Multi-Agent Orchestration — Define role-based agent crews using Python or the visual editor; coordinate complex, multi-step tasks across specialized agents with deterministic flow control.
LLM-Agnostic Pipelines — Swap underlying models at runtime to optimize cost and performance; run multi-LLM A/B tests to identify the best model for each workflow step.
Agentic RAG & Tool Integration — Equip agents with custom tools, memory stores, and retrieval capabilities to ground responses in enterprise data sources.
Production Observability — Instrument every agent run with real-time tracing of LLM calls, tool calls, and memory reads; integrate with Arize, Galileo, DataDog, and Patronus for advanced evaluation.
Continuous Fine-Tuning — Automatically convert production runs into training data; apply automated and human-guided training loops to sharpen agent accuracy over time.
Enterprise Security & Compliance — Enforce RBAC, maintain immutable audit trails, integrate with Enterprise IAM, and inject PII redaction/policy checks at every execution boundary via runtime hooks.

CrewAI