Practical guides for shipping AI systems.

Trackly Resources is a structured library of practical guides on AI agents, RAG pipelines, LangChain, cost intelligence, run comparison, and optimizing performance for real Python apps.

Start with What are AI Agents?Building agents? Improve your AI systems

Chapter 1

AI Agents

From zero to building production-grade AI agents.

5 article(s)Explore

Chapter 2

RAG

From basic retrieval to more adaptive RAG workflows.

4 article(s)Explore

Chapter 3

LangChain

Practical LangChain guides for getting from idea to working agent.

3 article(s)Explore

Chapter 4

LLM Costs

Understand provider pricing, token costs, and how to monitor spend.

3 article(s)Explore

Chapter 5

Ship AI Without Guesswork

Practical Trackly workflows for tracking tokens, running model what-if analysis, and tracing real agent flows.

3 article(s)Explore

Start with practical guides, then go deeper.

These are the most useful hands-on reads if you want to move from rough LLM prototypes to observable, cost-aware production workflows.

Ship AI Without Guesswork

4 min read

Track Token Usage Like a Product Team

Instrument a real Python feature with Trackly so you can see prompt tokens, completion tokens, spend, and latency by feature.

Ship AI Without Guesswork

3 min read

Use Playground Before You Switch Models

Track real traffic in Python, then use Trackly Playground to compare your current model against cheaper or faster alternatives before shipping a change.

Ship AI Without Guesswork

3 min read

Trace Agent Runs With Graphs

Use Trackly traces, spans, and graph views to understand where an agent workflow spent time, tokens, and money.