Skip to content

Building Agents

Best LLMs for Building AI Agents in 2026

Search for the best LLM for AI agents and you will drown in leaderboards that rank models on trivia, essays, and math word problems — none of which tell you whether a model can reliably call your tools without hallucinating an argument. The honest answer is that there is no single "best" model; there is only the best model for your workload, measured on your tasks. This guide walks through what actually matters when a model has to drive an agent, and shows you the practical trick that lets you A/B two models on your own tasks by swapping a single string — no rewrite required.

Build Your First AI Agent with Promptise in 10 Minutes

You can build an AI agent with Promptise in about ten minutes, starting from an empty file and ending with a tool-using agent you can chat with from your terminal. No orchestration boilerplate, no schema wiring, no abstractions to learn before you write the first useful line. This Promptise tutorial walks through every step — install, one API key, a first agent, real tools, and launching it from the CLI — and everything here is copy-pasteable. By the end you'll have a running agent and know exactly which parameter to flip when you're ready for production.

Connect Your AI Agent to MCP Tools with Promptise

To connect an AI agent to MCP tools in production, you need two things: reliable auto-discovery so you never hand-wire schemas, and a way to stop a large tool catalog from bloating every prompt. Most tutorials give you the first and quietly skip the second — then your token bill climbs as you add servers. This post shows the production path with Promptise: point the agent at your MCP servers, let it discover every tool, and flip tool optimization to SEMANTIC so local embeddings select only the tools each query actually needs. By the end you'll have a working config, the air-gapped local-embeddings variant, and a clear sense of when you don't need any of it.

How to Build an AI Agent in Python: The Complete Guide

If you have searched for how to build an AI agent in Python, you have probably found tutorials that hand-wire tool schemas and hard-code a single model. They work until the first thing changes — you swap gpt-4o-mini for a local Llama, add a second tool, or move from a notebook to a service — and then you are rewriting glue code instead of building features. This guide takes a different route. By the end you will have a real LLM agent in Python, understand how it discovers and calls tools automatically, and know which parts of the stack you should build yourself versus let a framework handle.

Model-Agnostic AI Agents: Claude, GPT, Ollama & Local

Model-agnostic AI agents let you change the model behind your agent without touching the rest of your code. In Promptise Foundry the model is just a string you pass to build_agent(), so the same agent — same tools, same memory, same guardrails — can run on openai:gpt-5-mini today, anthropic:claude-sonnet-4.5 tomorrow, and a local ollama:llama3 in an air-gapped deployment, with no rewrite in between. By the end of this post you'll know exactly how that swap works, how to run entirely offline, and when pinning to one provider is actually the smarter choice.

Python AI Agent Frameworks Compared: An Honest Guide

Any honest python ai agent framework comparison has to start with a concession: there is no single winner. The right choice depends on whether you're prototyping a demo, building a RAG search product, or shipping multi-tenant agents to production. This guide compares LangChain, LlamaIndex, CrewAI, Pydantic AI, and Promptise Foundry without strawmen — it says plainly where each tool is the better fit, then pinpoints the one design decision that separates them: how tools reach your agent. By the end you'll know which framework matches your stack and why an MCP-first approach changes the calculus.

What Is a Python AI Agent Framework? (And When You Need One)

A Python AI agent framework is a library that handles the plumbing between a large language model and the real world — tool discovery, memory, conversation persistence, guardrails, and observability — so you don't rebuild it for every project. The term gets thrown around loosely, and plenty of "frameworks" are really just thin wrappers around a chat completion call. This post cuts through that: it defines exactly what a framework adds over a hand-rolled loop, shows the moving parts concretely, and is honest about the times a plain script is the better choice. By the end you'll be able to look at your own project and decide whether you actually need one.

Tool Calling in Python: Connect an LLM to Tools

Tool calling in Python is the mechanism that turns a language model from a text generator into something that can actually do things — look up an order, query a database, call an internal API. The concept is simple, but the standard tutorials bury you in JSON Schema boilerplate: for every function you want to expose, you hand-write a schema describing its name, parameters, and types, and keep that schema in sync with the code forever. By the end of this post you'll understand exactly how tool calling works under the hood, and you'll see the shortcut most guides skip — pointing your agent at an MCP server so tools appear auto-discovered, with schemas derived straight from your Python type hints.