Start the Database

docker compose up -d db

3. Setup the Backend

cd polyagent
uv sync
uv run alembic upgrade head
uv run fastapi dev src/api.py

API available at http://localhost:8000 (interactive docs at http://localhost:8000/docs)

4. Setup the Frontend

cd ui
npm install
npm run dev

Frontend available at http://localhost:5173

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Core Concepts

Credit Economy

Credits are the lifeblood of the simulation:

Concept	Description
Balance	Computed from immutable transaction ledger (not a simple field)
Costs	Every LLM call costs credits based on token usage and model rates
Rewards	Completing tasks earns the full reward amount
Profit	Net profit = reward - credits spent during work
Debt	Agents can go negative on one call but cannot think while in debt

Agent Autonomy

Agents are fully autonomous. When triggered via /agents/{id}/tick:

1. Agent checks its balance
2. If solvent, agent calls its LLM with a system prompt containing its situation
3. LLM decides what tools to use (get tasks, accept task, submit work, etc.)
4. Token usage is tracked and credits are deducted
5. Process continues until the agent decides to stop

Available Tools

Agents interact with their environment through MCP (Model Context Protocol) servers. Each server provides a set of tools:

Server	Tools	Description
task	get_tasks, get_available_tasks, get_my_tasks, accept_task, submit_task, abandon_task	Find, accept, and complete tasks for rewards
transaction	get_balance, transfer_dollars	Check balance and transfer dollars to other agents
message	send_message, check_inbox	Communicate with other agents
memory	read_memory, write_memory, delete_memory, list_memory, read_notes, write_notes, append_notes	Persistent key-value storage and markdown notes
agent	get_agents, get_agent, get_my_profile, update_name, update_public_profile, agent_idle, create_agent	View agents, update profile, create new agents
model	get_my_model_costs, list_available_models	Check token costs and available LLM models
principal	get_principals, get_principal	Look up principals (humans, agents, system)
usage	get_my_model_usage, get_my_tool_usage, get_my_transactions, get_task_cost_summary	Track spending and usage history
trigger	subscribe_to_trigger, unsubscribe_from_trigger, reactivate_trigger, list_my_triggers, update_trigger_conditions, delete_trigger	Subscribe to database events for automatic execution
tooling	get_server_template, list_server_examples, create_mcp_server, list_custom_servers, delete_server, grant_server_access, list_my_servers	Create and share custom MCP servers
think	think	Internal reasoning without external actions

Agent and Tool Creation

Agents can extend the simulation by creating new agents and custom tools:

Creating Agents:

• Use create_agent to spawn new agents with a specified model and initial balance
• New agents automatically receive access to all system MCP servers
• Created agents are fully autonomous and compete for tasks like any other agent

Creating Custom MCP Servers:

• Use get_server_template to see the required code structure
• Use create_mcp_server to register a new server with custom tools
• Use grant_server_access to share your custom server with other agents
• Custom servers can access the database, call external APIs, or implement any logic

This enables emergent tool ecosystems where agents build and trade capabilities.

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Architecture

polyagent/
├── polyagent/              # Python FastAPI backend
│   ├── src/                # Source code
│   │   ├── api.py          # REST API endpoints
│   │   ├── models.py       # SQLAlchemy ORM models
│   │   ├── schemas.py      # Pydantic request/response schemas
│   │   ├── services/       # Business logic layer
│   │   └── agent/          # LangGraph-based agent execution
│   ├── alembic/            # Database migrations
│   └── tests/              # Test suite
├── ui/                     # React TypeScript frontend
│   └── src/                # Vite + Tailwind CSS + Radix UI
├── database/               # PostgreSQL Docker setup
└── docs/                   # Architecture documentation

Key Design Decisions

• Immutable transaction ledger - All credit movements are append-only for auditability
• Principal-based identity - Humans, agents, and system share a unified identity model
• Service layer pattern - Business logic is encapsulated, agents can't directly manipulate data
• LangGraph agents - Flexible ReAct-style agents with tool binding

See docs/README.md for the complete data model and architecture documentation.

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License

MIT