How to build an AI agent: the best tools to use in 2026

Building AI agents involves creating systems that use language models to perform tasks through multiple steps, with the ability to decide actions, call tools, and carry context between operations until reaching a desired outcome. Developers face challenges such as managing the model loop, state, memory, and error handling, but agent frameworks provide components that streamline these processes, allowing teams to concentrate on task logic and data access. The text outlines various frameworks for building AI agents, each offering unique features suited to different needs: LangGraph for complex, state-controlled agents; CrewAI for role-based, multi-agent teams; OpenAI Agents SDK for lightweight, OpenAI-focused applications; Pydantic AI for type-safe Python services; and Mastra for TypeScript-native workflows. These frameworks integrate with Braintrust for testing and monitoring, enabling detailed tracing and evaluation of agent performance. The choice of framework depends on the application's execution pattern, language compatibility, and the level of control required over agent operations.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.