Programming Languages for AI Agents

As AI agents move from experimental prototypes to production systems, a critical question emerges: Are we using the optimal development tools, or do we need to upgrade our foundation before building these digital architectures?

The conventional wisdom among your 10X developer is clear - there’s no universal “best” language for agent development. Python remains the ecosystem leader, benefitting from its rich AI/ML libraries and rapid prototyping capabilities. However, languages like Rust, Go, and Node.js are carving out significant niche for specialized agent workloads where performance, concurrency, or deployment constraints matter most.

In 2024, Python overtook JavaScript as the most popular language on GitHub, while Jupyter Notebooks skyrocketed, both of which underscore the surge in data science and machine learning on GitHub. But the landscape of AI agent development spans multiple programming languages, each with distinct advantage for different use cases.

Here is a quick comparison matrix and scroll down for detailed analysis →

Ideal for rapid prototyping, general-purpose AI agents like cognitive/planning agents, multi-agent orchestration, academic and research prototypes.

1. Huge ecosystem for ML, LLMs (TensorFlow, PyTorch, scikit-learn, NumPy, pandas) and Agent frameworks (LangChain, CrewAI, Autogen, OpenAgents, Phidata)

2. Rapid prototyping, simple syntax and easy integration with APIs and services

3. Tight integration with tools like OpenAI, Hugging Face, Pincecone, Weaviate, etc.

4. Large community and extensive documentation

5. Excellent debugging and testing tools

• Not ideal for high-concurrency or low-latency systems (due to GIL: Global Interpreter Lock)

• Higher memory consumption

• Slower runtime performance than compiled languages

Ideal for agent backends, vector search engines, low-level agent infrastructure, blockchain-based agents and high-performance systems.

• Memory safety and concurrency without garbage collection

• Great for long-running, performance-critical agents (like crawlers, API daemons, or simulation engines)

• Used in agent containers (e.g., ReALM, inference runtimes, embeddings servers)

• Modern language features and growing ecosystem for AI/ML

• Slower development cycle, steep learning curve

• Fewer AI/ML libraries

• Longer compilation time

• Limited talent pool

• Still maturing for AI applications

Ideal for real-time distributed agents, micro services agents, inter-agent communication and task distribution.

• Built-in concurrency (goroutines), simplicity, fast compile times

• Good for building scalable agent-based microservices or distributed agent backbones

• Simple, clean syntax with built-in HTTP server capabilities

• Fast compilation and deployment

• Limited AI ecosystem/ML library ecosystem and smaller community for AI development

• Basic error handling

• Not ideal for mathematical computations

Ideal for Web UIs, Browser-based LLM agents and Plugins.

• Useful for browser-based agents, extensions, or agents embedded in web apps

• Fast event-driven concurrency

• Strong asynchronous programming model

• Fast development cycle

• Native JSON handling

• Easy deployment to cloud platforms

• Growing AI library support

• Limited traditional ML library support

• Single-threaded nature can be limiting

• Less mature AI/ML ecosystem compared to Python

• Not designed for heavy computation

• Memory management challenges

Enterprise agents, large-scale distributed systems, Android-based agents

• Excellent performance and stability

• Strong enterprise ecosystem

• Robust concurrency support

• Platform independence (JVM)

• Extensive testing frameworks

• Strong security features

• Great for microservices architecture

• Verbose syntax slows development

• Limited AI/ML library ecosystem

• Steeper learning curve

• Heavier resource consumption

• Less agile for rapid prototyping

High-performance agents, embedded systems, real-time applications, edge computing

• Maximum performance and efficiency

• Fine-grained memory control

• Excellent for embedded systems

• Direct hardware access

• Mature ecosystem for systems programming

• Predictable performance characteristics

• High development complexity and steep learning curve

• Longer development time

• Memory management challenges

• Limited high-level AI libraries

• Prone to security vulnerabilities

Ideal for scientific modeling agents and high-performance numerical applications

• High performance + simplicity for mathematical modeling

• Strong in scientific computing and differential programming

• Good parallel computing support

• Combines ease of Python with speed of C

• Smaller community and ecosystem

• Limited support for LLMs and agents

• Fewer learning resources

• Still maturing as a language

Good for Data Science applications, Statistical analysis agents and research-focused agents

• Excellent for statistical computing

• Rich ecosystem for data analysis

• Strong visualization capabilities

• Designed for data manipulation

• Great for research and analysis

• Limited general-purpose programming capabilities

• Performance issues with large datasets

• Memory management challenges

• Steep learning curve for non-statisticians

• Limited production deployment options

Ideal for big data agents, Apache Spark applications, functional programming approaches

• Excellent for big data processing

• Functional programming paradigms

• Strong type system

• JVM performance benefits

• Good for distributed computing

• Complex syntax and steep learning curve

• Limited AI-specific libraries

• Smaller community

• Longer compilation time

iOS/macOS agents, TensorFlow Swift applications

• First-class iOS/macOS integration

• Growing ML support with TensorFlow Swift

• Modern language features

• Strong performance

• Apple ecosystem advantage

• Limited to Apple platform

• Small AI/ML ecosystem

• Limited cross-platform support

• Relatively new for AI development

• LLM orchestration and unmatched AI ecosystem: Python

• High-performance agents with memory efficiency: Rust

• Real-time agent networks with simple deployment: Go

• Web-embedded agents and async superpowers: Javascript

• Scientific modeling, Statistical computing agents: Julia/ R

• Battle-tested, Enterprise-grade tooling: Java

In 2025, the best programming language for your agentic workflow will have to balance performance, adaptability and community support. While Python leads with its vast AI libraries and ease of use, Rust and Go offer compelling advantages in speed and concurrency. Ultimately, the best choice depends on your agent’s complexity, deployment needs, and long-term scalability. As agent architectures mature, polyglot systems may become the norm, blending strengths form multiple languages.

There are some exciting emerging languages and startups innovating in the agent AI and systems programming space, such as Mojo, Val, Jai and Modular, Outerbounds, Martian, which I will cover in detail in another blog post.

The views expressed are those of the author and do not necessarily reflect the views of any investment firm or portfolio company.