Added on Nov 08,2026

Deep-Agnt is a hands-on learning platform for becoming an AI engineer by implementing AI systems from scratch, from the team behind Deep ML.
Every problem is one you implement yourself in real Python: the agent loop, the tools, the retrieval and the evals. Most problems are graded against a real model that the platform runs for you, so no API key is needed, while others replay a scripted model so that any failure comes from your own code.
The catalogue has more than 120 problems across sixteen curriculum tracks, covering LLM APIs, search, RAG, tool calling, agents, MCP, evals and production. Some problems give you only the standard library, while others provide PyTorch and a real Hugging Face model.
Challenges go beyond implementation. You may need to debug a loop that fails when a tool raises an error, repair a RAG pipeline that retrieves the wrong documents, or optimise a correct solution that runs over its budget of calls, tokens and cost. Solutions are ranked on measures such as model calls, tokens and documents read.
Key features include multi-step projects, a personal coding agent with your work pushed to GitHub, a discussion area, collections with badges and an Interview Prep track for AI engineering roles.
Pricing: the Free plan includes every problem, the docs, reference solutions, unlimited scripted and local submissions and monthly credits for live grading. Premium is US$18 a month or US$12 a month billed yearly, adding more monthly credits, four levels of AI hints, premium projects, interview mocks and priority execution.
The first problem takes about five minutes and can be tried without an account.
It is suited to software engineers, students and developers who want practical experience building and debugging LLM agents and retrieval systems.
Popular alternatives to Deep-Agnt include SciSummary, TutorAI and Wisdolia. Compare their pricing and what each is best for below.
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