162 AI tools reviewed with real pricing, quickstart code, and honest gotchas
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AutoGPT is the original hype-beast of autonomous agents—fascinating to watch, but dangerous for your wallet if left unsupervised. While the core loop is brilliant for prototyping 'set it and forget it' tasks, it frequently gets stuck in logic loops that burn through OpenAI credits. It's perfect for researchers and pioneers wanting to push the boundaries of agentic behavior, but production engineering teams should treat its non-deterministic nature with extreme caution.
AutoGen is the heavy hitter for developers who want agents that actually *do* things, not just talk. Its standout feature is the ability to execute code safely in Docker containers, making it the go-to choice for building coding assistants or data analysis bots. However, the conversation-driven paradigm can be chaotic to debug compared to graph-based flows. Use this if you need powerful, autonomous code execution; avoid it if you just need a simple, linear chain of LLM calls.
MCP is basically 'USB-C for AI tools'—a long-overdue standard that stops us from writing the same Google Drive connector five times for five different frameworks. It's a brilliant move by Anthropic to commoditize the integration layer, making it easier to switch models without rewriting your tool stack. If you're building internal AI tools or agentic platforms, adopt this standard immediately; if you're just hacking a simple script, the client-server architecture might be overkill.
