CrewAI

CrewAI is a framework for building collaborative multi-agent AI systems where specialized agents work together as a crew to accomplish complex tasks through role-based collaboration.

Why we're trialing CrewAI:

• Role-Based Agent Design: Define agents with specific roles, goals, and backstories
• Collaborative Workflows: Agents can delegate tasks and share context naturally
• Built-in Orchestration: Sequential and hierarchical task execution patterns
• LangChain Integration: Leverages existing LangChain tools and integrations
• Human-in-the-Loop: Support for human oversight and intervention

Core concepts:

• Agents: Specialized AI entities with defined roles and capabilities
• Tasks: Specific objectives with clear success criteria and context
• Crews: Collections of agents working together on related tasks
• Processes: Orchestration patterns (Sequential, Hierarchical, Consensual)
• Tools: Shared utilities that agents can use to accomplish tasks

Key advantages:

• Specialization: Each agent can focus on specific domain expertise
• Natural Delegation: Agents automatically route tasks to appropriate team members
• Context Sharing: Shared memory and knowledge between crew members
• Scalable Collaboration: Add new agent roles without rewriting existing logic
• Quality Control: Agents can review and improve each other's work

Integration with our platform:

• Deploy crews as containerized services on Kubernetes
• Use our LiteLLM proxy for centralized model access
• Leverage existing monitoring and logging infrastructure
• Connect to business databases and APIs through standard tools

Evaluation criteria:

• Task Complexity: Compare single-agent vs. multi-agent approaches
• Coordination Overhead: Measure communication costs between agents
• Result Quality: Assess collaborative output vs. individual agent work
• Resource Utilization: Monitor compute and token usage patterns

Use cases for evaluation:

• Document Analysis Pipeline: Specialized agents for extraction, summarization, validation
• Customer Support Workflow: Triage, research, response, and escalation agents
• Code Review Process: Analysis, testing, documentation, and approval agents

Best practices:

• Define clear agent roles with non-overlapping responsibilities
• Implement task validation and quality checks
• Use hierarchical structures for complex decision-making
• Monitor agent interactions for optimization opportunities