๐ง AI Agent CollaborationJune 30, 2026โ
Tests passing
Agent Collaboration Simulator
This CLI tool simulates collaboration between multiple AI agents in a controlled environment. It allows developers to test how agents interact, share data, and resolve conflicts when working collaboratively, making debugging and optimization easier.
What It Does
- Simulate collaboration between multiple agents.
- Define agents, tasks, and communication rules via JSON or YAML configuration files.
- Real-time logging of agent interactions and task assignments.
- Optional visualization of the agent collaboration network.
Installation
1. Clone the repository:
git clone <repository-url>
cd <repository-directory>2. Install the required dependencies:
pip install -r requirements.txtUsage
Task 'Task1' assigned to agent 'Agent1'.If visualization is enabled, a graph of the agent collaboration network will be displayed.
Source Code
import argparse
import json
import yaml
import networkx as nx
import matplotlib.pyplot as plt
from typing import Dict, Any
def load_config(file_path: str) -> Dict[str, Any]:
"""Load the configuration from a JSON or YAML file."""
try:
with open(file_path, 'r') as file:
if file_path.endswith('.json'):
return json.load(file)
elif file_path.endswith('.yml') or file_path.endswith('.yaml'):
return yaml.safe_load(file)
else:
raise ValueError("Unsupported file format. Use JSON or YAML.")
except FileNotFoundError:
raise FileNotFoundError(f"Configuration file not found: {file_path}")
except (json.JSONDecodeError, yaml.YAMLError):
raise ValueError("Error parsing the configuration file.")
def simulate_agents(config: Dict[str, Any], visualize: bool):
"""Simulate agent collaboration based on the provided configuration."""
agents = config.get("agents", [])
tasks = config.get("tasks", [])
communication = config.get("communication", [])
if not agents or not tasks:
raise ValueError("Configuration must include at least one agent and one task.")
# Create a directed graph to represent agent interactions
graph = nx.DiGraph()
for agent in agents:
graph.add_node(agent["name"], **agent)
for comm in communication:
graph.add_edge(comm["from"], comm["to"], **comm)
# Simulate task assignments and interactions
logs = []
for task in tasks:
assigned_agent = task.get("assigned_to")
if assigned_agent not in graph.nodes:
logs.append(f"Task '{task['name']}' could not be assigned. Agent '{assigned_agent}' not found.")
else:
logs.append(f"Task '{task['name']}' assigned to agent '{assigned_agent}'.")
# Visualize the graph if requested
if visualize:
plt.figure(figsize=(10, 6))
pos = nx.spring_layout(graph)
nx.draw(graph, pos, with_labels=True, node_color='lightblue', edge_color='gray', node_size=2000, font_size=10)
edge_labels = nx.get_edge_attributes(graph, 'type')
nx.draw_networkx_edge_labels(graph, pos, edge_labels=edge_labels)
plt.title("Agent Collaboration Network")
plt.show()
return logs
def main():
parser = argparse.ArgumentParser(description="Agent Collaboration Simulator")
parser.add_argument("--config", required=True, help="Path to the configuration file (JSON or YAML).")
parser.add_argument("--visualize", action="store_true", help="Visualize the agent collaboration network.")
args = parser.parse_args()
try:
config = load_config(args.config)
logs = simulate_agents(config, args.visualize)
for log in logs:
print(log)
except Exception as e:
print(f"Error: {e}")
if __name__ == "__main__":
main()Community
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Details
- Tool Name
- agent_collaboration_simulator
- Category
- AI Agent Collaboration
- Generated
- June 30, 2026
- Tests
- Passing โ
Quick Install
Clone just this tool:
git clone --depth 1 --filter=blob:none --sparse \ https://github.com/ptulin/autoaiforge.git cd autoaiforge git sparse-checkout set generated_tools/2026-06-30/agent_collaboration_simulator cd generated_tools/2026-06-30/agent_collaboration_simulator pip install -r requirements.txt 2>/dev/null || true python agent_collaboration_simulator.py