What programming languages are best for building AI agents?
Top choices
Python dominates because of libraries like LangChain, LlamaIndex, CrewAI, and AutoGen, plus direct SDKs for OpenAI, Anthropic, and others. Most tutorials and examples are in Python.
JavaScript and TypeScript are also widely used, particularly with the Vercel AI SDK and LangChain.js. They're a good fit if your agent needs to run in a browser or integrate tightly with a web app.
Other languages like Go, Rust, and Java have growing AI ecosystems, but they're less common for agent development. They can be useful for performance-critical components or when integrating with existing enterprise systems.
- Python: best overall for AI/ML libraries and community support
- JavaScript/TypeScript: great for web and serverless agents
- Go: good for high-performance, concurrent agents
- Rust: emerging for safe, fast agents but fewer libraries
- Java: used in enterprise settings with frameworks like Spring AI
How to choose
If you're new to AI agents, start with Python. The abundance of examples and pre-built tools will speed up your learning. You can always call Python services from other languages later.
If your agent is part of a web application, JavaScript or TypeScript might be more convenient because you can keep everything in one language. The trade-off is a smaller ecosystem of agent-specific tools.
For production systems, consider the language your team already knows well. The overhead of learning a new language often outweighs minor technical benefits.
Common mistakes
- Assuming you must use Python—other languages work fine, especially for web integration.
- Choosing a language solely because of hype without considering your team's existing skills.
- Overlooking the importance of library support; a language with few AI libraries will slow you down.
