Python still owns most AI agent tutorials. If you search best python libraries for building ai agents 2025 2026, you want a shortlist that survives production, not notebook demos.
Tier 1 for production control
LangGraph
Stateful graphs, checkpoints, human-in-the-loop. The default when reliability matters more than demo speed.
Pydantic AI
Typed tools and structured outputs with less ceremony. Good when your team already uses FastAPI and strict schemas.
Google ADK (Python)
Strong if you are on Gemini and GCP. Useful for multimodal steps (documents, images) in enterprise SA accounts.
Tier 2 for fast multi-agent prototypes
CrewAI
Role-based agents (researcher, writer, verifier). Ship a prototype in days; refactor to LangGraph when workflows harden.
LangChain (core)
Still fine for RAG glue and retrievers. Many teams use LangChain pieces inside LangGraph, not the old AgentExecutor alone.
SA production habits
- Secrets in env, never in prompts
- Supabase RLS for user-scoped memory
- Webhook idempotency for Paystack and Ozow tools
- LangSmith or OpenTelemetry for traces
Cross-stack overview: build AI agents libraries 2026. Go teams: best Go libraries.