Welcome to Agent Party
This guide will help you get started with Agent Party, the enterprise platform for AI agent collaboration. Whether you’re a developer, organization, or AI enthusiast, we’ll walk you through the steps to begin using the platform.
For Developers
1. Clone the Repository
Start by cloning the Agent Party repository:
git clone https://github.com/agent-party/agent-party.git
cd agent-party
2. Install Dependencies
Agent Party uses Poetry for dependency management:
# Install Poetry if you don't have it
curl -sSL https://install.python-poetry.org | python3 -
# Install dependencies
poetry install
3. Set Up the Environment
Create a .env
file with necessary configuration:
cp .env.example .env
# Edit .env with your settings
Key configuration items:
- Database connection strings
- API keys for language models
- Storage credentials
- Kafka connection details
4. Run the Dev Environment
Start the development environment using Docker Compose:
docker-compose up -d
This will start:
- Neo4j database
- Kafka and Zookeeper
- Redis
- MinIO object storage
5. Run the Application
Start the Agent Party application:
poetry run python -m agent_party
The API will be available at http://localhost:8000 and the web UI at http://localhost:8000/ui.
6. Try the Quickstart Example
Explore the quickstart example to see Agent Party in action:
poetry run python examples/quickstart.py
This example demonstrates:
- Agent registration
- Team formation
- Task execution
- Result visualization
For Organizations
If you’re evaluating Agent Party for your organization, follow these steps:
1. Assess Your Requirements
Consider your specific needs:
- What kind of AI collaboration do you need?
- What existing tools need integration?
- What security and compliance requirements do you have?
- What is your expected scale?
2. Explore the Enterprise Evaluation Guide
Download our Enterprise Evaluation Guide for a comprehensive assessment framework, including:
- Technical requirements
- Security checklist
- Integration points
- ROI calculator
- Implementation timeline
3. Schedule a Demo
Contact our team to schedule a personalized demo tailored to your use case.
4. Plan Your Implementation
Review our implementation resources:
5. Explore Success Stories
Learn from organizations already using Agent Party:
Core Components Walkthrough
To understand the platform better, let’s walk through its core components:
Doorman (Trust & Access Control)
The security gateway managing identity, permissions, and trust.
Getting Started with Doorman:
from agent_party.components import Doorman
# Initialize with your authentication settings
doorman = Doorman(
auth_provider="oauth",
auth_config={
"client_id": "your-client-id",
"client_secret": "your-client-secret",
"redirect_uri": "https://your-app.com/callback"
}
)
# Verify and decode tokens
user_info = doorman.authenticate_token(token)
# Check permissions
if doorman.authorize(user_id, "team:create"):
# User is authorized to create teams
...
DJ (Intelligent Orchestration)
The coordination engine that routes requests and manages workflows.
Getting Started with DJ:
from agent_party.components import DJ
# Initialize the DJ component
dj = DJ()
# Get team recommendations for a task
recommended_team = await dj.recommend_team(
task_id="task-123",
required_capabilities=["research", "writing", "data_analysis"],
max_team_size=3
)
# Print recommended team
for agent in recommended_team:
print(f"Agent: {agent['name']}, Role: {agent['role']}")
Bartender (User Interface)
The human-facing layer making AI collaboration intuitive.
Getting Started with Bartender:
from agent_party.components import Bartender
# Initialize the Bartender
bartender = Bartender()
# Create a team for a task
team_id = await bartender.create_team(
task_id="task-123",
team_name="Research Team",
agents=recommended_team
)
# Submit a task to the team
response = await bartender.submit_task(
team_id=team_id,
instructions="Research the impact of AI on healthcare and provide a summary",
format="markdown"
)
# Display the team's response
print(response.result)
Key Workflows
Creating an Agent
from agent_party.agent import AgentFactory
# Create an agent factory
factory = AgentFactory()
# Create a specialized agent
agent_id = await factory.create_agent(
template_id="researcher-template",
name="Healthcare Researcher",
parameters={
"expertise": ["medical_research", "clinical_trials", "healthcare_policy"],
"communication_style": "detailed",
"research_depth": "thorough"
}
)
print(f"Created agent with ID: {agent_id}")
Forming a Team
from agent_party.team import TeamFormation
# Initialize team formation service
team_formation = TeamFormation()
# Form a team based on task requirements
team = await team_formation.form_team(
task_description="Analyze clinical trial data and prepare a research report",
required_capabilities=[
{"name": "data_analysis", "importance": 0.9},
{"name": "medical_knowledge", "importance": 0.8},
{"name": "report_writing", "importance": 0.7}
],
max_team_size=3
)
print(f"Team formed with {len(team.agents)} agents")
Executing a Task
from agent_party.task import TaskExecution
# Initialize task execution service
task_execution = TaskExecution()
# Execute a task with a team
task_result = await task_execution.execute_task(
team_id=team.id,
instructions="Analyze the attached clinical trial data and provide a detailed report on efficacy and safety findings",
attachments=["clinical_trial_data.csv"],
output_format="markdown"
)
print(f"Task completed with status: {task_result.status}")
print(f"Output: {task_result.output}")
Next Steps
After getting started, explore these resources to deepen your understanding:
- Architecture Documentation
- Agent System Reference
- Team Formation Guide
- API Reference
- Contribution Guidelines
Community Resources
Join the Agent Party community:
Support
If you need help: