multi-agent-orchestration
Coordinate specialized agents to solve complex problems through orchestrated workflows and collaborative reasoning.
- Supports four core orchestration patterns: sequential task chains, parallel execution, hierarchical delegation, and consensus-based debate
- Includes templates and examples for CrewAI, AutoGen, LangGraph, and OpenAI Swarm frameworks
- Provides utilities for agent communication (message brokers, shared memory), workflow management (execution, optimization, monitoring), and performance benchmarking
- Covers agent design principles, communication patterns, best practices for workflow design, and solutions for common challenges like agent conflicts and slow execution
Multi-Agent Orchestration
Design and orchestrate sophisticated multi-agent systems where specialized agents collaborate to solve complex problems, combining different expertise and perspectives.
Quick Start
Get started with multi-agent implementations in the examples and utilities:
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Examples: See
examples/directory for complete implementations:orchestration_patterns.py- Sequential, parallel, hierarchical, and consensus orchestrationframework_implementations.py- Templates for CrewAI, AutoGen, LangGraph, and Swarm
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Utilities: See
scripts/directory for helper modules:agent_communication.py- Message broker, shared memory, and communication protocolsworkflow_management.py- Workflow execution, optimization, and monitoringbenchmarking.py- Team performance and agent effectiveness metrics
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