> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/gnosis/prediction-market-agent/llms.txt
> Use this file to discover all available pages before exploring further.
# Agent Gallery Overview
> Explore 60+ specialized prediction market agents with different strategies and capabilities
The Gnosis Prediction Market Agent platform includes over 60 specialized agents, each designed with unique trading strategies, models, and capabilities. These agents range from simple random traders to sophisticated AI-powered prediction systems.
## Agent Categories
Agents are organized into the following categories:
Basic agents including coinflip and known outcome predictors
Advanced agents with web research and analysis capabilities
LLM-powered agents using GPT-4, Claude, Gemini, and more
Self-learning agents with modifiable prompts and goal management
Purpose-built agents for arbitrage, social media, and market monitoring
## Running Agents
All agents in the gallery can be run using the `run_agent.py` entrypoint:
```bash theme={null}
python prediction_market_agent/run_agent.py
```
### Example
```bash theme={null}
# Run the GPT-4o Prophet agent on Omen markets
python prediction_market_agent/run_agent.py prophet_gpt4o omen
# Run the coinflip agent on Manifold markets
python prediction_market_agent/run_agent.py coinflip manifold
```
## Agent Architecture
All agents inherit from the `DeployableAgent` base class from the [prediction-market-agent-tooling](https://github.com/gnosis/prediction-market-agent-tooling) library. This provides:
* Market fetching and filtering
* Trade execution and position management
* Betting strategy configuration
* Monitoring and logging
* Deployment utilities for GKE
### Key Agent Methods
Agents implement these core methods:
```python theme={null}
class DeployableAgent:
def load(self) -> None:
"""Initialize agent resources and models"""
def answer_binary_market(self, market: AgentMarket) -> ProbabilisticAnswer | None:
"""Generate prediction for binary markets"""
def verify_market(self, market_type: MarketType, market: AgentMarket) -> bool:
"""Filter markets to trade on"""
def get_betting_strategy(self, market: AgentMarket) -> BettingStrategy:
"""Configure bet sizing and risk management"""
```
## Performance Tracking
Agent performance can be tracked using:
* **Langfuse**: All agents support tracing via `enable_langfuse=True`
* **Database Storage**: Predictions and trades are stored in PostgreSQL
* **Metrics**: PnL, accuracy, and bet statistics available via the API
## Adding New Agents
To add a new agent to the platform:
1. Create your agent class inheriting from `DeployableAgent`
2. Add it to the `RunnableAgent` enum in `run_agent.py`
3. Register it in the `RUNNABLE_AGENTS` dictionary
4. Deploy to GKE or run locally
```python theme={null}
from prediction_market_agent_tooling.deploy.agent import DeployableTraderAgent
class MyCustomAgent(DeployableTraderAgent):
def answer_binary_market(self, market: AgentMarket) -> ProbabilisticAnswer | None:
# Your prediction logic here
return ProbabilisticAnswer(
p_yes=Probability(0.7),
confidence=0.8,
reasoning="My analysis shows..."
)
```
## Next Steps
Start with basic agents
Explore LLM-powered predictions