Tools · February 14, 2025 · 8 min read

AI Agent Tools: A No-BS Beginner's Guide (2025)

Everyone's selling AI agent tools. Most of them are demos dressed up as products. Here's what we actually use in production — and what's not worth your time.

We run AI agents that handle real work: monitoring dashboards, writing content, managing projects, even deploying code. Not as experiments. As employees. This guide is based on that experience.

The Tool Categories You Actually Need

Forget the 47-category taxonomy charts. An AI agent needs exactly four things:

A brain — the LLM (Claude, GPT, Gemini)
A body — the runtime that lets it act (shell, browser, APIs)
A memory — persistent context across sessions
A communication channel — how it talks to you

That's it. Everything else is nice-to-have.

LLMs: The Brain

Claude (Anthropic)

Best for: agentic work, following complex instructions, coding. The Sonnet/Opus models handle multi-step tasks without losing the plot. Tool use is native and reliable.

🏆 Our pick for agent work. Claude Opus for complex reasoning, Sonnet for daily tasks.

GPT-4o / o1 (OpenAI)

Best for: broad general knowledge, vision tasks, creative writing. Tool use works but sometimes hallucinates tool calls or gets stuck in loops.

Good backup. Better at some creative tasks.

Gemini 2.0 (Google)

Best for: multimodal tasks (video, images), long context windows (1M tokens). Still catching up on pure agent reliability but improving fast.

Promising. Great for research and analysis. Less reliable for autonomous execution.

Runtimes: The Body

This is where most people go wrong. They pick a fancy "AI agent framework" when what they actually need is a solid runtime.

OpenClaw

Gateway that runs AI agents as always-on processes with shell access, browser control, cron jobs, and messaging integrations (Telegram, WhatsApp, Discord). Think of it as giving an LLM a laptop and a phone.

🏆 Our daily driver. It's what this entire operation runs on.

Claude Code (Anthropic CLI)

Terminal-based coding agent. Excellent at file operations, git workflows, and multi-file refactors. Works great as a sub-agent when you need focused coding work.

Best-in-class for code-specific tasks.

LangChain / LangGraph

Python framework for chaining LLM calls. Popular but over-engineered for most use cases. You'll spend more time debugging the framework than solving your actual problem.

⚠️ Use only if you're building a complex multi-agent system and know Python well.

CrewAI

Multi-agent framework where you define "crews" of agents with roles. Nice concept but the orchestration layer adds unpredictability. Hard to debug when things go wrong.

⚠️ Cool for demos. Frustrating in production.

⚡ Quick Shortcut

Skip months of trial and error

The AI Employee Playbook gives you production-ready templates, prompts, and workflows — everything in this guide and more, ready to deploy.

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Memory: The Missing Piece

This is what separates a chatbot from an agent. Without persistent memory, every conversation starts from zero.

The 3-File Framework (SOUL.md + AGENTS.md + USER.md)

Simple markdown files that give your agent identity, instructions, and context about you. No database needed. Version-controlled. Human-readable. The agent reads them on startup.

🏆 Start here. Seriously. Read our deep dive →

Daily Memory Files (YYYY-MM-DD.md)

Your agent writes daily notes about what it did, what it learned, and what's pending. Like a work journal. Combined with semantic search, this gives context across sessions.

Essential companion to the 3-file framework.

Vector Databases (Pinecone, Weaviate, Chroma)

Store embeddings for semantic search over large document collections. Useful when your agent needs to reference thousands of documents. Overkill for most solopreneur setups.

Skip until you outgrow markdown files.

Communication: How It Talks to You

Best balance of features and simplicity. Bot API is robust, supports rich media, inline buttons, and works everywhere. Our primary channel for agent communication.

🏆 Best default choice. 5-minute setup via BotFather.

Where your customers already are. More complex to set up (Business API or bridge), but unbeatable for client-facing agents.

Use for customer-facing agents.

Discord

Great for team/community agents. Rich bot features, slash commands, threads. Can feel noisy for personal use.

Best for team collaboration and communities.

The Tools We Skipped (And Why)

AutoGPT: Pioneer that proved the concept. Now outpaced by everything on this list.
MetaGPT: Interesting research project. Not production-ready.
SuperAGI: UI-heavy platform. More clicks than code. Doesn't fit the operator mindset.
Devin: Overhyped coding agent. Claude Code does it better for free.

Our Recommended Stack

If you're starting from scratch today, here's what we'd set up:

LLM: Claude Sonnet (daily) + Opus (complex tasks)
Runtime: OpenClaw on a Mac Mini or cheap VPS
Memory: 3-File Framework + daily notes + semantic search
Comms: Telegram for you, WhatsApp bridge for clients
Coding: Claude Code as a sub-agent for dev work

Total monthly cost: ~$50-100 in API credits. That's it. No $500/month SaaS subscriptions.

The best AI agent stack is the one you actually understand and can fix at 2am when it breaks.

Want the Complete Setup Guide?

The AI Employee Playbook walks you through building this exact stack, step by step. From zero to a working AI agent in a weekend.

Get the Playbook — €29 →