Advanced agent-based economic modeling with heterogeneous AI agents, neural decision-making, and MCP communication protocols.
Cutting-edge capabilities that set AgentsMCP apart from traditional economic models
Advanced 3-layer neural networks with attention mechanisms for intelligent agent decisions
Six distinct agent types with unique behavioral patterns and strategies
Evolving network topology based on agent decision similarity and performance
Model Context Protocol for sophisticated inter-agent communication
Integration with live market data and comprehensive performance analysis
Outperforms traditional DSGE and VAR models across multiple metrics
Experience the power of AgentsMCP with our live interactive demonstration
Watch agent performance and network evolution in real-time
Adjust simulation parameters and see immediate results
Download simulation data and visualizations
AgentsMCP consistently outperforms traditional econometric models
Comprehensive guides and references for AgentsMCP
Quick start guide with installation and basic usage examples
Deep dive into ANNEM architecture and customization options
Understanding dynamic network formation and evolution
Complete API documentation with function signatures
Real-world applications and research examples
Common issues and solutions for AgentsMCP users
Get AgentsMCP up and running in minutes
# Install devtools if needed
if (!require(devtools)) install.packages("devtools")
# Install AgentsMCP
devtools::install_github("avishekb9/AgentsMCP")# Load the package
library(AgentsMCP)
# Run quick analysis
results <- run_annem_analysis(
symbols = c("AAPL", "MSFT"),
n_agents = 100,
n_steps = 50
)
# View results
annem_summary(results)