AI Agent Framework Radar — Q1 2026

Adopt

State machine approach to multi-agent coordination has become the industry standard for complex agent topologies. Production-ready with strong observability tooling.

Document processing and RAG workflows are where LlamaIndex truly shines. Massive ecosystem, production deployments at scale across all industries.

For no-code and low-code automation teams, n8n now rivals Zapier with superior AI integration depth. Best choice when Python expertise is limited.

Trial

Role-based multi-agent collaboration model is intuitive and has strong community adoption. Still maturing in production stability but trajectory is strong.

Microsoft's backing and research pedigree make it worth trialing for enterprise multi-agent scenarios, but production hardening is still needed.

Claude 3's superior instruction-following and longer context makes it a serious production LLM choice, especially for document-heavy workflows.

Assess

Enterprise search and RAG specialist with strong NLP roots. Best fit for teams with heavy document retrieval needs and European data residency requirements.

Managed agent hosting reduces ops burden but creates deep vendor lock-in. Worth assessing for rapid prototyping, evaluate carefully for production.

Microsoft's enterprise-focused orchestration framework. Growing adoption in .NET shops but Python ecosystem lags. Watch for enterprise integrations.

Hold

Without orchestration layer, raw function-calling agents are brittle and hard to debug. Always use an orchestration framework above this layer.

Building your own orchestration framework from scratch is expensive to maintain and isolates you from ecosystem improvements. Use established frameworks.