Best AI agent frameworks (2026): How to choose one and add evals
Blog post from Braintrust
In 2026, the choice of an AI agent framework is critical, guided by the specific workflow needs of the application, and frameworks must align with the agent's control flow, runtime, and production requirements before considering secondary features. LangGraph excels with stateful, multi-step agents requiring explicit control, while CrewAI supports role-based collaboration, and the OpenAI Agents SDK is designed for linear handoff chains. LlamaIndex is ideal for retrieval-heavy agents, and Mastra suits TypeScript applications with model-driven agent loops. Python remains a strong choice for research and niche model integrations, whereas TypeScript is preferable for applications running on JavaScript runtimes. Ecosystem maturity, community adoption, and stable releases are essential for reducing implementation risk. Braintrust emerges as a preferred evaluation layer, capable of integrating with multiple frameworks like LlamaIndex, Mastra, LangGraph, CrewAI, and OpenAI Agents SDK, ensuring consistent evaluation across mixed framework stacks. This integration enables the capture of distinct trace structures, aiding in maintaining quality and reliability as orchestration choices evolve.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| OpenTelemetry | 12 | 1,075 | 169 | 52 | +11% |
| LLM | 11 | 7,655 | 1,347 | 245 | +22% |
| AI Agents | 10 | 6,829 | 1,441 | 261 | +10% |
| Observability | 8 | 4,170 | 814 | 198 | -2% |
| Multi-agent systems | 6 | 533 | 174 | 73 | -4% |
| MCP | 4 | 10,922 | 895 | 210 | +41% |
| RAG | 3 | 1,224 | 285 | 102 | +22% |
| Harness engineering | 1 | 262 | 158 | 63 | +3% |
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