Do You Really Need Python to Build AI Agents and Test Their Security?

A common piece of advice when starting with AI agents is to learn Python. This is sound guidance, but the full picture is more nuanced. By 2026, you can create a functional agent using TypeScript, visually orchestrate one without extensive coding, and conduct a comprehensive security assessment primarily through YAML files. Thus, is Python an absolute necessity?

The straightforward answer is: not for building, almost for testing. Let's explore why. Part 1: Creating agents An agent operates on a loop: a model receives a goal, decides on an action, calls a tool, receives the result, then decides again. This loop can be coded in any language capable of making HTTP requests and parsing JSON.

Python's strength Python continues to boast the most extensive ecosystem: platforms such as LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, and most vendor SDKs primarily release Python versions first. Local inference tools like transformers, vLLM, and llama-cpp bindings are natively supported in Python. If you aim to fine-tune a compact model or integrate a bespoke embedding pipeline into your agent's memory, Python is the most straightforward path.

Alternatives Python is not strictly required. TypeScript/JavaScript: LangChain.js, Vercel AI SDK, Mastra, and OpenAI Agents SDK for JS all facilitate tool calling, streaming, and multi-step agents. When your agent resides within a web application, utilizing TypeScript sidesteps the need for a secondary runtime. Other languages: Go, Rust, Java, and C# possess official or community SDKs for major model APIs.

Tool calling is executed via structured JSON. Low-code orchestration: tools such as n8n enable you to visually connect a model, tools, and memory. This proves beneficial for prototypes and internal automation. Model Context Protocol (MCP): tools accessed via MCP servers are completely language-agnostic. Your TypeScript agent can call a Python-based tool, and vice versa.

Verdict for building: Python is the standard, not a prerequisite. Choose the language your product is currently built upon. Part 2: Evaluating agent security This is where the situation shifts. Agents pose a larger attack surface compared to conventional chatbots. They can ingest untrusted content, store credentials, and perform actions.

A successful prompt injection against a chatbot results in an undesirable response; against an agent, it can lead to an undesirable action. Assessing the attack surface AI agent security provides a breakdown of attack surfaces. Fundamental attack vectors include direct and indirect injection, tool abuse, data exfiltration, goal hijacking across numerous turns, and retrieval poisoning.

Tools for testing garak (Python) is a scanner designed to probe models for jailbreaks, injection, leakage, and more. PyRIT (Python) is Microsoft's framework for orchestrating automated, multi-turn attacks. DeepTeam (Python) focuses on red teaming, centered around LLM vulnerability categories. promptfoo (Node.js) offers configuration-driven evaluations and red teaming, predominantly written in YAML.

Meaningful assessments can be conducted without Python expertise. Yet, venturing beyond pre-packaged probes, Python becomes difficult to evade. Why red teaming pulls you toward Python Real AI red teaming is an iterative adversarial process: generate an attack, send it, evaluate the response, mutate, and repeat. Increasingly, one model is attacking another.

Constructing this loop, crafting custom scorers, reproducing multi-turn scenarios, and analyzing results en masse using Python libraries like pandas and notebooks leverages Python's ecosystem effectively. A practical reason: most published research, proof-of-concept attacks, and datasets in adversarial ML are presented as Python code.

If you cannot comprehend it, you cannot replicate it. Verdict for security: scanning without Python is possible. However, conducting real red teaming without it is impractical. Pentesting vs red teaming: which approach is necessary? A traditional pentest scrutinizes the application surrounding the agent: authentication, APIs, permissions, secret management.

This task is language-agnostic, employing standard Application Security (AppSec) tools. Red teaming targets the model's behavior itself. Most agent deployments necessitate both approaches. A breakdown of pentesting versus red teaming for AI systems offers further clarity. A pragmatic approach You are a web developer: construct your agent using TypeScript.

Initiate security testing with promptfoo. Acquire sufficient Python knowledge to run and read garak or PyRIT. If you are a security professional: your existing pentesting skills cover the application layer. To address agent-specific challenges, incorporate Python for automating attack loops and scoring. If you are starting from scratch: learn Python.

It represents the quickest route to both building and breaking AI agents. Remember the frameworks Tools uncover vulnerabilities; frameworks outline what to test and how to report. OWASP's Top 10 for LLM Applications, MITRE ATLAS, NIST AI RMF, and the EU AI Act all influence the nature of a credible assessment. An overview of AI security frameworks aids in defining the scope of an AI audit.

Finally, ensure testing occurs within a documented mandate: refer to ethical AI hacking. Conclusion Building AI agents does not require Python; any language capable of handling JSON is sufficient. Conversely, securing agents is an experimental, adversarial discipline, and this discipline thrives within Python. Construct your agents using your preferred language. To break them, learn Python.